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For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. diff --git a/README.md b/README.md index 4c8e5839a7ebab6f5dc5ff19a2eaa5fea90e8587..e0d149efdc05afa411dcd850a563c48062dca8aa 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,74 @@ ---- -title: IF3D -emoji: 👁 -colorFrom: blue -colorTo: pink -sdk: docker -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposition +## [Paper](https://arxiv.org/abs/2302.11566) | [Video Youtube](https://youtu.be/EGi47YeIeGQ) | [Project Page](https://moygcc.github.io/vid2avatar/) | [SynWild Data](https://synwild.ait.ethz.ch/) + + +Official Repository for CVPR 2023 paper [*Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposition*](https://arxiv.org/abs/2302.11566). + + + +## Getting Started +* Clone this repo: `git clone https://github.com/MoyGcc/vid2avatar` +* Create a python virtual environment and activate. `conda create -n v2a python=3.7` and `conda activate v2a` +* Install dependenices. `cd vid2avatar`, `pip install -r requirement.txt` and `cd code; python setup.py develop` +* Install [Kaolin](https://kaolin.readthedocs.io/en/v0.10.0/notes/installation.html). We use version 0.10.0. +* Download [SMPL model](https://smpl.is.tue.mpg.de/download.php) (1.0.0 for Python 2.7 (10 shape PCs)) and move them to the corresponding places: +``` +mkdir code/lib/smpl/smpl_model/ +mv /path/to/smpl/models/basicModel_f_lbs_10_207_0_v1.0.0.pkl code/lib/smpl/smpl_model/SMPL_FEMALE.pkl +mv /path/to/smpl/models/basicmodel_m_lbs_10_207_0_v1.0.0.pkl code/lib/smpl/smpl_model/SMPL_MALE.pkl +``` +## Download preprocessed demo data +You can quickly start trying out Vid2Avatar with a preprocessed demo sequence including the pre-trained checkpoint. This can be downloaded from [Google drive](https://drive.google.com/drive/u/1/folders/1AUtKSmib7CvpWBCFO6mQ9spVrga_CTU4) which is originally a video clip provided by [NeuMan](https://github.com/apple/ml-neuman). Put this preprocessed demo data under the folder `data/` and put the folder `checkpoints` under `outputs/parkinglot/`. + +## Training +Before training, make sure that the `metaninfo` in the data config file `/code/confs/dataset/video.yaml` does match the expected training video. You can also continue the training by changing the flag `is_continue` in the model config file `code/confs/model/model_w_bg`. And then run: +``` +cd code +python train.py +``` +The training usually takes 24-48 hours. The validation results can be found at `outputs/`. +## Test +Run the following command to obtain the final outputs. By default, this loads the latest checkpoint. +``` +cd code +python test.py +``` +## 3D Visualization +We use [AITViewer](https://github.com/eth-ait/aitviewer) to visualize the human models in 3D. First install AITViewer: `pip install aitviewer imgui==1.4.1`, and then run the following command to visualize the canonical mesh (--mode static) or deformed mesh sequence (--mode dynamic): +``` +cd visualization +python vis.py --mode {MODE} --path {PATH} +``` +

+ +

+ +## Play on custom video +* We use [ROMP](https://github.com/Arthur151/ROMP#installation) to obtain initial SMPL shape and poses: `pip install --upgrade simple-romp` +* Install [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/installation/0_index.md) as well as the python bindings. +* Put the video frames under the folder `preprocessing/raw_data/{SEQUENCE_NAME}/frames` +* Modify the preprocessing script `preprocessing/run_preprocessing.sh` accordingly: the data source, sequence name, and the gender. The data source is by default "custom" which will estimate camera intrinsics. If the camera intrinsics are known, it's better if the true camera parameters can be given. +* Run preprocessing: `cd preprocessing` and `bash run_preprocessing.sh`. The processed data will be stored in `data/`. The intermediate outputs of the preprocessing can be found at `preprocessing/raw_data/{SEQUENCE_NAME}/` +* Launch training and test in the same way as above. The `metainfo` in the data config file `/code/confs/dataset/video.yaml` should be changed according to the custom video. + +

+ +

+ +## Acknowledgement +We have used codes from other great research work, including [VolSDF](https://github.com/lioryariv/volsdf), [NeRF++](https://github.com/Kai-46/nerfplusplus), [SMPL-X](https://github.com/vchoutas/smplx), [Anim-NeRF](https://github.com/JanaldoChen/Anim-NeRF), [I M Avatar](https://github.com/zhengyuf/IMavatar) and [SNARF](https://github.com/xuchen-ethz/snarf). We sincerely thank the authors for their awesome work! We also thank the authors of [ICON](https://github.com/YuliangXiu/ICON) and [SelfRecon](https://github.com/jby1993/SelfReconCode) for discussing experiment. + +## Related Works +Here are more recent related human body reconstruction projects from our team: +* [Jiang and Chen et. al. - InstantAvatar: Learning Avatars from Monocular Video in 60 Seconds](https://github.com/tijiang13/InstantAvatar) +* [Shen and Guo et. al. - X-Avatar: Expressive Human Avatars](https://skype-line.github.io/projects/X-Avatar/) +* [Yin et. al. - Hi4D: 4D Instance Segmentation of Close Human Interaction](https://yifeiyin04.github.io/Hi4D/) + +``` +@inproceedings{guo2023vid2avatar, + title={Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposition}, + author={Guo, Chen and Jiang, Tianjian and Chen, Xu and Song, Jie and Hilliges, Otmar}, + booktitle = {Computer Vision and Pattern Recognition (CVPR)}, + year = {2023} + } +``` diff --git a/assets/exstrimalik.gif 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+1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:353410716a784c880a252328b10743981a61737a3efa02db7055801347c35b28 +size 1065367 diff --git a/code/check_cuda.py b/code/check_cuda.py new file mode 100644 index 0000000000000000000000000000000000000000..3cb4950968332f1fd3213c02af66d225c2eb0a63 --- /dev/null +++ b/code/check_cuda.py @@ -0,0 +1,11 @@ +import torch + +print("Number of GPUs:", torch.cuda.device_count()) + +print("Torch version:",torch.__version__) + +print("Is CUDA enabled?",torch.cuda.is_available()) + +print(torch.cuda.device_count()) + +# pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html \ No newline at end of file diff --git a/code/confs/base.yaml b/code/confs/base.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d027190340674f8663e19f86e492df180e95444e --- /dev/null +++ b/code/confs/base.yaml @@ -0,0 +1,13 @@ +hydra: + run: + dir: "../outputs/${exp}/${run}" + +defaults: + - model: model_w_bg + - dataset: video + - _self_ + +seed: 42 +project_name: "model_w_bg" +exp: ${dataset.train.type} +run: ${dataset.metainfo.subject} \ No newline at end of file diff --git a/code/confs/dataset/video.yaml b/code/confs/dataset/video.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ee541e3c2d37473305d54cb9cce65e182ca22073 --- /dev/null +++ b/code/confs/dataset/video.yaml @@ -0,0 +1,37 @@ +metainfo: + gender: 'male' + data_dir : C:\Users\leob3\vid2avatar\data\parkinglot + subject: "parkinglot" + start_frame: 0 + end_frame: 42 + +train: + type: "Video" + batch_size: 1 + drop_last: False + shuffle: True + worker: 8 + + num_sample : 512 + +valid: + type: "VideoVal" + image_id: 0 + batch_size: 1 + drop_last: False + shuffle: False + worker: 8 + + num_sample : -1 + pixel_per_batch: 2048 + +test: + type: "VideoTest" + image_id: 0 + batch_size: 1 + drop_last: False + shuffle: False + worker: 8 + + num_sample : -1 + pixel_per_batch: 2048 \ No newline at end of file diff --git a/code/confs/model/model_w_bg.yaml b/code/confs/model/model_w_bg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..afcfa6d66f2e9d8f6187f73eb2ab7c9fea0dff47 --- /dev/null +++ b/code/confs/model/model_w_bg.yaml @@ -0,0 +1,77 @@ +learning_rate : 5.0e-4 +sched_milestones : [200,500] +sched_factor : 0.5 +smpl_init: True +is_continue: False +use_body_parsing: False +with_bkgd: True +using_inpainting: False +use_smpl_deformer: True +use_bbox_sampler: False + +implicit_network: + feature_vector_size: 256 + d_in: 3 + d_out: 1 + dims: [ 256, 256, 256, 256, 256, 256, 256, 256 ] + init: 'geometry' + bias: 0.6 + skip_in: [4] + weight_norm: True + embedder_mode: 'fourier' + multires: 6 + cond: 'smpl' + scene_bounding_sphere: 3.0 +rendering_network: + feature_vector_size: 256 + mode: "pose" + d_in: 14 + d_out: 3 + dims: [ 256, 256, 256, 256] + weight_norm: True + multires_view: -1 +bg_implicit_network: + feature_vector_size: 256 + d_in: 4 + d_out: 1 + dims: [ 256, 256, 256, 256, 256, 256, 256, 256 ] + init: 'none' + bias: 0.0 + skip_in: [4] + weight_norm: False + embedder_mode: 'fourier' + multires: 10 + cond: 'frame' + dim_frame_encoding: 32 +bg_rendering_network: + feature_vector_size: 256 + mode: 'nerf_frame_encoding' + d_in: 3 + d_out: 3 + dims: [128] + weight_norm: False + multires_view: 4 + dim_frame_encoding: 32 +shadow_network: + d_in: 3 + d_out: 1 + dims: [128, 128] + weight_norm: False +density: + params_init: {beta: 0.1} + beta_min: 0.0001 +ray_sampler: + near: 0.0 + N_samples: 64 + N_samples_eval: 128 + N_samples_extra: 32 + eps: 0.1 + beta_iters: 10 + max_total_iters: 5 + N_samples_inverse_sphere: 32 + add_tiny: 1.0e-6 +loss: + eikonal_weight : 0.1 + bce_weight: 5.0e-3 + opacity_sparse_weight: 3.0e-3 + in_shape_weight: 1.0e-2 diff --git a/code/lib/datasets/__init__.py b/code/lib/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e3e66cf81b242e79b136b147e244da3c93438735 --- /dev/null +++ b/code/lib/datasets/__init__.py @@ -0,0 +1,26 @@ +from .dataset import Dataset, ValDataset, TestDataset +from torch.utils.data import DataLoader + +def find_dataset_using_name(name): + mapping = { + "Video": Dataset, + "VideoVal": ValDataset, + "VideoTest": TestDataset, + } + cls = mapping.get(name, None) + if cls is None: + raise ValueError(f"Fail to find dataset {name}") + return cls + + +def create_dataset(metainfo, split): + dataset_cls = find_dataset_using_name(split.type) + dataset = dataset_cls(metainfo, split) + return DataLoader( + dataset, + batch_size=split.batch_size, + drop_last=split.drop_last, + shuffle=split.shuffle, + num_workers=split.worker, + pin_memory=True + ) \ No newline at end of file diff --git a/code/lib/datasets/dataset.py b/code/lib/datasets/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..709b0f632a0b36b415ed71a75821458fb30b992d --- /dev/null +++ b/code/lib/datasets/dataset.py @@ -0,0 +1,175 @@ +import os +import glob +import hydra +import cv2 +import numpy as np +import torch +from lib.utils import utils + + +class Dataset(torch.utils.data.Dataset): + def __init__(self, metainfo, split): + root = os.path.join("../data", metainfo.data_dir) + root = hydra.utils.to_absolute_path(root) + + self.start_frame = metainfo.start_frame + self.end_frame = metainfo.end_frame + self.skip_step = 1 + self.images, self.img_sizes = [], [] + self.training_indices = list(range(metainfo.start_frame, metainfo.end_frame, self.skip_step)) + + # images + img_dir = os.path.join(root, "image") + self.img_paths = sorted(glob.glob(f"{img_dir}/*.png")) + + # only store the image paths to avoid OOM + self.img_paths = [self.img_paths[i] for i in self.training_indices] + self.img_size = cv2.imread(self.img_paths[0]).shape[:2] + self.n_images = len(self.img_paths) + + # coarse projected SMPL masks, only for sampling + mask_dir = os.path.join(root, "mask") + self.mask_paths = sorted(glob.glob(f"{mask_dir}/*.png")) + self.mask_paths = [self.mask_paths[i] for i in self.training_indices] + + self.shape = np.load(os.path.join(root, "mean_shape.npy")) + self.poses = np.load(os.path.join(root, 'poses.npy'))[self.training_indices] + self.trans = np.load(os.path.join(root, 'normalize_trans.npy'))[self.training_indices] + # cameras + camera_dict = np.load(os.path.join(root, "cameras_normalize.npz")) + scale_mats = [camera_dict['scale_mat_%d' % idx].astype(np.float32) for idx in self.training_indices] + world_mats = [camera_dict['world_mat_%d' % idx].astype(np.float32) for idx in self.training_indices] + + self.scale = 1 / scale_mats[0][0, 0] + + self.intrinsics_all = [] + self.pose_all = [] + for scale_mat, world_mat in zip(scale_mats, world_mats): + P = world_mat @ scale_mat + P = P[:3, :4] + intrinsics, pose = utils.load_K_Rt_from_P(None, P) + self.intrinsics_all.append(torch.from_numpy(intrinsics).float()) + self.pose_all.append(torch.from_numpy(pose).float()) + assert len(self.intrinsics_all) == len(self.pose_all) + + # other properties + self.num_sample = split.num_sample + self.sampling_strategy = "weighted" + + def __len__(self): + return self.n_images + + def __getitem__(self, idx): + # normalize RGB + img = cv2.imread(self.img_paths[idx]) + # preprocess: BGR -> RGB -> Normalize + + img = img[:, :, ::-1] / 255 + + mask = cv2.imread(self.mask_paths[idx]) + # preprocess: BGR -> Gray -> Mask + mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY) > 0 + + img_size = self.img_size + + uv = np.mgrid[:img_size[0], :img_size[1]].astype(np.int32) + uv = np.flip(uv, axis=0).copy().transpose(1, 2, 0).astype(np.float32) + + smpl_params = torch.zeros([86]).float() + smpl_params[0] = torch.from_numpy(np.asarray(self.scale)).float() + + smpl_params[1:4] = torch.from_numpy(self.trans[idx]).float() + smpl_params[4:76] = torch.from_numpy(self.poses[idx]).float() + smpl_params[76:] = torch.from_numpy(self.shape).float() + + if self.num_sample > 0: + data = { + "rgb": img, + "uv": uv, + "object_mask": mask, + } + + samples, index_outside = utils.weighted_sampling(data, img_size, self.num_sample) + inputs = { + "uv": samples["uv"].astype(np.float32), + "intrinsics": self.intrinsics_all[idx], + "pose": self.pose_all[idx], + "smpl_params": smpl_params, + 'index_outside': index_outside, + "idx": idx + } + images = {"rgb": samples["rgb"].astype(np.float32)} + return inputs, images + else: + inputs = { + "uv": uv.reshape(-1, 2).astype(np.float32), + "intrinsics": self.intrinsics_all[idx], + "pose": self.pose_all[idx], + "smpl_params": smpl_params, + "idx": idx + } + images = { + "rgb": img.reshape(-1, 3).astype(np.float32), + "img_size": self.img_size + } + return inputs, images + +class ValDataset(torch.utils.data.Dataset): + def __init__(self, metainfo, split): + self.dataset = Dataset(metainfo, split) + self.img_size = self.dataset.img_size + + self.total_pixels = np.prod(self.img_size) + self.pixel_per_batch = split.pixel_per_batch + + def __len__(self): + return 1 + + def __getitem__(self, idx): + image_id = int(np.random.choice(len(self.dataset), 1)) + self.data = self.dataset[image_id] + inputs, images = self.data + + inputs = { + "uv": inputs["uv"], + "intrinsics": inputs['intrinsics'], + "pose": inputs['pose'], + "smpl_params": inputs["smpl_params"], + 'image_id': image_id, + "idx": inputs['idx'] + } + images = { + "rgb": images["rgb"], + "img_size": images["img_size"], + 'pixel_per_batch': self.pixel_per_batch, + 'total_pixels': self.total_pixels + } + return inputs, images + +class TestDataset(torch.utils.data.Dataset): + def __init__(self, metainfo, split): + self.dataset = Dataset(metainfo, split) + + self.img_size = self.dataset.img_size + + self.total_pixels = np.prod(self.img_size) + self.pixel_per_batch = split.pixel_per_batch + def __len__(self): + return len(self.dataset) + + def __getitem__(self, idx): + data = self.dataset[idx] + + inputs, images = data + inputs = { + "uv": inputs["uv"], + "intrinsics": inputs['intrinsics'], + "pose": inputs['pose'], + "smpl_params": inputs["smpl_params"], + "idx": inputs['idx'] + } + images = { + "rgb": images["rgb"], + "img_size": images["img_size"] + } + return inputs, images, self.pixel_per_batch, self.total_pixels, idx diff --git a/code/lib/libmise/mise.cp37-win_amd64.pyd b/code/lib/libmise/mise.cp37-win_amd64.pyd new file mode 100644 index 0000000000000000000000000000000000000000..63db7d2b926058fb92ed5057dc3832933802246b Binary files /dev/null and b/code/lib/libmise/mise.cp37-win_amd64.pyd differ diff --git a/code/lib/libmise/mise.cpp b/code/lib/libmise/mise.cpp new file mode 100644 index 0000000000000000000000000000000000000000..66690a77e68cc7b676d2c2e283491a4fb970c129 --- /dev/null +++ b/code/lib/libmise/mise.cpp @@ -0,0 +1,25450 @@ +/* Generated by Cython 0.29.20 */ + +/* BEGIN: Cython Metadata +{ + "distutils": { + "depends": [], + "language": "c++", + "name": "lib.libmise.mise", + "sources": [ + "lib/libmise/mise.pyx" + ] + }, + "module_name": "lib.libmise.mise" +} +END: Cython Metadata */ + +#define PY_SSIZE_T_CLEAN +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) + #error Cython requires Python 2.6+ or Python 3.3+. +#else +#define CYTHON_ABI "0_29_20" +#define CYTHON_HEX_VERSION 0x001D14F0 +#define CYTHON_FUTURE_DIVISION 0 +#include +#ifndef offsetof + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#define __PYX_COMMA , +#ifndef HAVE_LONG_LONG + #if PY_VERSION_HEX >= 0x02070000 + #define HAVE_LONG_LONG + #endif +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 +#elif defined(PYSTON_VERSION) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #elif !defined(CYTHON_USE_PYTYPE_LOOKUP) + #define CYTHON_USE_PYTYPE_LOOKUP 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #elif !defined(CYTHON_USE_PYLONG_INTERNALS) + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif + #ifndef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000) + #endif + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1) + #endif + #ifndef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1) + #endif + #ifndef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3) + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) +#endif +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #undef SHIFT + #undef BASE + #undef MASK + #ifdef SIZEOF_VOID_P + enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; + #endif +#endif +#ifndef __has_attribute + #define __has_attribute(x) 0 +#endif +#ifndef __has_cpp_attribute + #define __has_cpp_attribute(x) 0 +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) +#ifdef _MSC_VER + #ifndef _MSC_STDINT_H_ + #if _MSC_VER < 1300 + typedef unsigned char uint8_t; + typedef unsigned int uint32_t; + #else + typedef unsigned __int8 uint8_t; + typedef unsigned __int32 uint32_t; + #endif + #endif +#else + #include +#endif +#ifndef CYTHON_FALLTHROUGH + #if defined(__cplusplus) && __cplusplus >= 201103L + #if __has_cpp_attribute(fallthrough) + #define CYTHON_FALLTHROUGH [[fallthrough]] + #elif __has_cpp_attribute(clang::fallthrough) + #define CYTHON_FALLTHROUGH [[clang::fallthrough]] + #elif __has_cpp_attribute(gnu::fallthrough) + #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_attribute(fallthrough) + #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) + #else + #define CYTHON_FALLTHROUGH + #endif + #endif + #if defined(__clang__ ) && defined(__apple_build_version__) + #if __apple_build_version__ < 7000000 + #undef CYTHON_FALLTHROUGH + #define CYTHON_FALLTHROUGH + #endif + #endif +#endif + +#ifndef __cplusplus + #error "Cython files generated with the C++ option must be compiled with a C++ compiler." +#endif +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #else + #define CYTHON_INLINE inline + #endif +#endif +template +void __Pyx_call_destructor(T& x) { + x.~T(); +} +template +class __Pyx_FakeReference { + public: + __Pyx_FakeReference() : ptr(NULL) { } + __Pyx_FakeReference(const T& ref) : ptr(const_cast(&ref)) { } + T *operator->() { return ptr; } + T *operator&() { return ptr; } + operator T&() { return *ptr; } + template bool operator ==(U other) { return *ptr == other; } + template bool operator !=(U other) { return *ptr != other; } + private: + T *ptr; +}; + +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) + #define Py_OptimizeFlag 0 +#endif +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" +#if PY_VERSION_HEX >= 0x030800A4 && PY_VERSION_HEX < 0x030800B2 + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, 0, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#else + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#endif + #define __Pyx_DefaultClassType PyType_Type +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#ifndef METH_STACKLESS + #define METH_STACKLESS 0 +#endif +#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) + #ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + #endif + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); + typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast + #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords +#endif +#if CYTHON_FAST_PYCCALL +#define __Pyx_PyFastCFunction_Check(func)\ + ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))))) +#else +#define __Pyx_PyFastCFunction_Check(func) 0 +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1 + #define PyMem_RawMalloc(n) PyMem_Malloc(n) + #define PyMem_RawRealloc(p, n) PyMem_Realloc(p, n) + #define PyMem_RawFree(p) PyMem_Free(p) +#endif +#if CYTHON_COMPILING_IN_PYSTON + #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#elif PY_VERSION_HEX >= 0x03060000 + #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() +#elif PY_VERSION_HEX >= 0x03000000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#else + #define __Pyx_PyThreadState_Current _PyThreadState_Current +#endif +#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) +#include "pythread.h" +#define Py_tss_NEEDS_INIT 0 +typedef int Py_tss_t; +static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { + *key = PyThread_create_key(); + return 0; +} +static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { + Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); + *key = Py_tss_NEEDS_INIT; + return key; +} +static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { + PyObject_Free(key); +} +static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { + return *key != Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { + PyThread_delete_key(*key); + *key = Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { + return PyThread_set_key_value(*key, value); +} +static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { + return PyThread_get_key_value(*key); +} +#endif +#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) +#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) +#else +#define __Pyx_PyDict_NewPresized(n) PyDict_New() +#endif +#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS +#define __Pyx_PyDict_GetItemStr(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) +#else +#define __Pyx_PyDict_GetItemStr(dict, name) PyDict_GetItem(dict, name) +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#ifndef PyObject_Unicode + #define PyObject_Unicode PyObject_Str +#endif +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#if PY_VERSION_HEX >= 0x030900A4 + #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size) +#else + #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size) +#endif +#if CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) +#else + #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : (Py_INCREF(func), func)) +#else + #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) +#endif +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif +#else + #define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef __Pyx_PyAsyncMethodsStruct + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; +#endif + +#if defined(WIN32) || defined(MS_WINDOWS) + #define _USE_MATH_DEFINES +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + +#define __PYX_MARK_ERR_POS(f_index, lineno) \ + { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; } +#define __PYX_ERR(f_index, lineno, Ln_error) \ + { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; } + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__lib__libmise__mise +#define __PYX_HAVE_API__lib__libmise__mise +/* Early includes */ +#include +#include "ios" +#include "new" +#include "stdexcept" +#include "typeinfo" +#include +#include + + #if __cplusplus > 199711L + #include + + namespace cython_std { + template typename std::remove_reference::type&& move(T& t) noexcept { return std::move(t); } + template typename std::remove_reference::type&& move(T&& t) noexcept { return std::move(t); } + } + + #endif + +#include +#include +#include "pythread.h" +#include +#include +#include +#include "pystate.h" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8) +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { + return (size_t) i < (size_t) limit; +} +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) + #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +#define __Pyx_PySequence_Tuple(obj)\ + (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_ASSUME_SAFE_MACROS +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ +static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } + +static PyObject *__pyx_m = NULL; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_cython_runtime = NULL; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static PyObject *__pyx_empty_unicode; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + + +static const char *__pyx_f[] = { + "lib\\libmise\\mise.pyx", + "stringsource", +}; +/* MemviewSliceStruct.proto */ +struct __pyx_memoryview_obj; +typedef struct { + struct __pyx_memoryview_obj *memview; + char *data; + Py_ssize_t shape[8]; + Py_ssize_t strides[8]; + Py_ssize_t suboffsets[8]; +} __Pyx_memviewslice; +#define __Pyx_MemoryView_Len(m) (m.shape[0]) + +/* Atomics.proto */ +#include +#ifndef CYTHON_ATOMICS + #define CYTHON_ATOMICS 1 +#endif +#define __pyx_atomic_int_type int +#if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||\ + (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) &&\ + !defined(__i386__) + #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1) + #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using GNU atomics" + #endif +#elif CYTHON_ATOMICS && defined(_MSC_VER) && 0 + #include + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type LONG + #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value) + #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value) + #ifdef __PYX_DEBUG_ATOMICS + #pragma message ("Using MSVC atomics") + #endif +#elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0 + #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value) + #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using Intel atomics" + #endif +#else + #undef CYTHON_ATOMICS + #define CYTHON_ATOMICS 0 + #ifdef __PYX_DEBUG_ATOMICS + #warning "Not using atomics" + #endif +#endif +typedef volatile __pyx_atomic_int_type __pyx_atomic_int; +#if CYTHON_ATOMICS + #define __pyx_add_acquisition_count(memview)\ + __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) +#else + #define __pyx_add_acquisition_count(memview)\ + __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) +#endif + +/* ForceInitThreads.proto */ +#ifndef __PYX_FORCE_INIT_THREADS + #define __PYX_FORCE_INIT_THREADS 0 +#endif + +/* NoFastGil.proto */ +#define __Pyx_PyGILState_Ensure PyGILState_Ensure +#define __Pyx_PyGILState_Release PyGILState_Release +#define __Pyx_FastGIL_Remember() +#define __Pyx_FastGIL_Forget() +#define __Pyx_FastGilFuncInit() + +/* BufferFormatStructs.proto */ +#define IS_UNSIGNED(type) (((type) -1) > 0) +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + + +/*--- Type declarations ---*/ +struct __pyx_obj_3lib_7libmise_4mise_MISE; +struct __pyx_array_obj; +struct __pyx_MemviewEnum_obj; +struct __pyx_memoryview_obj; +struct __pyx_memoryviewslice_obj; +struct __pyx_t_3lib_7libmise_4mise_Vector3D; +struct __pyx_t_3lib_7libmise_4mise_Voxel; +struct __pyx_t_3lib_7libmise_4mise_GridPoint; + +/* "lib/libmise/mise.pyx":11 + * + * + * cdef struct Vector3D: # <<<<<<<<<<<<<< + * int x, y, z + * + */ +struct __pyx_t_3lib_7libmise_4mise_Vector3D { + int x; + int y; + int z; +}; + +/* "lib/libmise/mise.pyx":15 + * + * + * cdef struct Voxel: # <<<<<<<<<<<<<< + * Vector3D loc + * unsigned int level + */ +struct __pyx_t_3lib_7libmise_4mise_Voxel { + struct __pyx_t_3lib_7libmise_4mise_Vector3D loc; + unsigned int level; + int is_leaf; + unsigned long children[2][2][2]; +}; + +/* "lib/libmise/mise.pyx":22 + * + * + * cdef struct GridPoint: # <<<<<<<<<<<<<< + * Vector3D loc + * double value + */ +struct __pyx_t_3lib_7libmise_4mise_GridPoint { + struct __pyx_t_3lib_7libmise_4mise_Vector3D loc; + double value; + int known; +}; + +/* "lib/libmise/mise.pyx":34 + * + * + * cdef class MISE: # <<<<<<<<<<<<<< + * cdef vector[Voxel] voxels + * cdef vector[GridPoint] grid_points + */ +struct __pyx_obj_3lib_7libmise_4mise_MISE { + PyObject_HEAD + struct __pyx_vtabstruct_3lib_7libmise_4mise_MISE *__pyx_vtab; + std::vector voxels; + std::vector grid_points; + std::map grid_point_hash; + int resolution_0; + int depth; + double threshold; + int voxel_size_0; + int resolution; +}; + + +/* "View.MemoryView":105 + * + * @cname("__pyx_array") + * cdef class array: # <<<<<<<<<<<<<< + * + * cdef: + */ +struct __pyx_array_obj { + PyObject_HEAD + struct __pyx_vtabstruct_array *__pyx_vtab; + char *data; + Py_ssize_t len; + char *format; + int ndim; + Py_ssize_t *_shape; + Py_ssize_t *_strides; + Py_ssize_t itemsize; + PyObject *mode; + PyObject *_format; + void (*callback_free_data)(void *); + int free_data; + int dtype_is_object; +}; + + +/* "View.MemoryView":279 + * + * @cname('__pyx_MemviewEnum') + * cdef class Enum(object): # <<<<<<<<<<<<<< + * cdef object name + * def __init__(self, name): + */ +struct __pyx_MemviewEnum_obj { + PyObject_HEAD + PyObject *name; +}; + + +/* "View.MemoryView":330 + * + * @cname('__pyx_memoryview') + * cdef class memoryview(object): # <<<<<<<<<<<<<< + * + * cdef object obj + */ +struct __pyx_memoryview_obj { + PyObject_HEAD + struct __pyx_vtabstruct_memoryview *__pyx_vtab; + PyObject *obj; + PyObject *_size; + PyObject *_array_interface; + PyThread_type_lock lock; + __pyx_atomic_int acquisition_count[2]; + __pyx_atomic_int *acquisition_count_aligned_p; + Py_buffer view; + int flags; + int dtype_is_object; + __Pyx_TypeInfo *typeinfo; +}; + + +/* "View.MemoryView":965 + * + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< + * "Internal class for passing memoryview slices to Python" + * + */ +struct __pyx_memoryviewslice_obj { + struct __pyx_memoryview_obj __pyx_base; + __Pyx_memviewslice from_slice; + PyObject *from_object; + PyObject *(*to_object_func)(char *); + int (*to_dtype_func)(char *, PyObject *); +}; + + + +/* "lib/libmise/mise.pyx":34 + * + * + * cdef class MISE: # <<<<<<<<<<<<<< + * cdef vector[Voxel] voxels + * cdef vector[GridPoint] grid_points + */ + +struct __pyx_vtabstruct_3lib_7libmise_4mise_MISE { + void (*subdivide_voxels)(struct __pyx_obj_3lib_7libmise_4mise_MISE *); + void (*subdivide_voxel)(struct __pyx_obj_3lib_7libmise_4mise_MISE *, long); 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}} while(0) + #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) + #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) + #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) +#else + #define __Pyx_RefNannyDeclarations + #define __Pyx_RefNannySetupContext(name, acquire_gil) + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XINCREF(r) Py_XINCREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) + #define __Pyx_XGOTREF(r) + #define __Pyx_XGIVEREF(r) +#endif +#define __Pyx_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_XDECREF(tmp);\ + } while (0) +#define __Pyx_DECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_DECREF(tmp);\ + } while (0) +#define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) +#define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* RaiseArgTupleInvalid.proto */ +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ + const char* function_name); + +/* BufferIndexError.proto */ +static void __Pyx_RaiseBufferIndexError(int axis); + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; +#define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#define __Pyx_PyErr_Occurred() PyErr_Occurred() +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) +#else +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#endif +#else +#define __Pyx_PyErr_Clear() PyErr_Clear() +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* MemviewSliceInit.proto */ +#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d +#define __Pyx_MEMVIEW_DIRECT 1 +#define __Pyx_MEMVIEW_PTR 2 +#define __Pyx_MEMVIEW_FULL 4 +#define __Pyx_MEMVIEW_CONTIG 8 +#define __Pyx_MEMVIEW_STRIDED 16 +#define __Pyx_MEMVIEW_FOLLOW 32 +#define __Pyx_IS_C_CONTIG 1 +#define __Pyx_IS_F_CONTIG 2 +static int __Pyx_init_memviewslice( + struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference); +static CYTHON_INLINE int __pyx_add_acquisition_count_locked( + __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); +static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( + __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); +#define __pyx_get_slice_count_pointer(memview) (memview->acquisition_count_aligned_p) +#define __pyx_get_slice_count(memview) (*__pyx_get_slice_count_pointer(memview)) +#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) +#define __PYX_XDEC_MEMVIEW(slice, have_gil) __Pyx_XDEC_MEMVIEW(slice, have_gil, __LINE__) +static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); +static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *, int, int); + +/* PyDictVersioning.proto */ +#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +#define __PYX_DICT_VERSION_INIT ((PY_UINT64_T) -1) +#define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ + (version_var) = __PYX_GET_DICT_VERSION(dict);\ + (cache_var) = (value); +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ + (VAR) = __pyx_dict_cached_value;\ + } else {\ + (VAR) = __pyx_dict_cached_value = (LOOKUP);\ + __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ + }\ +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj); +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version); +#else +#define __PYX_GET_DICT_VERSION(dict) (0) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); +#endif + +/* GetModuleGlobalName.proto */ +#if CYTHON_USE_DICT_VERSIONS +#define __Pyx_GetModuleGlobalName(var, name) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ + (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ + __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} +#define __Pyx_GetModuleGlobalNameUncached(var, name) {\ + PY_UINT64_T __pyx_dict_version;\ + PyObject *__pyx_dict_cached_value;\ + (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); +#else +#define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) +#define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); +#endif + +/* PyFunctionFastCall.proto */ +#if CYTHON_FAST_PYCALL +#define __Pyx_PyFunction_FastCall(func, args, nargs)\ + __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); +#else +#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) +#endif +#define __Pyx_BUILD_ASSERT_EXPR(cond)\ + (sizeof(char [1 - 2*!(cond)]) - 1) +#ifndef Py_MEMBER_SIZE +#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) +#endif + static size_t __pyx_pyframe_localsplus_offset = 0; + #include "frameobject.h" + #define __Pxy_PyFrame_Initialize_Offsets()\ + ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ + (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) + #define __Pyx_PyFrame_GetLocalsplus(frame)\ + (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) +#endif + +/* PyCFunctionFastCall.proto */ +#if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); +#else +#define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) +#endif + +/* WriteUnraisableException.proto */ +static void __Pyx_WriteUnraisable(const char *name, int clineno, + int lineno, const char *filename, + int full_traceback, int nogil); + +/* ArgTypeTest.proto */ +#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ + ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\ + __Pyx__ArgTypeTest(obj, type, name, exact)) +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); + +/* PyObjectCall2Args.proto */ +static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2); + +/* PyObjectCallMethO.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +/* PyObjectCallOneArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +/* IncludeStringH.proto */ +#include + +/* BytesEquals.proto */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); + +/* UnicodeEquals.proto */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); + +/* StrEquals.proto */ +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals +#else +#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals +#endif + +/* None.proto */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); + +/* UnaryNegOverflows.proto */ +#define UNARY_NEG_WOULD_OVERFLOW(x)\ + (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) + +static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ +/* GetAttr.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); + +/* GetItemInt.proto */ +#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ + __Pyx_GetItemInt_Generic(o, to_py_func(i)))) +#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, + int is_list, int wraparound, int boundscheck); + +/* ObjectGetItem.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key); +#else +#define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) +#endif + +/* decode_c_string_utf16.proto */ +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = 0; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = -1; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = 1; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} + +/* decode_c_string.proto */ +static CYTHON_INLINE PyObject* __Pyx_decode_c_string( + const char* cstring, Py_ssize_t start, Py_ssize_t stop, + const char* encoding, const char* errors, + PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)); + +/* PyErrExceptionMatches.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); +#else +#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) +#endif + +/* GetAttr3.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); + +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* RaiseNoneIterError.proto */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +/* ExtTypeTest.proto */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +/* GetTopmostException.proto */ +#if CYTHON_USE_EXC_INFO_STACK +static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); +#endif + +/* SaveResetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +#else +#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) +#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) +#endif + +/* GetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* SwapException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* FastTypeChecks.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); +#else +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) +#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) +#endif +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) + +static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +/* ListCompAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len)) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + __Pyx_SET_SIZE(list, len + 1); + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) +#endif + +/* PyIntBinop.proto */ +#if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check); +#else +#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\ + (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) +#endif + +/* ListExtend.proto */ +static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) { +#if CYTHON_COMPILING_IN_CPYTHON + PyObject* none = _PyList_Extend((PyListObject*)L, v); + if (unlikely(!none)) + return -1; + Py_DECREF(none); + return 0; +#else + return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v); +#endif +} + +/* ListAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + __Pyx_SET_SIZE(list, len + 1); + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_PyList_Append(L,x) PyList_Append(L,x) +#endif + +/* None.proto */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); + +/* None.proto */ +static CYTHON_INLINE long __Pyx_div_long(long, long); + +/* ImportFrom.proto */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); + +/* HasAttr.proto */ +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); + +/* PyObject_GenericGetAttrNoDict.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr +#endif + +/* PyObject_GenericGetAttr.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr +#endif + +/* SetVTable.proto */ +static int __Pyx_SetVtable(PyObject *dict, void *vtable); + +/* PyObjectGetAttrStrNoError.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name); + +/* SetupReduce.proto */ +static int __Pyx_setup_reduce(PyObject* type_obj); + +/* CLineInTraceback.proto */ +#ifdef CYTHON_CLINE_IN_TRACEBACK +#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) +#else +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); +#endif + +/* CodeObjectCache.proto */ +typedef struct { + PyCodeObject* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +/* None.proto */ +#include + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +/* BufferStructDeclare.proto */ +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +/* MemviewSliceIsContig.proto */ +static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); + +/* OverlappingSlices.proto */ +static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize); + +/* Capsule.proto */ +static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig); + +/* IsLittleEndian.proto */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); + +/* BufferFormatCheck.proto */ +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type); + +/* TypeInfoCompare.proto */ +static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); + +/* MemviewSliceValidateAndInit.proto */ +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_nn_int64_t(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *, int writable_flag); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +struct __pyx_t_3lib_7libmise_4mise_Vector3D; +static PyObject* __pyx_convert__to_py_struct____pyx_t_3lib_7libmise_4mise_Vector3D(struct __pyx_t_3lib_7libmise_4mise_Vector3D s); +/* CppExceptionConversion.proto */ +#ifndef __Pyx_CppExn2PyErr +#include +#include +#include +#include +static void __Pyx_CppExn2PyErr() { + try { + if (PyErr_Occurred()) + ; // let the latest Python exn pass through and ignore the current one + else + throw; + } catch (const std::bad_alloc& exn) { + PyErr_SetString(PyExc_MemoryError, exn.what()); + } catch (const std::bad_cast& exn) { + PyErr_SetString(PyExc_TypeError, exn.what()); + } catch (const std::bad_typeid& exn) { + PyErr_SetString(PyExc_TypeError, exn.what()); + } catch (const std::domain_error& exn) { + PyErr_SetString(PyExc_ValueError, exn.what()); + } catch (const std::invalid_argument& exn) { + PyErr_SetString(PyExc_ValueError, exn.what()); + } catch (const std::ios_base::failure& exn) { + PyErr_SetString(PyExc_IOError, exn.what()); + } catch (const std::out_of_range& exn) { + PyErr_SetString(PyExc_IndexError, exn.what()); + } catch (const std::overflow_error& exn) { + PyErr_SetString(PyExc_OverflowError, exn.what()); + } catch (const std::range_error& exn) { + PyErr_SetString(PyExc_ArithmeticError, exn.what()); + } catch (const std::underflow_error& exn) { + PyErr_SetString(PyExc_ArithmeticError, exn.what()); + } catch (const std::exception& exn) { + PyErr_SetString(PyExc_RuntimeError, exn.what()); + } + catch (...) + { + PyErr_SetString(PyExc_RuntimeError, "Unknown exception"); + } +} +#endif + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int64_t(int64_t value); + +/* MemviewSliceCopyTemplate.proto */ +static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsdsds_double(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_long(PyObject *, int writable_flag); + +/* CheckBinaryVersion.proto */ +static int __Pyx_check_binary_version(void); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + +static void __pyx_f_3lib_7libmise_4mise_4MISE_subdivide_voxels(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self); /* proto*/ +static void __pyx_f_3lib_7libmise_4mise_4MISE_subdivide_voxel(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self, long __pyx_v_idx); /* proto*/ +static long __pyx_f_3lib_7libmise_4mise_4MISE_get_voxel_idx(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self, struct __pyx_t_3lib_7libmise_4mise_Vector3D __pyx_v_loc); /* proto*/ +static CYTHON_INLINE void __pyx_f_3lib_7libmise_4mise_4MISE_add_grid_point(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self, struct __pyx_t_3lib_7libmise_4mise_Vector3D __pyx_v_loc); /* proto*/ +static CYTHON_INLINE int __pyx_f_3lib_7libmise_4mise_4MISE_get_grid_point_idx(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self, struct __pyx_t_3lib_7libmise_4mise_Vector3D __pyx_v_loc); /* proto*/ +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ +static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ +static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ + +/* Module declarations from 'cython.view' */ + +/* Module declarations from 'cython' */ + +/* Module declarations from 'libc.stdint' */ + +/* Module declarations from 'libcpp.vector' */ + +/* Module declarations from 'libcpp.utility' */ + +/* Module declarations from 'libcpp.map' */ + +/* Module declarations from 'libc.math' */ + +/* Module declarations from 'lib.libmise.mise' */ +static PyTypeObject *__pyx_ptype_3lib_7libmise_4mise_MISE = 0; +static PyTypeObject *__pyx_array_type = 0; +static PyTypeObject *__pyx_MemviewEnum_type = 0; +static PyTypeObject *__pyx_memoryview_type = 0; +static PyTypeObject *__pyx_memoryviewslice_type = 0; +static PyObject *generic = 0; +static PyObject *strided = 0; +static PyObject *indirect = 0; +static PyObject *contiguous = 0; +static PyObject *indirect_contiguous = 0; +static int __pyx_memoryview_thread_locks_used; +static PyThread_type_lock __pyx_memoryview_thread_locks[8]; +static CYTHON_INLINE unsigned long __pyx_f_3lib_7libmise_4mise_vec_to_idx(struct __pyx_t_3lib_7libmise_4mise_Vector3D, long); /*proto*/ +static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ +static void *__pyx_align_pointer(void *, size_t); /*proto*/ +static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ +static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ +static PyObject *_unellipsify(PyObject *, int); /*proto*/ +static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ +static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ +static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ +static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ +static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ +static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ +static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ +static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ +static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ +static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/ +static int __pyx_memoryview_err(PyObject *, char *); /*proto*/ +static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ +static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ +static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ +static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ +static __Pyx_TypeInfo __Pyx_TypeInfo_nn_int64_t = { "int64_t", NULL, sizeof(int64_t), { 0 }, 0, IS_UNSIGNED(int64_t) ? 'U' : 'I', IS_UNSIGNED(int64_t), 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_long = { "long", NULL, sizeof(long), { 0 }, 0, IS_UNSIGNED(long) ? 'U' : 'I', IS_UNSIGNED(long), 0 }; +#define __Pyx_MODULE_NAME "lib.libmise.mise" +extern int __pyx_module_is_main_lib__libmise__mise; +int __pyx_module_is_main_lib__libmise__mise = 0; + +/* Implementation of 'lib.libmise.mise' */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_TypeError; +static PyObject *__pyx_builtin_MemoryError; +static PyObject *__pyx_builtin_enumerate; +static PyObject *__pyx_builtin_Ellipsis; +static PyObject *__pyx_builtin_id; +static PyObject *__pyx_builtin_IndexError; +static const char __pyx_k_O[] = "O"; +static const char __pyx_k_c[] = "c"; +static const char __pyx_k_x[] = "x"; +static const char __pyx_k_y[] = "y"; +static const char __pyx_k_z[] = "z"; +static const char __pyx_k_id[] = "id"; +static const char __pyx_k_np[] = "np"; +static const char __pyx_k_loc[] = "loc"; +static const char __pyx_k_nan[] = "nan"; +static const char __pyx_k_new[] = "__new__"; +static const char __pyx_k_obj[] = "obj"; +static const char __pyx_k_MISE[] = "MISE"; +static const char __pyx_k_base[] = "base"; +static const char __pyx_k_dict[] = "__dict__"; +static const char __pyx_k_full[] = "full"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_mode[] = "mode"; +static const char __pyx_k_name[] = "name"; +static const char __pyx_k_ndim[] = "ndim"; +static const char __pyx_k_pack[] = "pack"; +static const char __pyx_k_size[] = "size"; +static const char __pyx_k_step[] = "step"; +static const char __pyx_k_stop[] = "stop"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_ASCII[] = "ASCII"; +static const char __pyx_k_class[] = "__class__"; +static const char __pyx_k_depth[] = "depth"; +static const char __pyx_k_dtype[] = "dtype"; +static const char __pyx_k_error[] = "error"; +static const char __pyx_k_flags[] = "flags"; +static const char __pyx_k_int64[] = "int64"; +static const char __pyx_k_known[] = "known"; +static const char __pyx_k_level[] = "level"; +static const char __pyx_k_numpy[] = "numpy"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_shape[] = "shape"; +static const char __pyx_k_start[] = "start"; +static const char __pyx_k_value[] = "value"; +static const char __pyx_k_zeros[] = "zeros"; +static const char __pyx_k_encode[] = "encode"; +static const char __pyx_k_format[] = "format"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_name_2[] = "__name__"; +static const char __pyx_k_pickle[] = "pickle"; +static const char __pyx_k_points[] = "points"; +static const char __pyx_k_reduce[] = "__reduce__"; +static const char __pyx_k_struct[] = "struct"; +static const char __pyx_k_unpack[] = "unpack"; +static const char __pyx_k_update[] = "update"; +static const char __pyx_k_values[] = "values"; +static const char __pyx_k_float64[] = "float64"; +static const char __pyx_k_fortran[] = "fortran"; +static const char __pyx_k_is_leaf[] = "is_leaf"; +static const char __pyx_k_memview[] = "memview"; +static const char __pyx_k_Ellipsis[] = "Ellipsis"; +static const char __pyx_k_getstate[] = "__getstate__"; +static const char __pyx_k_itemsize[] = "itemsize"; +static const char __pyx_k_pyx_type[] = "__pyx_type"; +static const char __pyx_k_setstate[] = "__setstate__"; +static const char __pyx_k_TypeError[] = "TypeError"; +static const char __pyx_k_enumerate[] = "enumerate"; +static const char __pyx_k_pyx_state[] = "__pyx_state"; +static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; +static const char __pyx_k_threshold[] = "threshold"; +static const char __pyx_k_IndexError[] = "IndexError"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_pyx_result[] = "__pyx_result"; +static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; +static const char __pyx_k_MemoryError[] = "MemoryError"; +static const char __pyx_k_PickleError[] = "PickleError"; +static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; +static const char __pyx_k_resolution_0[] = "resolution_0"; +static const char __pyx_k_stringsource[] = "stringsource"; +static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer"; +static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; +static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; +static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; +static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; +static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; +static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; +static const char __pyx_k_Point_not_in_grid[] = "Point not in grid!"; +static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; +static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; +static const char __pyx_k_strided_and_direct[] = ""; +static const char __pyx_k_strided_and_indirect[] = ""; +static const char __pyx_k_contiguous_and_direct[] = ""; +static const char __pyx_k_MemoryView_of_r_object[] = ""; +static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; +static const char __pyx_k_contiguous_and_indirect[] = ""; +static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'"; +static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d."; +static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; +static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; +static const char __pyx_k_strided_and_direct_or_indirect[] = ""; +static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; +static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; +static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; +static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; +static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; +static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))"; +static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; +static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s"; +static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)"; +static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; +static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)"; +static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; +static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; +static PyObject *__pyx_n_s_ASCII; +static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; +static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; +static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor; +static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi; +static PyObject *__pyx_kp_s_Cannot_index_with_type_s; +static PyObject *__pyx_n_s_Ellipsis; +static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; +static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0; +static PyObject *__pyx_n_s_IndexError; +static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; +static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr; +static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d; +static PyObject *__pyx_n_s_MISE; +static PyObject *__pyx_n_s_MemoryError; +static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; +static PyObject *__pyx_kp_s_MemoryView_of_r_object; +static PyObject *__pyx_n_b_O; +static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a; +static PyObject *__pyx_n_s_PickleError; +static PyObject *__pyx_kp_s_Point_not_in_grid; +static PyObject *__pyx_n_s_TypeError; +static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; +static PyObject *__pyx_n_s_ValueError; +static PyObject *__pyx_n_s_View_MemoryView; +static PyObject *__pyx_n_s_allocate_buffer; +static PyObject *__pyx_n_s_base; +static PyObject *__pyx_n_s_c; +static PyObject *__pyx_n_u_c; +static PyObject *__pyx_n_s_class; +static PyObject *__pyx_n_s_cline_in_traceback; +static PyObject *__pyx_kp_s_contiguous_and_direct; +static PyObject *__pyx_kp_s_contiguous_and_indirect; +static PyObject *__pyx_n_s_depth; +static PyObject *__pyx_n_s_dict; +static PyObject *__pyx_n_s_dtype; +static PyObject *__pyx_n_s_dtype_is_object; +static PyObject *__pyx_n_s_encode; +static PyObject *__pyx_n_s_enumerate; +static PyObject *__pyx_n_s_error; +static PyObject *__pyx_n_s_flags; +static PyObject *__pyx_n_s_float64; +static PyObject *__pyx_n_s_format; +static PyObject *__pyx_n_s_fortran; +static PyObject *__pyx_n_u_fortran; +static PyObject *__pyx_n_s_full; +static PyObject *__pyx_n_s_getstate; +static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi; +static PyObject *__pyx_n_s_id; +static PyObject *__pyx_n_s_import; +static PyObject *__pyx_n_s_int64; +static PyObject *__pyx_n_s_is_leaf; +static PyObject *__pyx_n_s_itemsize; +static PyObject *__pyx_kp_s_itemsize_0_for_cython_array; +static PyObject *__pyx_n_s_known; +static PyObject *__pyx_n_s_level; +static PyObject *__pyx_n_s_loc; +static PyObject *__pyx_n_s_main; +static PyObject *__pyx_n_s_memview; +static PyObject *__pyx_n_s_mode; +static PyObject *__pyx_n_s_name; +static PyObject *__pyx_n_s_name_2; +static PyObject *__pyx_n_s_nan; +static PyObject *__pyx_n_s_ndim; +static PyObject *__pyx_n_s_new; +static PyObject *__pyx_kp_s_no_default___reduce___due_to_non; +static PyObject *__pyx_n_s_np; +static PyObject *__pyx_n_s_numpy; +static PyObject *__pyx_n_s_obj; +static PyObject *__pyx_n_s_pack; +static PyObject *__pyx_n_s_pickle; +static PyObject *__pyx_n_s_points; +static PyObject *__pyx_n_s_pyx_PickleError; +static PyObject *__pyx_n_s_pyx_checksum; +static PyObject *__pyx_n_s_pyx_getbuffer; +static PyObject *__pyx_n_s_pyx_result; +static PyObject *__pyx_n_s_pyx_state; +static PyObject *__pyx_n_s_pyx_type; +static PyObject *__pyx_n_s_pyx_unpickle_Enum; +static PyObject *__pyx_n_s_pyx_vtable; +static PyObject *__pyx_n_s_range; +static PyObject *__pyx_n_s_reduce; +static PyObject *__pyx_n_s_reduce_cython; +static PyObject *__pyx_n_s_reduce_ex; +static PyObject *__pyx_n_s_resolution_0; +static PyObject *__pyx_n_s_setstate; +static PyObject *__pyx_n_s_setstate_cython; +static PyObject *__pyx_n_s_shape; +static PyObject *__pyx_n_s_size; +static PyObject *__pyx_n_s_start; +static PyObject *__pyx_n_s_step; +static PyObject *__pyx_n_s_stop; +static PyObject *__pyx_kp_s_strided_and_direct; +static PyObject *__pyx_kp_s_strided_and_direct_or_indirect; +static PyObject *__pyx_kp_s_strided_and_indirect; +static PyObject *__pyx_kp_s_stringsource; +static PyObject *__pyx_n_s_struct; +static PyObject *__pyx_n_s_test; +static PyObject *__pyx_n_s_threshold; +static PyObject *__pyx_kp_s_unable_to_allocate_array_data; +static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; +static PyObject *__pyx_n_s_unpack; +static PyObject *__pyx_n_s_update; +static PyObject *__pyx_n_s_value; +static PyObject *__pyx_n_s_values; +static PyObject *__pyx_n_s_x; +static PyObject *__pyx_n_s_y; +static PyObject *__pyx_n_s_z; +static PyObject *__pyx_n_s_zeros; +static int __pyx_pf_3lib_7libmise_4mise_4MISE___cinit__(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self, int __pyx_v_resolution_0, int __pyx_v_depth, double __pyx_v_threshold); /* proto */ +static PyObject *__pyx_pf_3lib_7libmise_4mise_4MISE_2update(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self, __Pyx_memviewslice __pyx_v_points, __Pyx_memviewslice __pyx_v_values); /* proto */ +static PyObject *__pyx_pf_3lib_7libmise_4mise_4MISE_4query(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_3lib_7libmise_4mise_4MISE_6to_dense(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_3lib_7libmise_4mise_4MISE_8get_points(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_3lib_7libmise_4mise_4MISE_12resolution_0___get__(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_3lib_7libmise_4mise_4MISE_5depth___get__(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_3lib_7libmise_4mise_4MISE_9threshold___get__(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_3lib_7libmise_4mise_4MISE_12voxel_size_0___get__(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_3lib_7libmise_4mise_4MISE_10resolution___get__(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_3lib_7libmise_4mise_4MISE_10__reduce_cython__(CYTHON_UNUSED struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_3lib_7libmise_4mise_4MISE_12__setstate_cython__(CYTHON_UNUSED struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ +static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ +static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ +static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); 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dtype=np.float64) + */ + + /* function exit code */ + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_1); + __Pyx_XDECREF(__pyx_t_2); + __Pyx_XDECREF(__pyx_t_3); + __Pyx_XDECREF(__pyx_t_4); + __Pyx_XDECREF(__pyx_t_5); + __PYX_XDEC_MEMVIEW(&__pyx_t_6, 1); + __PYX_XDEC_MEMVIEW(&__pyx_t_7, 1); + __Pyx_AddTraceback("lib.libmise.mise.MISE.get_points", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = NULL; + __pyx_L0:; + __Pyx_XDECREF(__pyx_v_points_np); + __Pyx_XDECREF(__pyx_v_values_np); + __PYX_XDEC_MEMVIEW(&__pyx_v_points_view, 1); + __PYX_XDEC_MEMVIEW(&__pyx_v_values_view, 1); + __Pyx_XGIVEREF(__pyx_r); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + +/* "lib/libmise/mise.pyx":185 + * return points_np, values_np + * + * cdef void subdivide_voxels(self) except +: # <<<<<<<<<<<<<< + * cdef vector[bint] next_to_positive + * cdef vector[bint] next_to_negative + */ + +static void __pyx_f_3lib_7libmise_4mise_4MISE_subdivide_voxels(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self) { + std::vector __pyx_v_next_to_positive; + std::vector __pyx_v_next_to_negative; + int __pyx_v_i; + int __pyx_v_j; + int __pyx_v_k; + long __pyx_v_idx; + struct __pyx_t_3lib_7libmise_4mise_Vector3D __pyx_v_loc; + struct __pyx_t_3lib_7libmise_4mise_Vector3D __pyx_v_adj_loc; + struct __pyx_t_3lib_7libmise_4mise_GridPoint __pyx_v_grid_point; + int __pyx_v_n_subdivide; + __Pyx_RefNannyDeclarations + std::vector ::iterator __pyx_t_1; + std::vector *__pyx_t_2; + struct __pyx_t_3lib_7libmise_4mise_GridPoint __pyx_t_3; + struct __pyx_t_3lib_7libmise_4mise_Vector3D __pyx_t_4; + int __pyx_t_5; + int __pyx_t_6; + int __pyx_t_7; + int __pyx_t_8; + long __pyx_t_9; + std::vector ::size_type __pyx_t_10; + std::vector ::size_type __pyx_t_11; + int __pyx_t_12; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + __Pyx_RefNannySetupContext("subdivide_voxels", 0); + + /* "lib/libmise/mise.pyx":193 + * + * # Initialize vectors + * 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&__pyx_v_self->grid_points; + __pyx_t_1 = __pyx_t_2->begin(); + for (;;) { + if (!(__pyx_t_1 != __pyx_t_2->end())) break; + __pyx_t_3 = *__pyx_t_1; + ++__pyx_t_1; + __pyx_v_grid_point = __pyx_t_3; + + /* "lib/libmise/mise.pyx":199 + * # TODO: can move this to update operation and add attibute to voxel + * for grid_point in self.grid_points: + * loc = grid_point.loc # <<<<<<<<<<<<<< + * if not grid_point.known: + * continue + */ + __pyx_t_4 = __pyx_v_grid_point.loc; + __pyx_v_loc = __pyx_t_4; + + /* "lib/libmise/mise.pyx":200 + * for grid_point in self.grid_points: + * loc = grid_point.loc + * if not grid_point.known: # <<<<<<<<<<<<<< + * continue + * + */ + __pyx_t_5 = ((!(__pyx_v_grid_point.known != 0)) != 0); + if (__pyx_t_5) { + + /* "lib/libmise/mise.pyx":201 + * loc = grid_point.loc + * if not grid_point.known: + * continue # <<<<<<<<<<<<<< + * + * # Iterate over the 8 adjacent voxels + */ + goto __pyx_L3_continue; + + /* "lib/libmise/mise.pyx":200 + * for grid_point in 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(__pyx_v_next_to_positive[__pyx_v_idx]) = 1; + + /* "lib/libmise/mise.pyx":216 + * continue + * + * if grid_point.value >= self.threshold: # <<<<<<<<<<<<<< + * next_to_positive[idx] = True + * if grid_point.value <= self.threshold: + */ + } + + /* "lib/libmise/mise.pyx":218 + * if grid_point.value >= self.threshold: + * next_to_positive[idx] = True + * if grid_point.value <= self.threshold: # <<<<<<<<<<<<<< + * next_to_negative[idx] = True + * + */ + __pyx_t_5 = ((__pyx_v_grid_point.value <= __pyx_v_self->threshold) != 0); + if (__pyx_t_5) { + + /* "lib/libmise/mise.pyx":219 + * next_to_positive[idx] = True + * if grid_point.value <= self.threshold: + * next_to_negative[idx] = True # <<<<<<<<<<<<<< + * + * cdef int n_subdivide = 0 + */ + (__pyx_v_next_to_negative[__pyx_v_idx]) = 1; + + /* "lib/libmise/mise.pyx":218 + * if grid_point.value >= self.threshold: + * next_to_positive[idx] = True + * if grid_point.value <= self.threshold: # <<<<<<<<<<<<<< + * next_to_negative[idx] = True + * + */ + } + __pyx_L10_continue:; + } + } + } + + /* "lib/libmise/mise.pyx":198 + * # Iterate over grid points and mark voxels active + * # TODO: can move this to update operation and add attibute to voxel + * for grid_point in self.grid_points: # <<<<<<<<<<<<<< + * loc = grid_point.loc + * if not grid_point.known: + */ + __pyx_L3_continue:; + } + + /* "lib/libmise/mise.pyx":221 + * next_to_negative[idx] = True + * + * cdef int n_subdivide = 0 # <<<<<<<<<<<<<< + * + * for idx in range(self.voxels.size()): + */ + __pyx_v_n_subdivide = 0; + + /* "lib/libmise/mise.pyx":223 + * cdef int n_subdivide = 0 + * + * for idx in range(self.voxels.size()): # <<<<<<<<<<<<<< + * if not self.voxels[idx].is_leaf or self.voxels[idx].level == self.depth: + * continue + */ + __pyx_t_10 = __pyx_v_self->voxels.size(); + __pyx_t_11 = __pyx_t_10; + for (__pyx_t_9 = 0; __pyx_t_9 < __pyx_t_11; __pyx_t_9+=1) { + __pyx_v_idx = __pyx_t_9; + + /* "lib/libmise/mise.pyx":224 + * + * for idx in range(self.voxels.size()): + * if not self.voxels[idx].is_leaf or self.voxels[idx].level == self.depth: # <<<<<<<<<<<<<< + * continue + * if next_to_positive[idx] and next_to_negative[idx]: + */ + __pyx_t_12 = ((!((__pyx_v_self->voxels[__pyx_v_idx]).is_leaf != 0)) != 0); + if (!__pyx_t_12) { + } else { + __pyx_t_5 = __pyx_t_12; + goto __pyx_L18_bool_binop_done; + } + __pyx_t_12 = (((__pyx_v_self->voxels[__pyx_v_idx]).level == __pyx_v_self->depth) != 0); + __pyx_t_5 = __pyx_t_12; + __pyx_L18_bool_binop_done:; + if (__pyx_t_5) { + + /* "lib/libmise/mise.pyx":225 + * for idx in range(self.voxels.size()): + * if not self.voxels[idx].is_leaf or self.voxels[idx].level == self.depth: + * continue # <<<<<<<<<<<<<< + * if next_to_positive[idx] and next_to_negative[idx]: + * n_subdivide += 1 + */ + goto __pyx_L15_continue; + + /* "lib/libmise/mise.pyx":224 + * + * for idx in range(self.voxels.size()): + * if not self.voxels[idx].is_leaf or self.voxels[idx].level == self.depth: # <<<<<<<<<<<<<< + * continue + * if next_to_positive[idx] and next_to_negative[idx]: + */ + } + + /* "lib/libmise/mise.pyx":226 + * if not self.voxels[idx].is_leaf or self.voxels[idx].level == self.depth: + * continue + * if next_to_positive[idx] and next_to_negative[idx]: # <<<<<<<<<<<<<< + * n_subdivide += 1 + * + */ + __pyx_t_12 = ((__pyx_v_next_to_positive[__pyx_v_idx]) != 0); + if (__pyx_t_12) { + } else { + __pyx_t_5 = __pyx_t_12; + goto __pyx_L21_bool_binop_done; + } + __pyx_t_12 = ((__pyx_v_next_to_negative[__pyx_v_idx]) != 0); + __pyx_t_5 = __pyx_t_12; + __pyx_L21_bool_binop_done:; + if (__pyx_t_5) { + + /* "lib/libmise/mise.pyx":227 + * continue + * if next_to_positive[idx] and next_to_negative[idx]: + * n_subdivide += 1 # <<<<<<<<<<<<<< + * + * self.voxels.reserve(self.voxels.size() + 8 * n_subdivide) + */ + __pyx_v_n_subdivide = (__pyx_v_n_subdivide + 1); + + /* "lib/libmise/mise.pyx":226 + * if not self.voxels[idx].is_leaf or self.voxels[idx].level == self.depth: + * continue + * if next_to_positive[idx] and next_to_negative[idx]: # <<<<<<<<<<<<<< + * n_subdivide += 1 + * + */ + } + __pyx_L15_continue:; + } + + /* "lib/libmise/mise.pyx":229 + * n_subdivide += 1 + * + * self.voxels.reserve(self.voxels.size() + 8 * n_subdivide) # <<<<<<<<<<<<<< + * self.grid_points.reserve(self.voxels.size() + 19 * n_subdivide) + * + */ + __pyx_v_self->voxels.reserve((__pyx_v_self->voxels.size() + (8 * __pyx_v_n_subdivide))); + + /* "lib/libmise/mise.pyx":230 + * + * self.voxels.reserve(self.voxels.size() + 8 * n_subdivide) + * self.grid_points.reserve(self.voxels.size() + 19 * n_subdivide) # <<<<<<<<<<<<<< + * + * for idx in range(self.voxels.size()): + */ + __pyx_v_self->grid_points.reserve((__pyx_v_self->voxels.size() + (19 * __pyx_v_n_subdivide))); + + /* "lib/libmise/mise.pyx":232 + * self.grid_points.reserve(self.voxels.size() + 19 * n_subdivide) + * + * for idx in range(self.voxels.size()): # <<<<<<<<<<<<<< + * if not self.voxels[idx].is_leaf or self.voxels[idx].level == self.depth: + * continue + */ + __pyx_t_10 = __pyx_v_self->voxels.size(); + __pyx_t_11 = __pyx_t_10; + for (__pyx_t_9 = 0; __pyx_t_9 < __pyx_t_11; __pyx_t_9+=1) { + __pyx_v_idx = __pyx_t_9; + + /* "lib/libmise/mise.pyx":233 + * + * for idx in range(self.voxels.size()): + * if not self.voxels[idx].is_leaf or self.voxels[idx].level == self.depth: # <<<<<<<<<<<<<< + * continue + * if next_to_positive[idx] and next_to_negative[idx]: + */ + __pyx_t_12 = ((!((__pyx_v_self->voxels[__pyx_v_idx]).is_leaf != 0)) != 0); + if (!__pyx_t_12) { + } else { + __pyx_t_5 = __pyx_t_12; + goto __pyx_L26_bool_binop_done; + } + __pyx_t_12 = (((__pyx_v_self->voxels[__pyx_v_idx]).level == __pyx_v_self->depth) != 0); + __pyx_t_5 = __pyx_t_12; + __pyx_L26_bool_binop_done:; + if (__pyx_t_5) { + + /* "lib/libmise/mise.pyx":234 + * for idx in range(self.voxels.size()): + * if not self.voxels[idx].is_leaf or self.voxels[idx].level == self.depth: + * continue # <<<<<<<<<<<<<< + * if next_to_positive[idx] and next_to_negative[idx]: + * self.subdivide_voxel(idx) + */ + goto __pyx_L23_continue; + + /* "lib/libmise/mise.pyx":233 + * + * for idx in range(self.voxels.size()): + * if not self.voxels[idx].is_leaf or self.voxels[idx].level == self.depth: # <<<<<<<<<<<<<< + * continue + * if next_to_positive[idx] and next_to_negative[idx]: + */ + } + + /* "lib/libmise/mise.pyx":235 + * if not self.voxels[idx].is_leaf or self.voxels[idx].level == self.depth: + * continue + * if next_to_positive[idx] and next_to_negative[idx]: # <<<<<<<<<<<<<< + * self.subdivide_voxel(idx) + * + */ + __pyx_t_12 = ((__pyx_v_next_to_positive[__pyx_v_idx]) != 0); + if (__pyx_t_12) { + } else { + __pyx_t_5 = __pyx_t_12; + goto __pyx_L29_bool_binop_done; + } + __pyx_t_12 = ((__pyx_v_next_to_negative[__pyx_v_idx]) != 0); + __pyx_t_5 = __pyx_t_12; + __pyx_L29_bool_binop_done:; + if (__pyx_t_5) { + + /* "lib/libmise/mise.pyx":236 + * continue + * if next_to_positive[idx] and next_to_negative[idx]: + * self.subdivide_voxel(idx) # <<<<<<<<<<<<<< + * + * cdef void subdivide_voxel(self, long idx): + */ + ((struct __pyx_vtabstruct_3lib_7libmise_4mise_MISE *)__pyx_v_self->__pyx_vtab)->subdivide_voxel(__pyx_v_self, __pyx_v_idx); + + /* "lib/libmise/mise.pyx":235 + * if not self.voxels[idx].is_leaf or self.voxels[idx].level == self.depth: + * continue + * if next_to_positive[idx] and next_to_negative[idx]: # <<<<<<<<<<<<<< + * self.subdivide_voxel(idx) + * + */ + } + __pyx_L23_continue:; + } + + /* "lib/libmise/mise.pyx":185 + * return points_np, values_np + * + * cdef void subdivide_voxels(self) except +: # <<<<<<<<<<<<<< + * cdef vector[bint] next_to_positive + * cdef vector[bint] next_to_negative + */ + + /* function exit code */ + goto __pyx_L0; + __pyx_L1_error:; + __Pyx_AddTraceback("lib.libmise.mise.MISE.subdivide_voxels", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_L0:; + __Pyx_RefNannyFinishContext(); +} + +/* "lib/libmise/mise.pyx":238 + * self.subdivide_voxel(idx) + * + 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self.voxels[idx].loc # <<<<<<<<<<<<<< + * cdef Vector3D loc + * cdef int new_level = self.voxels[idx].level + 1 + */ + __pyx_t_1 = (__pyx_v_self->voxels[__pyx_v_idx]).loc; + __pyx_v_loc0 = __pyx_t_1; + + /* "lib/libmise/mise.pyx":243 + * cdef Vector3D loc0 = self.voxels[idx].loc + * cdef Vector3D loc + * cdef int new_level = self.voxels[idx].level + 1 # <<<<<<<<<<<<<< + * cdef int new_size = 1 << (self.depth - new_level) + * assert(new_level <= self.depth) + */ + __pyx_v_new_level = ((__pyx_v_self->voxels[__pyx_v_idx]).level + 1); + + /* "lib/libmise/mise.pyx":244 + * cdef Vector3D loc + * cdef int new_level = self.voxels[idx].level + 1 + * cdef int new_size = 1 << (self.depth - new_level) # <<<<<<<<<<<<<< + * assert(new_level <= self.depth) + * assert(1 <= new_size <= self.voxel_size_0) + */ + __pyx_v_new_size = (1 << (__pyx_v_self->depth - __pyx_v_new_level)); + + /* "lib/libmise/mise.pyx":245 + * cdef int new_level = self.voxels[idx].level + 1 + * cdef int new_size = 1 << (self.depth - new_level) + * assert(new_level <= self.depth) # <<<<<<<<<<<<<< + * assert(1 <= new_size <= self.voxel_size_0) + * + */ + #ifndef CYTHON_WITHOUT_ASSERTIONS + if (unlikely(!Py_OptimizeFlag)) { + if (unlikely(!((__pyx_v_new_level <= __pyx_v_self->depth) != 0))) { + PyErr_SetNone(PyExc_AssertionError); + __PYX_ERR(0, 245, __pyx_L1_error) + } + } + #endif + + /* "lib/libmise/mise.pyx":246 + * cdef int new_size = 1 << (self.depth - new_level) + * assert(new_level <= self.depth) + * assert(1 <= new_size <= self.voxel_size_0) # <<<<<<<<<<<<<< + * + * # Current voxel is not leaf anymore + */ + #ifndef CYTHON_WITHOUT_ASSERTIONS + if (unlikely(!Py_OptimizeFlag)) { + __pyx_t_2 = (1 <= __pyx_v_new_size); + if (__pyx_t_2) { + __pyx_t_2 = (__pyx_v_new_size <= __pyx_v_self->voxel_size_0); + } + if (unlikely(!(__pyx_t_2 != 0))) { + PyErr_SetNone(PyExc_AssertionError); + __PYX_ERR(0, 246, __pyx_L1_error) + } + } + #endif + + /* "lib/libmise/mise.pyx":249 + * + * # Current voxel is not 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"lib/libmise/mise.pyx":263 + * loc=loc, + * level=new_level, + * is_leaf=True # <<<<<<<<<<<<<< + * ) + * + */ + __pyx_t_6.is_leaf = 1; + __pyx_v_voxel = __pyx_t_6; + + /* "lib/libmise/mise.pyx":266 + * ) + * + * self.voxels[idx].children[i][j][k] = self.voxels.size() # <<<<<<<<<<<<<< + * self.voxels.push_back(voxel) + * + */ + ((((__pyx_v_self->voxels[__pyx_v_idx]).children[__pyx_v_i])[__pyx_v_j])[__pyx_v_k]) = __pyx_v_self->voxels.size(); + + /* "lib/libmise/mise.pyx":267 + * + * self.voxels[idx].children[i][j][k] = self.voxels.size() + * self.voxels.push_back(voxel) # <<<<<<<<<<<<<< + * + * # Add new grid points + */ + try { + __pyx_v_self->voxels.push_back(__pyx_v_voxel); + } catch(...) { + __Pyx_CppExn2PyErr(); + __PYX_ERR(0, 267, __pyx_L1_error) + } + } + } + } + + /* "lib/libmise/mise.pyx":270 + * + * # Add new grid points + * for i in range(3): # <<<<<<<<<<<<<< + * for j in range(3): + * for k in range(3): + */ + for (__pyx_t_3 = 0; __pyx_t_3 < 3; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; + + /* "lib/libmise/mise.pyx":271 + * # Add new grid points + * for i in range(3): + * for j in range(3): # <<<<<<<<<<<<<< + * for k in range(3): + * loc = Vector3D( + */ + for (__pyx_t_4 = 0; __pyx_t_4 < 3; __pyx_t_4+=1) { + __pyx_v_j = __pyx_t_4; + + /* "lib/libmise/mise.pyx":272 + * for i in range(3): + * for j in range(3): + * for k in range(3): # <<<<<<<<<<<<<< + * loc = Vector3D( + * loc0.x + i * new_size, + */ + for (__pyx_t_5 = 0; __pyx_t_5 < 3; __pyx_t_5+=1) { + __pyx_v_k = __pyx_t_5; + + /* "lib/libmise/mise.pyx":274 + * for k in range(3): + * loc = Vector3D( + * loc0.x + i * new_size, # <<<<<<<<<<<<<< + * loc0.y + j * new_size, + * loc0.z + k * new_size, + */ + __pyx_t_1.x = (__pyx_v_loc0.x + (__pyx_v_i * __pyx_v_new_size)); + + /* "lib/libmise/mise.pyx":275 + * loc = Vector3D( + * loc0.x + i * new_size, + * loc0.y + j * new_size, # <<<<<<<<<<<<<< + * loc0.z + k * new_size, + * ) + */ + __pyx_t_1.y = (__pyx_v_loc0.y + (__pyx_v_j * __pyx_v_new_size)); + + /* "lib/libmise/mise.pyx":276 + * loc0.x + i * new_size, + * loc0.y + j * new_size, + * loc0.z + k * new_size, # <<<<<<<<<<<<<< + * ) + * + */ + __pyx_t_1.z = (__pyx_v_loc0.z + (__pyx_v_k * __pyx_v_new_size)); + __pyx_v_loc = __pyx_t_1; + + /* "lib/libmise/mise.pyx":280 + * + * # Only add new grid points + * if self.get_grid_point_idx(loc) == -1: # <<<<<<<<<<<<<< + * self.add_grid_point(loc) + * + */ + __pyx_t_2 = ((__pyx_f_3lib_7libmise_4mise_4MISE_get_grid_point_idx(__pyx_v_self, __pyx_v_loc) == -1L) != 0); + if (__pyx_t_2) { + + /* "lib/libmise/mise.pyx":281 + * # Only add new grid points + * if self.get_grid_point_idx(loc) == -1: + * self.add_grid_point(loc) # <<<<<<<<<<<<<< + * + * + */ + __pyx_f_3lib_7libmise_4mise_4MISE_add_grid_point(__pyx_v_self, __pyx_v_loc); + + /* "lib/libmise/mise.pyx":280 + * + * # Only add new grid points + * if self.get_grid_point_idx(loc) == -1: # <<<<<<<<<<<<<< + * self.add_grid_point(loc) + * + */ + } + } + } + } + + /* "lib/libmise/mise.pyx":238 + * self.subdivide_voxel(idx) + * + * cdef void subdivide_voxel(self, long idx): # <<<<<<<<<<<<<< + * cdef Voxel voxel + * cdef GridPoint point + */ + + /* function exit code */ + goto __pyx_L0; + __pyx_L1_error:; + __Pyx_WriteUnraisable("lib.libmise.mise.MISE.subdivide_voxel", __pyx_clineno, __pyx_lineno, __pyx_filename, 1, 0); + __pyx_L0:; + __Pyx_RefNannyFinishContext(); +} + +/* "lib/libmise/mise.pyx":285 + * + * @cython.cdivision(True) + * cdef long get_voxel_idx(self, Vector3D loc) except +: # <<<<<<<<<<<<<< + * """Utility function for getting voxel index corresponding to 3D coordinates.""" + * # Shorthands + */ + +static long __pyx_f_3lib_7libmise_4mise_4MISE_get_voxel_idx(struct __pyx_obj_3lib_7libmise_4mise_MISE *__pyx_v_self, struct __pyx_t_3lib_7libmise_4mise_Vector3D __pyx_v_loc) { + long __pyx_v_resolution; + long __pyx_v_resolution_0; + long __pyx_v_depth; + long __pyx_v_voxel_size_0; + struct __pyx_t_3lib_7libmise_4mise_Vector3D __pyx_v_loc0; + int __pyx_v_idx; + struct __pyx_t_3lib_7libmise_4mise_Voxel __pyx_v_voxel; + struct __pyx_t_3lib_7libmise_4mise_Vector3D __pyx_v_loc_rel; + struct __pyx_t_3lib_7libmise_4mise_Vector3D __pyx_v_loc_offset; + long __pyx_v_voxel_size; + long __pyx_r; + __Pyx_RefNannyDeclarations + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + struct __pyx_t_3lib_7libmise_4mise_Vector3D __pyx_t_5; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + __Pyx_RefNannySetupContext("get_voxel_idx", 0); + + /* "lib/libmise/mise.pyx":288 + * """Utility function for getting voxel index corresponding to 3D coordinates.""" + * # Shorthands + * cdef long resolution = self.resolution # <<<<<<<<<<<<<< + * cdef long resolution_0 = self.resolution_0 + * cdef long depth = self.depth + */ + __pyx_t_1 = __pyx_v_self->resolution; + __pyx_v_resolution = __pyx_t_1; + + /* "lib/libmise/mise.pyx":289 + * # Shorthands + * cdef long resolution = self.resolution + * cdef long resolution_0 = self.resolution_0 # <<<<<<<<<<<<<< + * cdef long depth = self.depth + * cdef long voxel_size_0 = self.voxel_size_0 + */ + __pyx_t_1 = __pyx_v_self->resolution_0; + __pyx_v_resolution_0 = __pyx_t_1; + + /* "lib/libmise/mise.pyx":290 + * cdef long resolution = self.resolution + * cdef long resolution_0 = self.resolution_0 + * cdef long depth = self.depth # <<<<<<<<<<<<<< + * cdef long voxel_size_0 = self.voxel_size_0 + * + */ + __pyx_t_1 = __pyx_v_self->depth; + __pyx_v_depth = __pyx_t_1; + + /* "lib/libmise/mise.pyx":291 + * cdef long resolution_0 = self.resolution_0 + * cdef long depth = self.depth + * cdef long voxel_size_0 = self.voxel_size_0 # <<<<<<<<<<<<<< + * + * # Return -1 if point lies outside bounds + */ + __pyx_t_1 = __pyx_v_self->voxel_size_0; + __pyx_v_voxel_size_0 = __pyx_t_1; + + /* "lib/libmise/mise.pyx":294 + * + * # Return -1 if point lies outside bounds + * if not (0 <= loc.x < resolution and 0<= loc.y < resolution and 0 <= loc.z < resolution): # <<<<<<<<<<<<<< + * return -1 + * + */ + __pyx_t_3 = (0 <= __pyx_v_loc.x); + if (__pyx_t_3) { + __pyx_t_3 = (__pyx_v_loc.x < __pyx_v_resolution); + } + __pyx_t_4 = (__pyx_t_3 != 0); + if (__pyx_t_4) { + } else { + __pyx_t_2 = __pyx_t_4; + goto __pyx_L4_bool_binop_done; + } + __pyx_t_4 = (0 <= __pyx_v_loc.y); + if (__pyx_t_4) { + __pyx_t_4 = (__pyx_v_loc.y < __pyx_v_resolution); + } + __pyx_t_3 = (__pyx_t_4 != 0); + if (__pyx_t_3) { + } else { + __pyx_t_2 = __pyx_t_3; + goto __pyx_L4_bool_binop_done; + } + __pyx_t_3 = (0 <= __pyx_v_loc.z); + if (__pyx_t_3) { + __pyx_t_3 = (__pyx_v_loc.z < __pyx_v_resolution); + } + __pyx_t_4 = (__pyx_t_3 != 0); + __pyx_t_2 = __pyx_t_4; + __pyx_L4_bool_binop_done:; + __pyx_t_4 = ((!__pyx_t_2) != 0); + if (__pyx_t_4) { + + /* "lib/libmise/mise.pyx":295 + * # Return -1 if point lies outside bounds + * if not (0 <= loc.x < resolution and 0<= loc.y < resolution and 0 <= loc.z < resolution): + * return -1 # <<<<<<<<<<<<<< + * + * # Coordinates in coarse voxel grid + */ + __pyx_r 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__Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(1, 778, __pyx_L1_error) } + + /* "View.MemoryView":779 + * return memoryview_fromslice(dst, new_ndim, + * memviewsliceobj.to_object_func, + * memviewsliceobj.to_dtype_func, # <<<<<<<<<<<<<< + * memview.dtype_is_object) + * else: + */ + if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(1, 779, __pyx_L1_error) } + + /* "View.MemoryView":777 + * + * if isinstance(memview, _memoryviewslice): + * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< + * memviewsliceobj.to_object_func, + * memviewsliceobj.to_dtype_func, + */ + __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 777, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_3); + if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(1, 777, __pyx_L1_error) + __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); + __pyx_t_3 = 0; + goto __pyx_L0; + + /* "View.MemoryView":776 + * new_ndim += 1 + * + * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< + * return memoryview_fromslice(dst, new_ndim, + * memviewsliceobj.to_object_func, + */ + } + + /* "View.MemoryView":782 + * memview.dtype_is_object) + * else: + * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< + * memview.dtype_is_object) + * + */ + /*else*/ { + __Pyx_XDECREF(((PyObject *)__pyx_r)); + + /* "View.MemoryView":783 + * else: + * return memoryview_fromslice(dst, new_ndim, NULL, NULL, + * memview.dtype_is_object) # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 782, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_3); + + /* "View.MemoryView":782 + * memview.dtype_is_object) + * else: + * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< + * memview.dtype_is_object) + * + */ + if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(1, 782, __pyx_L1_error) + __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); + __pyx_t_3 = 0; + goto __pyx_L0; + } + + /* "View.MemoryView":710 + * + * @cname('__pyx_memview_slice') + * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< + * cdef int new_ndim = 0, suboffset_dim = -1, dim + * cdef bint negative_step + */ + + /* function exit code */ + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_3); + __Pyx_XDECREF(__pyx_t_9); + __Pyx_AddTraceback("View.MemoryView.memview_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = 0; + __pyx_L0:; + __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj); + __Pyx_XDECREF(__pyx_v_index); + __Pyx_XGIVEREF((PyObject *)__pyx_r); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + +/* "View.MemoryView":807 + * + * @cname('__pyx_memoryview_slice_memviewslice') + * cdef int slice_memviewslice( # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, + */ + +static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) { + Py_ssize_t __pyx_v_new_shape; + int __pyx_v_negative_step; + int __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + + /* "View.MemoryView":827 + * cdef bint negative_step + * + * if not is_slice: # <<<<<<<<<<<<<< + * + * if start < 0: + */ + __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":829 + * if not is_slice: + * + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if not 0 <= start < shape: + */ + __pyx_t_1 = ((__pyx_v_start < 0) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":830 + * + * if start < 0: + * start += shape # <<<<<<<<<<<<<< + * if not 0 <= start < shape: + * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) + */ + __pyx_v_start = (__pyx_v_start + __pyx_v_shape); + + /* "View.MemoryView":829 + * if not is_slice: + * + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if not 0 <= start < shape: + */ + } + + /* "View.MemoryView":831 + * if start < 0: + * start += shape + * if not 0 <= start < shape: # <<<<<<<<<<<<<< + * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) + * else: + */ + __pyx_t_1 = (0 <= __pyx_v_start); + if (__pyx_t_1) { + __pyx_t_1 = (__pyx_v_start < __pyx_v_shape); + } + __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":832 + * start += shape + * if not 0 <= start < shape: + * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) # <<<<<<<<<<<<<< + * else: + * + */ + __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 832, __pyx_L1_error) + + /* "View.MemoryView":831 + * if start < 0: + * start += shape + * if not 0 <= start < shape: # <<<<<<<<<<<<<< + * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) + * else: + */ + } + + /* "View.MemoryView":827 + * cdef bint negative_step + * + * if not is_slice: # <<<<<<<<<<<<<< + * + * if start < 0: + */ + goto __pyx_L3; + } + + /* "View.MemoryView":835 + * else: + * + * negative_step = have_step != 0 and step < 0 # <<<<<<<<<<<<<< + * + * if have_step and step == 0: + */ + /*else*/ { + __pyx_t_1 = ((__pyx_v_have_step != 0) != 0); + if (__pyx_t_1) { + } else { + __pyx_t_2 = __pyx_t_1; + goto __pyx_L6_bool_binop_done; + } + __pyx_t_1 = ((__pyx_v_step < 0) != 0); + __pyx_t_2 = __pyx_t_1; + __pyx_L6_bool_binop_done:; + __pyx_v_negative_step = __pyx_t_2; + + /* "View.MemoryView":837 + * negative_step = have_step != 0 and step < 0 + * + * if have_step and step == 0: # <<<<<<<<<<<<<< + * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) + * + */ + __pyx_t_1 = (__pyx_v_have_step != 0); + if (__pyx_t_1) { + } else { + __pyx_t_2 = __pyx_t_1; + goto __pyx_L9_bool_binop_done; + } + __pyx_t_1 = ((__pyx_v_step == 0) != 0); + __pyx_t_2 = __pyx_t_1; + __pyx_L9_bool_binop_done:; + if (__pyx_t_2) { + + /* "View.MemoryView":838 + * + * if have_step and step == 0: + * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 838, __pyx_L1_error) + + /* "View.MemoryView":837 + * negative_step = have_step != 0 and step < 0 + * + * if have_step and step == 0: # <<<<<<<<<<<<<< + * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) + * + */ + } + + /* "View.MemoryView":841 + * + * + * if have_start: # <<<<<<<<<<<<<< + * if start < 0: + * start += shape + */ + __pyx_t_2 = (__pyx_v_have_start != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":842 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: + */ + __pyx_t_2 = ((__pyx_v_start < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":843 + * if have_start: + * if start < 0: + * start += shape # <<<<<<<<<<<<<< + * if start < 0: + * start = 0 + */ + __pyx_v_start = (__pyx_v_start + __pyx_v_shape); + + /* "View.MemoryView":844 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: + */ + __pyx_t_2 = ((__pyx_v_start < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":845 + * start += shape + * if start < 0: + * start = 0 # <<<<<<<<<<<<<< + * elif start >= shape: + * if negative_step: + */ + __pyx_v_start = 0; + + /* "View.MemoryView":844 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: + */ + } + + /* "View.MemoryView":842 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: + */ + goto __pyx_L12; + } + + /* "View.MemoryView":846 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 + */ + __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":847 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + __pyx_t_2 = (__pyx_v_negative_step != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":848 + * elif start >= shape: + * if negative_step: + * start = shape - 1 # <<<<<<<<<<<<<< + * else: + * start = shape + */ + __pyx_v_start = (__pyx_v_shape - 1); + + /* "View.MemoryView":847 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + goto __pyx_L14; + } + + /* "View.MemoryView":850 + * start = shape - 1 + * else: + * start = shape # <<<<<<<<<<<<<< + * else: + * if negative_step: + */ + /*else*/ { + __pyx_v_start = __pyx_v_shape; + } + __pyx_L14:; + + /* "View.MemoryView":846 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 + */ + } + __pyx_L12:; + + /* "View.MemoryView":841 + * + * + * if have_start: # <<<<<<<<<<<<<< + * if start < 0: + * start += shape + */ + goto __pyx_L11; + } + + /* "View.MemoryView":852 + * start = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + /*else*/ { + __pyx_t_2 = (__pyx_v_negative_step != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":853 + * else: + * if negative_step: + * start = shape - 1 # <<<<<<<<<<<<<< + * else: + * start = 0 + */ + __pyx_v_start = (__pyx_v_shape - 1); + + /* "View.MemoryView":852 + * start = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + goto __pyx_L15; + } + + /* "View.MemoryView":855 + * start = shape - 1 + * else: + * start = 0 # <<<<<<<<<<<<<< + * + * if have_stop: + */ + /*else*/ { + __pyx_v_start = 0; + } + __pyx_L15:; + } + __pyx_L11:; + + /* "View.MemoryView":857 + * start = 0 + * + * if have_stop: # <<<<<<<<<<<<<< + * if stop < 0: + * stop += shape + */ + __pyx_t_2 = (__pyx_v_have_stop != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":858 + * + * if have_stop: + * if stop < 0: # <<<<<<<<<<<<<< + * stop += shape + * if stop < 0: + */ + __pyx_t_2 = ((__pyx_v_stop < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":859 + * if have_stop: + * if stop < 0: + * stop += shape # <<<<<<<<<<<<<< + * if stop < 0: + * stop = 0 + */ + __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); + + /* "View.MemoryView":860 + * if stop < 0: + * stop += shape + * if stop < 0: # <<<<<<<<<<<<<< + * stop = 0 + * elif stop > shape: + */ + __pyx_t_2 = ((__pyx_v_stop < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":861 + * stop += shape + * if stop < 0: + * stop = 0 # <<<<<<<<<<<<<< + * elif stop > shape: + * stop = shape + */ + __pyx_v_stop = 0; + + /* "View.MemoryView":860 + * if stop < 0: + * stop += shape + * if stop < 0: # <<<<<<<<<<<<<< + * stop = 0 + * elif stop > shape: + */ + } + + /* "View.MemoryView":858 + * + * if have_stop: + * if stop < 0: # <<<<<<<<<<<<<< + * stop += shape + * if stop < 0: + */ + goto __pyx_L17; + } + + /* "View.MemoryView":862 + * if stop < 0: + * stop = 0 + * elif stop > shape: # <<<<<<<<<<<<<< + * stop = shape + * else: + */ + __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":863 + * stop = 0 + * elif stop > shape: + * stop = shape # <<<<<<<<<<<<<< + * else: + * if negative_step: + */ + __pyx_v_stop = __pyx_v_shape; + + /* "View.MemoryView":862 + * if stop < 0: + * stop = 0 + * elif stop > shape: # <<<<<<<<<<<<<< + * stop = shape + * else: + */ + } + __pyx_L17:; + + /* "View.MemoryView":857 + * start = 0 + * + * if have_stop: # <<<<<<<<<<<<<< + * if stop < 0: + * stop += shape + */ + goto __pyx_L16; + } + + /* "View.MemoryView":865 + * stop = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * stop = -1 + * else: + */ + /*else*/ { + __pyx_t_2 = (__pyx_v_negative_step != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":866 + * else: + * if negative_step: + * stop = -1 # <<<<<<<<<<<<<< + * else: + * stop = shape + */ + __pyx_v_stop = -1L; + + /* "View.MemoryView":865 + * stop = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * stop = -1 + * else: + */ + goto __pyx_L19; + } + + /* "View.MemoryView":868 + * stop = -1 + * else: + * stop = shape # <<<<<<<<<<<<<< + * + * if not have_step: + */ + /*else*/ { + __pyx_v_stop = __pyx_v_shape; + } + __pyx_L19:; + } + __pyx_L16:; + + /* "View.MemoryView":870 + * stop = shape + * + * if not have_step: # <<<<<<<<<<<<<< + * step = 1 + * + */ + __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":871 + * + * if not have_step: + * step = 1 # <<<<<<<<<<<<<< + * + * + */ + __pyx_v_step = 1; + + /* "View.MemoryView":870 + * stop = shape + * + * if not have_step: # <<<<<<<<<<<<<< + * step = 1 + * + */ + } + + /* "View.MemoryView":875 + * + * with cython.cdivision(True): + * new_shape = (stop - start) // step # <<<<<<<<<<<<<< + * + * if (stop - start) - step * new_shape: + */ + __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step); + + /* "View.MemoryView":877 + * new_shape = (stop - start) // step + * + * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< + * new_shape += 1 + * + */ + __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":878 + * + * if (stop - start) - step * new_shape: + * new_shape += 1 # <<<<<<<<<<<<<< + * + * if new_shape < 0: + */ + __pyx_v_new_shape = (__pyx_v_new_shape + 1); + + /* "View.MemoryView":877 + * new_shape = (stop - start) // step + * + * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< + * new_shape += 1 + * + */ + } + + /* "View.MemoryView":880 + * new_shape += 1 + * + * if new_shape < 0: # <<<<<<<<<<<<<< + * new_shape = 0 + * + */ + __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":881 + * + * if new_shape < 0: + * new_shape = 0 # <<<<<<<<<<<<<< + * + * + */ + __pyx_v_new_shape = 0; + + /* "View.MemoryView":880 + * new_shape += 1 + * + * if new_shape < 0: # <<<<<<<<<<<<<< + * new_shape = 0 + * + */ + } + + /* "View.MemoryView":884 + * + * + * dst.strides[new_ndim] = stride * step # <<<<<<<<<<<<<< + * dst.shape[new_ndim] = new_shape + * dst.suboffsets[new_ndim] = suboffset + */ + (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step); + + /* 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(*to_dtype_func)(char *, object) except 0 + * + * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< + * to_object_func = (<_memoryviewslice> memview).to_object_func + * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func + */ + __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); + __pyx_t_2 = (__pyx_t_1 != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1095 + * + * if isinstance(memview, _memoryviewslice): + * to_object_func = (<_memoryviewslice> memview).to_object_func # <<<<<<<<<<<<<< + * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func + * else: + */ + __pyx_t_3 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_object_func; + __pyx_v_to_object_func = __pyx_t_3; + + /* "View.MemoryView":1096 + * if isinstance(memview, _memoryviewslice): + * to_object_func = (<_memoryviewslice> memview).to_object_func + * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func # <<<<<<<<<<<<<< + * else: + * to_object_func = NULL + */ + __pyx_t_4 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_dtype_func; + __pyx_v_to_dtype_func = __pyx_t_4; + + /* "View.MemoryView":1094 + * cdef int (*to_dtype_func)(char *, object) except 0 + * + * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< + * to_object_func = (<_memoryviewslice> memview).to_object_func + * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1098 + * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func + * else: + * to_object_func = NULL # <<<<<<<<<<<<<< + * to_dtype_func = NULL + * + */ + /*else*/ { + __pyx_v_to_object_func = NULL; + + /* "View.MemoryView":1099 + * else: + * to_object_func = NULL + * to_dtype_func = NULL # <<<<<<<<<<<<<< + * + * return memoryview_fromslice(memviewslice[0], memview.view.ndim, + */ + __pyx_v_to_dtype_func = NULL; + } + __pyx_L3:; + + /* "View.MemoryView":1101 + * to_dtype_func = NULL + * + * return memoryview_fromslice(memviewslice[0], memview.view.ndim, # <<<<<<<<<<<<<< + * to_object_func, to_dtype_func, + * memview.dtype_is_object) + */ + __Pyx_XDECREF(__pyx_r); + + /* "View.MemoryView":1103 + * return memoryview_fromslice(memviewslice[0], memview.view.ndim, + * to_object_func, to_dtype_func, + * memview.dtype_is_object) # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) __PYX_ERR(1, 1101, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_5); + __pyx_r = __pyx_t_5; + __pyx_t_5 = 0; + goto __pyx_L0; + + /* "View.MemoryView":1087 + * + * @cname('__pyx_memoryview_copy_object_from_slice') + * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< + * """ + * Create a new memoryview object from a given memoryview object and slice. + */ + + /* function exit code */ + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_5); + __Pyx_AddTraceback("View.MemoryView.memoryview_copy_from_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = 0; + __pyx_L0:; + __Pyx_XGIVEREF(__pyx_r); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + +/* "View.MemoryView":1109 + * + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< + * if arg < 0: + * return -arg + */ + +static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { + Py_ssize_t __pyx_r; + int __pyx_t_1; + + /* "View.MemoryView":1110 + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: + * if arg < 0: # <<<<<<<<<<<<<< + * return -arg + * else: + */ + __pyx_t_1 = ((__pyx_v_arg < 0) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":1111 + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: + * if arg < 0: + * return -arg # <<<<<<<<<<<<<< + * else: + * return arg + */ + __pyx_r = (-__pyx_v_arg); + goto __pyx_L0; + + /* "View.MemoryView":1110 + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: + * if arg < 0: # <<<<<<<<<<<<<< + * return -arg + * else: + */ + } + + /* "View.MemoryView":1113 + * return -arg + * else: + * return arg # <<<<<<<<<<<<<< + * + * @cname('__pyx_get_best_slice_order') + */ + /*else*/ { + __pyx_r = __pyx_v_arg; + goto __pyx_L0; + } + + /* "View.MemoryView":1109 + * + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< + * if arg < 0: + * return -arg + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1116 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + +static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) { + int __pyx_v_i; + Py_ssize_t __pyx_v_c_stride; + Py_ssize_t __pyx_v_f_stride; + char __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1121 + * """ + * cdef int i + * cdef Py_ssize_t c_stride = 0 # <<<<<<<<<<<<<< + * cdef Py_ssize_t f_stride = 0 + * + */ + __pyx_v_c_stride = 0; + + /* "View.MemoryView":1122 + * cdef int i + * cdef Py_ssize_t c_stride = 0 + * cdef Py_ssize_t f_stride = 0 # <<<<<<<<<<<<<< + * + * for i in range(ndim - 1, -1, -1): + */ + __pyx_v_f_stride = 0; + + /* "View.MemoryView":1124 + * cdef Py_ssize_t f_stride = 0 + * + * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] + */ + for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { + __pyx_v_i = __pyx_t_1; + + /* "View.MemoryView":1125 + * + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * c_stride = mslice.strides[i] + * break + */ + __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1126 + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] # <<<<<<<<<<<<<< + * break + * + */ + __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1127 + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] + * break # <<<<<<<<<<<<<< + * + * for i in range(ndim): + */ + goto __pyx_L4_break; + + /* "View.MemoryView":1125 + * + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * c_stride = mslice.strides[i] + * break + */ + } + } + __pyx_L4_break:; + + /* "View.MemoryView":1129 + * break + * + * for i in range(ndim): # <<<<<<<<<<<<<< + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] + */ + __pyx_t_1 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_1; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_i = __pyx_t_4; + + /* "View.MemoryView":1130 + * + * for i in range(ndim): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * f_stride = mslice.strides[i] + * break + */ + __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1131 + * for i in range(ndim): + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< + * break + * + */ + __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1132 + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] + * break # <<<<<<<<<<<<<< + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + */ + goto __pyx_L7_break; + + /* "View.MemoryView":1130 + * + * for i in range(ndim): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * f_stride = mslice.strides[i] + * break + */ + } + } + __pyx_L7_break:; + + /* "View.MemoryView":1134 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1135 + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + * return 'C' # <<<<<<<<<<<<<< + * else: + * return 'F' + */ + __pyx_r = 'C'; + goto __pyx_L0; + + /* "View.MemoryView":1134 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + } + + /* "View.MemoryView":1137 + * return 'C' + * else: + * return 'F' # <<<<<<<<<<<<<< + * + * @cython.cdivision(True) + */ + /*else*/ { + __pyx_r = 'F'; + goto __pyx_L0; + } + + /* "View.MemoryView":1116 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1140 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + +static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + CYTHON_UNUSED Py_ssize_t __pyx_v_i; + CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; + Py_ssize_t __pyx_v_dst_extent; + Py_ssize_t __pyx_v_src_stride; + Py_ssize_t __pyx_v_dst_stride; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + Py_ssize_t __pyx_t_4; + Py_ssize_t __pyx_t_5; + Py_ssize_t __pyx_t_6; + + /* "View.MemoryView":1147 + * + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + */ + __pyx_v_src_extent = (__pyx_v_src_shape[0]); + + /* "View.MemoryView":1148 + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] + */ + __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); + + /* "View.MemoryView":1149 + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + */ + __pyx_v_src_stride = (__pyx_v_src_strides[0]); + + /* "View.MemoryView":1150 + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< + * + * if ndim == 1: + */ + __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); + + /* "View.MemoryView":1152 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":1153 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + + /* "View.MemoryView":1154 + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + */ + __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); + if (__pyx_t_2) { + __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); + } + __pyx_t_3 = (__pyx_t_2 != 0); + __pyx_t_1 = __pyx_t_3; + __pyx_L5_bool_binop_done:; + + /* "View.MemoryView":1153 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + if (__pyx_t_1) { + + /* "View.MemoryView":1155 + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); + + /* "View.MemoryView":1153 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + goto __pyx_L4; + } + + /* "View.MemoryView":1157 + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + */ + /*else*/ { + __pyx_t_4 = __pyx_v_dst_extent; + __pyx_t_5 = __pyx_t_4; + for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { + __pyx_v_i = __pyx_t_6; + + /* "View.MemoryView":1158 + * else: + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< + * src_data += src_stride + * dst_data += dst_stride + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); + + /* "View.MemoryView":1159 + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * else: + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1160 + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L4:; + + /* "View.MemoryView":1152 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1162 + * dst_data += dst_stride + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * _copy_strided_to_strided(src_data, src_strides + 1, + * dst_data, dst_strides + 1, + */ + /*else*/ { + __pyx_t_4 = __pyx_v_dst_extent; + __pyx_t_5 = __pyx_t_4; + for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { + __pyx_v_i = __pyx_t_6; + + /* "View.MemoryView":1163 + * else: + * for i in range(dst_extent): + * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< + * dst_data, dst_strides + 1, + * src_shape + 1, dst_shape + 1, + */ + _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); + + /* "View.MemoryView":1167 + * src_shape + 1, dst_shape + 1, + * ndim - 1, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1168 + * ndim - 1, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L3:; + + /* "View.MemoryView":1140 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + + /* function exit code */ +} + +/* "View.MemoryView":1170 + * dst_data += dst_stride + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) nogil: 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"Return the size of the memory occupied by the slice in number of bytes" + * cdef Py_ssize_t shape, size = src.memview.view.itemsize + */ + +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { + Py_ssize_t __pyx_v_shape; + Py_ssize_t __pyx_v_size; + Py_ssize_t __pyx_r; + Py_ssize_t __pyx_t_1; + Py_ssize_t *__pyx_t_2; + Py_ssize_t *__pyx_t_3; + Py_ssize_t *__pyx_t_4; + + /* "View.MemoryView":1179 + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef Py_ssize_t shape, size = src.memview.view.itemsize # <<<<<<<<<<<<<< + * + * for shape in src.shape[:ndim]: + */ + __pyx_t_1 = __pyx_v_src->memview->view.itemsize; + __pyx_v_size = __pyx_t_1; + + /* "View.MemoryView":1181 + * cdef Py_ssize_t shape, size = src.memview.view.itemsize + * + * for shape in src.shape[:ndim]: # <<<<<<<<<<<<<< + * size *= shape + * + */ + __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim); + for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { + __pyx_t_2 = __pyx_t_4; + __pyx_v_shape = (__pyx_t_2[0]); + + /* "View.MemoryView":1182 + * + * for shape in src.shape[:ndim]: + * size *= shape # <<<<<<<<<<<<<< + * + * return size + */ + __pyx_v_size = (__pyx_v_size * __pyx_v_shape); + } + + /* "View.MemoryView":1184 + * size *= shape + * + * return size # <<<<<<<<<<<<<< + * + * @cname('__pyx_fill_contig_strides_array') + */ + __pyx_r = __pyx_v_size; + goto __pyx_L0; + + /* "View.MemoryView":1177 + * + * @cname('__pyx_memoryview_slice_get_size') + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef Py_ssize_t shape, size = src.memview.view.itemsize + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1187 + * + * @cname('__pyx_fill_contig_strides_array') + * 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slice_is_contig(dst, 'F', ndim) + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); + + /* "View.MemoryView":1313 + * + * + * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + */ + goto __pyx_L12; + } + + /* "View.MemoryView":1315 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1316 + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< + * + * if direct_copy: + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); + + /* "View.MemoryView":1315 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + } + __pyx_L12:; + + /* "View.MemoryView":1318 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + */ + __pyx_t_2 = (__pyx_v_direct_copy != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1320 + * if direct_copy: + * + * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, True) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1321 + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< + * refcount_copying(&dst, dtype_is_object, ndim, True) + * free(tmpdata) + */ + (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim))); + + /* "View.MemoryView":1322 + * refcount_copying(&dst, dtype_is_object, ndim, False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< + * free(tmpdata) + * return 0 + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1323 + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, True) + * free(tmpdata) # <<<<<<<<<<<<<< + * return 0 + * + */ + free(__pyx_v_tmpdata); + + /* "View.MemoryView":1324 + * refcount_copying(&dst, dtype_is_object, ndim, True) + * free(tmpdata) + * return 0 # <<<<<<<<<<<<<< + * + * if order == 'F' == get_best_order(&dst, ndim): + */ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":1318 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + */ + } + + /* "View.MemoryView":1310 + * src = tmp + * + * if not broadcasting: # <<<<<<<<<<<<<< + * + * + */ + } + + /* "View.MemoryView":1326 + * return 0 + * + * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_2 = (__pyx_v_order == 'F'); + if (__pyx_t_2) { + __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); + } + __pyx_t_8 = (__pyx_t_2 != 0); + if (__pyx_t_8) { + + /* "View.MemoryView":1329 + * + * + * transpose_memslice(&src) # <<<<<<<<<<<<<< + * transpose_memslice(&dst) + * + */ + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1329, __pyx_L1_error) + + /* "View.MemoryView":1330 + * + * transpose_memslice(&src) + * transpose_memslice(&dst) # <<<<<<<<<<<<<< + * + * 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__pyx_array_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_array, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + 0, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_array, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_array, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + __pyx_tp_getattro_array, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_array, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/ + 0, /*tp_doc*/ + 0, /*tp_traverse*/ + 0, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_array, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_array, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_array, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif + #if PY_VERSION_HEX >= 0x030800b1 + 0, /*tp_vectorcall*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 + 0, /*tp_print*/ + #endif +}; + +static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { + struct __pyx_MemviewEnum_obj *p; + PyObject *o; + if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + p = ((struct __pyx_MemviewEnum_obj *)o); + p->name = Py_None; Py_INCREF(Py_None); + return o; +} + +static void __pyx_tp_dealloc_Enum(PyObject *o) { + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + PyObject_GC_UnTrack(o); + Py_CLEAR(p->name); + (*Py_TYPE(o)->tp_free)(o); +} + +static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + if (p->name) { + e = (*v)(p->name, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_Enum(PyObject *o) { + PyObject* tmp; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + tmp = ((PyObject*)p->name); + p->name = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + return 0; +} + +static PyMethodDef __pyx_methods_Enum[] = { + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static PyTypeObject __pyx_type___pyx_MemviewEnum = { + PyVarObject_HEAD_INIT(0, 0) + "lib.libmise.mise.Enum", /*tp_name*/ + sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_Enum, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_MemviewEnum___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_Enum, /*tp_traverse*/ + __pyx_tp_clear_Enum, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_Enum, /*tp_methods*/ + 0, /*tp_members*/ + 0, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + __pyx_MemviewEnum___init__, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_Enum, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif + #if PY_VERSION_HEX >= 0x030800b1 + 0, /*tp_vectorcall*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 + 0, /*tp_print*/ + #endif +}; +static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview; + +static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryview_obj *p; + PyObject *o; + if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + p = ((struct __pyx_memoryview_obj *)o); + p->__pyx_vtab = __pyx_vtabptr_memoryview; + p->obj = Py_None; Py_INCREF(Py_None); + p->_size = Py_None; Py_INCREF(Py_None); + p->_array_interface = Py_None; Py_INCREF(Py_None); + p->view.obj = NULL; + if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; + return o; + bad: + Py_DECREF(o); o = 0; + return NULL; +} + +static void __pyx_tp_dealloc_memoryview(PyObject *o) { + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); + __pyx_memoryview___dealloc__(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->obj); + Py_CLEAR(p->_size); + Py_CLEAR(p->_array_interface); + (*Py_TYPE(o)->tp_free)(o); +} + +static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + if (p->obj) { + e = (*v)(p->obj, a); if (e) return e; + } + if (p->_size) { + e = (*v)(p->_size, a); if (e) return e; + } + if (p->_array_interface) { + e = (*v)(p->_array_interface, a); if (e) return e; + } + if (p->view.obj) { + e = (*v)(p->view.obj, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_memoryview(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + tmp = ((PyObject*)p->obj); + p->obj = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_size); + p->_size = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_array_interface); + p->_array_interface = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + Py_CLEAR(p->view.obj); + return 0; +} +static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { + PyObject *r; + PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; + r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); + Py_DECREF(x); + return r; +} + +static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { + if (v) { + return __pyx_memoryview___setitem__(o, i, v); + } + else { + PyErr_Format(PyExc_NotImplementedError, + "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); + return -1; + } +} + +static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); +} + +static PyMethodDef __pyx_methods_memoryview[] = { + {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0}, + {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0}, + {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0}, + {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0}, + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_memoryview[] = { + {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, + {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, + {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, + {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0}, + {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0}, + {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0}, + {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0}, + {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0}, + {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; + +static PySequenceMethods __pyx_tp_as_sequence_memoryview = { + __pyx_memoryview___len__, /*sq_length*/ + 0, /*sq_concat*/ + 0, /*sq_repeat*/ + __pyx_sq_item_memoryview, /*sq_item*/ + 0, /*sq_slice*/ + 0, /*sq_ass_item*/ + 0, /*sq_ass_slice*/ + 0, /*sq_contains*/ + 0, /*sq_inplace_concat*/ + 0, /*sq_inplace_repeat*/ +}; + +static PyMappingMethods __pyx_tp_as_mapping_memoryview = { + __pyx_memoryview___len__, /*mp_length*/ + __pyx_memoryview___getitem__, /*mp_subscript*/ + __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ +}; + +static PyBufferProcs __pyx_tp_as_buffer_memoryview = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_memoryview_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; + +static PyTypeObject __pyx_type___pyx_memoryview = { + PyVarObject_HEAD_INIT(0, 0) + "lib.libmise.mise.memoryview", /*tp_name*/ + sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_memoryview___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + __pyx_memoryview___str__, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_memoryview, /*tp_traverse*/ + __pyx_tp_clear_memoryview, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_memoryview, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_memoryview, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_memoryview, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif + #if PY_VERSION_HEX >= 0x030800b1 + 0, /*tp_vectorcall*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 + 0, /*tp_print*/ + #endif +}; +static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; + +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryviewslice_obj *p; + PyObject *o = __pyx_tp_new_memoryview(t, a, k); + if (unlikely(!o)) return 0; + p = ((struct __pyx_memoryviewslice_obj *)o); + p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; + p->from_object = Py_None; Py_INCREF(Py_None); + p->from_slice.memview = NULL; + return o; +} + +static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); + __pyx_memoryviewslice___dealloc__(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->from_object); + PyObject_GC_Track(o); + __pyx_tp_dealloc_memoryview(o); +} + +static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; + if (p->from_object) { + e = (*v)(p->from_object, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear__memoryviewslice(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + __pyx_tp_clear_memoryview(o); + tmp = ((PyObject*)p->from_object); + p->from_object = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + __PYX_XDEC_MEMVIEW(&p->from_slice, 1); + return 0; +} + +static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o); +} + +static PyMethodDef __pyx_methods__memoryviewslice[] = { + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = { + {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; + +static PyTypeObject __pyx_type___pyx_memoryviewslice = { + PyVarObject_HEAD_INIT(0, 0) + "lib.libmise.mise._memoryviewslice", /*tp_name*/ + sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + #if CYTHON_COMPILING_IN_PYPY + __pyx_memoryview___repr__, /*tp_repr*/ + #else + 0, /*tp_repr*/ + #endif + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + #if CYTHON_COMPILING_IN_PYPY + __pyx_memoryview___str__, /*tp_str*/ + #else + 0, /*tp_str*/ + #endif + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + "Internal class for passing memoryview slices to Python", /*tp_doc*/ + __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/ + __pyx_tp_clear__memoryviewslice, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods__memoryviewslice, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets__memoryviewslice, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new__memoryviewslice, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif + #if PY_VERSION_HEX >= 0x030800b1 + 0, /*tp_vectorcall*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 + 0, /*tp_print*/ + #endif +}; + +static PyMethodDef __pyx_methods[] = { + {0, 0, 0, 0} +}; + +#if PY_MAJOR_VERSION >= 3 +#if CYTHON_PEP489_MULTI_PHASE_INIT +static PyObject* __pyx_pymod_create(PyObject *spec, PyModuleDef *def); /*proto*/ +static int __pyx_pymod_exec_mise(PyObject* module); /*proto*/ +static PyModuleDef_Slot __pyx_moduledef_slots[] = { + {Py_mod_create, (void*)__pyx_pymod_create}, + {Py_mod_exec, (void*)__pyx_pymod_exec_mise}, + {0, NULL} +}; +#endif + +static struct PyModuleDef __pyx_moduledef = { + PyModuleDef_HEAD_INIT, + "mise", + 0, /* m_doc */ + 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PyInterpreterState_GetID(PyThreadState_Get()->interp); + if (main_interpreter_id == -1) { + main_interpreter_id = current_id; + return (unlikely(current_id == -1)) ? -1 : 0; + } else if (unlikely(main_interpreter_id != current_id)) + #else + static PyInterpreterState *main_interpreter = NULL; + PyInterpreterState *current_interpreter = PyThreadState_Get()->interp; + if (!main_interpreter) { + main_interpreter = current_interpreter; + } else if (unlikely(main_interpreter != current_interpreter)) + #endif + { + PyErr_SetString( + PyExc_ImportError, + "Interpreter change detected - this module can only be loaded into one interpreter per process."); + return -1; + } + return 0; +} +static CYTHON_SMALL_CODE int __Pyx_copy_spec_to_module(PyObject *spec, PyObject *moddict, const char* from_name, const char* to_name, int allow_none) { + PyObject *value = PyObject_GetAttrString(spec, from_name); + int result = 0; + if (likely(value)) { + if (allow_none || value != Py_None) { + result = 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+ __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_1); + if (__pyx_m) { + if (__pyx_d) { + __Pyx_AddTraceback("init lib.libmise.mise", __pyx_clineno, __pyx_lineno, __pyx_filename); + } + Py_CLEAR(__pyx_m); + } else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_ImportError, "init lib.libmise.mise"); + } + __pyx_L0:; + __Pyx_RefNannyFinishContext(); + #if CYTHON_PEP489_MULTI_PHASE_INIT + return (__pyx_m != NULL) ? 0 : -1; + #elif PY_MAJOR_VERSION >= 3 + return __pyx_m; + #else + return; + #endif +} + +/* --- Runtime support code --- */ +/* Refnanny */ +#if CYTHON_REFNANNY +static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { + PyObject *m = NULL, *p = NULL; + void *r = NULL; + m = PyImport_ImportModule(modname); + if (!m) goto end; + p = PyObject_GetAttrString(m, "RefNannyAPI"); + if (!p) goto end; + r = PyLong_AsVoidPtr(p); +end: + Py_XDECREF(p); + Py_XDECREF(m); + return (__Pyx_RefNannyAPIStruct *)r; +} +#endif + +/* PyObjectGetAttrStr */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#endif + +/* GetBuiltinName */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name) { + PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); + if (unlikely(!result)) { + PyErr_Format(PyExc_NameError, +#if PY_MAJOR_VERSION >= 3 + "name '%U' is not defined", name); +#else + "name '%.200s' is not defined", PyString_AS_STRING(name)); +#endif + } + return result; +} + +/* RaiseArgTupleInvalid */ +static void __Pyx_RaiseArgtupleInvalid( + const char* func_name, + int exact, + Py_ssize_t num_min, + Py_ssize_t num_max, + Py_ssize_t num_found) +{ + Py_ssize_t num_expected; + const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +/* RaiseDoubleKeywords */ +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +/* ParseKeywords */ +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + while (PyDict_Next(kwds, &pos, &key, &value)) { + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = (**name == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +/* BufferIndexError */ +static void __Pyx_RaiseBufferIndexError(int axis) { + PyErr_Format(PyExc_IndexError, + "Out of bounds on buffer access (axis %d)", axis); +} + +/* PyObjectCall */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = func->ob_type->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyErrFetchRestore */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +} +#endif + +/* RaiseException */ +#if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + __Pyx_PyThreadState_declare + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_PyThreadState_assign + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } + if (cause) { + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if CYTHON_COMPILING_IN_PYPY + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#else + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +/* MemviewSliceInit */ +static int +__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference) +{ + __Pyx_RefNannyDeclarations + int i, retval=-1; + Py_buffer *buf = &memview->view; + __Pyx_RefNannySetupContext("init_memviewslice", 0); + if (unlikely(memviewslice->memview || memviewslice->data)) { + PyErr_SetString(PyExc_ValueError, + "memviewslice is already initialized!"); + goto fail; + } + if (buf->strides) { + for (i = 0; i < ndim; i++) { + memviewslice->strides[i] = buf->strides[i]; + } + } else { + Py_ssize_t stride = buf->itemsize; + for (i = ndim - 1; i >= 0; i--) { + memviewslice->strides[i] = stride; + stride *= buf->shape[i]; + } + } + for (i = 0; i < ndim; i++) { + memviewslice->shape[i] = buf->shape[i]; + if (buf->suboffsets) { + memviewslice->suboffsets[i] = buf->suboffsets[i]; + } else { + memviewslice->suboffsets[i] = -1; + } + } + memviewslice->memview = memview; + memviewslice->data = (char *)buf->buf; + if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { + Py_INCREF(memview); + } + retval = 0; + goto no_fail; +fail: + memviewslice->memview = 0; + memviewslice->data = 0; + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} +#ifndef Py_NO_RETURN +#define Py_NO_RETURN +#endif +static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { + va_list vargs; + char msg[200]; +#ifdef HAVE_STDARG_PROTOTYPES + va_start(vargs, fmt); +#else + va_start(vargs); +#endif + vsnprintf(msg, 200, fmt, vargs); + va_end(vargs); + Py_FatalError(msg); +} +static CYTHON_INLINE int +__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)++; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE int +__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)--; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE void +__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) +{ + int first_time; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (unlikely(!memview || (PyObject *) memview == Py_None)) + return; + if (unlikely(__pyx_get_slice_count(memview) < 0)) + __pyx_fatalerror("Acquisition count is %d (line %d)", + __pyx_get_slice_count(memview), lineno); + first_time = __pyx_add_acquisition_count(memview) == 0; + if (unlikely(first_time)) { + if (have_gil) { + Py_INCREF((PyObject *) memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_INCREF((PyObject *) memview); + PyGILState_Release(_gilstate); + } + } +} +static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, + int have_gil, int lineno) { + int last_time; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (unlikely(!memview || (PyObject *) memview == Py_None)) { + memslice->memview = NULL; + return; + } + if (unlikely(__pyx_get_slice_count(memview) <= 0)) + __pyx_fatalerror("Acquisition count is %d (line %d)", + __pyx_get_slice_count(memview), lineno); + last_time = __pyx_sub_acquisition_count(memview) == 1; + memslice->data = NULL; + if (unlikely(last_time)) { + if (have_gil) { + Py_CLEAR(memslice->memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_CLEAR(memslice->memview); + PyGILState_Release(_gilstate); + } + } else { + memslice->memview = NULL; + } +} + +/* PyDictVersioning */ +#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { + PyObject **dictptr = NULL; + Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; + if (offset) { +#if CYTHON_COMPILING_IN_CPYTHON + dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); +#else + dictptr = _PyObject_GetDictPtr(obj); +#endif + } + return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; +} +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) + return 0; + return obj_dict_version == __Pyx_get_object_dict_version(obj); +} +#endif + +/* GetModuleGlobalName */ +#if CYTHON_USE_DICT_VERSIONS +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) +#else +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) +#endif +{ + PyObject *result; +#if !CYTHON_AVOID_BORROWED_REFS +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 + result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } else if (unlikely(PyErr_Occurred())) { + return NULL; + } +#else + result = PyDict_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } +#endif +#else + result = PyObject_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } + PyErr_Clear(); +#endif + return __Pyx_GetBuiltinName(name); +} + +/* PyFunctionFastCall */ +#if CYTHON_FAST_PYCALL +static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, + PyObject *globals) { + PyFrameObject *f; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject **fastlocals; + Py_ssize_t i; + PyObject *result; + assert(globals != NULL); + /* XXX Perhaps we should create a specialized + PyFrame_New() that doesn't take locals, but does + take builtins without sanity checking them. + */ + assert(tstate != NULL); + f = PyFrame_New(tstate, co, globals, NULL); + if (f == NULL) { + return NULL; + } + fastlocals = __Pyx_PyFrame_GetLocalsplus(f); + for (i = 0; i < na; i++) { + Py_INCREF(*args); + fastlocals[i] = *args++; + } + result = PyEval_EvalFrameEx(f,0); + ++tstate->recursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; +#if PY_MAJOR_VERSION >= 3 + PyObject *kwdefs; +#endif + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { + return NULL; + } + if ( +#if PY_MAJOR_VERSION >= 3 + co->co_kwonlyargcount == 0 && +#endif + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); +#if PY_MAJOR_VERSION >= 3 + kwdefs = PyFunction_GET_KW_DEFAULTS(func); +#endif + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } +#if PY_MAJOR_VERSION >= 3 + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, closure); +#endif + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif +#endif + +/* PyCFunctionFastCall */ +#if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { + PyCFunctionObject *func = (PyCFunctionObject*)func_obj; + PyCFunction meth = PyCFunction_GET_FUNCTION(func); + PyObject *self = PyCFunction_GET_SELF(func); + int flags = PyCFunction_GET_FLAGS(func); + assert(PyCFunction_Check(func)); + assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))); + assert(nargs >= 0); + assert(nargs == 0 || args != NULL); + /* _PyCFunction_FastCallDict() must not be called with an exception set, + because it may clear it (directly or indirectly) and so the + caller loses its exception */ + assert(!PyErr_Occurred()); + if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { + return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL); + } else { + return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs); + } +} +#endif + +/* WriteUnraisableException */ +static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno, + CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename, + int full_traceback, CYTHON_UNUSED int nogil) { + PyObject *old_exc, *old_val, *old_tb; + PyObject *ctx; + __Pyx_PyThreadState_declare +#ifdef WITH_THREAD + PyGILState_STATE state; + if (nogil) + state = PyGILState_Ensure(); +#ifdef _MSC_VER + else state = (PyGILState_STATE)-1; +#endif +#endif + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&old_exc, &old_val, &old_tb); + if (full_traceback) { + Py_XINCREF(old_exc); + Py_XINCREF(old_val); + Py_XINCREF(old_tb); + __Pyx_ErrRestore(old_exc, old_val, old_tb); + PyErr_PrintEx(1); + } + #if PY_MAJOR_VERSION < 3 + ctx = PyString_FromString(name); + #else + ctx = PyUnicode_FromString(name); + #endif + __Pyx_ErrRestore(old_exc, old_val, old_tb); + if (!ctx) { + PyErr_WriteUnraisable(Py_None); + } else { + PyErr_WriteUnraisable(ctx); + Py_DECREF(ctx); + } +#ifdef WITH_THREAD + if (nogil) + PyGILState_Release(state); +#endif +} + +/* ArgTypeTest */ +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + else if (exact) { + #if PY_MAJOR_VERSION == 2 + if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(__Pyx_TypeCheck(obj, type))) return 1; + } + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); + return 0; +} + +/* PyObjectCall2Args */ +static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) { + PyObject *args, *result = NULL; + #if CYTHON_FAST_PYCALL + if (PyFunction_Check(function)) { + PyObject *args[2] = {arg1, arg2}; + return __Pyx_PyFunction_FastCall(function, args, 2); + } + #endif + #if CYTHON_FAST_PYCCALL + if (__Pyx_PyFastCFunction_Check(function)) { + PyObject *args[2] = {arg1, arg2}; + return __Pyx_PyCFunction_FastCall(function, args, 2); + } + #endif + args = PyTuple_New(2); + if (unlikely(!args)) goto done; + Py_INCREF(arg1); + PyTuple_SET_ITEM(args, 0, arg1); + Py_INCREF(arg2); + PyTuple_SET_ITEM(args, 1, arg2); + Py_INCREF(function); + result = __Pyx_PyObject_Call(function, args, NULL); + Py_DECREF(args); + Py_DECREF(function); +done: + return result; +} + +/* PyObjectCallMethO */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = PyCFunction_GET_FUNCTION(func); + self = PyCFunction_GET_SELF(func); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectCallOneArg */ +#if CYTHON_COMPILING_IN_CPYTHON +static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_New(1); + if (unlikely(!args)) return NULL; + Py_INCREF(arg); + PyTuple_SET_ITEM(args, 0, arg); + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { +#if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCall(func, &arg, 1); + } +#endif + if (likely(PyCFunction_Check(func))) { + if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { + return __Pyx_PyObject_CallMethO(func, arg); +#if CYTHON_FAST_PYCCALL + } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { + return __Pyx_PyCFunction_FastCall(func, &arg, 1); +#endif + } + } + return __Pyx__PyObject_CallOneArg(func, arg); +} +#else +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_Pack(1, arg); + if (unlikely(!args)) return NULL; + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +#endif + +/* BytesEquals */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY + return PyObject_RichCompareBool(s1, s2, equals); +#else + if (s1 == s2) { + return (equals == Py_EQ); + } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { + const char *ps1, *ps2; + Py_ssize_t length = PyBytes_GET_SIZE(s1); + if (length != PyBytes_GET_SIZE(s2)) + return (equals == Py_NE); + ps1 = PyBytes_AS_STRING(s1); + ps2 = PyBytes_AS_STRING(s2); + if (ps1[0] != ps2[0]) { + return (equals == Py_NE); + } else if (length == 1) { + return (equals == Py_EQ); + } else { + int result; +#if CYTHON_USE_UNICODE_INTERNALS + Py_hash_t hash1, hash2; + hash1 = ((PyBytesObject*)s1)->ob_shash; + hash2 = ((PyBytesObject*)s2)->ob_shash; + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + return (equals == Py_NE); + } +#endif + result = memcmp(ps1, ps2, (size_t)length); + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { + return (equals == Py_NE); + } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { + return (equals == Py_NE); + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +#endif +} + +/* UnicodeEquals */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY + return PyObject_RichCompareBool(s1, s2, equals); +#else +#if PY_MAJOR_VERSION < 3 + PyObject* owned_ref = NULL; +#endif + int s1_is_unicode, s2_is_unicode; + if (s1 == s2) { + goto return_eq; + } + s1_is_unicode = PyUnicode_CheckExact(s1); + s2_is_unicode = PyUnicode_CheckExact(s2); +#if PY_MAJOR_VERSION < 3 + if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { + owned_ref = PyUnicode_FromObject(s2); + if (unlikely(!owned_ref)) + return -1; + s2 = owned_ref; + s2_is_unicode = 1; + } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { + owned_ref = PyUnicode_FromObject(s1); + if (unlikely(!owned_ref)) + return -1; + s1 = owned_ref; + s1_is_unicode = 1; + } else if (((!s2_is_unicode) & (!s1_is_unicode))) { + return __Pyx_PyBytes_Equals(s1, s2, equals); + } +#endif + if (s1_is_unicode & s2_is_unicode) { + Py_ssize_t length; + int kind; + void *data1, *data2; + if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) + return -1; + length = __Pyx_PyUnicode_GET_LENGTH(s1); + if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { + goto return_ne; + } +#if CYTHON_USE_UNICODE_INTERNALS + { + Py_hash_t hash1, hash2; + #if CYTHON_PEP393_ENABLED + hash1 = ((PyASCIIObject*)s1)->hash; + hash2 = ((PyASCIIObject*)s2)->hash; + #else + hash1 = ((PyUnicodeObject*)s1)->hash; + hash2 = ((PyUnicodeObject*)s2)->hash; + #endif + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + goto return_ne; + } + } +#endif + kind = __Pyx_PyUnicode_KIND(s1); + if (kind != __Pyx_PyUnicode_KIND(s2)) { + goto return_ne; + } + data1 = __Pyx_PyUnicode_DATA(s1); + data2 = __Pyx_PyUnicode_DATA(s2); + if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { + goto return_ne; + } else if (length == 1) { + goto return_eq; + } else { + int result = memcmp(data1, data2, (size_t)(length * kind)); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & s2_is_unicode) { + goto return_ne; + } else if ((s2 == Py_None) & s1_is_unicode) { + goto return_ne; + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +return_eq: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ); +return_ne: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_NE); +#endif +} + +/* None */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { + Py_ssize_t q = a / b; + Py_ssize_t r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* GetAttr */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { +#if CYTHON_USE_TYPE_SLOTS +#if PY_MAJOR_VERSION >= 3 + if (likely(PyUnicode_Check(n))) +#else + if (likely(PyString_Check(n))) +#endif + return __Pyx_PyObject_GetAttrStr(o, n); +#endif + return PyObject_GetAttr(o, n); +} + +/* GetItemInt */ +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { + PyObject *r; + if (!j) return NULL; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyList_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyTuple_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); + if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { + PyObject *r = PyList_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } + else if (PyTuple_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); + if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } else { + PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; + if (likely(m && m->sq_item)) { + if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { + Py_ssize_t l = m->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return NULL; + PyErr_Clear(); + } + } + return m->sq_item(o, i); + } + } +#else + if (is_list || PySequence_Check(o)) { + return PySequence_GetItem(o, i); + } +#endif + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +} + +/* ObjectGetItem */ +#if CYTHON_USE_TYPE_SLOTS +static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) { + PyObject *runerr; + Py_ssize_t key_value; + PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence; + if (unlikely(!(m && m->sq_item))) { + PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name); + return NULL; + } + key_value = __Pyx_PyIndex_AsSsize_t(index); + if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { + return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); + } + if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { + PyErr_Clear(); + PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name); + } + return NULL; +} +static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) { + PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping; + if (likely(m && m->mp_subscript)) { + return m->mp_subscript(obj, key); + } + return __Pyx_PyObject_GetIndex(obj, key); +} +#endif + +/* decode_c_string */ +static CYTHON_INLINE PyObject* __Pyx_decode_c_string( + const char* cstring, Py_ssize_t start, Py_ssize_t stop, + const char* encoding, const char* errors, + PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { + Py_ssize_t length; + if (unlikely((start < 0) | (stop < 0))) { + size_t slen = strlen(cstring); + if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) { + PyErr_SetString(PyExc_OverflowError, + "c-string too long to convert to Python"); + return NULL; + } + length = (Py_ssize_t) slen; + if (start < 0) { + start += length; + if (start < 0) + start = 0; + } + if (stop < 0) + stop += length; + } + if (unlikely(stop <= start)) + return PyUnicode_FromUnicode(NULL, 0); + length = stop - start; + cstring += start; + if (decode_func) { + return decode_func(cstring, length, errors); + } else { + return PyUnicode_Decode(cstring, length, encoding, errors); + } +} + +/* PyErrExceptionMatches */ +#if CYTHON_FAST_THREAD_STATE +static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; icurexc_type; + if (exc_type == err) return 1; + if (unlikely(!exc_type)) return 0; + if (unlikely(PyTuple_Check(err))) + return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); + return __Pyx_PyErr_GivenExceptionMatches(exc_type, err); +} +#endif + +/* GetAttr3 */ +static PyObject *__Pyx_GetAttr3Default(PyObject *d) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + return NULL; + __Pyx_PyErr_Clear(); + Py_INCREF(d); + return d; +} +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { + PyObject *r = __Pyx_GetAttr(o, n); + return (likely(r)) ? r : __Pyx_GetAttr3Default(d); +} + +/* RaiseTooManyValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* RaiseNoneIterError */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +/* ExtTypeTest */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(__Pyx_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +/* GetTopmostException */ +#if CYTHON_USE_EXC_INFO_STACK +static _PyErr_StackItem * +__Pyx_PyErr_GetTopmostException(PyThreadState *tstate) +{ + _PyErr_StackItem *exc_info = tstate->exc_info; + while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) && + exc_info->previous_item != NULL) + { + exc_info = exc_info->previous_item; + } + return exc_info; +} +#endif + +/* SaveResetException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); + *type = exc_info->exc_type; + *value = exc_info->exc_value; + *tb = exc_info->exc_traceback; + #else + *type = tstate->exc_type; + *value = tstate->exc_value; + *tb = tstate->exc_traceback; + #endif + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); +} +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = type; + exc_info->exc_value = value; + exc_info->exc_traceback = tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = type; + tstate->exc_value = value; + tstate->exc_traceback = tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +#endif + +/* GetException */ +#if CYTHON_FAST_THREAD_STATE +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) +#endif +{ + PyObject *local_type, *local_value, *local_tb; +#if CYTHON_FAST_THREAD_STATE + PyObject *tmp_type, *tmp_value, *tmp_tb; + local_type = tstate->curexc_type; + local_value = tstate->curexc_value; + local_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif + PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_FAST_THREAD_STATE + if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif + goto bad; + #if PY_MAJOR_VERSION >= 3 + if (local_tb) { + if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) + goto bad; + } + #endif + Py_XINCREF(local_tb); + Py_XINCREF(local_type); + Py_XINCREF(local_value); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_FAST_THREAD_STATE + #if CYTHON_USE_EXC_INFO_STACK + { + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = local_type; + exc_info->exc_value = local_value; + exc_info->exc_traceback = local_tb; + } + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = local_type; + tstate->exc_value = local_value; + tstate->exc_traceback = local_tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif + return 0; +bad: + *type = 0; + *value = 0; + *tb = 0; + Py_XDECREF(local_type); + Py_XDECREF(local_value); + Py_XDECREF(local_tb); + return -1; +} + +/* SwapException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = *type; + exc_info->exc_value = *value; + exc_info->exc_traceback = *tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = *type; + tstate->exc_value = *value; + tstate->exc_traceback = *tb; + #endif + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); + PyErr_SetExcInfo(*type, *value, *tb); + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#endif + +/* Import */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_MAJOR_VERSION < 3 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) { + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_MAJOR_VERSION < 3 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, (PyObject *)NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } +bad: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +/* FastTypeChecks */ +#if CYTHON_COMPILING_IN_CPYTHON +static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { + while (a) { + a = a->tp_base; + if (a == b) + return 1; + } + return b == &PyBaseObject_Type; +} +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (a == b) return 1; + mro = a->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(a, b); +} +#if PY_MAJOR_VERSION == 2 +static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { + PyObject *exception, *value, *tb; + int res; + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&exception, &value, &tb); + res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + if (!res) { + res = PyObject_IsSubclass(err, exc_type2); + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + } + __Pyx_ErrRestore(exception, value, tb); + return res; +} +#else +static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { + int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; + if (!res) { + res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); + } + return res; +} +#endif +static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + assert(PyExceptionClass_Check(exc_type)); + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; i= 0 || (x^b) >= 0)) + return PyInt_FromLong(x); + return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + #endif + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op1))) { + const long b = intval; + long a, x; +#ifdef HAVE_LONG_LONG + const PY_LONG_LONG llb = intval; + PY_LONG_LONG lla, llx; +#endif + const digit* digits = ((PyLongObject*)op1)->ob_digit; + const Py_ssize_t size = Py_SIZE(op1); + if (likely(__Pyx_sst_abs(size) <= 1)) { + a = likely(size) ? digits[0] : 0; + if (size == -1) a = -a; + } else { + switch (size) { + case -2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case 2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case -3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case 3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case -4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case 4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + default: return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + } + x = a + b; + return PyLong_FromLong(x); +#ifdef HAVE_LONG_LONG + long_long: + llx = lla + llb; + return PyLong_FromLongLong(llx); +#endif + + + } + #endif + if (PyFloat_CheckExact(op1)) { + const long b = intval; + double a = PyFloat_AS_DOUBLE(op1); + double result; + PyFPE_START_PROTECT("add", return NULL) + result = ((double)a) + (double)b; + PyFPE_END_PROTECT(result) + return PyFloat_FromDouble(result); + } + return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); +} +#endif + +/* None */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { + PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); +} + +/* None */ +static CYTHON_INLINE long __Pyx_div_long(long a, long b) { + long q = a / b; + long r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* ImportFrom */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { + PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); + if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { + PyErr_Format(PyExc_ImportError, + #if PY_MAJOR_VERSION < 3 + "cannot import name %.230s", PyString_AS_STRING(name)); + #else + "cannot import name %S", name); + #endif + } + return value; +} + +/* HasAttr */ +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { + PyObject *r; + if (unlikely(!__Pyx_PyBaseString_Check(n))) { + PyErr_SetString(PyExc_TypeError, + "hasattr(): attribute name must be string"); + return -1; + } + r = __Pyx_GetAttr(o, n); + if (unlikely(!r)) { + PyErr_Clear(); + return 0; + } else { + Py_DECREF(r); + return 1; + } +} + +/* PyObject_GenericGetAttrNoDict */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { + PyErr_Format(PyExc_AttributeError, +#if PY_MAJOR_VERSION >= 3 + "'%.50s' object has no attribute '%U'", + tp->tp_name, attr_name); +#else + "'%.50s' object has no attribute '%.400s'", + tp->tp_name, PyString_AS_STRING(attr_name)); +#endif + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { + PyObject *descr; + PyTypeObject *tp = Py_TYPE(obj); + if (unlikely(!PyString_Check(attr_name))) { + return PyObject_GenericGetAttr(obj, attr_name); + } + assert(!tp->tp_dictoffset); + descr = _PyType_Lookup(tp, attr_name); + if (unlikely(!descr)) { + return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); + } + Py_INCREF(descr); + #if PY_MAJOR_VERSION < 3 + if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) + #endif + { + descrgetfunc f = Py_TYPE(descr)->tp_descr_get; + if (unlikely(f)) { + PyObject *res = f(descr, obj, (PyObject *)tp); + Py_DECREF(descr); + return res; + } + } + return descr; +} +#endif + +/* PyObject_GenericGetAttr */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { + if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { + return PyObject_GenericGetAttr(obj, attr_name); + } + return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); +} +#endif + +/* SetVTable */ +static int __Pyx_SetVtable(PyObject *dict, void *vtable) { +#if PY_VERSION_HEX >= 0x02070000 + PyObject *ob = PyCapsule_New(vtable, 0, 0); +#else + PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); +#endif + if (!ob) + goto bad; + if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0) + goto bad; + Py_DECREF(ob); + return 0; +bad: + Py_XDECREF(ob); + return -1; +} + +/* PyObjectGetAttrStrNoError */ +static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + __Pyx_PyErr_Clear(); +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) { + PyObject *result; +#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1 + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) { + return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1); + } +#endif + result = __Pyx_PyObject_GetAttrStr(obj, attr_name); + if (unlikely(!result)) { + __Pyx_PyObject_GetAttrStr_ClearAttributeError(); + } + return result; +} + +/* SetupReduce */ +static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { + int ret; + PyObject *name_attr; + name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2); + if (likely(name_attr)) { + ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); + } else { + ret = -1; + } + if (unlikely(ret < 0)) { + PyErr_Clear(); + ret = 0; + } + Py_XDECREF(name_attr); + return ret; +} +static int __Pyx_setup_reduce(PyObject* type_obj) { + int ret = 0; + PyObject *object_reduce = NULL; + PyObject *object_reduce_ex = NULL; + PyObject *reduce = NULL; + PyObject *reduce_ex = NULL; + PyObject *reduce_cython = NULL; + PyObject *setstate = NULL; + PyObject *setstate_cython = NULL; +#if CYTHON_USE_PYTYPE_LOOKUP + if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; +#else + if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; +#endif +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; +#else + object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; +#endif + reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; + if (reduce_ex == object_reduce_ex) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; +#else + object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; +#endif + reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; + if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { + reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython); + if (likely(reduce_cython)) { + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + } else if (reduce == object_reduce || PyErr_Occurred()) { + goto __PYX_BAD; + } + setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate); + if (!setstate) PyErr_Clear(); + if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { + setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython); + if (likely(setstate_cython)) { + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + } else if (!setstate || PyErr_Occurred()) { + goto __PYX_BAD; + } + } + PyType_Modified((PyTypeObject*)type_obj); + } + } + goto __PYX_GOOD; +__PYX_BAD: + if (!PyErr_Occurred()) + PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name); + ret = -1; +__PYX_GOOD: +#if !CYTHON_USE_PYTYPE_LOOKUP + Py_XDECREF(object_reduce); + Py_XDECREF(object_reduce_ex); +#endif + Py_XDECREF(reduce); + Py_XDECREF(reduce_ex); + Py_XDECREF(reduce_cython); + Py_XDECREF(setstate); + Py_XDECREF(setstate_cython); + return ret; +} + +/* CLineInTraceback */ +#ifndef CYTHON_CLINE_IN_TRACEBACK +static int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) { + PyObject *use_cline; + PyObject *ptype, *pvalue, *ptraceback; +#if CYTHON_COMPILING_IN_CPYTHON + PyObject **cython_runtime_dict; +#endif + if (unlikely(!__pyx_cython_runtime)) { + return c_line; + } + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); +#if CYTHON_COMPILING_IN_CPYTHON + cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); + if (likely(cython_runtime_dict)) { + __PYX_PY_DICT_LOOKUP_IF_MODIFIED( + use_cline, *cython_runtime_dict, + __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) + } else +#endif + { + PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); + if (use_cline_obj) { + use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; + Py_DECREF(use_cline_obj); + } else { + PyErr_Clear(); + use_cline = NULL; + } + } + if (!use_cline) { + c_line = 0; + PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); + } + else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { + c_line = 0; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + return c_line; +} +#endif + +/* CodeObjectCache */ +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +/* AddTraceback */ +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + if (c_line) { + c_line = __Pyx_CLineForTraceback(tstate, c_line); + } + py_code = __pyx_find_code_object(c_line ? -c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); + } + py_frame = PyFrame_New( + tstate, /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); + PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if ((0)) {} + view->obj = NULL; + Py_DECREF(obj); +} +#endif + + +/* MemviewSliceIsContig */ +static int +__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) +{ + int i, index, step, start; + Py_ssize_t itemsize = mvs.memview->view.itemsize; + if (order == 'F') { + step = 1; + start = 0; + } else { + step = -1; + start = ndim - 1; + } + for (i = 0; i < ndim; i++) { + index = start + step * i; + if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) + return 0; + itemsize *= mvs.shape[index]; + } + return 1; +} + +/* OverlappingSlices */ +static void +__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, + void **out_start, void **out_end, + int ndim, size_t itemsize) +{ + char *start, *end; + int i; + start = end = slice->data; + for (i = 0; i < ndim; i++) { + Py_ssize_t stride = slice->strides[i]; + Py_ssize_t extent = slice->shape[i]; + if (extent == 0) { + *out_start = *out_end = start; + return; + } else { + if (stride > 0) + end += stride * (extent - 1); + else + start += stride * (extent - 1); + } + } + *out_start = start; + *out_end = end + itemsize; +} +static int +__pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize) +{ + void *start1, *end1, *start2, *end2; + __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); + __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); + return (start1 < end2) && (start2 < end1); +} + +/* Capsule */ +static CYTHON_INLINE PyObject * +__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) +{ + PyObject *cobj; +#if PY_VERSION_HEX >= 0x02070000 + cobj = PyCapsule_New(p, sig, NULL); +#else + cobj = PyCObject_FromVoidPtr(p, NULL); +#endif + return cobj; +} + +/* CIntFromPyVerify */ +#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* IsLittleEndian */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) +{ + union { + uint32_t u32; + uint8_t u8[4]; + } S; + S.u32 = 0x01020304; + return S.u8[0] == 4; +} + +/* BufferFormatCheck */ +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t <= '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case '?': return "'bool'"; + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparseable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static PyObject * +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number, ndim; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ndim = ctx->head->field->type->ndim; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) + return PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + if (*ts != ',' && *ts != ')') + return PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + if (*ts == ',') ts++; + i++; + } + if (i != ndim) + return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return NULL; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return Py_None; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + CYTHON_FALLTHROUGH; + case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && + (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + CYTHON_FALLTHROUGH; + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} + +/* TypeInfoCompare */ + static int +__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) +{ + int i; + if (!a || !b) + return 0; + if (a == b) + return 1; + if (a->size != b->size || a->typegroup != b->typegroup || + a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { + if (a->typegroup == 'H' || b->typegroup == 'H') { + return a->size == b->size; + } else { + return 0; + } + } + if (a->ndim) { + for (i = 0; i < a->ndim; i++) + if (a->arraysize[i] != b->arraysize[i]) + return 0; + } + if (a->typegroup == 'S') { + if (a->flags != b->flags) + return 0; + if (a->fields || b->fields) { + if (!(a->fields && b->fields)) + return 0; + for (i = 0; a->fields[i].type && b->fields[i].type; i++) { + __Pyx_StructField *field_a = a->fields + i; + __Pyx_StructField *field_b = b->fields + i; + if (field_a->offset != field_b->offset || + !__pyx_typeinfo_cmp(field_a->type, field_b->type)) + return 0; + } + return !a->fields[i].type && !b->fields[i].type; + } + } + return 1; +} + +/* MemviewSliceValidateAndInit */ + static int +__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) +{ + if (buf->shape[dim] <= 1) + return 1; + if (buf->strides) { + if (spec & __Pyx_MEMVIEW_CONTIG) { + if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { + if (unlikely(buf->strides[dim] != sizeof(void *))) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly contiguous " + "in dimension %d.", dim); + goto fail; + } + } else if (unlikely(buf->strides[dim] != buf->itemsize)) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_FOLLOW) { + Py_ssize_t stride = buf->strides[dim]; + if (stride < 0) + stride = -stride; + if (unlikely(stride < buf->itemsize)) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + } else { + if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not contiguous in " + "dimension %d", dim); + goto fail; + } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not indirect in " + "dimension %d", dim); + goto fail; + } else if (unlikely(buf->suboffsets)) { + PyErr_SetString(PyExc_ValueError, + "Buffer exposes suboffsets but no strides"); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) +{ + if (spec & __Pyx_MEMVIEW_DIRECT) { + if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) { + PyErr_Format(PyExc_ValueError, + "Buffer not compatible with direct access " + "in dimension %d.", dim); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_PTR) { + if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly accessible " + "in dimension %d.", dim); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) +{ + int i; + if (c_or_f_flag & __Pyx_IS_F_CONTIG) { + Py_ssize_t stride = 1; + for (i = 0; i < ndim; i++) { + if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { + PyErr_SetString(PyExc_ValueError, + "Buffer not fortran contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { + Py_ssize_t stride = 1; + for (i = ndim - 1; i >- 1; i--) { + if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { + PyErr_SetString(PyExc_ValueError, + "Buffer not C contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } + return 1; +fail: + return 0; +} +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj) +{ + struct __pyx_memoryview_obj *memview, *new_memview; + __Pyx_RefNannyDeclarations + Py_buffer *buf; + int i, spec = 0, retval = -1; + __Pyx_BufFmt_Context ctx; + int from_memoryview = __pyx_memoryview_check(original_obj); + __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); + if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) + original_obj)->typeinfo)) { + memview = (struct __pyx_memoryview_obj *) original_obj; + new_memview = NULL; + } else { + memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + original_obj, buf_flags, 0, dtype); + new_memview = memview; + if (unlikely(!memview)) + goto fail; + } + buf = &memview->view; + if (unlikely(buf->ndim != ndim)) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + ndim, buf->ndim); + goto fail; + } + if (new_memview) { + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail; + } + if (unlikely((unsigned) buf->itemsize != dtype->size)) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " + "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", + buf->itemsize, + (buf->itemsize > 1) ? "s" : "", + dtype->name, + dtype->size, + (dtype->size > 1) ? "s" : ""); + goto fail; + } + for (i = 0; i < ndim; i++) { + spec = axes_specs[i]; + if (unlikely(!__pyx_check_strides(buf, i, ndim, spec))) + goto fail; + if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec))) + goto fail; + } + if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))) + goto fail; + if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, + new_memview != NULL) == -1)) { + goto fail; + } + retval = 0; + goto no_fail; +fail: + Py_XDECREF(new_memview); + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_nn_int64_t(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS_RO | writable_flag, 2, + &__Pyx_TypeInfo_nn_int64_t, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_double(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS_RO | writable_flag, 1, + &__Pyx_TypeInfo_double, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +static PyObject* __pyx_convert__to_py_struct____pyx_t_3lib_7libmise_4mise_Vector3D(struct __pyx_t_3lib_7libmise_4mise_Vector3D s) { + PyObject* res; + PyObject* member; + res = __Pyx_PyDict_NewPresized(3); if (unlikely(!res)) return NULL; + member = __Pyx_PyInt_From_int(s.x); if (unlikely(!member)) goto bad; + if (unlikely(PyDict_SetItem(res, __pyx_n_s_x, member) < 0)) goto bad; + Py_DECREF(member); + member = __Pyx_PyInt_From_int(s.y); if (unlikely(!member)) goto bad; + if (unlikely(PyDict_SetItem(res, __pyx_n_s_y, member) < 0)) goto bad; + Py_DECREF(member); + member = __Pyx_PyInt_From_int(s.z); if (unlikely(!member)) goto bad; + if (unlikely(PyDict_SetItem(res, __pyx_n_s_z, member) < 0)) goto bad; + Py_DECREF(member); + return res; + bad: + Py_XDECREF(member); + Py_DECREF(res); + return NULL; + } + /* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int64_t(int64_t value) { + const int64_t neg_one = (int64_t) ((int64_t) 0 - (int64_t) 1), const_zero = (int64_t) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int64_t) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int64_t) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int64_t) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int64_t) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int64_t) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int64_t), + little, !is_unsigned); + } +} + +/* MemviewSliceCopyTemplate */ + static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object) +{ + __Pyx_RefNannyDeclarations + int i; + __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; + struct __pyx_memoryview_obj *from_memview = from_mvs->memview; + Py_buffer *buf = &from_memview->view; + PyObject *shape_tuple = NULL; + PyObject *temp_int = NULL; + struct __pyx_array_obj *array_obj = NULL; + struct __pyx_memoryview_obj *memview_obj = NULL; + __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); + for (i = 0; i < ndim; i++) { + if (unlikely(from_mvs->suboffsets[i] >= 0)) { + PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " + "indirect dimensions (axis %d)", i); + goto fail; + } + } + shape_tuple = PyTuple_New(ndim); + if (unlikely(!shape_tuple)) { + goto fail; + } + __Pyx_GOTREF(shape_tuple); + for(i = 0; i < ndim; i++) { + temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); + if(unlikely(!temp_int)) { + goto fail; + } else { + PyTuple_SET_ITEM(shape_tuple, i, temp_int); + temp_int = NULL; + } + } + array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); + if (unlikely(!array_obj)) { + goto fail; + } + __Pyx_GOTREF(array_obj); + memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + (PyObject *) array_obj, contig_flag, + dtype_is_object, + from_mvs->memview->typeinfo); + if (unlikely(!memview_obj)) + goto fail; + if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) + goto fail; + if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, + dtype_is_object) < 0)) + goto fail; + goto no_fail; +fail: + __Pyx_XDECREF(new_mvs.memview); + new_mvs.memview = NULL; + new_mvs.data = NULL; +no_fail: + __Pyx_XDECREF(shape_tuple); + __Pyx_XDECREF(temp_int); + __Pyx_XDECREF(array_obj); + __Pyx_RefNannyFinishContext(); + return new_mvs; +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) + case -2: + if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *x) { + const size_t neg_one = (size_t) ((size_t) 0 - (size_t) 1), const_zero = (size_t) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(size_t) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(size_t, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (size_t) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (size_t) 0; + case 1: __PYX_VERIFY_RETURN_INT(size_t, digit, digits[0]) + case 2: + if (8 * sizeof(size_t) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) >= 2 * PyLong_SHIFT) { + return (size_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(size_t) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) >= 3 * PyLong_SHIFT) { + return (size_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(size_t) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) >= 4 * PyLong_SHIFT) { + return (size_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (size_t) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(size_t) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(size_t) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (size_t) 0; + case -1: __PYX_VERIFY_RETURN_INT(size_t, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(size_t, digit, +digits[0]) + case -2: + if (8 * sizeof(size_t) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) { + return (size_t) (((size_t)-1)*(((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(size_t) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) { + return (size_t) ((((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) { + return (size_t) (((size_t)-1)*(((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(size_t) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) { + return (size_t) ((((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) - 1 > 4 * PyLong_SHIFT) { + return (size_t) (((size_t)-1)*(((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(size_t) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(size_t) - 1 > 4 * PyLong_SHIFT) { + return (size_t) ((((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]))); + } + } + break; + } +#endif + if (sizeof(size_t) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(size_t, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(size_t) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(size_t, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + size_t val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (size_t) -1; + } + } else { + size_t val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (size_t) -1; + val = __Pyx_PyInt_As_size_t(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to size_t"); + return (size_t) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to size_t"); + return (size_t) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) + case -2: + if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { + const char neg_one = (char) ((char) 0 - (char) 1), const_zero = (char) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(char) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (char) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (char) 0; + case 1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0]) + case 2: + if (8 * sizeof(char) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) { + return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(char) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) { + return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(char) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) { + return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (char) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(char) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (char) 0; + case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(char, digit, +digits[0]) + case -2: + if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(char) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(char) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(char) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { + return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + } +#endif + if (sizeof(char) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + char val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (char) -1; + } + } else { + char val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (char) -1; + val = __Pyx_PyInt_As_char(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to char"); + return (char) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to char"); + return (char) -1; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsdsds_double(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS_RO | writable_flag, 3, + &__Pyx_TypeInfo_double, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_long(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS_RO | writable_flag, 2, + &__Pyx_TypeInfo_long, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* CheckBinaryVersion */ + static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + return PyErr_WarnEx(NULL, message, 1); + } + return 0; +} + +/* InitStrings */ + static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + if (PyObject_Hash(*t->p) == -1) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +#if !CYTHON_PEP393_ENABLED +static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +} +#else +static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (likely(PyUnicode_IS_ASCII(o))) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +} +#endif +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { + return __Pyx_PyUnicode_AsStringAndSize(o, length); + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { + int retval; + if (unlikely(!x)) return -1; + retval = __Pyx_PyObject_IsTrue(x); + Py_DECREF(x); + return retval; +} +static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { +#if PY_MAJOR_VERSION >= 3 + if (PyLong_Check(result)) { + if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, + "__int__ returned non-int (type %.200s). " + "The ability to return an instance of a strict subclass of int " + "is deprecated, and may be removed in a future version of Python.", + Py_TYPE(result)->tp_name)) { + Py_DECREF(result); + return NULL; + } + return result; + } +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + type_name, type_name, Py_TYPE(result)->tp_name); + Py_DECREF(result); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x) || PyLong_Check(x))) +#else + if (likely(PyLong_Check(x))) +#endif + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + #if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = m->nb_int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = m->nb_long(x); + } + #else + if (likely(m && m->nb_int)) { + name = "int"; + res = m->nb_int(x); + } + #endif +#else + if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { + res = PyNumber_Int(x); + } +#endif + if (likely(res)) { +#if PY_MAJOR_VERSION < 3 + if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { +#else + if (unlikely(!PyLong_CheckExact(res))) { +#endif + return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(b); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)b)->ob_digit; + const Py_ssize_t size = Py_SIZE(b); + if (likely(__Pyx_sst_abs(size) <= 1)) { + ival = likely(size) ? digits[0] : 0; + if (size == -1) ival = -ival; + return ival; + } else { + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { + return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +#endif /* Py_PYTHON_H */ diff --git a/code/lib/libmise/mise.pyx b/code/lib/libmise/mise.pyx new file mode 100644 index 0000000000000000000000000000000000000000..eb246c3e0a497c3746ee0c20b60b7bf73760d23f --- /dev/null +++ b/code/lib/libmise/mise.pyx @@ -0,0 +1,370 @@ +# distutils: language = c++ +cimport cython +from libc.stdint cimport int32_t, int64_t +from cython.operator cimport dereference as dref +from libcpp.vector cimport vector +from libcpp.map cimport map +from libc.math cimport isnan, NAN +import numpy as np + + +cdef struct Vector3D: + int x, y, z + + +cdef struct Voxel: + Vector3D loc + unsigned int level + bint is_leaf + unsigned long children[2][2][2] + + +cdef struct GridPoint: + Vector3D loc + double value + bint known + + +cdef inline unsigned long vec_to_idx(Vector3D coord, long resolution): + cdef unsigned long idx + idx = resolution * resolution * coord.x + resolution * coord.y + coord.z + return idx + + +cdef class MISE: + cdef vector[Voxel] voxels + cdef vector[GridPoint] grid_points + cdef map[long, long] grid_point_hash + cdef readonly int resolution_0 + cdef readonly int depth + cdef readonly double threshold + cdef readonly int voxel_size_0 + cdef readonly int resolution + + def __cinit__(self, int resolution_0, int depth, double threshold): + self.resolution_0 = resolution_0 + self.depth = depth + self.threshold = threshold + self.voxel_size_0 = (1 << depth) + self.resolution = resolution_0 * self.voxel_size_0 + + # Create initial voxels + self.voxels.reserve(resolution_0 * resolution_0 * resolution_0) + + cdef Voxel voxel + cdef GridPoint point + cdef Vector3D loc + cdef int i, j, k + for i in range(resolution_0): + for j in range(resolution_0): + for k in range (resolution_0): + loc = Vector3D( + i * self.voxel_size_0, + j * self.voxel_size_0, + k * self.voxel_size_0, + ) + voxel = Voxel( + loc=loc, + level=0, + is_leaf=True, + ) + + assert(self.voxels.size() == vec_to_idx(Vector3D(i, j, k), resolution_0)) + self.voxels.push_back(voxel) + + # Create initial grid points + self.grid_points.reserve((resolution_0 + 1) * (resolution_0 + 1) * (resolution_0 + 1)) + for i in range(resolution_0 + 1): + for j in range(resolution_0 + 1): + for k in range(resolution_0 + 1): + loc = Vector3D( + i * self.voxel_size_0, + j * self.voxel_size_0, + k * self.voxel_size_0, + ) + assert(self.grid_points.size() == vec_to_idx(Vector3D(i, j, k), resolution_0 + 1)) + self.add_grid_point(loc) + + def update(self, int64_t[:, :] points, double[:] values): + """Update points and set their values. Also determine all active voxels and subdivide them.""" + assert(points.shape[0] == values.shape[0]) + assert(points.shape[1] == 3) + cdef Vector3D loc + cdef long idx + cdef int i + + # Find all indices of point and set value + for i in range(points.shape[0]): + loc = Vector3D(points[i, 0], points[i, 1], points[i, 2]) + idx = self.get_grid_point_idx(loc) + if idx == -1: + raise ValueError('Point not in grid!') + self.grid_points[idx].value = values[i] + self.grid_points[idx].known = True + # Subdivide activate voxels and add new points + self.subdivide_voxels() + + def query(self): + """Query points to evaluate.""" + # Find all points with unknown value + cdef vector[Vector3D] points + cdef int n_unknown = 0 + for p in self.grid_points: + if not p.known: + n_unknown += 1 + + points.reserve(n_unknown) + for p in self.grid_points: + if not p.known: + points.push_back(p.loc) + + # Convert to numpy + points_np = np.zeros((points.size(), 3), dtype=np.int64) + cdef int64_t[:, :] points_view = points_np + for i in range(points.size()): + points_view[i, 0] = points[i].x + points_view[i, 1] = points[i].y + points_view[i, 2] = points[i].z + + return points_np + + def to_dense(self): + """Output dense matrix at highest resolution.""" + out_array = np.full((self.resolution + 1,) * 3, np.nan) + cdef double[:, :, :] out_view = out_array + cdef GridPoint point + cdef int i, j, k + + for point in self.grid_points: + # Take voxel for which points is upper left corner + # assert(point.known) + out_view[point.loc.x, point.loc.y, point.loc.z] = point.value + + # Complete along x axis + for i in range(1, self.resolution + 1): + for j in range(self.resolution + 1): + for k in range(self.resolution + 1): + if isnan(out_view[i, j, k]): + out_view[i, j, k] = out_view[i-1, j, k] + + # Complete along y axis + for i in range(self.resolution + 1): + for j in range(1, self.resolution + 1): + for k in range(self.resolution + 1): + if isnan(out_view[i, j, k]): + out_view[i, j, k] = out_view[i, j-1, k] + + + # Complete along z axis + for i in range(self.resolution + 1): + for j in range(self.resolution + 1): + for k in range(1, self.resolution + 1): + if isnan(out_view[i, j, k]): + out_view[i, j, k] = out_view[i, j, k-1] + assert(not isnan(out_view[i, j, k])) + return out_array + + def get_points(self): + points_np = np.zeros((self.grid_points.size(), 3), dtype=np.int64) + values_np = np.zeros((self.grid_points.size()), dtype=np.float64) + + cdef long[:, :] points_view = points_np + cdef double[:] values_view = values_np + cdef Vector3D loc + cdef int i + + for i in range(self.grid_points.size()): + loc = self.grid_points[i].loc + points_view[i, 0] = loc.x + points_view[i, 1] = loc.y + points_view[i, 2] = loc.z + values_view[i] = self.grid_points[i].value + + return points_np, values_np + + cdef void subdivide_voxels(self) except +: + cdef vector[bint] next_to_positive + cdef vector[bint] next_to_negative + cdef int i, j, k + cdef long idx + cdef Vector3D loc, adj_loc + + # Initialize vectors + next_to_positive.resize(self.voxels.size(), False) + next_to_negative.resize(self.voxels.size(), False) + + # Iterate over grid points and mark voxels active + # TODO: can move this to update operation and add attibute to voxel + for grid_point in self.grid_points: + loc = grid_point.loc + if not grid_point.known: + continue + + # Iterate over the 8 adjacent voxels + for i in range(-1, 1): + for j in range(-1, 1): + for k in range(-1, 1): + adj_loc = Vector3D( + x=loc.x + i, + y=loc.y + j, + z=loc.z + k, + ) + idx = self.get_voxel_idx(adj_loc) + if idx == -1: + continue + + if grid_point.value >= self.threshold: + next_to_positive[idx] = True + if grid_point.value <= self.threshold: + next_to_negative[idx] = True + + cdef int n_subdivide = 0 + + for idx in range(self.voxels.size()): + if not self.voxels[idx].is_leaf or self.voxels[idx].level == self.depth: + continue + if next_to_positive[idx] and next_to_negative[idx]: + n_subdivide += 1 + + self.voxels.reserve(self.voxels.size() + 8 * n_subdivide) + self.grid_points.reserve(self.voxels.size() + 19 * n_subdivide) + + for idx in range(self.voxels.size()): + if not self.voxels[idx].is_leaf or self.voxels[idx].level == self.depth: + continue + if next_to_positive[idx] and next_to_negative[idx]: + self.subdivide_voxel(idx) + + cdef void subdivide_voxel(self, long idx): + cdef Voxel voxel + cdef GridPoint point + cdef Vector3D loc0 = self.voxels[idx].loc + cdef Vector3D loc + cdef int new_level = self.voxels[idx].level + 1 + cdef int new_size = 1 << (self.depth - new_level) + assert(new_level <= self.depth) + assert(1 <= new_size <= self.voxel_size_0) + + # Current voxel is not leaf anymore + self.voxels[idx].is_leaf = False + # Add new voxels + cdef int i, j, k + for i in range(2): + for j in range(2): + for k in range(2): + loc = Vector3D( + x=loc0.x + i * new_size, + y=loc0.y + j * new_size, + z=loc0.z + k * new_size, + ) + voxel = Voxel( + loc=loc, + level=new_level, + is_leaf=True + ) + + self.voxels[idx].children[i][j][k] = self.voxels.size() + self.voxels.push_back(voxel) + + # Add new grid points + for i in range(3): + for j in range(3): + for k in range(3): + loc = Vector3D( + loc0.x + i * new_size, + loc0.y + j * new_size, + loc0.z + k * new_size, + ) + + # Only add new grid points + if self.get_grid_point_idx(loc) == -1: + self.add_grid_point(loc) + + + @cython.cdivision(True) + cdef long get_voxel_idx(self, Vector3D loc) except +: + """Utility function for getting voxel index corresponding to 3D coordinates.""" + # Shorthands + cdef long resolution = self.resolution + cdef long resolution_0 = self.resolution_0 + cdef long depth = self.depth + cdef long voxel_size_0 = self.voxel_size_0 + + # Return -1 if point lies outside bounds + if not (0 <= loc.x < resolution and 0<= loc.y < resolution and 0 <= loc.z < resolution): + return -1 + + # Coordinates in coarse voxel grid + cdef Vector3D loc0 = Vector3D( + x=loc.x >> depth, + y=loc.y >> depth, + z=loc.z >> depth, + ) + + # Initial voxels + cdef int idx = vec_to_idx(loc0, resolution_0) + cdef Voxel voxel = self.voxels[idx] + assert(voxel.loc.x == loc0.x * voxel_size_0) + assert(voxel.loc.y == loc0.y * voxel_size_0) + assert(voxel.loc.z == loc0.z * voxel_size_0) + + # Relative coordinates + cdef Vector3D loc_rel = Vector3D( + x=loc.x - (loc0.x << depth), + y=loc.y - (loc0.y << depth), + z=loc.z - (loc0.z << depth), + ) + + cdef Vector3D loc_offset + cdef long voxel_size = voxel_size_0 + + while not voxel.is_leaf: + voxel_size = voxel_size >> 1 + assert(voxel_size >= 1) + + # Determine child + loc_offset = Vector3D( + x=1 if (loc_rel.x >= voxel_size) else 0, + y=1 if (loc_rel.y >= voxel_size) else 0, + z=1 if (loc_rel.z >= voxel_size) else 0, + ) + # New voxel + idx = voxel.children[loc_offset.x][loc_offset.y][loc_offset.z] + voxel = self.voxels[idx] + + # New relative coordinates + loc_rel = Vector3D( + x=loc_rel.x - loc_offset.x * voxel_size, + y=loc_rel.y - loc_offset.y * voxel_size, + z=loc_rel.z - loc_offset.z * voxel_size, + ) + + assert(0<= loc_rel.x < voxel_size) + assert(0<= loc_rel.y < voxel_size) + assert(0<= loc_rel.z < voxel_size) + + + # Return idx + return idx + + + cdef inline void add_grid_point(self, Vector3D loc): + cdef GridPoint point = GridPoint( + loc=loc, + value=0., + known=False, + ) + self.grid_point_hash[vec_to_idx(loc, self.resolution + 1)] = self.grid_points.size() + self.grid_points.push_back(point) + + cdef inline int get_grid_point_idx(self, Vector3D loc): + p_idx = self.grid_point_hash.find(vec_to_idx(loc, self.resolution + 1)) + if p_idx == self.grid_point_hash.end(): + return -1 + + cdef int idx = dref(p_idx).second + assert(self.grid_points[idx].loc.x == loc.x) + assert(self.grid_points[idx].loc.y == loc.y) + assert(self.grid_points[idx].loc.z == loc.z) + + return idx \ No newline at end of file diff --git a/code/lib/model/body_model_params.py b/code/lib/model/body_model_params.py new file mode 100644 index 0000000000000000000000000000000000000000..617b8c37bfca7268fe7e7e452bc39bd247588e6a --- /dev/null +++ b/code/lib/model/body_model_params.py @@ -0,0 +1,49 @@ +import torch +import torch.nn as nn + +class BodyModelParams(nn.Module): + def __init__(self, num_frames, model_type='smpl'): + super(BodyModelParams, self).__init__() + self.num_frames = num_frames + self.model_type = model_type + self.params_dim = { + 'betas': 10, + 'global_orient': 3, + 'transl': 3, + } + if model_type == 'smpl': + self.params_dim.update({ + 'body_pose': 69, + }) + else: + assert ValueError(f'Unknown model type {model_type}, exiting!') + + self.param_names = self.params_dim.keys() + + for param_name in self.param_names: + if param_name == 'betas': + param = nn.Embedding(1, self.params_dim[param_name]) + param.weight.data.fill_(0) + param.weight.requires_grad = False + setattr(self, param_name, param) + else: + param = nn.Embedding(num_frames, self.params_dim[param_name]) + param.weight.data.fill_(0) + param.weight.requires_grad = False + setattr(self, param_name, param) + + def init_parameters(self, param_name, data, requires_grad=False): + getattr(self, param_name).weight.data = data[..., :self.params_dim[param_name]] + getattr(self, param_name).weight.requires_grad = requires_grad + + def set_requires_grad(self, param_name, requires_grad=True): + getattr(self, param_name).weight.requires_grad = requires_grad + + def forward(self, frame_ids): + params = {} + for param_name in self.param_names: + if param_name == 'betas': + params[param_name] = getattr(self, param_name)(torch.zeros_like(frame_ids)) + else: + params[param_name] = getattr(self, param_name)(frame_ids) + return params \ No newline at end of file diff --git a/code/lib/model/deformer.py b/code/lib/model/deformer.py new file mode 100644 index 0000000000000000000000000000000000000000..6009090a617147bd898b1a5dd4c301fc54bed253 --- /dev/null +++ b/code/lib/model/deformer.py @@ -0,0 +1,89 @@ +import torch +import torch.nn.functional as F +from .smpl import SMPLServer +from pytorch3d import ops + +class SMPLDeformer(): + def __init__(self, max_dist=0.1, K=1, gender='female', betas=None): + super().__init__() + + self.max_dist = max_dist + self.K = K + self.smpl = SMPLServer(gender=gender) + smpl_params_canoical = self.smpl.param_canonical.clone() + smpl_params_canoical[:, 76:] = torch.tensor(betas).float().to(self.smpl.param_canonical.device) + cano_scale, cano_transl, cano_thetas, cano_betas = torch.split(smpl_params_canoical, [1, 3, 72, 10], dim=1) + smpl_output = self.smpl(cano_scale, cano_transl, cano_thetas, cano_betas) + self.smpl_verts = smpl_output['smpl_verts'] + self.smpl_weights = smpl_output['smpl_weights'] + def forward(self, x, smpl_tfs, return_weights=True, inverse=False, smpl_verts=None): + if x.shape[0] == 0: return x + if smpl_verts is None: + weights, outlier_mask = self.query_skinning_weights_smpl_multi(x[None], smpl_verts=self.smpl_verts[0], smpl_weights=self.smpl_weights) + else: + weights, outlier_mask = self.query_skinning_weights_smpl_multi(x[None], smpl_verts=smpl_verts[0], smpl_weights=self.smpl_weights) + if return_weights: + return weights + + x_transformed = skinning(x.unsqueeze(0), weights, smpl_tfs, inverse=inverse) + + return x_transformed.squeeze(0), outlier_mask + def forward_skinning(self, xc, cond, smpl_tfs): + weights, _ = self.query_skinning_weights_smpl_multi(xc, smpl_verts=self.smpl_verts[0], smpl_weights=self.smpl_weights) + x_transformed = skinning(xc, weights, smpl_tfs, inverse=False) + + return x_transformed + + def query_skinning_weights_smpl_multi(self, pts, smpl_verts, smpl_weights): + + distance_batch, index_batch, neighbor_points = ops.knn_points(pts, smpl_verts.unsqueeze(0), + K=self.K, return_nn=True) + distance_batch = torch.clamp(distance_batch, max=4) + weights_conf = torch.exp(-distance_batch) + distance_batch = torch.sqrt(distance_batch) + weights_conf = weights_conf / weights_conf.sum(-1, keepdim=True) + index_batch = index_batch[0] + weights = smpl_weights[:, index_batch, :] + weights = torch.sum(weights * weights_conf.unsqueeze(-1), dim=-2).detach() + + outlier_mask = (distance_batch[..., 0] > self.max_dist)[0] + return weights, outlier_mask + + def query_weights(self, xc): + weights = self.forward(xc, None, return_weights=True, inverse=False) + return weights + + def forward_skinning_normal(self, xc, normal, cond, tfs, inverse = False): + if normal.ndim == 2: + normal = normal.unsqueeze(0) + w = self.query_weights(xc[0], cond) + + p_h = F.pad(normal, (0, 1), value=0) + + if inverse: + # p:num_point, n:num_bone, i,j: num_dim+1 + tf_w = torch.einsum('bpn,bnij->bpij', w.double(), tfs.double()) + p_h = torch.einsum('bpij,bpj->bpi', tf_w.inverse(), p_h.double()).float() + else: + p_h = torch.einsum('bpn, bnij, bpj->bpi', w.double(), tfs.double(), p_h.double()).float() + + return p_h[:, :, :3] + +def skinning(x, w, tfs, inverse=False): + """Linear blend skinning + Args: + x (tensor): canonical points. shape: [B, N, D] + w (tensor): conditional input. [B, N, J] + tfs (tensor): bone transformation matrices. shape: [B, J, D+1, D+1] + Returns: + x (tensor): skinned points. shape: [B, N, D] + """ + x_h = F.pad(x, (0, 1), value=1.0) + + if inverse: + # p:n_point, n:n_bone, i,k: n_dim+1 + w_tf = torch.einsum("bpn,bnij->bpij", w, tfs) + x_h = torch.einsum("bpij,bpj->bpi", w_tf.inverse(), x_h) + else: + x_h = torch.einsum("bpn,bnij,bpj->bpi", w, tfs, x_h) + return x_h[:, :, :3] \ No newline at end of file diff --git a/code/lib/model/density.py b/code/lib/model/density.py new file mode 100644 index 0000000000000000000000000000000000000000..2c848ffa33375b668981992e8fc3c0f30e825bef --- /dev/null +++ b/code/lib/model/density.py @@ -0,0 +1,46 @@ +import torch.nn as nn +import torch + +class Density(nn.Module): + def __init__(self, params_init={}): + super().__init__() + for p in params_init: + param = nn.Parameter(torch.tensor(params_init[p])) + setattr(self, p, param) + + def forward(self, sdf, beta=None): + return self.density_func(sdf, beta=beta) + + +class LaplaceDensity(Density): # alpha * Laplace(loc=0, scale=beta).cdf(-sdf) + def __init__(self, params_init={}, beta_min=0.0001): + super().__init__(params_init=params_init) + self.beta_min = torch.tensor(beta_min).cuda() + + def density_func(self, sdf, beta=None): + if beta is None: + beta = self.get_beta() + + alpha = 1 / beta + return alpha * (0.5 + 0.5 * sdf.sign() * torch.expm1(-sdf.abs() / beta)) + + def get_beta(self): + beta = self.beta.abs() + self.beta_min + return beta + + +class AbsDensity(Density): # like NeRF++ + def density_func(self, sdf, beta=None): + return torch.abs(sdf) + + +class SimpleDensity(Density): # like NeRF + def __init__(self, params_init={}, noise_std=1.0): + super().__init__(params_init=params_init) + self.noise_std = noise_std + + def density_func(self, sdf, beta=None): + if self.training and self.noise_std > 0.0: + noise = torch.randn(sdf.shape).cuda() * self.noise_std + sdf = sdf + noise + return torch.relu(sdf) diff --git a/code/lib/model/embedders.py b/code/lib/model/embedders.py new file mode 100644 index 0000000000000000000000000000000000000000..1fbb5b9a91d993db92221457e7f0152fc3a763ca --- /dev/null +++ b/code/lib/model/embedders.py @@ -0,0 +1,50 @@ +import torch + +class Embedder: + def __init__(self, **kwargs): + self.kwargs = kwargs + self.create_embedding_fn() + + def create_embedding_fn(self): + embed_fns = [] + d = self.kwargs['input_dims'] + out_dim = 0 + if self.kwargs['include_input']: + embed_fns.append(lambda x: x) + out_dim += d + + max_freq = self.kwargs['max_freq_log2'] + N_freqs = self.kwargs['num_freqs'] + + if self.kwargs['log_sampling']: + freq_bands = 2. ** torch.linspace(0., max_freq, N_freqs) + else: + freq_bands = torch.linspace(2.**0., 2.**max_freq, N_freqs) + + for freq in freq_bands: + for p_fn in self.kwargs['periodic_fns']: + embed_fns.append(lambda x, p_fn=p_fn, + freq=freq: p_fn(x * freq)) + out_dim += d + + self.embed_fns = embed_fns + self.out_dim = out_dim + + def embed(self, inputs): + return torch.cat([fn(inputs) for fn in self.embed_fns], -1) + +def get_embedder(multires, input_dims=3, mode='fourier'): + embed_kwargs = { + 'include_input': True, + 'input_dims': input_dims, + 'max_freq_log2': multires-1, + 'num_freqs': multires, + 'log_sampling': True, + 'periodic_fns': [torch.sin, torch.cos], + } + if mode == 'fourier': + embedder_obj = Embedder(**embed_kwargs) + + + def embed(x, eo=embedder_obj): return eo.embed(x) + return embed, embedder_obj.out_dim \ No newline at end of file diff --git a/code/lib/model/loss.py b/code/lib/model/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..065986330ffe8808e56fa136eb94f917d0881bc1 --- /dev/null +++ b/code/lib/model/loss.py @@ -0,0 +1,64 @@ +import torch +from torch import nn +from torch.nn import functional as F + +class Loss(nn.Module): + def __init__(self, opt): + super().__init__() + self.eikonal_weight = opt.eikonal_weight + self.bce_weight = opt.bce_weight + self.opacity_sparse_weight = opt.opacity_sparse_weight + self.in_shape_weight = opt.in_shape_weight + self.eps = 1e-6 + self.milestone = 200 + self.l1_loss = nn.L1Loss(reduction='mean') + self.l2_loss = nn.MSELoss(reduction='mean') + + # L1 reconstruction loss for RGB values + def get_rgb_loss(self, rgb_values, rgb_gt): + rgb_loss = self.l1_loss(rgb_values, rgb_gt) + return rgb_loss + + # Eikonal loss introduced in IGR + def get_eikonal_loss(self, grad_theta): + eikonal_loss = ((grad_theta.norm(2, dim=-1) - 1)**2).mean() + return eikonal_loss + + # BCE loss for clear boundary + def get_bce_loss(self, acc_map): + binary_loss = -1 * (acc_map * (acc_map + self.eps).log() + (1-acc_map) * (1 - acc_map + self.eps).log()).mean() * 2 + return binary_loss + + # Global opacity sparseness regularization + def get_opacity_sparse_loss(self, acc_map, index_off_surface): + opacity_sparse_loss = self.l1_loss(acc_map[index_off_surface], torch.zeros_like(acc_map[index_off_surface])) + return opacity_sparse_loss + + # Optional: This loss helps to stablize the training in the very beginning + def get_in_shape_loss(self, acc_map, index_in_surface): + in_shape_loss = self.l1_loss(acc_map[index_in_surface], torch.ones_like(acc_map[index_in_surface])) + return in_shape_loss + + def forward(self, model_outputs, ground_truth): + nan_filter = ~torch.any(model_outputs['rgb_values'].isnan(), dim=1) + rgb_gt = ground_truth['rgb'][0].cuda() + rgb_loss = self.get_rgb_loss(model_outputs['rgb_values'][nan_filter], rgb_gt[nan_filter]) + eikonal_loss = self.get_eikonal_loss(model_outputs['grad_theta']) + bce_loss = self.get_bce_loss(model_outputs['acc_map']) + opacity_sparse_loss = self.get_opacity_sparse_loss(model_outputs['acc_map'], model_outputs['index_off_surface']) + in_shape_loss = self.get_in_shape_loss(model_outputs['acc_map'], model_outputs['index_in_surface']) + curr_epoch_for_loss = min(self.milestone, model_outputs['epoch']) # will not increase after the milestone + + loss = rgb_loss + \ + self.eikonal_weight * eikonal_loss + \ + self.bce_weight * bce_loss + \ + self.opacity_sparse_weight * (1 + curr_epoch_for_loss ** 2 / 40) * opacity_sparse_loss + \ + self.in_shape_weight * (1 - curr_epoch_for_loss / self.milestone) * in_shape_loss + return { + 'loss': loss, + 'rgb_loss': rgb_loss, + 'eikonal_loss': eikonal_loss, + 'bce_loss': bce_loss, + 'opacity_sparse_loss': opacity_sparse_loss, + 'in_shape_loss': in_shape_loss, + } \ No newline at end of file diff --git a/code/lib/model/networks.py b/code/lib/model/networks.py new file mode 100644 index 0000000000000000000000000000000000000000..3f38cfa43d39e16ba08ec879acba0fd512244625 --- /dev/null +++ b/code/lib/model/networks.py @@ -0,0 +1,178 @@ +import torch.nn as nn +import torch +import numpy as np +from .embedders import get_embedder + +class ImplicitNet(nn.Module): + def __init__(self, opt): + super().__init__() + + dims = [opt.d_in] + list( + opt.dims) + [opt.d_out + opt.feature_vector_size] + self.num_layers = len(dims) + self.skip_in = opt.skip_in + self.embed_fn = None + self.opt = opt + + if opt.multires > 0: + embed_fn, input_ch = get_embedder(opt.multires, input_dims=opt.d_in, mode=opt.embedder_mode) + self.embed_fn = embed_fn + dims[0] = input_ch + self.cond = opt.cond + if self.cond == 'smpl': + self.cond_layer = [0] + self.cond_dim = 69 + elif self.cond == 'frame': + self.cond_layer = [0] + self.cond_dim = opt.dim_frame_encoding + self.dim_pose_embed = 0 + if self.dim_pose_embed > 0: + self.lin_p0 = nn.Linear(self.cond_dim, self.dim_pose_embed) + self.cond_dim = self.dim_pose_embed + for l in range(0, self.num_layers - 1): + if l + 1 in self.skip_in: + out_dim = dims[l + 1] - dims[0] + else: + out_dim = dims[l + 1] + + if self.cond != 'none' and l in self.cond_layer: + lin = nn.Linear(dims[l] + self.cond_dim, out_dim) + else: + lin = nn.Linear(dims[l], out_dim) + if opt.init == 'geometry': + if l == self.num_layers - 2: + torch.nn.init.normal_(lin.weight, + mean=np.sqrt(np.pi) / + np.sqrt(dims[l]), + std=0.0001) + torch.nn.init.constant_(lin.bias, -opt.bias) + elif opt.multires > 0 and l == 0: + torch.nn.init.constant_(lin.bias, 0.0) + torch.nn.init.constant_(lin.weight[:, 3:], 0.0) + torch.nn.init.normal_(lin.weight[:, :3], 0.0, + np.sqrt(2) / np.sqrt(out_dim)) + elif opt.multires > 0 and l in self.skip_in: + torch.nn.init.constant_(lin.bias, 0.0) + torch.nn.init.normal_(lin.weight, 0.0, + np.sqrt(2) / np.sqrt(out_dim)) + torch.nn.init.constant_(lin.weight[:, -(dims[0] - 3):], + 0.0) + else: + torch.nn.init.constant_(lin.bias, 0.0) + torch.nn.init.normal_(lin.weight, 0.0, + np.sqrt(2) / np.sqrt(out_dim)) + if opt.init == 'zero': + init_val = 1e-5 + if l == self.num_layers - 2: + torch.nn.init.constant_(lin.bias, 0.0) + torch.nn.init.uniform_(lin.weight, -init_val, init_val) + if opt.weight_norm: + lin = nn.utils.weight_norm(lin) + setattr(self, "lin" + str(l), lin) + self.softplus = nn.Softplus(beta=100) + + def forward(self, input, cond, current_epoch=None): + if input.ndim == 2: input = input.unsqueeze(0) + + num_batch, num_point, num_dim = input.shape + + if num_batch * num_point == 0: return input + + input = input.reshape(num_batch * num_point, num_dim) + + if self.cond != 'none': + num_batch, num_cond = cond[self.cond].shape + + input_cond = cond[self.cond].unsqueeze(1).expand(num_batch, num_point, num_cond) + + input_cond = input_cond.reshape(num_batch * num_point, num_cond) + + if self.dim_pose_embed: + input_cond = self.lin_p0(input_cond) + + if self.embed_fn is not None: + input = self.embed_fn(input) + + x = input + + for l in range(0, self.num_layers - 1): + lin = getattr(self, "lin" + str(l)) + if self.cond != 'none' and l in self.cond_layer: + x = torch.cat([x, input_cond], dim=-1) + if l in self.skip_in: + x = torch.cat([x, input], 1) / np.sqrt(2) + x = lin(x) + if l < self.num_layers - 2: + x = self.softplus(x) + + x = x.reshape(num_batch, num_point, -1) + + return x + + def gradient(self, x, cond): + x.requires_grad_(True) + y = self.forward(x, cond)[:, :1] + d_output = torch.ones_like(y, requires_grad=False, device=y.device) + gradients = torch.autograd.grad(outputs=y, + inputs=x, + grad_outputs=d_output, + create_graph=True, + retain_graph=True, + only_inputs=True)[0] + return gradients.unsqueeze(1) + + +class RenderingNet(nn.Module): + def __init__(self, opt): + super().__init__() + + self.mode = opt.mode + dims = [opt.d_in + opt.feature_vector_size] + list( + opt.dims) + [opt.d_out] + + self.embedview_fn = None + if opt.multires_view > 0: + embedview_fn, input_ch = get_embedder(opt.multires_view) + self.embedview_fn = embedview_fn + dims[0] += (input_ch - 3) + if self.mode == 'nerf_frame_encoding': + dims[0] += opt.dim_frame_encoding + if self.mode == 'pose': + self.dim_cond_embed = 8 + self.cond_dim = 69 # dimension of the body pose, global orientation excluded. + # lower the condition dimension + self.lin_pose = torch.nn.Linear(self.cond_dim, self.dim_cond_embed) + self.num_layers = len(dims) + for l in range(0, self.num_layers - 1): + out_dim = dims[l + 1] + lin = nn.Linear(dims[l], out_dim) + if opt.weight_norm: + lin = nn.utils.weight_norm(lin) + setattr(self, "lin" + str(l), lin) + self.relu = nn.ReLU() + self.sigmoid = nn.Sigmoid() + + def forward(self, points, normals, view_dirs, body_pose, feature_vectors, frame_latent_code=None): + if self.embedview_fn is not None: + if self.mode == 'nerf_frame_encoding': + view_dirs = self.embedview_fn(view_dirs) + + if self.mode == 'nerf_frame_encoding': + frame_latent_code = frame_latent_code.expand(view_dirs.shape[0], -1) + rendering_input = torch.cat([view_dirs, frame_latent_code, feature_vectors], dim=-1) + elif self.mode == 'pose': + num_points = points.shape[0] + body_pose = body_pose.unsqueeze(1).expand(-1, num_points, -1).reshape(num_points, -1) + body_pose = self.lin_pose(body_pose) + rendering_input = torch.cat([points, normals, body_pose, feature_vectors], dim=-1) + else: + raise NotImplementedError + + x = rendering_input + for l in range(0, self.num_layers - 1): + lin = getattr(self, "lin" + str(l)) + x = lin(x) + if l < self.num_layers - 2: + x = self.relu(x) + x = self.sigmoid(x) + return x diff --git a/code/lib/model/ray_sampler.py b/code/lib/model/ray_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..5aa3933f49f7d4c61e552be18b62d5997d13d21a --- /dev/null +++ b/code/lib/model/ray_sampler.py @@ -0,0 +1,234 @@ +import abc +import torch +from lib.utils import utils + +class RaySampler(metaclass=abc.ABCMeta): + def __init__(self,near, far): + self.near = near + self.far = far + + @abc.abstractmethod + def get_z_vals(self, ray_dirs, cam_loc, model): + pass + +class UniformSampler(RaySampler): + """Samples uniformly in the range [near, far] + """ + def __init__(self, scene_bounding_sphere, near, N_samples, take_sphere_intersection=False, far=-1): + super().__init__(near, 2.0 * scene_bounding_sphere if far == -1 else far) # default far is 2*R + self.N_samples = N_samples + self.scene_bounding_sphere = scene_bounding_sphere + self.take_sphere_intersection = take_sphere_intersection + + def get_z_vals(self, ray_dirs, cam_loc, model): + if not self.take_sphere_intersection: + near, far = self.near * torch.ones(ray_dirs.shape[0], 1).cuda(), self.far * torch.ones(ray_dirs.shape[0], 1).cuda() + else: + sphere_intersections = utils.get_sphere_intersections(cam_loc, ray_dirs, r=self.scene_bounding_sphere) + near = self.near * torch.ones(ray_dirs.shape[0], 1).cuda() + far = sphere_intersections[:,1:] + + t_vals = torch.linspace(0., 1., steps=self.N_samples).cuda() + z_vals = near * (1. - t_vals) + far * (t_vals) + + if model.training: + # get intervals between samples + mids = .5 * (z_vals[..., 1:] + z_vals[..., :-1]) + upper = torch.cat([mids, z_vals[..., -1:]], -1) + lower = torch.cat([z_vals[..., :1], mids], -1) + # stratified samples in those intervals + t_rand = torch.rand(z_vals.shape).cuda() + + z_vals = lower + (upper - lower) * t_rand + + return z_vals + +class ErrorBoundSampler(RaySampler): + def __init__(self, scene_bounding_sphere, near, N_samples, N_samples_eval, N_samples_extra, + eps, beta_iters, max_total_iters, + inverse_sphere_bg=False, N_samples_inverse_sphere=0, add_tiny=0.0): + super().__init__(near, 2.0 * scene_bounding_sphere) + self.N_samples = N_samples + self.N_samples_eval = N_samples_eval + self.uniform_sampler = UniformSampler(scene_bounding_sphere, near, N_samples_eval, take_sphere_intersection=inverse_sphere_bg) + + self.N_samples_extra = N_samples_extra + + self.eps = eps + self.beta_iters = beta_iters + self.max_total_iters = max_total_iters + self.scene_bounding_sphere = scene_bounding_sphere + self.add_tiny = add_tiny + + self.inverse_sphere_bg = inverse_sphere_bg + if inverse_sphere_bg: + N_samples_inverse_sphere = 32 + self.inverse_sphere_sampler = UniformSampler(1.0, 0.0, N_samples_inverse_sphere, False, far=1.0) + + def get_z_vals(self, ray_dirs, cam_loc, model, cond, smpl_tfs, eval_mode, smpl_verts): + beta0 = model.density.get_beta().detach() + + # Start with uniform sampling + z_vals = self.uniform_sampler.get_z_vals(ray_dirs, cam_loc, model) + samples, samples_idx = z_vals, None + + # Get maximum beta from the upper bound (Lemma 2) + dists = z_vals[:, 1:] - z_vals[:, :-1] + bound = (1.0 / (4.0 * torch.log(torch.tensor(self.eps + 1.0)))) * (dists ** 2.).sum(-1) + beta = torch.sqrt(bound) + + total_iters, not_converge = 0, True + + # VolSDF Algorithm 1 + while not_converge and total_iters < self.max_total_iters: + points = cam_loc.unsqueeze(1) + samples.unsqueeze(2) * ray_dirs.unsqueeze(1) + points_flat = points.reshape(-1, 3) + # Calculating the SDF only for the new sampled points + model.implicit_network.eval() + with torch.no_grad(): + samples_sdf = model.sdf_func_with_smpl_deformer(points_flat, cond, smpl_tfs, smpl_verts=smpl_verts)[0] + model.implicit_network.train() + if samples_idx is not None: + sdf_merge = torch.cat([sdf.reshape(-1, z_vals.shape[1] - samples.shape[1]), + samples_sdf.reshape(-1, samples.shape[1])], -1) + sdf = torch.gather(sdf_merge, 1, samples_idx).reshape(-1, 1) + else: + sdf = samples_sdf + + + # Calculating the bound d* (Theorem 1) + d = sdf.reshape(z_vals.shape) + dists = z_vals[:, 1:] - z_vals[:, :-1] + a, b, c = dists, d[:, :-1].abs(), d[:, 1:].abs() + first_cond = a.pow(2) + b.pow(2) <= c.pow(2) + second_cond = a.pow(2) + c.pow(2) <= b.pow(2) + d_star = torch.zeros(z_vals.shape[0], z_vals.shape[1] - 1).cuda() + d_star[first_cond] = b[first_cond] + d_star[second_cond] = c[second_cond] + s = (a + b + c) / 2.0 + area_before_sqrt = s * (s - a) * (s - b) * (s - c) + mask = ~first_cond & ~second_cond & (b + c - a > 0) + d_star[mask] = (2.0 * torch.sqrt(area_before_sqrt[mask])) / (a[mask]) + d_star = (d[:, 1:].sign() * d[:, :-1].sign() == 1) * d_star # Fixing the sign + + + # Updating beta using line search + curr_error = self.get_error_bound(beta0, model, sdf, z_vals, dists, d_star) + beta[curr_error <= self.eps] = beta0 + beta_min, beta_max = beta0.unsqueeze(0).repeat(z_vals.shape[0]), beta + for j in range(self.beta_iters): + beta_mid = (beta_min + beta_max) / 2. + curr_error = self.get_error_bound(beta_mid.unsqueeze(-1), model, sdf, z_vals, dists, d_star) + beta_max[curr_error <= self.eps] = beta_mid[curr_error <= self.eps] + beta_min[curr_error > self.eps] = beta_mid[curr_error > self.eps] + beta = beta_max + + + # Upsample more points + density = model.density(sdf.reshape(z_vals.shape), beta=beta.unsqueeze(-1)) + + dists = torch.cat([dists, torch.tensor([1e10]).cuda().unsqueeze(0).repeat(dists.shape[0], 1)], -1) + free_energy = dists * density + shifted_free_energy = torch.cat([torch.zeros(dists.shape[0], 1).cuda(), free_energy[:, :-1]], dim=-1) + alpha = 1 - torch.exp(-free_energy) + transmittance = torch.exp(-torch.cumsum(shifted_free_energy, dim=-1)) + weights = alpha * transmittance # probability of the ray hits something here + + # Check if we are done and this is the last sampling + total_iters += 1 + not_converge = beta.max() > beta0 + + if not_converge and total_iters < self.max_total_iters: + ''' Sample more points proportional to the current error bound''' + + N = self.N_samples_eval + + bins = z_vals + error_per_section = torch.exp(-d_star / beta.unsqueeze(-1)) * (dists[:,:-1] ** 2.) / (4 * beta.unsqueeze(-1) ** 2) + error_integral = torch.cumsum(error_per_section, dim=-1) + bound_opacity = (torch.clamp(torch.exp(error_integral),max=1.e6) - 1.0) * transmittance[:,:-1] + + pdf = bound_opacity + self.add_tiny + pdf = pdf / torch.sum(pdf, -1, keepdim=True) + cdf = torch.cumsum(pdf, -1) + cdf = torch.cat([torch.zeros_like(cdf[..., :1]), cdf], -1) + + else: + ''' Sample the final sample set to be used in the volume rendering integral ''' + + N = self.N_samples + + bins = z_vals + pdf = weights[..., :-1] + pdf = pdf + 1e-5 # prevent nans + pdf = pdf / torch.sum(pdf, -1, keepdim=True) + cdf = torch.cumsum(pdf, -1) + cdf = torch.cat([torch.zeros_like(cdf[..., :1]), cdf], -1) # (batch, len(bins)) + + + # Invert CDF + if (not_converge and total_iters < self.max_total_iters) or (not model.training): + u = torch.linspace(0., 1., steps=N).cuda().unsqueeze(0).repeat(cdf.shape[0], 1) + else: + u = torch.rand(list(cdf.shape[:-1]) + [N]).cuda() + u = u.contiguous() + + inds = torch.searchsorted(cdf, u, right=True) + below = torch.max(torch.zeros_like(inds - 1), inds - 1) + above = torch.min((cdf.shape[-1] - 1) * torch.ones_like(inds), inds) + inds_g = torch.stack([below, above], -1) # (batch, N_samples, 2) + + matched_shape = [inds_g.shape[0], inds_g.shape[1], cdf.shape[-1]] + cdf_g = torch.gather(cdf.unsqueeze(1).expand(matched_shape), 2, inds_g) + bins_g = torch.gather(bins.unsqueeze(1).expand(matched_shape), 2, inds_g) + + denom = (cdf_g[..., 1] - cdf_g[..., 0]) + denom = torch.where(denom < 1e-5, torch.ones_like(denom), denom) + t = (u - cdf_g[..., 0]) / denom + samples = bins_g[..., 0] + t * (bins_g[..., 1] - bins_g[..., 0]) + + + # Adding samples if we not converged + if not_converge and total_iters < self.max_total_iters: + z_vals, samples_idx = torch.sort(torch.cat([z_vals, samples], -1), -1) + + + z_samples = samples + + near, far = self.near * torch.ones(ray_dirs.shape[0], 1).cuda(), self.far * torch.ones(ray_dirs.shape[0],1).cuda() + if self.inverse_sphere_bg: # if inverse sphere then need to add the far sphere intersection + far = utils.get_sphere_intersections(cam_loc, ray_dirs, r=self.scene_bounding_sphere)[:,1:] + + if self.N_samples_extra > 0: + if model.training: + sampling_idx = torch.randperm(z_vals.shape[1])[:self.N_samples_extra] + else: + sampling_idx = torch.linspace(0, z_vals.shape[1]-1, self.N_samples_extra).long() + z_vals_extra = torch.cat([near, far, z_vals[:,sampling_idx]], -1) + else: + z_vals_extra = torch.cat([near, far], -1) + + z_vals, _ = torch.sort(torch.cat([z_samples, z_vals_extra], -1), -1) + + # add some of the near surface points + idx = torch.randint(z_vals.shape[-1], (z_vals.shape[0],)).cuda() + z_samples_eik = torch.gather(z_vals, 1, idx.unsqueeze(-1)) + + if self.inverse_sphere_bg: + z_vals_inverse_sphere = self.inverse_sphere_sampler.get_z_vals(ray_dirs, cam_loc, model) + z_vals_inverse_sphere = z_vals_inverse_sphere * (1./self.scene_bounding_sphere) + z_vals = (z_vals, z_vals_inverse_sphere) + + return z_vals, z_samples_eik + + def get_error_bound(self, beta, model, sdf, z_vals, dists, d_star): + density = model.density(sdf.reshape(z_vals.shape), beta=beta) + shifted_free_energy = torch.cat([torch.zeros(dists.shape[0], 1).cuda(), dists * density[:, :-1]], dim=-1) + integral_estimation = torch.cumsum(shifted_free_energy, dim=-1) + error_per_section = torch.exp(-d_star / beta) * (dists ** 2.) / (4 * beta ** 2) + error_integral = torch.cumsum(error_per_section, dim=-1) + bound_opacity = (torch.clamp(torch.exp(error_integral), max=1.e6) - 1.0) * torch.exp(-integral_estimation[:, :-1]) + + return bound_opacity.max(-1)[0] + + diff --git a/code/lib/model/sampler.py b/code/lib/model/sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..94d6ca43892df58d8e6c45e269c1236059b1e86f --- /dev/null +++ b/code/lib/model/sampler.py @@ -0,0 +1,29 @@ +import torch + + +class PointInSpace: + def __init__(self, global_sigma=0.5, local_sigma=0.01): + self.global_sigma = global_sigma + self.local_sigma = local_sigma + + def get_points(self, pc_input=None, local_sigma=None, global_ratio=0.125): + """Sample one point near each of the given point + 1/8 uniformly. + Args: + pc_input (tensor): sampling centers. shape: [B, N, D] + Returns: + samples (tensor): sampled points. shape: [B, N + N / 8, D] + """ + + batch_size, sample_size, dim = pc_input.shape + if local_sigma is None: + sample_local = pc_input + (torch.randn_like(pc_input) * self.local_sigma) + else: + sample_local = pc_input + (torch.randn_like(pc_input) * local_sigma) + sample_global = ( + torch.rand(batch_size, int(sample_size * global_ratio), dim, device=pc_input.device) + * (self.global_sigma * 2) + ) - self.global_sigma + + sample = torch.cat([sample_local, sample_global], dim=1) + + return sample \ No newline at end of file diff --git a/code/lib/model/smpl.py b/code/lib/model/smpl.py new file mode 100644 index 0000000000000000000000000000000000000000..a9e97372d01792cd7e948c8911b9e3680fd47a27 --- /dev/null +++ b/code/lib/model/smpl.py @@ -0,0 +1,94 @@ +import torch +import hydra +import numpy as np +from ..smpl.body_models import SMPL + +class SMPLServer(torch.nn.Module): + + def __init__(self, gender='neutral', betas=None, v_template=None): + super().__init__() + + + self.smpl = SMPL(model_path=hydra.utils.to_absolute_path('lib/smpl/smpl_model'), + gender=gender, + batch_size=1, + use_hands=False, + use_feet_keypoints=False, + dtype=torch.float32).cuda() + + self.bone_parents = self.smpl.bone_parents.astype(int) + self.bone_parents[0] = -1 + self.bone_ids = [] + self.faces = self.smpl.faces + for i in range(24): self.bone_ids.append([self.bone_parents[i], i]) + + if v_template is not None: + self.v_template = torch.tensor(v_template).float().cuda() + else: + self.v_template = None + + if betas is not None: + self.betas = torch.tensor(betas).float().cuda() + else: + self.betas = None + + # define the canonical pose + param_canonical = torch.zeros((1, 86),dtype=torch.float32).cuda() + param_canonical[0, 0] = 1 + param_canonical[0, 9] = np.pi / 6 + param_canonical[0, 12] = -np.pi / 6 + if self.betas is not None and self.v_template is None: + param_canonical[0,-10:] = self.betas + self.param_canonical = param_canonical + + output = self.forward(*torch.split(self.param_canonical, [1, 3, 72, 10], dim=1), absolute=True) + self.verts_c = output['smpl_verts'] + self.joints_c = output['smpl_jnts'] + self.tfs_c_inv = output['smpl_tfs'].squeeze(0).inverse() + + + def forward(self, scale, transl, thetas, betas, absolute=False): + """return SMPL output from params + Args: + scale : scale factor. shape: [B, 1] + transl: translation. shape: [B, 3] + thetas: pose. shape: [B, 72] + betas: shape. shape: [B, 10] + absolute (bool): if true return smpl_tfs wrt thetas=0. else wrt thetas=thetas_canonical. + Returns: + smpl_verts: vertices. shape: [B, 6893. 3] + smpl_tfs: bone transformations. shape: [B, 24, 4, 4] + smpl_jnts: joint positions. shape: [B, 25, 3] + """ + + output = {} + + # ignore betas if v_template is provided + if self.v_template is not None: + betas = torch.zeros_like(betas) + + + smpl_output = self.smpl.forward(betas=betas, + transl=torch.zeros_like(transl), + body_pose=thetas[:, 3:], + global_orient=thetas[:, :3], + return_verts=True, + return_full_pose=True, + v_template=self.v_template) + + verts = smpl_output.vertices.clone() + output['smpl_verts'] = verts * scale.unsqueeze(1) + transl.unsqueeze(1) * scale.unsqueeze(1) + + joints = smpl_output.joints.clone() + output['smpl_jnts'] = joints * scale.unsqueeze(1) + transl.unsqueeze(1) * scale.unsqueeze(1) + + tf_mats = smpl_output.T.clone() + tf_mats[:, :, :3, :] = tf_mats[:, :, :3, :] * scale.unsqueeze(1).unsqueeze(1) + tf_mats[:, :, :3, 3] = tf_mats[:, :, :3, 3] + transl.unsqueeze(1) * scale.unsqueeze(1) + + if not absolute: + tf_mats = torch.einsum('bnij,njk->bnik', tf_mats, self.tfs_c_inv) + + output['smpl_tfs'] = tf_mats + output['smpl_weights'] = smpl_output.weights + return output \ No newline at end of file diff --git a/code/lib/model/v2a.py b/code/lib/model/v2a.py new file mode 100644 index 0000000000000000000000000000000000000000..14a07dc2074e2e537861720ed47eb251485ab0f5 --- /dev/null +++ b/code/lib/model/v2a.py @@ -0,0 +1,368 @@ +from .networks import ImplicitNet, RenderingNet +from .density import LaplaceDensity, AbsDensity +from .ray_sampler import ErrorBoundSampler +from .deformer import SMPLDeformer +from .smpl import SMPLServer + +from .sampler import PointInSpace + +from ..utils import utils + +import numpy as np +import torch +import torch.nn as nn +from torch.autograd import grad +import hydra +import kaolin +from kaolin.ops.mesh import index_vertices_by_faces +class V2A(nn.Module): + def __init__(self, opt, betas_path, gender, num_training_frames): + super().__init__() + + # Foreground networks + self.implicit_network = ImplicitNet(opt.implicit_network) + self.rendering_network = RenderingNet(opt.rendering_network) + + # Background networks + self.bg_implicit_network = ImplicitNet(opt.bg_implicit_network) + self.bg_rendering_network = RenderingNet(opt.bg_rendering_network) + + # Frame latent encoder + self.frame_latent_encoder = nn.Embedding(num_training_frames, opt.bg_rendering_network.dim_frame_encoding) + self.sampler = PointInSpace() + + betas = np.load(betas_path) + self.use_smpl_deformer = opt.use_smpl_deformer + self.gender = gender + if self.use_smpl_deformer: + self.deformer = SMPLDeformer(betas=betas, gender=self.gender) + + # pre-defined bounding sphere + self.sdf_bounding_sphere = 3.0 + + # threshold for the out-surface points + self.threshold = 0.05 + + self.density = LaplaceDensity(**opt.density) + self.bg_density = AbsDensity() + + self.ray_sampler = ErrorBoundSampler(self.sdf_bounding_sphere, inverse_sphere_bg=True, **opt.ray_sampler) + self.smpl_server = SMPLServer(gender=self.gender, betas=betas) + + if opt.smpl_init: + smpl_model_state = torch.load(hydra.utils.to_absolute_path('../assets/smpl_init.pth')) + self.implicit_network.load_state_dict(smpl_model_state["model_state_dict"]) + + self.smpl_v_cano = self.smpl_server.verts_c + self.smpl_f_cano = torch.tensor(self.smpl_server.smpl.faces.astype(np.int64), device=self.smpl_v_cano.device) + + self.mesh_v_cano = self.smpl_server.verts_c + self.mesh_f_cano = torch.tensor(self.smpl_server.smpl.faces.astype(np.int64), device=self.smpl_v_cano.device) + self.mesh_face_vertices = index_vertices_by_faces(self.mesh_v_cano, self.mesh_f_cano) + + def sdf_func_with_smpl_deformer(self, x, cond, smpl_tfs, smpl_verts): + """ sdf_func_with_smpl_deformer method + Used to compute SDF values for input points using the SMPL deformer and the implicit network. + It handles the deforming of points, network inference, feature extraction, and handling of outlier points. + """ + if hasattr(self, "deformer"): + x_c, outlier_mask = self.deformer.forward(x, smpl_tfs, return_weights=False, inverse=True, smpl_verts=smpl_verts) + output = self.implicit_network(x_c, cond)[0] + sdf = output[:, 0:1] + feature = output[:, 1:] + if not self.training: + sdf[outlier_mask] = 4. # set a large SDF value for outlier points + + return sdf, x_c, feature + + def check_off_in_surface_points_cano_mesh(self, x_cano, N_samples, threshold=0.05): + """check_off_in_surface_points_cano_mesh method + Used to check whether points are off the surface or within the surface of a canonical mesh. + It calculates distances, signs, and signed distances to determine the position of points with respect to the mesh surface. + The method plays a role in identifying points that might be considered outliers or outside the reconstructed avatar's surface. + """ + + distance, _, _ = kaolin.metrics.trianglemesh.point_to_mesh_distance(x_cano.unsqueeze(0).contiguous(), self.mesh_face_vertices) + + distance = torch.sqrt(distance) # kaolin outputs squared distance + sign = kaolin.ops.mesh.check_sign(self.mesh_v_cano, self.mesh_f_cano, x_cano.unsqueeze(0)).float() + sign = 1 - 2 * sign # -1 for off-surface, 1 for in-surface + signed_distance = sign * distance + batch_size = x_cano.shape[0] // N_samples + signed_distance = signed_distance.reshape(batch_size, N_samples, 1) # The distances are reshaped to match the batch size and the number of samples + + minimum = torch.min(signed_distance, 1)[0] + index_off_surface = (minimum > threshold).squeeze(1) + index_in_surface = (minimum <= 0.).squeeze(1) + return index_off_surface, index_in_surface # Indexes of off-surface points and in-surface points + + def forward(self, input): + # Parse model input, prepares the necessary input data and SMPL parameters for subsequent calculations + torch.set_grad_enabled(True) + intrinsics = input["intrinsics"] + pose = input["pose"] + uv = input["uv"] + + scale = input['smpl_params'][:, 0] + smpl_pose = input["smpl_pose"] + smpl_shape = input["smpl_shape"] + smpl_trans = input["smpl_trans"] + smpl_output = self.smpl_server(scale, smpl_trans, smpl_pose, smpl_shape) # invokes the SMPL model to obtain the transformations for pose and shape changes + + smpl_tfs = smpl_output['smpl_tfs'] + + cond = {'smpl': smpl_pose[:, 3:]/np.pi} + if self.training: + if input['current_epoch'] < 20 or input['current_epoch'] % 20 == 0: # set the pose to zero for the first 20 epochs + cond = {'smpl': smpl_pose[:, 3:] * 0.} + ray_dirs, cam_loc = utils.get_camera_params(uv, pose, intrinsics) # get the ray directions and camera location + batch_size, num_pixels, _ = ray_dirs.shape + + cam_loc = cam_loc.unsqueeze(1).repeat(1, num_pixels, 1).reshape(-1, 3) # reshape to match the batch size and the number of pixels + ray_dirs = ray_dirs.reshape(-1, 3) # reshape to match the batch size and the number of pixels + + z_vals, _ = self.ray_sampler.get_z_vals(ray_dirs, cam_loc, self, cond, smpl_tfs, eval_mode=True, smpl_verts=smpl_output['smpl_verts']) # get the z values for each pixel + + z_vals, z_vals_bg = z_vals # unpack the z values for the foreground and the background + z_max = z_vals[:,-1] # get the maximum z value + z_vals = z_vals[:,:-1] # get the z values for the foreground + N_samples = z_vals.shape[1] # get the number of samples + + points = cam_loc.unsqueeze(1) + z_vals.unsqueeze(2) * ray_dirs.unsqueeze(1) # 3D points along the rays are calculated by adding z_vals scaled by ray directions to the camera location. The result is stored in the points tensor of shape (batch_size * num_pixels, N_samples, 3) + points_flat = points.reshape(-1, 3) # The points tensor is reshaped into a flattened tensor points_flat of shape (batch_size * num_pixels * N_samples, 3) + + dirs = ray_dirs.unsqueeze(1).repeat(1,N_samples,1) # The dirs tensor is created by repeating ray_dirs for each sample along the rays. The resulting tensor has shape (batch_size * num_pixels, N_samples, 3) + sdf_output, canonical_points, feature_vectors = self.sdf_func_with_smpl_deformer(points_flat, cond, smpl_tfs, smpl_output['smpl_verts']) # The sdf_func_with_smpl_deformer method is called to compute the signed distance functions (SDF) for the points + + sdf_output = sdf_output.unsqueeze(1) # The sdf_output tensor is reshaped by unsqueezing along the first dimension + + if self.training: + index_off_surface, index_in_surface = self.check_off_in_surface_points_cano_mesh(canonical_points, N_samples, threshold=self.threshold) + canonical_points = canonical_points.reshape(num_pixels, N_samples, 3) + + canonical_points = canonical_points.reshape(-1, 3) # The canonical points tensor flattened to shape (-1, 3) + + # sample canonical SMPL surface pnts for the eikonal loss + smpl_verts_c = self.smpl_server.verts_c.repeat(batch_size, 1,1) # The canonical SMPL surface vertices are repeated across the batch dimension + + indices = torch.randperm(smpl_verts_c.shape[1])[:num_pixels].cuda() # Random indices are generated to select a subset of vertices for sampling. The number of selected vertices is num_pixels + verts_c = torch.index_select(smpl_verts_c, 1, indices) # The selected vertices are gathered from smpl_verts_c, resulting in the tensor verts_c. + sample = self.sampler.get_points(verts_c, global_ratio=0.) # The get_points method of the sampler class is called to sample points around the canonical SMPL surface points. The global_ratio is set to 0.0, indicating local sampling + + sample.requires_grad_() # The sampled points are marked as requiring gradients + local_pred = self.implicit_network(sample, cond)[..., 0:1] # The sampled points (sample) are passed through the implicit network along with the conditioning (cond). The local prediction (SDF) for each sampled point is extracted using [..., 0:1] + grad_theta = gradient(sample, local_pred) # compute gradients with respect to the sampled points and their local predictions (local_pred). + + differentiable_points = canonical_points # The differentiable_points tensor is assigned the value of canonical_points + + else: + differentiable_points = canonical_points.reshape(num_pixels, N_samples, 3).reshape(-1, 3) + grad_theta = None + + sdf_output = sdf_output.reshape(num_pixels, N_samples, 1).reshape(-1, 1) # flattened to shape (num_pixels * N_samples, ) + z_vals = z_vals + view = -dirs.reshape(-1, 3) # The view vector is calculated as the negation of the reshaped dirs, giving the view directions for points along the rays. + + if differentiable_points.shape[0] > 0: # If there are differentiable points (indicating that gradient information is available) + fg_rgb_flat, others = self.get_rbg_value(points_flat, differentiable_points, view, + cond, smpl_tfs, feature_vectors=feature_vectors, is_training=self.training) # The returned values include fg_rgb_flat (foreground RGB values) and others (other calculated values, including normals) + normal_values = others['normals'] # The normal values are extracted from the others dictionary + + if 'image_id' in input.keys(): + frame_latent_code = self.frame_latent_encoder(input['image_id']) + else: + frame_latent_code = self.frame_latent_encoder(input['idx']) + + fg_rgb = fg_rgb_flat.reshape(-1, N_samples, 3) + normal_values = normal_values.reshape(-1, N_samples, 3) + weights, bg_transmittance = self.volume_rendering(z_vals, z_max, sdf_output) + + fg_rgb_values = torch.sum(weights.unsqueeze(-1) * fg_rgb, 1) + + # Background rendering + if input['idx'] is not None: + N_bg_samples = z_vals_bg.shape[1] + z_vals_bg = torch.flip(z_vals_bg, dims=[-1, ]) # 1--->0 + + bg_dirs = ray_dirs.unsqueeze(1).repeat(1,N_bg_samples,1) + bg_locs = cam_loc.unsqueeze(1).repeat(1,N_bg_samples,1) + + bg_points = self.depth2pts_outside(bg_locs, bg_dirs, z_vals_bg) # [..., N_samples, 4] + bg_points_flat = bg_points.reshape(-1, 4) + bg_dirs_flat = bg_dirs.reshape(-1, 3) + bg_output = self.bg_implicit_network(bg_points_flat, {'frame': frame_latent_code})[0] + bg_sdf = bg_output[:, :1] + bg_feature_vectors = bg_output[:, 1:] + + bg_rendering_output = self.bg_rendering_network(None, None, bg_dirs_flat, None, bg_feature_vectors, frame_latent_code) + if bg_rendering_output.shape[-1] == 4: + bg_rgb_flat = bg_rendering_output[..., :-1] + shadow_r = bg_rendering_output[..., -1] + bg_rgb = bg_rgb_flat.reshape(-1, N_bg_samples, 3) + shadow_r = shadow_r.reshape(-1, N_bg_samples, 1) + bg_rgb = (1 - shadow_r) * bg_rgb + else: + bg_rgb_flat = bg_rendering_output + bg_rgb = bg_rgb_flat.reshape(-1, N_bg_samples, 3) + bg_weights = self.bg_volume_rendering(z_vals_bg, bg_sdf) + bg_rgb_values = torch.sum(bg_weights.unsqueeze(-1) * bg_rgb, 1) + else: + bg_rgb_values = torch.ones_like(fg_rgb_values, device=fg_rgb_values.device) + + # Composite foreground and background + bg_rgb_values = bg_transmittance.unsqueeze(-1) * bg_rgb_values + rgb_values = fg_rgb_values + bg_rgb_values + + normal_values = torch.sum(weights.unsqueeze(-1) * normal_values, 1) + + if self.training: + output = { + 'points': points, + 'rgb_values': rgb_values, + 'normal_values': normal_values, + 'index_outside': input['index_outside'], + 'index_off_surface': index_off_surface, + 'index_in_surface': index_in_surface, + 'acc_map': torch.sum(weights, -1), + 'sdf_output': sdf_output, + 'grad_theta': grad_theta, + 'epoch': input['current_epoch'], + } + else: + fg_output_rgb = fg_rgb_values + bg_transmittance.unsqueeze(-1) * torch.ones_like(fg_rgb_values, device=fg_rgb_values.device) + output = { + 'acc_map': torch.sum(weights, -1), + 'rgb_values': rgb_values, + 'fg_rgb_values': fg_output_rgb, + 'normal_values': normal_values, + 'sdf_output': sdf_output, + } + return output + + def get_rbg_value(self, x, points, view_dirs, cond, tfs, feature_vectors, is_training=True): + pnts_c = points + others = {} + + _, gradients, feature_vectors = self.forward_gradient(x, pnts_c, cond, tfs, create_graph=is_training, retain_graph=is_training) + # ensure the gradient is normalized + normals = nn.functional.normalize(gradients, dim=-1, eps=1e-6) + fg_rendering_output = self.rendering_network(pnts_c, normals, view_dirs, cond['smpl'], + feature_vectors) + + rgb_vals = fg_rendering_output[:, :3] + others['normals'] = normals + return rgb_vals, others + + def forward_gradient(self, x, pnts_c, cond, tfs, create_graph=True, retain_graph=True): + if pnts_c.shape[0] == 0: + return pnts_c.detach() + pnts_c.requires_grad_(True) + + pnts_d = self.deformer.forward_skinning(pnts_c.unsqueeze(0), None, tfs).squeeze(0) + num_dim = pnts_d.shape[-1] + grads = [] + for i in range(num_dim): + d_out = torch.zeros_like(pnts_d, requires_grad=False, device=pnts_d.device) + d_out[:, i] = 1 + grad = torch.autograd.grad( + outputs=pnts_d, + inputs=pnts_c, + grad_outputs=d_out, + create_graph=create_graph, + retain_graph=True if i < num_dim - 1 else retain_graph, + only_inputs=True)[0] + grads.append(grad) + grads = torch.stack(grads, dim=-2) + grads_inv = grads.inverse() + + output = self.implicit_network(pnts_c, cond)[0] + sdf = output[:, :1] + + feature = output[:, 1:] + d_output = torch.ones_like(sdf, requires_grad=False, device=sdf.device) + gradients = torch.autograd.grad( + outputs=sdf, + inputs=pnts_c, + grad_outputs=d_output, + create_graph=create_graph, + retain_graph=retain_graph, + only_inputs=True)[0] + + return grads.reshape(grads.shape[0], -1), torch.nn.functional.normalize(torch.einsum('bi,bij->bj', gradients, grads_inv), dim=1), feature + + def volume_rendering(self, z_vals, z_max, sdf): + density_flat = self.density(sdf) + density = density_flat.reshape(-1, z_vals.shape[1]) # (batch_size * num_pixels) x N_samples + + # included also the dist from the sphere intersection + dists = z_vals[:, 1:] - z_vals[:, :-1] + dists = torch.cat([dists, z_max.unsqueeze(-1) - z_vals[:, -1:]], -1) + + # LOG SPACE + free_energy = dists * density + shifted_free_energy = torch.cat([torch.zeros(dists.shape[0], 1).cuda(), free_energy], dim=-1) # add 0 for transperancy 1 at t_0 + alpha = 1 - torch.exp(-free_energy) # probability of it is not empty here + transmittance = torch.exp(-torch.cumsum(shifted_free_energy, dim=-1)) # probability of everything is empty up to now + fg_transmittance = transmittance[:, :-1] + weights = alpha * fg_transmittance # probability of the ray hits something here + bg_transmittance = transmittance[:, -1] # factor to be multiplied with the bg volume rendering + + return weights, bg_transmittance + + def bg_volume_rendering(self, z_vals_bg, bg_sdf): + bg_density_flat = self.bg_density(bg_sdf) + bg_density = bg_density_flat.reshape(-1, z_vals_bg.shape[1]) # (batch_size * num_pixels) x N_samples + + bg_dists = z_vals_bg[:, :-1] - z_vals_bg[:, 1:] + bg_dists = torch.cat([bg_dists, torch.tensor([1e10]).cuda().unsqueeze(0).repeat(bg_dists.shape[0], 1)], -1) + + # LOG SPACE + bg_free_energy = bg_dists * bg_density + bg_shifted_free_energy = torch.cat([torch.zeros(bg_dists.shape[0], 1).cuda(), bg_free_energy[:, :-1]], dim=-1) # shift one step + bg_alpha = 1 - torch.exp(-bg_free_energy) # probability of it is not empty here + bg_transmittance = torch.exp(-torch.cumsum(bg_shifted_free_energy, dim=-1)) # probability of everything is empty up to now + bg_weights = bg_alpha * bg_transmittance # probability of the ray hits something here + + return bg_weights + + def depth2pts_outside(self, ray_o, ray_d, depth): + + ''' + ray_o, ray_d: [..., 3] + depth: [...]; inverse of distance to sphere origin + ''' + + o_dot_d = torch.sum(ray_d * ray_o, dim=-1) + under_sqrt = o_dot_d ** 2 - ((ray_o ** 2).sum(-1) - self.sdf_bounding_sphere ** 2) + d_sphere = torch.sqrt(under_sqrt) - o_dot_d + p_sphere = ray_o + d_sphere.unsqueeze(-1) * ray_d + p_mid = ray_o - o_dot_d.unsqueeze(-1) * ray_d + p_mid_norm = torch.norm(p_mid, dim=-1) + + rot_axis = torch.cross(ray_o, p_sphere, dim=-1) + rot_axis = rot_axis / torch.norm(rot_axis, dim=-1, keepdim=True) + phi = torch.asin(p_mid_norm / self.sdf_bounding_sphere) + theta = torch.asin(p_mid_norm * depth) # depth is inside [0, 1] + rot_angle = (phi - theta).unsqueeze(-1) # [..., 1] + + # now rotate p_sphere + # Rodrigues formula: https://en.wikipedia.org/wiki/Rodrigues%27_rotation_formula + p_sphere_new = p_sphere * torch.cos(rot_angle) + \ + torch.cross(rot_axis, p_sphere, dim=-1) * torch.sin(rot_angle) + \ + rot_axis * torch.sum(rot_axis * p_sphere, dim=-1, keepdim=True) * (1. - torch.cos(rot_angle)) + p_sphere_new = p_sphere_new / torch.norm(p_sphere_new, dim=-1, keepdim=True) + pts = torch.cat((p_sphere_new, depth.unsqueeze(-1)), dim=-1) + + return pts + +def gradient(inputs, outputs): + + d_points = torch.ones_like(outputs, requires_grad=False, device=outputs.device) + points_grad = grad( + outputs=outputs, + inputs=inputs, + grad_outputs=d_points, + create_graph=True, + retain_graph=True, + only_inputs=True)[0][:, :, -3:] + return points_grad \ No newline at end of file diff --git a/code/lib/smpl/body_models.py b/code/lib/smpl/body_models.py new file mode 100644 index 0000000000000000000000000000000000000000..4f643883f57478063a5cbb97f3585e9a1f494a9d --- /dev/null +++ b/code/lib/smpl/body_models.py @@ -0,0 +1,365 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems and the Max Planck Institute for Biological +# Cybernetics. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os +import os.path as osp + + +import pickle + +import numpy as np + +from collections import namedtuple + +import torch +import torch.nn as nn + +from .lbs import ( + lbs, vertices2joints, blend_shapes) + +from .vertex_ids import vertex_ids as VERTEX_IDS +from .utils import Struct, to_np, to_tensor +from .vertex_joint_selector import VertexJointSelector + + +ModelOutput = namedtuple('ModelOutput', + ['vertices','faces', 'joints', 'full_pose', 'betas', + 'global_orient', + 'body_pose', 'expression', + 'left_hand_pose', 'right_hand_pose', + 'jaw_pose', 'T', 'T_weighted', 'weights']) +ModelOutput.__new__.__defaults__ = (None,) * len(ModelOutput._fields) + +class SMPL(nn.Module): + + NUM_JOINTS = 23 + NUM_BODY_JOINTS = 23 + NUM_BETAS = 10 + + def __init__(self, model_path, data_struct=None, + create_betas=True, + betas=None, + create_global_orient=True, + global_orient=None, + create_body_pose=True, + body_pose=None, + create_transl=True, + transl=None, + dtype=torch.float32, + batch_size=1, + joint_mapper=None, gender='neutral', + vertex_ids=None, + pose_blend=True, + **kwargs): + ''' SMPL model constructor + + Parameters + ---------- + model_path: str + The path to the folder or to the file where the model + parameters are stored + data_struct: Strct + A struct object. If given, then the parameters of the model are + read from the object. Otherwise, the model tries to read the + parameters from the given `model_path`. (default = None) + create_global_orient: bool, optional + Flag for creating a member variable for the global orientation + of the body. (default = True) + global_orient: torch.tensor, optional, Bx3 + The default value for the global orientation variable. + (default = None) + create_body_pose: bool, optional + Flag for creating a member variable for the pose of the body. + (default = True) + body_pose: torch.tensor, optional, Bx(Body Joints * 3) + The default value for the body pose variable. + (default = None) + create_betas: bool, optional + Flag for creating a member variable for the shape space + (default = True). + betas: torch.tensor, optional, Bx10 + The default value for the shape member variable. + (default = None) + create_transl: bool, optional + Flag for creating a member variable for the translation + of the body. (default = True) + transl: torch.tensor, optional, Bx3 + The default value for the transl variable. + (default = None) + dtype: torch.dtype, optional + The data type for the created variables + batch_size: int, optional + The batch size used for creating the member variables + joint_mapper: object, optional + An object that re-maps the joints. Useful if one wants to + re-order the SMPL joints to some other convention (e.g. MSCOCO) + (default = None) + gender: str, optional + Which gender to load + vertex_ids: dict, optional + A dictionary containing the indices of the extra vertices that + will be selected + ''' + + self.gender = gender + self.pose_blend = pose_blend + + if data_struct is None: + if osp.isdir(model_path): + model_fn = 'SMPL_{}.{ext}'.format(gender.upper(), ext='pkl') + smpl_path = os.path.join(model_path, model_fn) + else: + smpl_path = model_path + assert osp.exists(smpl_path), 'Path {} does not exist!'.format( + smpl_path) + + with open(smpl_path, 'rb') as smpl_file: + data_struct = Struct(**pickle.load(smpl_file,encoding='latin1')) + super(SMPL, self).__init__() + self.batch_size = batch_size + + if vertex_ids is None: + # SMPL and SMPL-H share the same topology, so any extra joints can + # be drawn from the same place + vertex_ids = VERTEX_IDS['smplh'] + + self.dtype = dtype + + self.joint_mapper = joint_mapper + + self.vertex_joint_selector = VertexJointSelector( + vertex_ids=vertex_ids, **kwargs) + + self.faces = data_struct.f + self.register_buffer('faces_tensor', + to_tensor(to_np(self.faces, dtype=np.int64), + dtype=torch.long)) + + if create_betas: + if betas is None: + default_betas = torch.zeros([batch_size, self.NUM_BETAS], + dtype=dtype) + else: + if 'torch.Tensor' in str(type(betas)): + default_betas = betas.clone().detach() + else: + default_betas = torch.tensor(betas, + dtype=dtype) + + self.register_parameter('betas', nn.Parameter(default_betas, + requires_grad=True)) + + # The tensor that contains the global rotation of the model + # It is separated from the pose of the joints in case we wish to + # optimize only over one of them + if create_global_orient: + if global_orient is None: + default_global_orient = torch.zeros([batch_size, 3], + dtype=dtype) + else: + if 'torch.Tensor' in str(type(global_orient)): + default_global_orient = global_orient.clone().detach() + else: + default_global_orient = torch.tensor(global_orient, + dtype=dtype) + + global_orient = nn.Parameter(default_global_orient, + requires_grad=True) + self.register_parameter('global_orient', global_orient) + + if create_body_pose: + if body_pose is None: + default_body_pose = torch.zeros( + [batch_size, self.NUM_BODY_JOINTS * 3], dtype=dtype) + else: + if 'torch.Tensor' in str(type(body_pose)): + default_body_pose = body_pose.clone().detach() + else: + default_body_pose = torch.tensor(body_pose, + dtype=dtype) + self.register_parameter( + 'body_pose', + nn.Parameter(default_body_pose, requires_grad=True)) + + if create_transl: + if transl is None: + default_transl = torch.zeros([batch_size, 3], + dtype=dtype, + requires_grad=True) + else: + default_transl = torch.tensor(transl, dtype=dtype) + self.register_parameter( + 'transl', + nn.Parameter(default_transl, requires_grad=True)) + + # The vertices of the template model + self.register_buffer('v_template', + to_tensor(to_np(data_struct.v_template), + dtype=dtype)) + + # The shape components + shapedirs = data_struct.shapedirs[:, :, :self.NUM_BETAS] + # The shape components + self.register_buffer( + 'shapedirs', + to_tensor(to_np(shapedirs), dtype=dtype)) + + + j_regressor = to_tensor(to_np( + data_struct.J_regressor), dtype=dtype) + self.register_buffer('J_regressor', j_regressor) + + # if self.gender == 'neutral': + # joint_regressor = to_tensor(to_np( + # data_struct.cocoplus_regressor), dtype=dtype).permute(1,0) + # self.register_buffer('joint_regressor', joint_regressor) + + # Pose blend shape basis: 6890 x 3 x 207, reshaped to 6890*3 x 207 + num_pose_basis = data_struct.posedirs.shape[-1] + # 207 x 20670 + posedirs = np.reshape(data_struct.posedirs, [-1, num_pose_basis]).T + self.register_buffer('posedirs', + to_tensor(to_np(posedirs), dtype=dtype)) + + # indices of parents for each joints + parents = to_tensor(to_np(data_struct.kintree_table[0])).long() + parents[0] = -1 + self.register_buffer('parents', parents) + + self.bone_parents = to_np(data_struct.kintree_table[0]) + + self.register_buffer('lbs_weights', + to_tensor(to_np(data_struct.weights), dtype=dtype)) + + def create_mean_pose(self, data_struct): + pass + + @torch.no_grad() + def reset_params(self, **params_dict): + for param_name, param in self.named_parameters(): + if param_name in params_dict: + param[:] = torch.tensor(params_dict[param_name]) + else: + param.fill_(0) + + def get_T_hip(self, betas=None): + v_shaped = self.v_template + blend_shapes(betas, self.shapedirs) + J = vertices2joints(self.J_regressor, v_shaped) + T_hip = J[0,0] + return T_hip + + def get_num_verts(self): + return self.v_template.shape[0] + + def get_num_faces(self): + return self.faces.shape[0] + + def extra_repr(self): + return 'Number of betas: {}'.format(self.NUM_BETAS) + + def forward(self, betas=None, body_pose=None, global_orient=None, + transl=None, return_verts=True, return_full_pose=False,displacement=None,v_template=None, + **kwargs): + ''' Forward pass for the SMPL model + + Parameters + ---------- + global_orient: torch.tensor, optional, shape Bx3 + If given, ignore the member variable and use it as the global + rotation of the body. Useful if someone wishes to predicts this + with an external model. (default=None) + betas: torch.tensor, optional, shape Bx10 + If given, ignore the member variable `betas` and use it + instead. For example, it can used if shape parameters + `betas` are predicted from some external model. + (default=None) + body_pose: torch.tensor, optional, shape Bx(J*3) + If given, ignore the member variable `body_pose` and use it + instead. For example, it can used if someone predicts the + pose of the body joints are predicted from some external model. + It should be a tensor that contains joint rotations in + axis-angle format. (default=None) + transl: torch.tensor, optional, shape Bx3 + If given, ignore the member variable `transl` and use it + instead. For example, it can used if the translation + `transl` is predicted from some external model. + (default=None) + return_verts: bool, optional + Return the vertices. (default=True) + return_full_pose: bool, optional + Returns the full axis-angle pose vector (default=False) + + Returns + ------- + ''' + # If no shape and pose parameters are passed along, then use the + # ones from the module + global_orient = (global_orient if global_orient is not None else + self.global_orient) + body_pose = body_pose if body_pose is not None else self.body_pose + betas = betas if betas is not None else self.betas + + apply_trans = transl is not None or hasattr(self, 'transl') + if transl is None and hasattr(self, 'transl'): + transl = self.transl + + full_pose = torch.cat([global_orient, body_pose], dim=1) + + # if betas.shape[0] != self.batch_size: + # num_repeats = int(self.batch_size / betas.shape[0]) + # betas = betas.expand(num_repeats, -1) + + if v_template is None: + v_template = self.v_template + + if displacement is not None: + vertices, joints_smpl, T_weighted, W, T = lbs(betas, full_pose, v_template+displacement, + self.shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, dtype=self.dtype,pose_blend=self.pose_blend) + else: + vertices, joints_smpl,T_weighted, W, T = lbs(betas, full_pose, v_template, + self.shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, dtype=self.dtype,pose_blend=self.pose_blend) + + # if self.gender is not 'neutral': + joints = self.vertex_joint_selector(vertices, joints_smpl) + # else: + # joints = torch.matmul(vertices.permute(0,2,1),self.joint_regressor).permute(0,2,1) + # Map the joints to the current dataset + if self.joint_mapper is not None: + joints = self.joint_mapper(joints) + + if apply_trans: + joints_smpl = joints_smpl + transl.unsqueeze(dim=1) + joints = joints + transl.unsqueeze(dim=1) + vertices = vertices + transl.unsqueeze(dim=1) + + output = ModelOutput(vertices=vertices if return_verts else None, + faces=self.faces, + global_orient=global_orient, + body_pose=body_pose, + joints=joints_smpl, + betas=self.betas, + full_pose=full_pose if return_full_pose else None, + T=T, T_weighted=T_weighted, weights=W) + return output \ No newline at end of file diff --git a/code/lib/smpl/lbs.py b/code/lib/smpl/lbs.py new file mode 100644 index 0000000000000000000000000000000000000000..14827cf92e847a41b87581749e25ef5a0eca55ea --- /dev/null +++ b/code/lib/smpl/lbs.py @@ -0,0 +1,377 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems and the Max Planck Institute for Biological +# Cybernetics. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import numpy as np + +import torch +import torch.nn.functional as F + +from .utils import rot_mat_to_euler + + +def find_dynamic_lmk_idx_and_bcoords(vertices, pose, dynamic_lmk_faces_idx, + dynamic_lmk_b_coords, + neck_kin_chain, dtype=torch.float32): + ''' Compute the faces, barycentric coordinates for the dynamic landmarks + + + To do so, we first compute the rotation of the neck around the y-axis + and then use a pre-computed look-up table to find the faces and the + barycentric coordinates that will be used. + + Special thanks to Soubhik Sanyal (soubhik.sanyal@tuebingen.mpg.de) + for providing the original TensorFlow implementation and for the LUT. + + Parameters + ---------- + vertices: torch.tensor BxVx3, dtype = torch.float32 + The tensor of input vertices + pose: torch.tensor Bx(Jx3), dtype = torch.float32 + The current pose of the body model + dynamic_lmk_faces_idx: torch.tensor L, dtype = torch.long + The look-up table from neck rotation to faces + dynamic_lmk_b_coords: torch.tensor Lx3, dtype = torch.float32 + The look-up table from neck rotation to barycentric coordinates + neck_kin_chain: list + A python list that contains the indices of the joints that form the + kinematic chain of the neck. + dtype: torch.dtype, optional + + Returns + ------- + dyn_lmk_faces_idx: torch.tensor, dtype = torch.long + A tensor of size BxL that contains the indices of the faces that + will be used to compute the current dynamic landmarks. + dyn_lmk_b_coords: torch.tensor, dtype = torch.float32 + A tensor of size BxL that contains the indices of the faces that + will be used to compute the current dynamic landmarks. + ''' + + batch_size = vertices.shape[0] + + aa_pose = torch.index_select(pose.view(batch_size, -1, 3), 1, + neck_kin_chain) + rot_mats = batch_rodrigues( + aa_pose.view(-1, 3), dtype=dtype).view(batch_size, -1, 3, 3) + + rel_rot_mat = torch.eye(3, device=vertices.device, + dtype=dtype).unsqueeze_(dim=0) + for idx in range(len(neck_kin_chain)): + rel_rot_mat = torch.bmm(rot_mats[:, idx], rel_rot_mat) + + y_rot_angle = torch.round( + torch.clamp(-rot_mat_to_euler(rel_rot_mat) * 180.0 / np.pi, + max=39)).to(dtype=torch.long) + neg_mask = y_rot_angle.lt(0).to(dtype=torch.long) + mask = y_rot_angle.lt(-39).to(dtype=torch.long) + neg_vals = mask * 78 + (1 - mask) * (39 - y_rot_angle) + y_rot_angle = (neg_mask * neg_vals + + (1 - neg_mask) * y_rot_angle) + + dyn_lmk_faces_idx = torch.index_select(dynamic_lmk_faces_idx, + 0, y_rot_angle) + dyn_lmk_b_coords = torch.index_select(dynamic_lmk_b_coords, + 0, y_rot_angle) + + return dyn_lmk_faces_idx, dyn_lmk_b_coords + + +def vertices2landmarks(vertices, faces, lmk_faces_idx, lmk_bary_coords): + ''' Calculates landmarks by barycentric interpolation + + Parameters + ---------- + vertices: torch.tensor BxVx3, dtype = torch.float32 + The tensor of input vertices + faces: torch.tensor Fx3, dtype = torch.long + The faces of the mesh + lmk_faces_idx: torch.tensor L, dtype = torch.long + The tensor with the indices of the faces used to calculate the + landmarks. + lmk_bary_coords: torch.tensor Lx3, dtype = torch.float32 + The tensor of barycentric coordinates that are used to interpolate + the landmarks + + Returns + ------- + landmarks: torch.tensor BxLx3, dtype = torch.float32 + The coordinates of the landmarks for each mesh in the batch + ''' + # Extract the indices of the vertices for each face + # BxLx3 + batch_size, num_verts = vertices.shape[:2] + device = vertices.device + + lmk_faces = torch.index_select(faces, 0, lmk_faces_idx.view(-1)).expand( + batch_size, -1, -1).long() + + lmk_faces = lmk_faces + torch.arange( + batch_size, dtype=torch.long, device=device).view(-1, 1, 1) * num_verts + + lmk_vertices = vertices.view(-1, 3)[lmk_faces].view( + batch_size, -1, 3, 3) + + landmarks = torch.einsum('blfi,blf->bli', [lmk_vertices, lmk_bary_coords]) + return landmarks + + +def lbs(betas, pose, v_template, shapedirs, posedirs, J_regressor, parents, + lbs_weights, pose2rot=True, dtype=torch.float32, pose_blend=True): + ''' Performs Linear Blend Skinning with the given shape and pose parameters + + Parameters + ---------- + betas : torch.tensor BxNB + The tensor of shape parameters + pose : torch.tensor Bx(J + 1) * 3 + The pose parameters in axis-angle format + v_template torch.tensor BxVx3 + The template mesh that will be deformed + shapedirs : torch.tensor 1xNB + The tensor of PCA shape displacements + posedirs : torch.tensor Px(V * 3) + The pose PCA coefficients + J_regressor : torch.tensor JxV + The regressor array that is used to calculate the joints from + the position of the vertices + parents: torch.tensor J + The array that describes the kinematic tree for the model + lbs_weights: torch.tensor N x V x (J + 1) + The linear blend skinning weights that represent how much the + rotation matrix of each part affects each vertex + pose2rot: bool, optional + Flag on whether to convert the input pose tensor to rotation + matrices. The default value is True. If False, then the pose tensor + should already contain rotation matrices and have a size of + Bx(J + 1)x9 + dtype: torch.dtype, optional + + Returns + ------- + verts: torch.tensor BxVx3 + The vertices of the mesh after applying the shape and pose + displacements. + joints: torch.tensor BxJx3 + The joints of the model + ''' + + batch_size = max(betas.shape[0], pose.shape[0]) + device = betas.device + + # Add shape contribution + v_shaped = v_template + blend_shapes(betas, shapedirs) + + # Get the joints + # NxJx3 array + J = vertices2joints(J_regressor, v_shaped) + + # 3. Add pose blend shapes + # N x J x 3 x 3 + ident = torch.eye(3, dtype=dtype, device=device) + + + if pose2rot: + rot_mats = batch_rodrigues( + pose.view(-1, 3), dtype=dtype).view([batch_size, -1, 3, 3]) + + pose_feature = (rot_mats[:, 1:, :, :] - ident).view([batch_size, -1]) + # (N x P) x (P, V * 3) -> N x V x 3 + pose_offsets = torch.matmul(pose_feature, posedirs) \ + .view(batch_size, -1, 3) + else: + pose_feature = pose[:, 1:].view(batch_size, -1, 3, 3) - ident + rot_mats = pose.view(batch_size, -1, 3, 3) + + pose_offsets = torch.matmul(pose_feature.view(batch_size, -1), + posedirs).view(batch_size, -1, 3) + + if pose_blend: + v_posed = pose_offsets + v_shaped + else: + v_posed = v_shaped + + # 4. Get the global joint location + J_transformed, A = batch_rigid_transform(rot_mats, J, parents, dtype=dtype) + + # 5. Do skinning: + # W is N x V x (J + 1) + W = lbs_weights.unsqueeze(dim=0).expand([batch_size, -1, -1]) + # (N x V x (J + 1)) x (N x (J + 1) x 16) + num_joints = J_regressor.shape[0] + T = torch.matmul(W, A.view(batch_size, num_joints, 16)) \ + .view(batch_size, -1, 4, 4) + + homogen_coord = torch.ones([batch_size, v_posed.shape[1], 1], + dtype=dtype, device=device) + v_posed_homo = torch.cat([v_posed, homogen_coord], dim=2) + v_homo = torch.matmul(T, torch.unsqueeze(v_posed_homo, dim=-1)) + + verts = v_homo[:, :, :3, 0] + + return verts, J_transformed, T, W, A.view(batch_size, num_joints, 4,4) + + +def vertices2joints(J_regressor, vertices): + ''' Calculates the 3D joint locations from the vertices + + Parameters + ---------- + J_regressor : torch.tensor JxV + The regressor array that is used to calculate the joints from the + position of the vertices + vertices : torch.tensor BxVx3 + The tensor of mesh vertices + + Returns + ------- + torch.tensor BxJx3 + The location of the joints + ''' + + return torch.einsum('bik,ji->bjk', [vertices, J_regressor]) + + +def blend_shapes(betas, shape_disps): + ''' Calculates the per vertex displacement due to the blend shapes + + + Parameters + ---------- + betas : torch.tensor Bx(num_betas) + Blend shape coefficients + shape_disps: torch.tensor Vx3x(num_betas) + Blend shapes + + Returns + ------- + torch.tensor BxVx3 + The per-vertex displacement due to shape deformation + ''' + + # Displacement[b, m, k] = sum_{l} betas[b, l] * shape_disps[m, k, l] + # i.e. Multiply each shape displacement by its corresponding beta and + # then sum them. + blend_shape = torch.einsum('bl,mkl->bmk', [betas, shape_disps]) + return blend_shape + + +def batch_rodrigues(rot_vecs, epsilon=1e-8, dtype=torch.float32): + ''' Calculates the rotation matrices for a batch of rotation vectors + Parameters + ---------- + rot_vecs: torch.tensor Nx3 + array of N axis-angle vectors + Returns + ------- + R: torch.tensor Nx3x3 + The rotation matrices for the given axis-angle parameters + ''' + + batch_size = rot_vecs.shape[0] + device = rot_vecs.device + + angle = torch.norm(rot_vecs + 1e-8, dim=1, keepdim=True) + rot_dir = rot_vecs / angle + + cos = torch.unsqueeze(torch.cos(angle), dim=1) + sin = torch.unsqueeze(torch.sin(angle), dim=1) + + # Bx1 arrays + rx, ry, rz = torch.split(rot_dir, 1, dim=1) + K = torch.zeros((batch_size, 3, 3), dtype=dtype, device=device) + + zeros = torch.zeros((batch_size, 1), dtype=dtype, device=device) + K = torch.cat([zeros, -rz, ry, rz, zeros, -rx, -ry, rx, zeros], dim=1) \ + .view((batch_size, 3, 3)) + + ident = torch.eye(3, dtype=dtype, device=device).unsqueeze(dim=0) + rot_mat = ident + sin * K + (1 - cos) * torch.bmm(K, K) + return rot_mat + + +def transform_mat(R, t): + ''' Creates a batch of transformation matrices + Args: + - R: Bx3x3 array of a batch of rotation matrices + - t: Bx3x1 array of a batch of translation vectors + Returns: + - T: Bx4x4 Transformation matrix + ''' + # No padding left or right, only add an extra row + return torch.cat([F.pad(R, [0, 0, 0, 1]), + F.pad(t, [0, 0, 0, 1], value=1)], dim=2) + + +def batch_rigid_transform(rot_mats, joints, parents, dtype=torch.float32): + """ + Applies a batch of rigid transformations to the joints + + Parameters + ---------- + rot_mats : torch.tensor BxNx3x3 + Tensor of rotation matrices + joints : torch.tensor BxNx3 + Locations of joints + parents : torch.tensor BxN + The kinematic tree of each object + dtype : torch.dtype, optional: + The data type of the created tensors, the default is torch.float32 + + Returns + ------- + posed_joints : torch.tensor BxNx3 + The locations of the joints after applying the pose rotations + rel_transforms : torch.tensor BxNx4x4 + The relative (with respect to the root joint) rigid transformations + for all the joints + """ + + joints = torch.unsqueeze(joints, dim=-1) + + rel_joints = joints.clone() + rel_joints[:, 1:] = rel_joints[:, 1:] - joints[:, parents[1:]] + + transforms_mat = transform_mat( + rot_mats.reshape(-1, 3, 3), + rel_joints.reshape(-1, 3, 1)).reshape(-1, joints.shape[1], 4, 4) + + transform_chain = [transforms_mat[:, 0]] + for i in range(1, parents.shape[0]): + # Subtract the joint location at the rest pose + # No need for rotation, since it's identity when at rest + curr_res = torch.matmul(transform_chain[parents[i]], + transforms_mat[:, i]) + transform_chain.append(curr_res) + + transforms = torch.stack(transform_chain, dim=1) + + # The last column of the transformations contains the posed joints + posed_joints = transforms[:, :, :3, 3] + + # The last column of the transformations contains the posed joints + posed_joints = transforms[:, :, :3, 3] + + joints_homogen = F.pad(joints, [0, 0, 0, 1]) + + rel_transforms = transforms - F.pad( + torch.matmul(transforms, joints_homogen), [3, 0, 0, 0, 0, 0, 0, 0]) + + return posed_joints, rel_transforms diff --git a/code/lib/smpl/smpl_model/SMPL_FEMALE.pkl b/code/lib/smpl/smpl_model/SMPL_FEMALE.pkl new file mode 100644 index 0000000000000000000000000000000000000000..92a201f4839bd95c1c1986437c7c6a02d7d1ae99 --- /dev/null +++ b/code/lib/smpl/smpl_model/SMPL_FEMALE.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a583c1b98e4afc19042641f1bae5cd8a1f712a6724886291a7627ec07acd408d +size 39056454 diff --git a/code/lib/smpl/smpl_model/SMPL_MALE.pkl b/code/lib/smpl/smpl_model/SMPL_MALE.pkl new file mode 100644 index 0000000000000000000000000000000000000000..43dfecc57d9b7aa99cd2398df818ba252be7f605 --- /dev/null +++ b/code/lib/smpl/smpl_model/SMPL_MALE.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e8c0bbbbc635dcb166ed29c303fb4bef16ea5f623e5a89263495a9e403575bd +size 39056404 diff --git a/code/lib/smpl/utils.py b/code/lib/smpl/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2e238761914fa529c37fc870fde6f5ed2a2028a3 --- /dev/null +++ b/code/lib/smpl/utils.py @@ -0,0 +1,49 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems and the Max Planck Institute for Biological +# Cybernetics. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from __future__ import print_function +from __future__ import absolute_import +from __future__ import division + +import numpy as np +import torch + + +def to_tensor(array, dtype=torch.float32): + if 'torch.tensor' not in str(type(array)): + return torch.tensor(array, dtype=dtype) + + +class Struct(object): + def __init__(self, **kwargs): + for key, val in kwargs.items(): + setattr(self, key, val) + + +def to_np(array, dtype=np.float32): + if 'scipy.sparse' in str(type(array)): + array = array.todense() + return np.array(array, dtype=dtype) + + +def rot_mat_to_euler(rot_mats): + # Calculates rotation matrix to euler angles + # Careful for extreme cases of eular angles like [0.0, pi, 0.0] + + sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] + + rot_mats[:, 1, 0] * rot_mats[:, 1, 0]) + return torch.atan2(-rot_mats[:, 2, 0], sy) diff --git a/code/lib/smpl/vertex_ids.py b/code/lib/smpl/vertex_ids.py new file mode 100644 index 0000000000000000000000000000000000000000..5571c8181727baa9d2661090bc876cd37d0891be --- /dev/null +++ b/code/lib/smpl/vertex_ids.py @@ -0,0 +1,71 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems and the Max Planck Institute for Biological +# Cybernetics. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from __future__ import print_function +from __future__ import absolute_import +from __future__ import division + +# Joint name to vertex mapping. SMPL/SMPL-H/SMPL-X vertices that correspond to +# MSCOCO and OpenPose joints +vertex_ids = { + 'smplh': { + 'nose': 332, + 'reye': 6260, + 'leye': 2800, + 'rear': 4071, + 'lear': 583, + 'rthumb': 6191, + 'rindex': 5782, + 'rmiddle': 5905, + 'rring': 6016, + 'rpinky': 6133, + 'lthumb': 2746, + 'lindex': 2319, + 'lmiddle': 2445, + 'lring': 2556, + 'lpinky': 2673, + 'LBigToe': 3216, + 'LSmallToe': 3226, + 'LHeel': 3387, + 'RBigToe': 6617, + 'RSmallToe': 6624, + 'RHeel': 6787 + }, + 'smplx': { + 'nose': 9120, + 'reye': 9929, + 'leye': 9448, + 'rear': 616, + 'lear': 6, + 'rthumb': 8079, + 'rindex': 7669, + 'rmiddle': 7794, + 'rring': 7905, + 'rpinky': 8022, + 'lthumb': 5361, + 'lindex': 4933, + 'lmiddle': 5058, + 'lring': 5169, + 'lpinky': 5286, + 'LBigToe': 5770, + 'LSmallToe': 5780, + 'LHeel': 8846, + 'RBigToe': 8463, + 'RSmallToe': 8474, + 'RHeel': 8635 + } +} diff --git a/code/lib/smpl/vertex_joint_selector.py b/code/lib/smpl/vertex_joint_selector.py new file mode 100644 index 0000000000000000000000000000000000000000..bc9be78bccbb00faff91c983e53082022a474223 --- /dev/null +++ b/code/lib/smpl/vertex_joint_selector.py @@ -0,0 +1,77 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems and the Max Planck Institute for Biological +# Cybernetics. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import numpy as np + +import torch +import torch.nn as nn + +from .utils import to_tensor + + +class VertexJointSelector(nn.Module): + + def __init__(self, vertex_ids=None, + use_hands=True, + use_feet_keypoints=True, **kwargs): + super(VertexJointSelector, self).__init__() + + extra_joints_idxs = [] + + face_keyp_idxs = np.array([ + vertex_ids['nose'], + vertex_ids['reye'], + vertex_ids['leye'], + vertex_ids['rear'], + vertex_ids['lear']], dtype=np.int64) + + extra_joints_idxs = np.concatenate([extra_joints_idxs, + face_keyp_idxs]) + + if use_feet_keypoints: + feet_keyp_idxs = np.array([vertex_ids['LBigToe'], + vertex_ids['LSmallToe'], + vertex_ids['LHeel'], + vertex_ids['RBigToe'], + vertex_ids['RSmallToe'], + vertex_ids['RHeel']], dtype=np.int32) + + extra_joints_idxs = np.concatenate( + [extra_joints_idxs, feet_keyp_idxs]) + + if use_hands: + self.tip_names = ['thumb', 'index', 'middle', 'ring', 'pinky'] + + tips_idxs = [] + for hand_id in ['l', 'r']: + for tip_name in self.tip_names: + tips_idxs.append(vertex_ids[hand_id + tip_name]) + + extra_joints_idxs = np.concatenate( + [extra_joints_idxs, tips_idxs]) + + self.register_buffer('extra_joints_idxs', + to_tensor(extra_joints_idxs, dtype=torch.long)) + + def forward(self, vertices, joints): + extra_joints = torch.index_select(vertices, 1, self.extra_joints_idxs) + joints = torch.cat([joints, extra_joints], dim=1) + return joints diff --git a/code/lib/utils/meshing.py b/code/lib/utils/meshing.py new file mode 100644 index 0000000000000000000000000000000000000000..3afc28d676a63ad68b3db423d03bdb42aca52c9a --- /dev/null +++ b/code/lib/utils/meshing.py @@ -0,0 +1,63 @@ +import numpy as np +import torch +from skimage import measure +from lib.libmise import mise +import trimesh + +def generate_mesh(func, verts, level_set=0, res_init=32, res_up=3, point_batch=5000): + + scale = 1.1 # Scale of the padded bbox regarding the tight one. + verts = verts.data.cpu().numpy() + + gt_bbox = np.stack([verts.min(axis=0), verts.max(axis=0)], axis=0) + gt_center = (gt_bbox[0] + gt_bbox[1]) * 0.5 + gt_scale = (gt_bbox[1] - gt_bbox[0]).max() + + mesh_extractor = mise.MISE(res_init, res_up, level_set) + points = mesh_extractor.query() + + # query occupancy grid + while points.shape[0] != 0: + + orig_points = points + points = points.astype(np.float32) + points = (points / mesh_extractor.resolution - 0.5) * scale + points = points * gt_scale + gt_center + points = torch.tensor(points).float().cuda() + + values = [] + for _, pnts in enumerate((torch.split(points,point_batch,dim=0))): + out = func(pnts) + values.append(out['sdf'].data.cpu().numpy()) + values = np.concatenate(values, axis=0).astype(np.float64)[:,0] + + mesh_extractor.update(orig_points, values) + + points = mesh_extractor.query() + + value_grid = mesh_extractor.to_dense() + + # marching cube + verts, faces, normals, values = measure.marching_cubes_lewiner( + volume=value_grid, + gradient_direction='ascent', + level=level_set) + + verts = (verts / mesh_extractor.resolution - 0.5) * scale + verts = verts * gt_scale + gt_center + faces = faces[:, [0,2,1]] + meshexport = trimesh.Trimesh(verts, faces, normals, vertex_colors=values) + + #remove disconnect part + connected_comp = meshexport.split(only_watertight=False) + max_area = 0 + max_comp = None + for comp in connected_comp: + if comp.area > max_area: + max_area = comp.area + max_comp = comp + meshexport = max_comp + + return meshexport + + diff --git a/code/lib/utils/utils.py b/code/lib/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b67932438e7077d431f432a61e14532da66a8531 --- /dev/null +++ b/code/lib/utils/utils.py @@ -0,0 +1,232 @@ +import numpy as np +import cv2 +import torch +from torch.nn import functional as F + + +def split_input(model_input, total_pixels, n_pixels = 10000): + ''' + Split the input to fit Cuda memory for large resolution. + Can decrease the value of n_pixels in case of cuda out of memory error. + ''' + + split = [] + + for i, indx in enumerate(torch.split(torch.arange(total_pixels).cuda(), n_pixels, dim=0)): + data = model_input.copy() + data['uv'] = torch.index_select(model_input['uv'], 1, indx) + split.append(data) + return split + + +def merge_output(res, total_pixels, batch_size): + ''' Merge the split output. ''' + + model_outputs = {} + for entry in res[0]: + if res[0][entry] is None: + continue + if len(res[0][entry].shape) == 1: + model_outputs[entry] = torch.cat([r[entry].reshape(batch_size, -1, 1) for r in res], + 1).reshape(batch_size * total_pixels) + else: + model_outputs[entry] = torch.cat([r[entry].reshape(batch_size, -1, r[entry].shape[-1]) for r in res], + 1).reshape(batch_size * total_pixels, -1) + return model_outputs + + +def get_psnr(img1, img2, normalize_rgb=False): + if normalize_rgb: # [-1,1] --> [0,1] + img1 = (img1 + 1.) / 2. + img2 = (img2 + 1. ) / 2. + + mse = torch.mean((img1 - img2) ** 2) + psnr = -10. * torch.log(mse) / torch.log(torch.Tensor([10.]).cuda()) + + return psnr + + +def load_K_Rt_from_P(filename, P=None): + if P is None: + lines = open(filename).read().splitlines() + if len(lines) == 4: + lines = lines[1:] + lines = [[x[0], x[1], x[2], x[3]] for x in (x.split(" ") for x in lines)] + P = np.asarray(lines).astype(np.float32).squeeze() + + out = cv2.decomposeProjectionMatrix(P) + K = out[0] + R = out[1] + t = out[2] + + K = K/K[2,2] + intrinsics = np.eye(4) + intrinsics[:3, :3] = K + + pose = np.eye(4, dtype=np.float32) + pose[:3, :3] = R.transpose() + pose[:3,3] = (t[:3] / t[3])[:,0] + + return intrinsics, pose + + +def get_camera_params(uv, pose, intrinsics): + if pose.shape[1] == 7: #In case of quaternion vector representation + cam_loc = pose[:, 4:] + R = quat_to_rot(pose[:,:4]) + p = torch.eye(4).repeat(pose.shape[0],1,1).cuda().float() + p[:, :3, :3] = R + p[:, :3, 3] = cam_loc + else: # In case of pose matrix representation + cam_loc = pose[:, :3, 3] + p = pose + + batch_size, num_samples, _ = uv.shape + + depth = torch.ones((batch_size, num_samples)).cuda() + x_cam = uv[:, :, 0].view(batch_size, -1) + y_cam = uv[:, :, 1].view(batch_size, -1) + z_cam = depth.view(batch_size, -1) + + pixel_points_cam = lift(x_cam, y_cam, z_cam, intrinsics=intrinsics) + + # permute for batch matrix product + pixel_points_cam = pixel_points_cam.permute(0, 2, 1) + + world_coords = torch.bmm(p, pixel_points_cam).permute(0, 2, 1)[:, :, :3] + ray_dirs = world_coords - cam_loc[:, None, :] + ray_dirs = F.normalize(ray_dirs, dim=2) + + return ray_dirs, cam_loc + +def lift(x, y, z, intrinsics): + # parse intrinsics + intrinsics = intrinsics.cuda() + fx = intrinsics[:, 0, 0] + fy = intrinsics[:, 1, 1] + cx = intrinsics[:, 0, 2] + cy = intrinsics[:, 1, 2] + sk = intrinsics[:, 0, 1] + + x_lift = (x - cx.unsqueeze(-1) + cy.unsqueeze(-1)*sk.unsqueeze(-1)/fy.unsqueeze(-1) - sk.unsqueeze(-1)*y/fy.unsqueeze(-1)) / fx.unsqueeze(-1) * z + y_lift = (y - cy.unsqueeze(-1)) / fy.unsqueeze(-1) * z + + # homogeneous + return torch.stack((x_lift, y_lift, z, torch.ones_like(z).cuda()), dim=-1) + + +def quat_to_rot(q): + batch_size, _ = q.shape + q = F.normalize(q, dim=1) + R = torch.ones((batch_size, 3,3)).cuda() + qr=q[:,0] + qi = q[:, 1] + qj = q[:, 2] + qk = q[:, 3] + R[:, 0, 0]=1-2 * (qj**2 + qk**2) + R[:, 0, 1] = 2 * (qj *qi -qk*qr) + R[:, 0, 2] = 2 * (qi * qk + qr * qj) + R[:, 1, 0] = 2 * (qj * qi + qk * qr) + R[:, 1, 1] = 1-2 * (qi**2 + qk**2) + R[:, 1, 2] = 2*(qj*qk - qi*qr) + R[:, 2, 0] = 2 * (qk * qi-qj * qr) + R[:, 2, 1] = 2 * (qj*qk + qi*qr) + R[:, 2, 2] = 1-2 * (qi**2 + qj**2) + return R + + +def rot_to_quat(R): + batch_size, _,_ = R.shape + q = torch.ones((batch_size, 4)).cuda() + + R00 = R[:, 0,0] + R01 = R[:, 0, 1] + R02 = R[:, 0, 2] + R10 = R[:, 1, 0] + R11 = R[:, 1, 1] + R12 = R[:, 1, 2] + R20 = R[:, 2, 0] + R21 = R[:, 2, 1] + R22 = R[:, 2, 2] + + q[:,0]=torch.sqrt(1.0+R00+R11+R22)/2 + q[:, 1]=(R21-R12)/(4*q[:,0]) + q[:, 2] = (R02 - R20) / (4 * q[:, 0]) + q[:, 3] = (R10 - R01) / (4 * q[:, 0]) + return q + + +def get_sphere_intersections(cam_loc, ray_directions, r = 1.0): + # Input: n_rays x 3 ; n_rays x 3 + # Output: n_rays x 1, n_rays x 1 (close and far) + + ray_cam_dot = torch.bmm(ray_directions.view(-1, 1, 3), + cam_loc.view(-1, 3, 1)).squeeze(-1) + under_sqrt = ray_cam_dot ** 2 - (cam_loc.norm(2, 1, keepdim=True) ** 2 - r ** 2) + + # sanity check + if (under_sqrt <= 0).sum() > 0: + print('BOUNDING SPHERE PROBLEM!') + exit() + + sphere_intersections = torch.sqrt(under_sqrt) * torch.Tensor([-1, 1]).cuda().float() - ray_cam_dot + sphere_intersections = sphere_intersections.clamp_min(0.0) + + return sphere_intersections + +def bilinear_interpolation(xs, ys, dist_map): + x1 = np.floor(xs).astype(np.int32) + y1 = np.floor(ys).astype(np.int32) + x2 = x1 + 1 + y2 = y1 + 1 + + dx = np.expand_dims(np.stack([x2 - xs, xs - x1], axis=1), axis=1) + dy = np.expand_dims(np.stack([y2 - ys, ys - y1], axis=1), axis=2) + Q = np.stack([ + dist_map[x1, y1], dist_map[x1, y2], dist_map[x2, y1], dist_map[x2, y2] + ], axis=1).reshape(-1, 2, 2) + return np.squeeze(dx @ Q @ dy) # ((x2 - x1) * (y2 - y1)) = 1 + +def get_index_outside_of_bbox(samples_uniform, bbox_min, bbox_max): + samples_uniform_row = samples_uniform[:, 0] + samples_uniform_col = samples_uniform[:, 1] + index_outside = np.where((samples_uniform_row < bbox_min[0]) | (samples_uniform_row > bbox_max[0]) | (samples_uniform_col < bbox_min[1]) | (samples_uniform_col > bbox_max[1]))[0] + return index_outside + + +def weighted_sampling(data, img_size, num_sample, bbox_ratio=0.9): + """ + More sampling within the bounding box + """ + + # calculate bounding box + mask = data["object_mask"] + where = np.asarray(np.where(mask)) + bbox_min = where.min(axis=1) + bbox_max = where.max(axis=1) + + num_sample_bbox = int(num_sample * bbox_ratio) + samples_bbox = np.random.rand(num_sample_bbox, 2) + samples_bbox = samples_bbox * (bbox_max - bbox_min) + bbox_min + + num_sample_uniform = num_sample - num_sample_bbox + samples_uniform = np.random.rand(num_sample_uniform, 2) + samples_uniform *= (img_size[0] - 1, img_size[1] - 1) + + # get indices for uniform samples outside of bbox + index_outside = get_index_outside_of_bbox(samples_uniform, bbox_min, bbox_max) + num_sample_bbox + + indices = np.concatenate([samples_bbox, samples_uniform], axis=0) + output = {} + for key, val in data.items(): + if len(val.shape) == 3: + new_val = np.stack([ + bilinear_interpolation(indices[:, 0], indices[:, 1], val[:, :, i]) + for i in range(val.shape[2]) + ], axis=-1) + else: + new_val = bilinear_interpolation(indices[:, 0], indices[:, 1], val) + new_val = new_val.reshape(-1, *val.shape[2:]) + output[key] = new_val + + return output, index_outside \ No newline at end of file diff --git a/code/setup.py b/code/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..e12c85aaaaa36f5916ae2d262fa472d05a65f0a7 --- /dev/null +++ b/code/setup.py @@ -0,0 +1,34 @@ +# Copyright 2020 The TensorFlow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Set-up script for installing extension modules.""" +from Cython.Build import cythonize +import numpy +from setuptools import Extension +from setuptools import setup + +# Get the numpy include directory. +numpy_include_dir = numpy.get_include() + +# mise (efficient mesh extraction) +mise_module = Extension( + "lib.libmise.mise", + sources=["lib/libmise/mise.pyx"], +) + +# Gather all extension modules +ext_modules = [ + mise_module, +] + +setup(ext_modules=cythonize(ext_modules),) diff --git a/code/test.py b/code/test.py new file mode 100644 index 0000000000000000000000000000000000000000..918f215fc7ac11f1bcf21bfaa75651c1a3ecaa6c --- /dev/null +++ b/code/test.py @@ -0,0 +1,39 @@ +from v2a_model import V2AModel +from lib.datasets import create_dataset +import hydra +import pytorch_lightning as pl +from pytorch_lightning.loggers import WandbLogger +import os +import glob + +@hydra.main(config_path="confs", config_name="base") +def main(opt): + pl.seed_everything(42) + print("Working dir:", os.getcwd()) + + checkpoint_callback = pl.callbacks.ModelCheckpoint( + dirpath="checkpoints/", + filename="{epoch:04d}-{loss}", + save_on_train_epoch_end=True, + save_last=True) + logger = WandbLogger(project=opt.project_name, name=f"{opt.exp}/{opt.run}") + + trainer = pl.Trainer( + gpus=1, + accelerator="gpu", + callbacks=[checkpoint_callback], + max_epochs=8000, + check_val_every_n_epoch=50, + logger=logger, + log_every_n_steps=1, + num_sanity_val_steps=0 + ) + + model = V2AModel(opt) + checkpoint = sorted(glob.glob("checkpoints/*.ckpt"))[-1] + testset = create_dataset(opt.dataset.metainfo, opt.dataset.test) + + trainer.test(model, testset, ckpt_path=checkpoint) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/code/train.py b/code/train.py new file mode 100644 index 0000000000000000000000000000000000000000..26434229c70786c4ad5c1a93f566a1f26de9c8a8 --- /dev/null +++ b/code/train.py @@ -0,0 +1,45 @@ +from v2a_model import V2AModel +from lib.datasets import create_dataset +import hydra +import pytorch_lightning as pl +from pytorch_lightning.loggers import WandbLogger +import os +import glob + +@hydra.main(config_path="confs", config_name="base") +def main(opt): + pl.seed_everything(42) + print("Working dir:", os.getcwd()) + + checkpoint_callback = pl.callbacks.ModelCheckpoint( + dirpath="checkpoints/", + filename="{epoch:04d}-{loss}", + save_on_train_epoch_end=True, + save_last=True) + logger = WandbLogger(project=opt.project_name, name=f"{opt.exp}/{opt.run}") + + trainer = pl.Trainer( + gpus=1, + accelerator="gpu", + callbacks=[checkpoint_callback], + max_epochs=8000, + check_val_every_n_epoch=50, + logger=logger, + log_every_n_steps=1, + num_sanity_val_steps=0 + ) + + + model = V2AModel(opt) + trainset = create_dataset(opt.dataset.metainfo, opt.dataset.train) + validset = create_dataset(opt.dataset.metainfo, opt.dataset.valid) + + if opt.model.is_continue == True: + checkpoint = sorted(glob.glob("checkpoints/*.ckpt"))[-1] + trainer.fit(model, trainset, validset, ckpt_path=checkpoint) + else: + trainer.fit(model, trainset, validset) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/code/v2a_model.py b/code/v2a_model.py new file mode 100644 index 0000000000000000000000000000000000000000..4649d88dfc4d85ae7dd062bf719d7c6af10f8c9d --- /dev/null +++ b/code/v2a_model.py @@ -0,0 +1,311 @@ +import pytorch_lightning as pl +import torch.optim as optim +from lib.model.v2a import V2A +from lib.model.body_model_params import BodyModelParams +from lib.model.deformer import SMPLDeformer +import cv2 +import torch +from lib.model.loss import Loss +import hydra +import os +import numpy as np +from lib.utils.meshing import generate_mesh +from kaolin.ops.mesh import index_vertices_by_faces +import trimesh +from lib.model.deformer import skinning +from lib.utils import utils +class V2AModel(pl.LightningModule): + def __init__(self, opt) -> None: + super().__init__() + + self.opt = opt + num_training_frames = opt.dataset.metainfo.end_frame - opt.dataset.metainfo.start_frame + self.betas_path = os.path.join(hydra.utils.to_absolute_path('..'), 'data', opt.dataset.metainfo.data_dir, 'mean_shape.npy') + self.gender = opt.dataset.metainfo.gender + self.model = V2A(opt.model, self.betas_path, self.gender, num_training_frames) + self.start_frame = opt.dataset.metainfo.start_frame + self.end_frame = opt.dataset.metainfo.end_frame + self.training_modules = ["model"] + + self.training_indices = list(range(self.start_frame, self.end_frame)) + self.body_model_params = BodyModelParams(num_training_frames, model_type='smpl') + self.load_body_model_params() + optim_params = self.body_model_params.param_names + for param_name in optim_params: + self.body_model_params.set_requires_grad(param_name, requires_grad=True) + self.training_modules += ['body_model_params'] + + self.loss = Loss(opt.model.loss) + + def load_body_model_params(self): + body_model_params = {param_name: [] for param_name in self.body_model_params.param_names} + data_root = os.path.join('../data', self.opt.dataset.metainfo.data_dir) + data_root = hydra.utils.to_absolute_path(data_root) + + body_model_params['betas'] = torch.tensor(np.load(os.path.join(data_root, 'mean_shape.npy'))[None], dtype=torch.float32) + body_model_params['global_orient'] = torch.tensor(np.load(os.path.join(data_root, 'poses.npy'))[self.training_indices][:, :3], dtype=torch.float32) + body_model_params['body_pose'] = torch.tensor(np.load(os.path.join(data_root, 'poses.npy'))[self.training_indices] [:, 3:], dtype=torch.float32) + body_model_params['transl'] = torch.tensor(np.load(os.path.join(data_root, 'normalize_trans.npy'))[self.training_indices], dtype=torch.float32) + + for param_name in body_model_params.keys(): + self.body_model_params.init_parameters(param_name, body_model_params[param_name], requires_grad=False) + + def configure_optimizers(self): + params = [{'params': self.model.parameters(), 'lr':self.opt.model.learning_rate}] + params.append({'params': self.body_model_params.parameters(), 'lr':self.opt.model.learning_rate*0.1}) + self.optimizer = optim.Adam(params, lr=self.opt.model.learning_rate, eps=1e-8) + self.scheduler = optim.lr_scheduler.MultiStepLR( + self.optimizer, milestones=self.opt.model.sched_milestones, gamma=self.opt.model.sched_factor) + return [self.optimizer], [self.scheduler] + + def training_step(self, batch): + inputs, targets = batch + + batch_idx = inputs["idx"] + + body_model_params = self.body_model_params(batch_idx) + inputs['smpl_pose'] = torch.cat((body_model_params['global_orient'], body_model_params['body_pose']), dim=1) + inputs['smpl_shape'] = body_model_params['betas'] + inputs['smpl_trans'] = body_model_params['transl'] + + inputs['current_epoch'] = self.current_epoch + model_outputs = self.model(inputs) + + loss_output = self.loss(model_outputs, targets) + for k, v in loss_output.items(): + if k in ["loss"]: + self.log(k, v.item(), prog_bar=True, on_step=True) + else: + self.log(k, v.item(), prog_bar=True, on_step=True) + return loss_output["loss"] + + def training_epoch_end(self, outputs) -> None: + # Canonical mesh update every 20 epochs + if self.current_epoch != 0 and self.current_epoch % 20 == 0: + cond = {'smpl': torch.zeros(1, 69).float().cuda()} + mesh_canonical = generate_mesh(lambda x: self.query_oc(x, cond), self.model.smpl_server.verts_c[0], point_batch=10000, res_up=2) + self.model.mesh_v_cano = torch.tensor(mesh_canonical.vertices[None], device = self.model.smpl_v_cano.device).float() + self.model.mesh_f_cano = torch.tensor(mesh_canonical.faces.astype(np.int64), device=self.model.smpl_v_cano.device) + self.model.mesh_face_vertices = index_vertices_by_faces(self.model.mesh_v_cano, self.model.mesh_f_cano) + return super().training_epoch_end(outputs) + + def query_oc(self, x, cond): + + x = x.reshape(-1, 3) + mnfld_pred = self.model.implicit_network(x, cond)[:,:,0].reshape(-1,1) + return {'sdf':mnfld_pred} + + def query_wc(self, x): + + x = x.reshape(-1, 3) + w = self.model.deformer.query_weights(x) + + return w + + def query_od(self, x, cond, smpl_tfs, smpl_verts): + + x = x.reshape(-1, 3) + x_c, _ = self.model.deformer.forward(x, smpl_tfs, return_weights=False, inverse=True, smpl_verts=smpl_verts) + output = self.model.implicit_network(x_c, cond)[0] + sdf = output[:, 0:1] + + return {'sdf': sdf} + + def get_deformed_mesh_fast_mode(self, verts, smpl_tfs): + verts = torch.tensor(verts).cuda().float() + weights = self.model.deformer.query_weights(verts) + verts_deformed = skinning(verts.unsqueeze(0), weights, smpl_tfs).data.cpu().numpy()[0] + return verts_deformed + + def validation_step(self, batch, *args, **kwargs): + + output = {} + inputs, targets = batch + inputs['current_epoch'] = self.current_epoch + self.model.eval() + + body_model_params = self.body_model_params(inputs['image_id']) + inputs['smpl_pose'] = torch.cat((body_model_params['global_orient'], body_model_params['body_pose']), dim=1) + inputs['smpl_shape'] = body_model_params['betas'] + inputs['smpl_trans'] = body_model_params['transl'] + + cond = {'smpl': inputs["smpl_pose"][:, 3:]/np.pi} + mesh_canonical = generate_mesh(lambda x: self.query_oc(x, cond), self.model.smpl_server.verts_c[0], point_batch=10000, res_up=3) + + mesh_canonical = trimesh.Trimesh(mesh_canonical.vertices, mesh_canonical.faces) + + output.update({ + 'canonical_mesh':mesh_canonical + }) + + split = utils.split_input(inputs, targets["total_pixels"][0], n_pixels=min(targets['pixel_per_batch'], targets["img_size"][0] * targets["img_size"][1])) + + res = [] + for s in split: + + out = self.model(s) + + for k, v in out.items(): + try: + out[k] = v.detach() + except: + out[k] = v + + res.append({ + 'rgb_values': out['rgb_values'].detach(), + 'normal_values': out['normal_values'].detach(), + 'fg_rgb_values': out['fg_rgb_values'].detach(), + }) + batch_size = targets['rgb'].shape[0] + + model_outputs = utils.merge_output(res, targets["total_pixels"][0], batch_size) + + output.update({ + "rgb_values": model_outputs["rgb_values"].detach().clone(), + "normal_values": model_outputs["normal_values"].detach().clone(), + "fg_rgb_values": model_outputs["fg_rgb_values"].detach().clone(), + **targets, + }) + + return output + + def validation_step_end(self, batch_parts): + return batch_parts + + def validation_epoch_end(self, outputs) -> None: + img_size = outputs[0]["img_size"] + + rgb_pred = torch.cat([output["rgb_values"] for output in outputs], dim=0) + rgb_pred = rgb_pred.reshape(*img_size, -1) + + fg_rgb_pred = torch.cat([output["fg_rgb_values"] for output in outputs], dim=0) + fg_rgb_pred = fg_rgb_pred.reshape(*img_size, -1) + + normal_pred = torch.cat([output["normal_values"] for output in outputs], dim=0) + normal_pred = (normal_pred.reshape(*img_size, -1) + 1) / 2 + + rgb_gt = torch.cat([output["rgb"] for output in outputs], dim=1).squeeze(0) + rgb_gt = rgb_gt.reshape(*img_size, -1) + if 'normal' in outputs[0].keys(): + normal_gt = torch.cat([output["normal"] for output in outputs], dim=1).squeeze(0) + normal_gt = (normal_gt.reshape(*img_size, -1) + 1) / 2 + normal = torch.cat([normal_gt, normal_pred], dim=0).cpu().numpy() + else: + normal = torch.cat([normal_pred], dim=0).cpu().numpy() + + rgb = torch.cat([rgb_gt, rgb_pred], dim=0).cpu().numpy() + rgb = (rgb * 255).astype(np.uint8) + + fg_rgb = torch.cat([fg_rgb_pred], dim=0).cpu().numpy() + fg_rgb = (fg_rgb * 255).astype(np.uint8) + + normal = (normal * 255).astype(np.uint8) + + os.makedirs("rendering", exist_ok=True) + os.makedirs("normal", exist_ok=True) + os.makedirs('fg_rendering', exist_ok=True) + + canonical_mesh = outputs[0]['canonical_mesh'] + canonical_mesh.export(f"rendering/{self.current_epoch}.ply") + + cv2.imwrite(f"rendering/{self.current_epoch}.png", rgb[:, :, ::-1]) + cv2.imwrite(f"normal/{self.current_epoch}.png", normal[:, :, ::-1]) + cv2.imwrite(f"fg_rendering/{self.current_epoch}.png", fg_rgb[:, :, ::-1]) + + def test_step(self, batch, *args, **kwargs): + inputs, targets, pixel_per_batch, total_pixels, idx = batch + num_splits = (total_pixels + pixel_per_batch - + 1) // pixel_per_batch + results = [] + + scale, smpl_trans, smpl_pose, smpl_shape = torch.split(inputs["smpl_params"], [1, 3, 72, 10], dim=1) + + body_model_params = self.body_model_params(inputs['idx']) + smpl_shape = body_model_params['betas'] if body_model_params['betas'].dim() == 2 else body_model_params['betas'].unsqueeze(0) + smpl_trans = body_model_params['transl'] + smpl_pose = torch.cat((body_model_params['global_orient'], body_model_params['body_pose']), dim=1) + + smpl_outputs = self.model.smpl_server(scale, smpl_trans, smpl_pose, smpl_shape) + smpl_tfs = smpl_outputs['smpl_tfs'] + cond = {'smpl': smpl_pose[:, 3:]/np.pi} + + mesh_canonical = generate_mesh(lambda x: self.query_oc(x, cond), self.model.smpl_server.verts_c[0], point_batch=10000, res_up=4) + self.model.deformer = SMPLDeformer(betas=np.load(self.betas_path), gender=self.gender, K=7) + verts_deformed = self.get_deformed_mesh_fast_mode(mesh_canonical.vertices, smpl_tfs) + mesh_deformed = trimesh.Trimesh(vertices=verts_deformed, faces=mesh_canonical.faces, process=False) + + os.makedirs("test_mask", exist_ok=True) + os.makedirs("test_rendering", exist_ok=True) + os.makedirs("test_fg_rendering", exist_ok=True) + os.makedirs("test_normal", exist_ok=True) + os.makedirs("test_mesh", exist_ok=True) + + mesh_canonical.export(f"test_mesh/{int(idx.cpu().numpy()):04d}_canonical.ply") + mesh_deformed.export(f"test_mesh/{int(idx.cpu().numpy()):04d}_deformed.ply") + self.model.deformer = SMPLDeformer(betas=np.load(self.betas_path), gender=self.gender) + for i in range(num_splits): + indices = list(range(i * pixel_per_batch, + min((i + 1) * pixel_per_batch, total_pixels))) + batch_inputs = {"uv": inputs["uv"][:, indices], + "intrinsics": inputs['intrinsics'], + "pose": inputs['pose'], + "smpl_params": inputs["smpl_params"], + "smpl_pose": inputs["smpl_params"][:, 4:76], + "smpl_shape": inputs["smpl_params"][:, 76:], + "smpl_trans": inputs["smpl_params"][:, 1:4], + "idx": inputs["idx"] if 'idx' in inputs.keys() else None} + + body_model_params = self.body_model_params(inputs['idx']) + + batch_inputs.update({'smpl_pose': torch.cat((body_model_params['global_orient'], body_model_params['body_pose']), dim=1)}) + batch_inputs.update({'smpl_shape': body_model_params['betas']}) + batch_inputs.update({'smpl_trans': body_model_params['transl']}) + + batch_targets = {"rgb": targets["rgb"][:, indices].detach().clone() if 'rgb' in targets.keys() else None, + "img_size": targets["img_size"]} + + with torch.no_grad(): + model_outputs = self.model(batch_inputs) + results.append({"rgb_values":model_outputs["rgb_values"].detach().clone(), + "fg_rgb_values":model_outputs["fg_rgb_values"].detach().clone(), + "normal_values": model_outputs["normal_values"].detach().clone(), + "acc_map": model_outputs["acc_map"].detach().clone(), + **batch_targets}) + + img_size = results[0]["img_size"] + rgb_pred = torch.cat([result["rgb_values"] for result in results], dim=0) + rgb_pred = rgb_pred.reshape(*img_size, -1) + + fg_rgb_pred = torch.cat([result["fg_rgb_values"] for result in results], dim=0) + fg_rgb_pred = fg_rgb_pred.reshape(*img_size, -1) + + normal_pred = torch.cat([result["normal_values"] for result in results], dim=0) + normal_pred = (normal_pred.reshape(*img_size, -1) + 1) / 2 + + pred_mask = torch.cat([result["acc_map"] for result in results], dim=0) + pred_mask = pred_mask.reshape(*img_size, -1) + + if results[0]['rgb'] is not None: + rgb_gt = torch.cat([result["rgb"] for result in results], dim=1).squeeze(0) + rgb_gt = rgb_gt.reshape(*img_size, -1) + rgb = torch.cat([rgb_gt, rgb_pred], dim=0).cpu().numpy() + else: + rgb = torch.cat([rgb_pred], dim=0).cpu().numpy() + if 'normal' in results[0].keys(): + normal_gt = torch.cat([result["normal"] for result in results], dim=1).squeeze(0) + normal_gt = (normal_gt.reshape(*img_size, -1) + 1) / 2 + normal = torch.cat([normal_gt, normal_pred], dim=0).cpu().numpy() + else: + normal = torch.cat([normal_pred], dim=0).cpu().numpy() + + rgb = (rgb * 255).astype(np.uint8) + + fg_rgb = torch.cat([fg_rgb_pred], dim=0).cpu().numpy() + fg_rgb = (fg_rgb * 255).astype(np.uint8) + + normal = (normal * 255).astype(np.uint8) + + cv2.imwrite(f"test_mask/{int(idx.cpu().numpy()):04d}.png", pred_mask.cpu().numpy() * 255) + cv2.imwrite(f"test_rendering/{int(idx.cpu().numpy()):04d}.png", rgb[:, :, ::-1]) + cv2.imwrite(f"test_normal/{int(idx.cpu().numpy()):04d}.png", normal[:, :, ::-1]) + cv2.imwrite(f"test_fg_rendering/{int(idx.cpu().numpy()):04d}.png", fg_rgb[:, :, ::-1]) \ No 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b/data/parkinglot/poses.npy new file mode 100644 index 0000000000000000000000000000000000000000..e1cd9f4ec2fea9cbdb3464007570072bef3cf26c --- /dev/null +++ b/data/parkinglot/poses.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6773eb66b573b8f830c4a783afe0db5f97b86a143c6687a481be93e7149c5ea1 +size 12224 diff --git a/preprocessing/loss.py b/preprocessing/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..4694b400a7b34205be27433d7544b85ad17e4fa1 --- /dev/null +++ b/preprocessing/loss.py @@ -0,0 +1,25 @@ +from preprocessing_utils import GMoF +import torch +num_joints = 25 +joints_to_ign = [1,9,12] +joint_weights = torch.ones(num_joints) +joint_weights[joints_to_ign] = 0 +joint_weights = joint_weights.reshape((-1,1)).cuda() + +robustifier = GMoF(rho=100) + +def get_loss_weights(): + loss_weight = {'J2D_Loss': lambda cst, it: 1e-2 * cst, + 'Temporal_Loss': lambda cst, it: 6e0 * cst, + } + return loss_weight + +def joints_2d_loss(gt_joints_2d=None, joints_2d=None, joint_confidence=None): + + joint_diff = robustifier(gt_joints_2d - joints_2d) + joints_2dloss = torch.mean((joint_confidence*joint_weights[:, 0]).unsqueeze(-1) ** 2 * joint_diff) + return joints_2dloss + +def pose_temporal_loss(last_pose, param_pose): + temporal_loss = torch.mean(torch.square(last_pose - param_pose)) + return temporal_loss \ No newline at end of file diff --git a/preprocessing/normalize_cameras.py b/preprocessing/normalize_cameras.py new file mode 100644 index 0000000000000000000000000000000000000000..1a9146209652498c2534fe05ad87c7b374a3c8a8 --- /dev/null +++ b/preprocessing/normalize_cameras.py @@ -0,0 +1,67 @@ +# Adapted from https://github.com/lioryariv/volsdf/blob/main/DATA_CONVENTION.md +import cv2 +import numpy as np +import argparse + +def get_center_point(num_cams,cameras): + A = np.zeros((3 * num_cams, 3 + num_cams)) + b = np.zeros((3 * num_cams, 1)) + camera_centers=np.zeros((3,num_cams)) + for i in range(num_cams): + if 'cam_%d' % i in cameras: + P0 = cameras['cam_%d' % i][:3, :] + else: + P0 = cameras[i] + K = cv2.decomposeProjectionMatrix(P0)[0] + R = cv2.decomposeProjectionMatrix(P0)[1] + c = cv2.decomposeProjectionMatrix(P0)[2] + c = c / c[3] + camera_centers[:,i]=c[:3].flatten() + + v=R[2,:] + A[3 * i:(3 * i + 3), :3] = np.eye(3) + A[3 * i:(3 * i + 3), 3 + i] = -v + b[3 * i:(3 * i + 3)] = c[:3] + + return camera_centers + +def normalize_cameras(original_cameras_filename,output_cameras_filename,num_of_cameras, scene_bounding_sphere=3.0): + cameras = np.load(original_cameras_filename) + if num_of_cameras==-1: + all_files=cameras.files + maximal_ind=0 + for field in all_files: + maximal_ind=np.maximum(maximal_ind,int(field.split('_')[-1])) + num_of_cameras=maximal_ind+1 + + camera_centers = get_center_point(num_of_cameras, cameras) + + center = np.array([0, 0, 0]) + + max_radius = np.linalg.norm((center[:, np.newaxis] - camera_centers), axis=0).max() * 1.1 + + normalization = np.eye(4).astype(np.float32) + + normalization[0, 0] = max_radius / scene_bounding_sphere + normalization[1, 1] = max_radius / scene_bounding_sphere + normalization[2, 2] = max_radius / scene_bounding_sphere + + cameras_new = {} + for i in range(num_of_cameras): + cameras_new['scale_mat_%d' % i] = normalization + cameras_new['world_mat_%d' % i] = cameras['cam_%d' % i].copy() + + np.savez(output_cameras_filename, **cameras_new) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='Normalizing cameras') + parser.add_argument('--input_cameras_file', type=str, default="cameras.npz", + help='the input cameras file') + parser.add_argument('--output_cameras_file', type=str, default="cameras_normalize.npz", + help='the output cameras file') + parser.add_argument('--number_of_cams',type=int, default=-1, + help='Number of cameras, if -1 use all') + + args = parser.parse_args() + normalize_cameras(args.input_cameras_file, args.output_cameras_file, args.number_of_cams) \ No newline at end of file diff --git a/preprocessing/preprocessing.py b/preprocessing/preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..3122cfbc7ee2f4890344fae47aa0bb39773a662c --- /dev/null +++ b/preprocessing/preprocessing.py @@ -0,0 +1,265 @@ +import numpy as np +import pickle as pkl +import torch +import trimesh +import cv2 +import os +from tqdm import tqdm +import glob +import argparse +from preprocessing_utils import (smpl_to_pose, PerspectiveCamera, Renderer, render_trimesh, \ + estimate_translation_cv2, transform_smpl) +from loss import joints_2d_loss, pose_temporal_loss, get_loss_weights + +def main(args): + device = torch.device("cuda:0") + seq = args.seq + gender = args.gender + DIR = './raw_data' + img_dir = f'{DIR}/{seq}/frames' + romp_file_dir = f'{DIR}/{seq}/ROMP' + img_paths = sorted(glob.glob(f"{img_dir}/*.png")) + romp_file_paths = sorted(glob.glob(f"{romp_file_dir}/*.npz")) + + from smplx import SMPL + smpl_model = SMPL('../code/lib/smpl/smpl_model', gender=gender).to(device) + + input_img = cv2.imread(img_paths[0]) + if args.source == 'custom': + focal_length = max(input_img.shape[0], input_img.shape[1]) + cam_intrinsics = np.array([[focal_length, 0., input_img.shape[1]//2], + [0., focal_length, input_img.shape[0]//2], + [0., 0., 1.]]) + elif args.source == 'neuman': + NeuMan_DIR = '' # path to NeuMan dataset + with open(f'{NeuMan_DIR}/{seq}/sparse/cameras.txt') as f: + lines = f.readlines() + cam_params = lines[3].split() + cam_intrinsics = np.array([[float(cam_params[4]), 0., float(cam_params[6])], + [0., float(cam_params[5]), float(cam_params[7])], + [0., 0., 1.]]) + elif args.source == 'deepcap': + DeepCap_DIR = '' # path to DeepCap dataset + with open(f'{DeepCap_DIR}/monocularCalibrationBM.calibration') as f: + lines = f.readlines() + + cam_params = lines[5].split() + cam_intrinsics = np.array([[float(cam_params[1]), 0., float(cam_params[3])], + [0., float(cam_params[6]), float(cam_params[7])], + [0., 0., 1.]]) + else: + print('Please specify the source of the dataset (custom, neuman, deepcap). We will continue to update the sources in the future.') + raise NotImplementedError + renderer = Renderer(img_size = [input_img.shape[0], input_img.shape[1]], cam_intrinsic=cam_intrinsics) + + if args.mode == 'mask': + if not os.path.exists(f'{DIR}/{seq}/init_mask'): + os.makedirs(f'{DIR}/{seq}/init_mask') + elif args.mode == 'refine': + if not os.path.exists(f'{DIR}/{seq}/init_refined_smpl'): + os.makedirs(f'{DIR}/{seq}/init_refined_smpl') + if not os.path.exists(f'{DIR}/{seq}/init_refined_mask'): + os.makedirs(f'{DIR}/{seq}/init_refined_mask') + if not os.path.exists(f'{DIR}/{seq}/init_refined_smpl_files'): + os.makedirs(f'{DIR}/{seq}/init_refined_smpl_files') + openpose_dir = f'{DIR}/{seq}/openpose' + openpose_paths = sorted(glob.glob(f"{openpose_dir}/*.npy")) + opt_num_iters=150 + weight_dict = get_loss_weights() + cam = PerspectiveCamera(focal_length_x=torch.tensor(cam_intrinsics[0, 0], dtype=torch.float32), + focal_length_y=torch.tensor(cam_intrinsics[1, 1], dtype=torch.float32), + center=torch.tensor(cam_intrinsics[0:2, 2]).unsqueeze(0)).to(device) + mean_shape = [] + smpl2op_mapping = torch.tensor(smpl_to_pose(model_type='smpl', use_hands=False, use_face=False, + use_face_contour=False, openpose_format='coco25'), dtype=torch.long).cuda() + elif args.mode == 'final': + refined_smpl_dir = f'{DIR}/{seq}/init_refined_smpl_files' + refined_smpl_mask_dir = f'{DIR}/{seq}/init_refined_mask' + refined_smpl_paths = sorted(glob.glob(f"{refined_smpl_dir}/*.pkl")) + refined_smpl_mask_paths = sorted(glob.glob(f"{refined_smpl_mask_dir}/*.png")) + + save_dir = f'../data/{seq}' + if not os.path.exists(os.path.join(save_dir, 'image')): + os.makedirs(os.path.join(save_dir, 'image')) + if not os.path.exists(os.path.join(save_dir, 'mask')): + os.makedirs(os.path.join(save_dir, 'mask')) + + scale_factor = args.scale_factor + smpl_shape = np.load(f'{DIR}/{seq}/mean_shape.npy') + T_hip = smpl_model.get_T_hip(betas=torch.tensor(smpl_shape)[None].float().to(device)).squeeze().cpu().numpy() + + K = np.eye(4) + K[:3, :3] = cam_intrinsics + K[0, 0] = K[0, 0] / scale_factor + K[1, 1] = K[1, 1] / scale_factor + K[0, 2] = K[0, 2] / scale_factor + K[1, 2] = K[1, 2] / scale_factor + + dial_kernel = np.ones((20, 20),np.uint8) + + output_trans = [] + output_pose = [] + output_P = {} + + last_j3d = None + actor_id = 0 + cam_extrinsics = np.eye(4) + R = torch.tensor(cam_extrinsics[:3,:3])[None].float() + T = torch.tensor(cam_extrinsics[:3, 3])[None].float() + for idx, img_path in enumerate(tqdm(img_paths)): + input_img = cv2.imread(img_path) + if args.mode == 'mask' or args.mode == 'refine': + seq_file = np.load(romp_file_paths[idx], allow_pickle=True)['results'][()] + + # tracking in case of two persons or wrong ROMP detection + if len(seq_file['smpl_thetas']) >= 2: + dist = [] + if idx == 0: + last_j3d = seq_file['joints'][actor_id] + for i in range(len(seq_file['smpl_thetas'])): + dist.append(np.linalg.norm(seq_file['joints'][i].mean(0) - last_j3d.mean(0, keepdims=True))) + actor_id = np.argmin(dist) + smpl_verts = seq_file['verts'][actor_id] + pj2d_org = seq_file['pj2d_org'][actor_id] + joints3d = seq_file['joints'][actor_id] + last_j3d = joints3d.copy() + tra_pred = estimate_translation_cv2(joints3d, pj2d_org, proj_mat=cam_intrinsics) + + smpl_verts += tra_pred + + if args.mode == 'refine': + openpose = np.load(openpose_paths[idx]) + openpose_j2d = torch.tensor(openpose[:, :2][None], dtype=torch.float32, requires_grad=False, device=device) + openpose_conf = torch.tensor(openpose[:, -1][None], dtype=torch.float32, requires_grad=False, device=device) + + smpl_shape = seq_file['smpl_betas'][actor_id][:10] + smpl_pose = seq_file['smpl_thetas'][actor_id] + smpl_trans = tra_pred + + opt_betas = torch.tensor(smpl_shape[None], dtype=torch.float32, requires_grad=True, device=device) + opt_pose = torch.tensor(smpl_pose[None], dtype=torch.float32, requires_grad=True, device=device) + opt_trans = torch.tensor(smpl_trans[None], dtype=torch.float32, requires_grad=True, device=device) + + opt_params = [{'params': opt_betas, 'lr': 1e-3}, + {'params': opt_pose, 'lr': 1e-3}, + {'params': opt_trans, 'lr': 1e-3}] + optimizer = torch.optim.Adam(opt_params, lr=2e-3, betas=(0.9, 0.999)) + if idx == 0: + last_pose = [opt_pose.detach().clone()] + loop = tqdm(range(opt_num_iters)) + for it in loop: + optimizer.zero_grad() + + smpl_output = smpl_model(betas=opt_betas, + body_pose=opt_pose[:,3:], + global_orient=opt_pose[:,:3], + transl=opt_trans) + smpl_verts = smpl_output.vertices.data.cpu().numpy().squeeze() + + smpl_joints_2d = cam(torch.index_select(smpl_output.joints, 1, smpl2op_mapping)) + + loss = dict() + loss['J2D_Loss'] = joints_2d_loss(openpose_j2d, smpl_joints_2d, openpose_conf) + loss['Temporal_Loss'] = pose_temporal_loss(last_pose[0], opt_pose) + w_loss = dict() + for k in loss: + w_loss[k] = weight_dict[k](loss[k], it) + + tot_loss = list(w_loss.values()) + tot_loss = torch.stack(tot_loss).sum() + tot_loss.backward() + optimizer.step() + + l_str = 'Iter: %d' % it + for k in loss: + l_str += ', %s: %0.4f' % (k, weight_dict[k](loss[k], it).mean().item()) + loop.set_description(l_str) + + smpl_mesh = trimesh.Trimesh(smpl_verts, smpl_model.faces, process=False) + R = torch.tensor(cam_extrinsics[:3,:3])[None].float() + T = torch.tensor(cam_extrinsics[:3, 3])[None].float() + rendered_image = render_trimesh(renderer, smpl_mesh, R, T, 'n') + if input_img.shape[0] < input_img.shape[1]: + rendered_image = rendered_image[abs(input_img.shape[0]-input_img.shape[1])//2:(input_img.shape[0]+input_img.shape[1])//2,...] + else: + rendered_image = rendered_image[:,abs(input_img.shape[0]-input_img.shape[1])//2:(input_img.shape[0]+input_img.shape[1])//2] + valid_mask = (rendered_image[:,:,-1] > 0)[:, :, np.newaxis] + + if args.mode == 'mask': + cv2.imwrite(os.path.join(f'{DIR}/{seq}/init_mask', '%04d.png' % idx), valid_mask*255) + elif args.mode == 'refine': + output_img = (rendered_image[:,:,:-1] * valid_mask + input_img * (1 - valid_mask)).astype(np.uint8) + cv2.imwrite(os.path.join(f'{DIR}/{seq}/init_refined_smpl', '%04d.png' % idx), output_img) + cv2.imwrite(os.path.join(f'{DIR}/{seq}/init_refined_mask', '%04d.png' % idx), valid_mask*255) + last_pose.pop(0) + last_pose.append(opt_pose.detach().clone()) + smpl_dict = {} + smpl_dict['pose'] = opt_pose.data.squeeze().cpu().numpy() + smpl_dict['trans'] = opt_trans.data.squeeze().cpu().numpy() + smpl_dict['shape'] = opt_betas.data.squeeze().cpu().numpy() + + mean_shape.append(smpl_dict['shape']) + pkl.dump(smpl_dict, open(os.path.join(f'{DIR}/{seq}/init_refined_smpl_files', '%04d.pkl' % idx), 'wb')) + elif args.mode == 'final': + input_img = cv2.resize(input_img, (input_img.shape[1] // scale_factor, input_img.shape[0] // scale_factor)) + seq_file = pkl.load(open(refined_smpl_paths[idx], 'rb')) + + mask = cv2.imread(refined_smpl_mask_paths[idx]) + mask = cv2.resize(mask, (mask.shape[1] // scale_factor, mask.shape[0] // scale_factor)) + + # dilate mask to obtain a coarse bbox + mask = cv2.dilate(mask, dial_kernel) + + cv2.imwrite(os.path.join(save_dir, 'image/%04d.png' % idx), input_img) + cv2.imwrite(os.path.join(save_dir, 'mask/%04d.png' % idx), mask) + + smpl_pose = seq_file['pose'] + smpl_trans = seq_file['trans'] + + # transform the spaces such that our camera has the same orientation as the OpenGL camera + target_extrinsic = np.eye(4) + target_extrinsic[1:3] *= -1 + target_extrinsic, smpl_pose, smpl_trans = transform_smpl(cam_extrinsics, target_extrinsic, smpl_pose, smpl_trans, T_hip) + smpl_output = smpl_model(betas=torch.tensor(smpl_shape)[None].float().to(device), + body_pose=torch.tensor(smpl_pose[3:])[None].float().to(device), + global_orient=torch.tensor(smpl_pose[:3])[None].float().to(device), + transl=torch.tensor(smpl_trans)[None].float().to(device)) + smpl_verts = smpl_output.vertices.data.cpu().numpy().squeeze() + + # we need to center the human for every frame due to the potentially large global movement + v_max = smpl_verts.max(axis=0) + v_min = smpl_verts.min(axis=0) + normalize_shift = -(v_max + v_min) / 2. + + trans = smpl_trans + normalize_shift + + target_extrinsic[:3, -1] = target_extrinsic[:3, -1] - (target_extrinsic[:3, :3] @ normalize_shift) + + P = K @ target_extrinsic + output_trans.append(trans) + output_pose.append(smpl_pose) + output_P[f"cam_{idx}"] = P + + if args.mode == 'refine': + mean_shape = np.array(mean_shape) + np.save(f'{DIR}/{seq}/mean_shape.npy', mean_shape.mean(0)) + if args.mode == 'final': + np.save(os.path.join(save_dir, 'poses.npy'), np.array(output_pose)) + np.save(os.path.join(save_dir, 'mean_shape.npy'), smpl_shape) + np.save(os.path.join(save_dir, 'normalize_trans.npy'), np.array(output_trans)) + np.savez(os.path.join(save_dir, "cameras.npz"), **output_P) + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description="Preprocessing data") + # video source + parser.add_argument('--source', type=str, default='custom', help="custom video or dataset video") + # sequence name + parser.add_argument('--seq', type=str) + # gender + parser.add_argument('--gender', type=str, help="gender of the actor: MALE or FEMALE") + # mode + parser.add_argument('--mode', type=str, help="mask mode or refine mode: mask or refine or final") + # scale factor for the input image + parser.add_argument('--scale_factor', type=int, default=2, help="scale factor for the input image") + args = parser.parse_args() + main(args) \ No newline at end of file diff --git a/preprocessing/preprocessing_utils.py b/preprocessing/preprocessing_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..feb156dceb7e39e6e2b55180b61fcd6a22b5ea94 --- /dev/null +++ b/preprocessing/preprocessing_utils.py @@ -0,0 +1,370 @@ +"""This module contains simple helper functions and classes for preprocessing """ +import numpy as np +import cv2 +import torch +import torch.nn as nn +import torch.nn.functional as F +from pytorch3d.renderer import ( + SfMPerspectiveCameras, + RasterizationSettings, + MeshRenderer, + MeshRasterizer, + SoftPhongShader, + PointLights, +) +from pytorch3d.structures import Meshes +from pytorch3d.renderer.mesh import Textures +DEFAULT_DTYPE = torch.float32 +INVALID_TRANS=np.ones(3)*-1 + +def smpl_to_pose(model_type='smplx', use_hands=True, use_face=True, + use_face_contour=False, openpose_format='coco25'): + ''' Returns the indices of the permutation that maps OpenPose to SMPL + Parameters + ---------- + model_type: str, optional + The type of SMPL-like model that is used. The default mapping + returned is for the SMPLX model + use_hands: bool, optional + Flag for adding to the returned permutation the mapping for the + hand keypoints. Defaults to True + use_face: bool, optional + Flag for adding to the returned permutation the mapping for the + face keypoints. Defaults to True + use_face_contour: bool, optional + Flag for appending the facial contour keypoints. Defaults to False + openpose_format: bool, optional + The output format of OpenPose. For now only COCO-25 and COCO-19 is + supported. Defaults to 'coco25' + ''' + if openpose_format.lower() == 'coco25': + if model_type == 'smpl': + return np.array([24, 12, 17, 19, 21, 16, 18, 20, 0, 2, 5, 8, 1, 4, + 7, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], + dtype=np.int32) + elif model_type == 'smplh': + body_mapping = np.array([52, 12, 17, 19, 21, 16, 18, 20, 0, 2, 5, + 8, 1, 4, 7, 53, 54, 55, 56, 57, 58, 59, + 60, 61, 62], dtype=np.int32) + mapping = [body_mapping] + if use_hands: + lhand_mapping = np.array([20, 34, 35, 36, 63, 22, 23, 24, 64, + 25, 26, 27, 65, 31, 32, 33, 66, 28, + 29, 30, 67], dtype=np.int32) + rhand_mapping = np.array([21, 49, 50, 51, 68, 37, 38, 39, 69, + 40, 41, 42, 70, 46, 47, 48, 71, 43, + 44, 45, 72], dtype=np.int32) + mapping += [lhand_mapping, rhand_mapping] + return np.concatenate(mapping) + # SMPLX + elif model_type == 'smplx': + body_mapping = np.array([55, 12, 17, 19, 21, 16, 18, 20, 0, 2, 5, + 8, 1, 4, 7, 56, 57, 58, 59, 60, 61, 62, + 63, 64, 65], dtype=np.int32) + mapping = [body_mapping] + if use_hands: + lhand_mapping = np.array([20, 37, 38, 39, 66, 25, 26, 27, + 67, 28, 29, 30, 68, 34, 35, 36, 69, + 31, 32, 33, 70], dtype=np.int32) + rhand_mapping = np.array([21, 52, 53, 54, 71, 40, 41, 42, 72, + 43, 44, 45, 73, 49, 50, 51, 74, 46, + 47, 48, 75], dtype=np.int32) + + mapping += [lhand_mapping, rhand_mapping] + + if use_face: + # end_idx = 127 + 17 * use_face_contour + face_mapping = np.arange(76, 127 + 17 * use_face_contour, + dtype=np.int32) + mapping += [face_mapping] + + return np.concatenate(mapping) + else: + raise ValueError('Unknown model type: {}'.format(model_type)) + elif openpose_format == 'coco19': + if model_type == 'smpl': + return np.array([24, 12, 17, 19, 21, 16, 18, 20, 2, 5, 8, + 1, 4, 7, 25, 26, 27, 28], + dtype=np.int32) + elif model_type == 'smpl_neutral': + return np.array([14, 12, 8, 7, 6, 9, 10, 11, 2, 1, 0, 3, 4, 5, 16, 15,18, 17,], + dtype=np.int32) + + elif model_type == 'smplh': + body_mapping = np.array([52, 12, 17, 19, 21, 16, 18, 20, 0, 2, 5, + 8, 1, 4, 7, 53, 54, 55, 56], + dtype=np.int32) + mapping = [body_mapping] + if use_hands: + lhand_mapping = np.array([20, 34, 35, 36, 57, 22, 23, 24, 58, + 25, 26, 27, 59, 31, 32, 33, 60, 28, + 29, 30, 61], dtype=np.int32) + rhand_mapping = np.array([21, 49, 50, 51, 62, 37, 38, 39, 63, + 40, 41, 42, 64, 46, 47, 48, 65, 43, + 44, 45, 66], dtype=np.int32) + mapping += [lhand_mapping, rhand_mapping] + return np.concatenate(mapping) + # SMPLX + elif model_type == 'smplx': + body_mapping = np.array([55, 12, 17, 19, 21, 16, 18, 20, 0, 2, 5, + 8, 1, 4, 7, 56, 57, 58, 59], + dtype=np.int32) + mapping = [body_mapping] + if use_hands: + lhand_mapping = np.array([20, 37, 38, 39, 60, 25, 26, 27, + 61, 28, 29, 30, 62, 34, 35, 36, 63, + 31, 32, 33, 64], dtype=np.int32) + rhand_mapping = np.array([21, 52, 53, 54, 65, 40, 41, 42, 66, + 43, 44, 45, 67, 49, 50, 51, 68, 46, + 47, 48, 69], dtype=np.int32) + + mapping += [lhand_mapping, rhand_mapping] + if use_face: + face_mapping = np.arange(70, 70 + 51 + + 17 * use_face_contour, + dtype=np.int32) + mapping += [face_mapping] + + return np.concatenate(mapping) + else: + raise ValueError('Unknown model type: {}'.format(model_type)) + elif openpose_format == 'h36': + if model_type == 'smpl': + return np.array([2,5,8,1,4,7,12,24,16,18,20,17,19,21],dtype=np.int32) + elif model_type == 'smpl_neutral': + #return np.array([2,1,0,3,4,5,12,13,9,10,11,8,7,6], dtype=np.int32) + return [6, 5, 4, 1, 2, 3, 16, 15, 14, 11, 12, 13, 8, 10] + + else: + raise ValueError('Unknown joint format: {}'.format(openpose_format)) + +def render_trimesh(renderer,mesh,R,T, mode='np'): + + verts = torch.tensor(mesh.vertices).cuda().float()[None] + faces = torch.tensor(mesh.faces).cuda()[None] + colors = torch.tensor(mesh.visual.vertex_colors).float().cuda()[None,...,:3]/255 + renderer.set_camera(R,T) + image = renderer.render_mesh_recon(verts, faces, colors=colors, mode=mode)[0] + image = (255*image).data.cpu().numpy().astype(np.uint8) + + return image + +def estimate_translation_cv2(joints_3d, joints_2d, focal_length=600, img_size=np.array([512.,512.]), proj_mat=None, cam_dist=None): + if proj_mat is None: + camK = np.eye(3) + camK[0,0], camK[1,1] = focal_length, focal_length + camK[:2,2] = img_size//2 + else: + camK = proj_mat + _, _, tvec,inliers = cv2.solvePnPRansac(joints_3d, joints_2d, camK, cam_dist,\ + flags=cv2.SOLVEPNP_EPNP,reprojectionError=20,iterationsCount=100) + + if inliers is None: + return INVALID_TRANS + else: + tra_pred = tvec[:,0] + return tra_pred + +class JointMapper(nn.Module): + def __init__(self, joint_maps=None): + super(JointMapper, self).__init__() + if joint_maps is None: + self.joint_maps = joint_maps + else: + self.register_buffer('joint_maps', + torch.tensor(joint_maps, dtype=torch.long)) + + def forward(self, joints, **kwargs): + if self.joint_maps is None: + return joints + else: + return torch.index_select(joints, 1, self.joint_maps) + +def transform_mat(R, t): + ''' Creates a batch of transformation matrices + Args: + - R: Bx3x3 array of a batch of rotation matrices + - t: Bx3x1 array of a batch of translation vectors + Returns: + - T: Bx4x4 Transformation matrix + ''' + # No padding left or right, only add an extra row + return torch.cat([F.pad(R, [0, 0, 0, 1]), + F.pad(t, [0, 0, 0, 1], value=1)], dim=2) + +# transform SMPL such that the target camera extrinsic will be met +def transform_smpl(curr_extrinsic, target_extrinsic, smpl_pose, smpl_trans, T_hip): + + R_root = cv2.Rodrigues(smpl_pose[:3])[0] + transf_global_ori = np.linalg.inv(target_extrinsic[:3,:3]) @ curr_extrinsic[:3,:3] @ R_root + + target_extrinsic[:3, -1] = curr_extrinsic[:3,:3] @ (smpl_trans + T_hip) + curr_extrinsic[:3, -1] - smpl_trans - target_extrinsic[:3,:3] @ T_hip + + smpl_pose[:3] = cv2.Rodrigues(transf_global_ori)[0].reshape(3) + smpl_trans = np.linalg.inv(target_extrinsic[:3,:3]) @ smpl_trans # we assume + + return target_extrinsic, smpl_pose, smpl_trans + +class GMoF(nn.Module): + def __init__(self, rho=1): + super(GMoF, self).__init__() + self.rho = rho + + def extra_repr(self): + return 'rho = {}'.format(self.rho) + + def forward(self, residual): + squared_res = residual ** 2 + dist = torch.div(squared_res, squared_res + self.rho ** 2) + return self.rho ** 2 * dist + +class PerspectiveCamera(nn.Module): + + FOCAL_LENGTH = 50*128 + + def __init__(self, rotation=None, translation=None, + focal_length_x=None, focal_length_y=None, + batch_size=1, + center=None, dtype=torch.float32): + super(PerspectiveCamera, self).__init__() + self.batch_size = batch_size + self.dtype = dtype + # Make a buffer so that PyTorch does not complain when creating + # the camera matrix + self.register_buffer('zero', + torch.zeros([batch_size], dtype=dtype)) + + if focal_length_x is None or type(focal_length_x) == float: + focal_length_x = torch.full( + [batch_size], + self.FOCAL_LENGTH if focal_length_x is None else + focal_length_x, + dtype=dtype) + + if focal_length_y is None or type(focal_length_y) == float: + focal_length_y = torch.full( + [batch_size], + self.FOCAL_LENGTH if focal_length_y is None else + focal_length_y, + dtype=dtype) + + self.register_buffer('focal_length_x', focal_length_x) + self.register_buffer('focal_length_y', focal_length_y) + + if center is None: + center = torch.zeros([batch_size, 2], dtype=dtype) + self.register_buffer('center', center) + + if rotation is None: + rotation = torch.eye( + 3, dtype=dtype).unsqueeze(dim=0).repeat(batch_size, 1, 1) + + rotation = nn.Parameter(rotation, requires_grad=False) + self.register_parameter('rotation', rotation) + + if translation is None: + translation = torch.zeros([batch_size, 3], dtype=dtype) + + translation = nn.Parameter(translation, + requires_grad=True) + self.register_parameter('translation', translation) + + def forward(self, points): + device = points.device + with torch.no_grad(): + camera_mat = torch.zeros([self.batch_size, 2, 2], + dtype=self.dtype, device=points.device) + camera_mat[:, 0, 0] = self.focal_length_x + camera_mat[:, 1, 1] = self.focal_length_y + + camera_transform = transform_mat(self.rotation, + self.translation.unsqueeze(dim=-1)) + + homog_coord = torch.ones(list(points.shape)[:-1] + [1], + dtype=points.dtype, + device=device) + # Convert the points to homogeneous coordinates + points_h = torch.cat([points, homog_coord], dim=-1) + + projected_points = torch.einsum('bki,bji->bjk', + [camera_transform, points_h]) + + img_points = torch.div(projected_points[:, :, :2], + projected_points[:, :, 2].unsqueeze(dim=-1)) + img_points = torch.einsum('bki,bji->bjk', [camera_mat, img_points]) \ + + self.center.unsqueeze(dim=1) + return img_points + +class Renderer(): + + def __init__(self, principal_point=None, img_size=None, cam_intrinsic = None): + + super().__init__() + + self.device = torch.device("cuda:0") + torch.cuda.set_device(self.device) + self.cam_intrinsic = cam_intrinsic + self.image_size = img_size + self.render_img_size = np.max(img_size) + + principal_point = [-(self.cam_intrinsic[0,2]-self.image_size[1]/2.)/(self.image_size[1]/2.), -(self.cam_intrinsic[1,2]-self.image_size[0]/2.)/(self.image_size[0]/2.)] + self.principal_point = torch.tensor(principal_point, device=self.device).unsqueeze(0) + + self.cam_R = torch.from_numpy(np.array([[-1., 0., 0.], + [0., -1., 0.], + [0., 0., 1.]])).cuda().float().unsqueeze(0) + + self.cam_T = torch.zeros((1,3)).cuda().float() + + half_max_length = max(self.cam_intrinsic[0:2,2]) + self.focal_length = torch.tensor([(self.cam_intrinsic[0,0]/half_max_length).astype(np.float32), \ + (self.cam_intrinsic[1,1]/half_max_length).astype(np.float32)]).unsqueeze(0) + + self.cameras = SfMPerspectiveCameras(focal_length=self.focal_length, principal_point=self.principal_point, R=self.cam_R, T=self.cam_T, device=self.device) + + self.lights = PointLights(device=self.device,location=[[0.0, 0.0, 0.0]], ambient_color=((1,1,1),),diffuse_color=((0,0,0),),specular_color=((0,0,0),)) + + self.raster_settings = RasterizationSettings(image_size=self.render_img_size, faces_per_pixel=10, blur_radius=0, max_faces_per_bin=30000) + self.rasterizer = MeshRasterizer(cameras=self.cameras, raster_settings=self.raster_settings) + + self.shader = SoftPhongShader(device=self.device, cameras=self.cameras, lights=self.lights) + + self.renderer = MeshRenderer(rasterizer=self.rasterizer, shader=self.shader) + + def set_camera(self, R, T): + self.cam_R = R + self.cam_T = T + self.cam_R[:, :2, :] *= -1.0 + self.cam_T[:, :2] *= -1.0 + self.cam_R = torch.transpose(self.cam_R,1,2) + self.cameras = SfMPerspectiveCameras(focal_length=self.focal_length, principal_point=self.principal_point, R=self.cam_R, T=self.cam_T, device=self.device) + self.rasterizer = MeshRasterizer(cameras=self.cameras, raster_settings=self.raster_settings) + self.shader = SoftPhongShader(device=self.device, cameras=self.cameras, lights=self.lights) + self.renderer = MeshRenderer(rasterizer=self.rasterizer, shader=self.shader) + + def render_mesh_recon(self, verts, faces, R=None, T=None, colors=None, mode='npat'): + ''' + mode: normal, phong, texture + ''' + with torch.no_grad(): + + mesh = Meshes(verts, faces) + + normals = torch.stack(mesh.verts_normals_list()) + front_light = -torch.tensor([0,0,-1]).float().to(verts.device) + shades = (normals * front_light.view(1,1,3)).sum(-1).clamp(min=0).unsqueeze(-1).expand(-1,-1,3) + results = [] + # shading + if 'p' in mode: + mesh_shading = Meshes(verts, faces, textures=Textures(verts_rgb=shades)) + image_phong = self.renderer(mesh_shading) + results.append(image_phong) + # normal + if 'n' in mode: + normals_vis = normals* 0.5 + 0.5 + normals_vis = normals_vis[:,:,[2,1,0]] + mesh_normal = Meshes(verts, faces, textures=Textures(verts_rgb=normals_vis)) + image_normal = self.renderer(mesh_normal) + results.append(image_normal) + return torch.cat(results, axis=1) + diff --git a/preprocessing/run_openpose.py b/preprocessing/run_openpose.py new file mode 100644 index 0000000000000000000000000000000000000000..8ff2aea2936f758ad54455070233b58a4309894d --- /dev/null +++ b/preprocessing/run_openpose.py @@ -0,0 +1,84 @@ +import sys +import cv2 +import os +import numpy as np +import argparse +import time +import glob +from sklearn.neighbors import NearestNeighbors +def get_bbox_center(img_path, mask_path): + _img = cv2.imread(img_path) + W, H = _img.shape[1], _img.shape[0] + + mask = cv2.imread(mask_path)[:, :, 0] + where = np.asarray(np.where(mask)) + bbox_min = where.min(axis=1) + bbox_max = where.max(axis=1) + left, top, right, bottom = bbox_min[1], bbox_min[0], bbox_max[1], bbox_max[ + 0] + left = max(left, 0) + top = max(top, 0) + right = min(right, W) + bottom = min(bottom, H) + bbox_center = np.array([left + (right - left) / 2, top + (bottom - top) / 2]) + return bbox_center + +def main(args): + try: + sys.path.append(args.openpose_dir + '/build/python') + # we use the python binding of openpose + from openpose import pyopenpose as op + DIR = './raw_data' + # Flags + params = dict() + params['model_folder'] = args.openpose_dir + '/models/' + params['scale_number'] = 1 + params['scale_gap'] = 0.25 + params['net_resolution'] = '720x480' + + # Starting OpenPose + opWrapper = op.WrapperPython() + opWrapper.configure(params) + opWrapper.start() + + # Read frames on directory + img_dir = f'{DIR}/{args.seq}/frames' + imagePaths = op.get_images_on_directory(img_dir) + maskPaths = sorted(glob.glob(f'{img_dir}/../init_mask/*.png')) + start = time.time() + + if not os.path.exists(f'{img_dir}/../openpose'): + os.makedirs(f'{img_dir}/../openpose') + + # Process and display images + nbrs = NearestNeighbors(n_neighbors=1) + for idx, imagePath in enumerate(imagePaths): + datum = op.Datum() + imageToProcess = cv2.imread(imagePath) + maskPath = maskPaths[idx] + bbox_center = get_bbox_center(imagePath, maskPath) + datum.cvInputData = imageToProcess + opWrapper.emplaceAndPop(op.VectorDatum([datum])) + + poseKeypoints = datum.poseKeypoints + + nbrs.fit(poseKeypoints[:, 8, :2]) + + actor = nbrs.kneighbors(bbox_center.reshape(1, -1), return_distance=False).ravel()[0] + poseKeypoints = poseKeypoints[actor] + np.save(f'{img_dir}/../openpose/%04d.npy' % idx, poseKeypoints) + cv2.imwrite(f'{img_dir}/../openpose/%04d.png' % idx, datum.cvOutputData) + end = time.time() + print("OpenPose demo successfully finished. Total time: " + str(end - start) + " seconds") + except Exception as e: + print(e) + sys.exit(-1) + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description="Run OpenPose on a sequence") + # directory of openpose + parser.add_argument('--openpose_dir', type=str, help="Directory of openpose") + # sequence name + parser.add_argument('--seq', type=str, help="Process a directory of images. Read all standard formats (jpg, png, bmp, etc.).") + args = parser.parse_args() + main(args) \ No newline at end of file diff --git a/preprocessing/run_preprocessing.sh b/preprocessing/run_preprocessing.sh new file mode 100644 index 0000000000000000000000000000000000000000..1734ac8c8cc2dd106b229a1fc039b8ee8adfc7e9 --- /dev/null +++ b/preprocessing/run_preprocessing.sh @@ -0,0 +1,29 @@ +# pre-define data +source="custom" +seq="" +gender="FEMALE" + +# run ROMP to get initial SMPL parameters +echo "Running ROMP" +romp --mode=video --calc_smpl --render_mesh -i=./raw_data/$seq/frames -o=./raw_data/$seq/ROMP --smpl_path ./romp/SMPL_$gender.pth + +# obtain the projected masks through estimated perspective camera (so that OpenPose detection) +echo "Getting projected SMPL masks" +python preprocessing.py --source $source --seq $seq --gender $gender --mode mask + +# run OpenPose to get 2D keypoints +echo "Running OpenPose" +python run_openpose.py --openpose_dir {PATH_TO_OPENPOSE} --seq $seq + +# offline refine poses +echo "Refining poses offline" +python preprocessing.py --source $source --seq $seq --gender $gender --mode refine + +# scale images and center the human in 3D space +echo "Scaling images and centering human in 3D space" +python preprocessing.py --source $source --seq $seq --gender $gender --mode final --scale_factor 1 + +# normalize cameras such that all cameras are within the sphere of radius 3 +echo "Normalizing cameras" +python normalize_cameras.py --input_cameras_file ../data/$seq/cameras.npz \ + --output_cameras_file ../data/$seq/cameras_normalize.npz \ No newline at end of file diff --git a/preprocessing/smplx/__init__.py b/preprocessing/smplx/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..886949df670691d1ef5995737cafa285224826c4 --- /dev/null +++ b/preprocessing/smplx/__init__.py @@ -0,0 +1,30 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from .body_models import ( + create, + SMPL, + SMPLH, + SMPLX, + MANO, + FLAME, + build_layer, + SMPLLayer, + SMPLHLayer, + SMPLXLayer, + MANOLayer, + FLAMELayer, +) diff --git a/preprocessing/smplx/body_models.py b/preprocessing/smplx/body_models.py new file mode 100644 index 0000000000000000000000000000000000000000..da07237f7bb485dbdc6f7394c90fe9d8d4d9cb38 --- /dev/null +++ b/preprocessing/smplx/body_models.py @@ -0,0 +1,2376 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from typing import Optional, Dict, Union +import os +import os.path as osp + +import pickle + +import numpy as np + +import torch +import torch.nn as nn + +from .lbs import ( + lbs, vertices2landmarks, find_dynamic_lmk_idx_and_bcoords, vertices2joints, blend_shapes) + +from .vertex_ids import vertex_ids as VERTEX_IDS +from .utils import ( + Struct, to_np, to_tensor, Tensor, Array, + SMPLOutput, + SMPLHOutput, + SMPLXOutput, + MANOOutput, + FLAMEOutput, + find_joint_kin_chain) +from .vertex_joint_selector import VertexJointSelector + + +class SMPL(nn.Module): + + NUM_JOINTS = 23 + NUM_BODY_JOINTS = 23 + SHAPE_SPACE_DIM = 300 + + def __init__( + self, model_path: str, + data_struct: Optional[Struct] = None, + create_betas: bool = True, + betas: Optional[Tensor] = None, + num_betas: int = 10, + create_global_orient: bool = True, + global_orient: Optional[Tensor] = None, + create_body_pose: bool = True, + body_pose: Optional[Tensor] = None, + create_transl: bool = True, + transl: Optional[Tensor] = None, + dtype=torch.float32, + batch_size: int = 1, + joint_mapper=None, + gender: str = 'neutral', + vertex_ids: Dict[str, int] = None, + v_template: Optional[Union[Tensor, Array]] = None + ) -> None: + ''' SMPL model constructor + + Parameters + ---------- + model_path: str + The path to the folder or to the file where the model + parameters are stored + data_struct: Strct + A struct object. If given, then the parameters of the model are + read from the object. Otherwise, the model tries to read the + parameters from the given `model_path`. (default = None) + create_global_orient: bool, optional + Flag for creating a member variable for the global orientation + of the body. (default = True) + global_orient: torch.tensor, optional, Bx3 + The default value for the global orientation variable. + (default = None) + create_body_pose: bool, optional + Flag for creating a member variable for the pose of the body. + (default = True) + body_pose: torch.tensor, optional, Bx(Body Joints * 3) + The default value for the body pose variable. + (default = None) + num_betas: int, optional + Number of shape components to use + (default = 10). + create_betas: bool, optional + Flag for creating a member variable for the shape space + (default = True). + betas: torch.tensor, optional, Bx10 + The default value for the shape member variable. + (default = None) + create_transl: bool, optional + Flag for creating a member variable for the translation + of the body. (default = True) + transl: torch.tensor, optional, Bx3 + The default value for the transl variable. + (default = None) + dtype: torch.dtype, optional + The data type for the created variables + batch_size: int, optional + The batch size used for creating the member variables + joint_mapper: object, optional + An object that re-maps the joints. Useful if one wants to + re-order the SMPL joints to some other convention (e.g. MSCOCO) + (default = None) + gender: str, optional + Which gender to load + vertex_ids: dict, optional + A dictionary containing the indices of the extra vertices that + will be selected + ''' + + self.gender = gender + + if data_struct is None: + if osp.isdir(model_path): + model_fn = 'SMPL_{}.{ext}'.format(gender.upper(), ext='pkl') + smpl_path = os.path.join(model_path, model_fn) + else: + smpl_path = model_path + assert osp.exists(smpl_path), 'Path {} does not exist!'.format( + smpl_path) + + with open(smpl_path, 'rb') as smpl_file: + data_struct = Struct(**pickle.load(smpl_file, + encoding='latin1')) + + super(SMPL, self).__init__() + self.batch_size = batch_size + shapedirs = data_struct.shapedirs + if (shapedirs.shape[-1] < self.SHAPE_SPACE_DIM): + print(f'WARNING: You are using a {self.name()} model, with only' + ' 10 shape coefficients.') + num_betas = min(num_betas, 10) + else: + num_betas = min(num_betas, self.SHAPE_SPACE_DIM) + + self._num_betas = num_betas + shapedirs = shapedirs[:, :, :num_betas] + # The shape components + self.register_buffer( + 'shapedirs', + to_tensor(to_np(shapedirs), dtype=dtype)) + + if vertex_ids is None: + # SMPL and SMPL-H share the same topology, so any extra joints can + # be drawn from the same place + vertex_ids = VERTEX_IDS['smplh'] + + self.dtype = dtype + + self.joint_mapper = joint_mapper + + self.vertex_joint_selector = VertexJointSelector( + vertex_ids=vertex_ids) + + self.faces = data_struct.f + self.register_buffer('faces_tensor', + to_tensor(to_np(self.faces, dtype=np.int64), + dtype=torch.long)) + + if create_betas: + if betas is None: + default_betas = torch.zeros( + [batch_size, self.num_betas], dtype=dtype) + else: + if torch.is_tensor(betas): + default_betas = betas.clone().detach() + else: + default_betas = torch.tensor(betas, dtype=dtype) + + self.register_parameter( + 'betas', nn.Parameter(default_betas, requires_grad=True)) + + # The tensor that contains the global rotation of the model + # It is separated from the pose of the joints in case we wish to + # optimize only over one of them + if create_global_orient: + if global_orient is None: + default_global_orient = torch.zeros( + [batch_size, 3], dtype=dtype) + else: + if torch.is_tensor(global_orient): + default_global_orient = global_orient.clone().detach() + else: + default_global_orient = torch.tensor( + global_orient, dtype=dtype) + + global_orient = nn.Parameter(default_global_orient, + requires_grad=True) + self.register_parameter('global_orient', global_orient) + + if create_body_pose: + if body_pose is None: + default_body_pose = torch.zeros( + [batch_size, self.NUM_BODY_JOINTS * 3], dtype=dtype) + else: + if torch.is_tensor(body_pose): + default_body_pose = body_pose.clone().detach() + else: + default_body_pose = torch.tensor(body_pose, + dtype=dtype) + self.register_parameter( + 'body_pose', + nn.Parameter(default_body_pose, requires_grad=True)) + + if create_transl: + if transl is None: + default_transl = torch.zeros([batch_size, 3], + dtype=dtype, + requires_grad=True) + else: + default_transl = torch.tensor(transl, dtype=dtype) + self.register_parameter( + 'transl', nn.Parameter(default_transl, requires_grad=True)) + + if v_template is None: + v_template = data_struct.v_template + if not torch.is_tensor(v_template): + v_template = to_tensor(to_np(v_template), dtype=dtype) + # The vertices of the template model + self.register_buffer('v_template', v_template) + + j_regressor = to_tensor(to_np( + data_struct.J_regressor), dtype=dtype) + self.register_buffer('J_regressor', j_regressor) + + # Pose blend shape basis: 6890 x 3 x 207, reshaped to 6890*3 x 207 + num_pose_basis = data_struct.posedirs.shape[-1] + # 207 x 20670 + posedirs = np.reshape(data_struct.posedirs, [-1, num_pose_basis]).T + self.register_buffer('posedirs', + to_tensor(to_np(posedirs), dtype=dtype)) + + # indices of parents for each joints + parents = to_tensor(to_np(data_struct.kintree_table[0])).long() + parents[0] = -1 + self.register_buffer('parents', parents) + + self.register_buffer( + 'lbs_weights', to_tensor(to_np(data_struct.weights), dtype=dtype)) + + @property + def num_betas(self): + return self._num_betas + + @property + def num_expression_coeffs(self): + return 0 + + def create_mean_pose(self, data_struct) -> Tensor: + pass + + def name(self) -> str: + return 'SMPL' + + @torch.no_grad() + def reset_params(self, **params_dict) -> None: + for param_name, param in self.named_parameters(): + if param_name in params_dict: + param[:] = torch.tensor(params_dict[param_name]) + else: + param.fill_(0) + + def get_num_verts(self) -> int: + return self.v_template.shape[0] + + def get_num_faces(self) -> int: + return self.faces.shape[0] + + def get_T_hip(self, betas=None, displacement=None): + if displacement is not None: + v_shaped = self.v_template+displacement + blend_shapes(betas, self.shapedirs) + else: + v_shaped = self.v_template + blend_shapes(betas, self.shapedirs) + J = vertices2joints(self.J_regressor, v_shaped) + T_hip = J[0,0] + return T_hip + + def extra_repr(self) -> str: + msg = [ + f'Gender: {self.gender.upper()}', + f'Number of joints: {self.J_regressor.shape[0]}', + f'Betas: {self.num_betas}', + ] + return '\n'.join(msg) + + def forward( + self, + betas: Optional[Tensor] = None, + body_pose: Optional[Tensor] = None, + global_orient: Optional[Tensor] = None, + transl: Optional[Tensor] = None, + return_verts=True, + return_full_pose: bool = False, + pose2rot: bool = True, + displacement=None, + absolute_displacement=True, + ) -> SMPLOutput: + ''' Forward pass for the SMPL model + + Parameters + ---------- + global_orient: torch.tensor, optional, shape Bx3 + If given, ignore the member variable and use it as the global + rotation of the body. Useful if someone wishes to predicts this + with an external model. (default=None) + betas: torch.tensor, optional, shape BxN_b + If given, ignore the member variable `betas` and use it + instead. For example, it can used if shape parameters + `betas` are predicted from some external model. + (default=None) + body_pose: torch.tensor, optional, shape Bx(J*3) + If given, ignore the member variable `body_pose` and use it + instead. For example, it can used if someone predicts the + pose of the body joints are predicted from some external model. + It should be a tensor that contains joint rotations in + axis-angle format. (default=None) + transl: torch.tensor, optional, shape Bx3 + If given, ignore the member variable `transl` and use it + instead. For example, it can used if the translation + `transl` is predicted from some external model. + (default=None) + return_verts: bool, optional + Return the vertices. (default=True) + return_full_pose: bool, optional + Returns the full axis-angle pose vector (default=False) + + Returns + ------- + ''' + # If no shape and pose parameters are passed along, then use the + # ones from the module + global_orient = (global_orient if global_orient is not None else + self.global_orient) + body_pose = body_pose if body_pose is not None else self.body_pose + betas = betas if betas is not None else self.betas + + apply_trans = transl is not None or hasattr(self, 'transl') + if transl is None and hasattr(self, 'transl'): + transl = self.transl + + full_pose = torch.cat([global_orient, body_pose], dim=1) + + batch_size = max(betas.shape[0], global_orient.shape[0], + body_pose.shape[0]) + + if betas.shape[0] != batch_size: + num_repeats = int(batch_size / betas.shape[0]) + betas = betas.expand(num_repeats, -1) + if displacement is not None: + if absolute_displacement: + vertices, joints = lbs(betas, full_pose, displacement, + self.shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, pose2rot=pose2rot) + else: + vertices, joints = lbs(betas, full_pose, self.v_template+displacement, + self.shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, pose2rot=pose2rot) + else: + vertices, joints = lbs(betas, full_pose, self.v_template, + self.shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, pose2rot=pose2rot) + joints = self.vertex_joint_selector(vertices, joints) + # Map the joints to the current dataset + if self.joint_mapper is not None: + joints = self.joint_mapper(joints) + + if apply_trans: + joints += transl.unsqueeze(dim=1) + vertices += transl.unsqueeze(dim=1) + + output = SMPLOutput(vertices=vertices if return_verts else None, + faces=self.faces, + global_orient=global_orient, + body_pose=body_pose, + joints=joints, + betas=betas, + full_pose=full_pose if return_full_pose else None) + + return output + + +class SMPLLayer(SMPL): + def __init__( + self, + *args + ) -> None: + # Just create a SMPL module without any member variables + super(SMPLLayer, self).__init__( + create_body_pose=False, + create_betas=False, + create_global_orient=False, + create_transl=False, + *args, + ) + + def forward( + self, + betas: Optional[Tensor] = None, + body_pose: Optional[Tensor] = None, + global_orient: Optional[Tensor] = None, + transl: Optional[Tensor] = None, + return_verts=True, + return_full_pose: bool = False, + pose2rot: bool = True + ) -> SMPLOutput: + ''' Forward pass for the SMPL model + + Parameters + ---------- + global_orient: torch.tensor, optional, shape Bx3x3 + Global rotation of the body. Useful if someone wishes to + predicts this with an external model. It is expected to be in + rotation matrix format. (default=None) + betas: torch.tensor, optional, shape BxN_b + Shape parameters. For example, it can used if shape parameters + `betas` are predicted from some external model. + (default=None) + body_pose: torch.tensor, optional, shape BxJx3x3 + Body pose. For example, it can used if someone predicts the + pose of the body joints are predicted from some external model. + It should be a tensor that contains joint rotations in + rotation matrix format. (default=None) + transl: torch.tensor, optional, shape Bx3 + Translation vector of the body. + For example, it can used if the translation + `transl` is predicted from some external model. + (default=None) + return_verts: bool, optional + Return the vertices. (default=True) + return_full_pose: bool, optional + Returns the full axis-angle pose vector (default=False) + + Returns + ------- + ''' + model_vars = [betas, global_orient, body_pose, transl] + batch_size = 1 + for var in model_vars: + if var is None: + continue + batch_size = max(batch_size, len(var)) + device, dtype = self.shapedirs.device, self.shapedirs.dtype + if global_orient is None: + global_orient = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous() + if body_pose is None: + body_pose = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand( + batch_size, self.NUM_BODY_JOINTS, -1, -1).contiguous() + if betas is None: + betas = torch.zeros([batch_size, self.num_betas], + dtype=dtype, device=device) + if transl is None: + transl = torch.zeros([batch_size, 3], dtype=dtype, device=device) + full_pose = torch.cat( + [global_orient.reshape(-1, 1, 3, 3), + body_pose.reshape(-1, self.NUM_BODY_JOINTS, 3, 3)], + dim=1) + + vertices, joints = lbs(betas, full_pose, self.v_template, + self.shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, + pose2rot=False) + + joints = self.vertex_joint_selector(vertices, joints) + # Map the joints to the current dataset + if self.joint_mapper is not None: + joints = self.joint_mapper(joints) + + if transl is not None: + joints += transl.unsqueeze(dim=1) + vertices += transl.unsqueeze(dim=1) + + output = SMPLOutput(vertices=vertices if return_verts else None, + global_orient=global_orient, + body_pose=body_pose, + joints=joints, + betas=betas, + full_pose=full_pose if return_full_pose else None) + + return output + + +class SMPLH(SMPL): + + # The hand joints are replaced by MANO + NUM_BODY_JOINTS = SMPL.NUM_JOINTS - 2 + NUM_HAND_JOINTS = 15 + NUM_JOINTS = NUM_BODY_JOINTS + 2 * NUM_HAND_JOINTS + + def __init__( + self, model_path, + data_struct: Optional[Struct] = None, + create_left_hand_pose: bool = True, + left_hand_pose: Optional[Tensor] = None, + create_right_hand_pose: bool = True, + right_hand_pose: Optional[Tensor] = None, + use_pca: bool = True, + num_pca_comps: int = 6, + flat_hand_mean: bool = False, + batch_size: int = 1, + gender: str = 'neutral', + dtype=torch.float32, + vertex_ids=None, + use_compressed: bool = True, + ext: str = 'pkl' + ) -> None: + ''' SMPLH model constructor + + Parameters + ---------- + model_path: str + The path to the folder or to the file where the model + parameters are stored + data_struct: Strct + A struct object. If given, then the parameters of the model are + read from the object. Otherwise, the model tries to read the + parameters from the given `model_path`. (default = None) + create_left_hand_pose: bool, optional + Flag for creating a member variable for the pose of the left + hand. (default = True) + left_hand_pose: torch.tensor, optional, BxP + The default value for the left hand pose member variable. + (default = None) + create_right_hand_pose: bool, optional + Flag for creating a member variable for the pose of the right + hand. (default = True) + right_hand_pose: torch.tensor, optional, BxP + The default value for the right hand pose member variable. + (default = None) + num_pca_comps: int, optional + The number of PCA components to use for each hand. + (default = 6) + flat_hand_mean: bool, optional + If False, then the pose of the hand is initialized to False. + batch_size: int, optional + The batch size used for creating the member variables + gender: str, optional + Which gender to load + dtype: torch.dtype, optional + The data type for the created variables + vertex_ids: dict, optional + A dictionary containing the indices of the extra vertices that + will be selected + ''' + + self.num_pca_comps = num_pca_comps + # If no data structure is passed, then load the data from the given + # model folder + if data_struct is None: + # Load the model + if osp.isdir(model_path): + model_fn = 'SMPLH_{}.{ext}'.format(gender.upper(), ext=ext) + smplh_path = os.path.join(model_path, model_fn) + else: + smplh_path = model_path + assert osp.exists(smplh_path), 'Path {} does not exist!'.format( + smplh_path) + + if ext == 'pkl': + with open(smplh_path, 'rb') as smplh_file: + model_data = pickle.load(smplh_file, encoding='latin1') + elif ext == 'npz': + model_data = np.load(smplh_path, allow_pickle=True) + else: + raise ValueError('Unknown extension: {}'.format(ext)) + data_struct = Struct(**model_data) + + if vertex_ids is None: + vertex_ids = VERTEX_IDS['smplh'] + + super(SMPLH, self).__init__( + model_path=model_path, + data_struct=data_struct, + batch_size=batch_size, vertex_ids=vertex_ids, gender=gender, + use_compressed=use_compressed, dtype=dtype, ext=ext) + + self.use_pca = use_pca + self.num_pca_comps = num_pca_comps + self.flat_hand_mean = flat_hand_mean + + left_hand_components = data_struct.hands_componentsl[:num_pca_comps] + right_hand_components = data_struct.hands_componentsr[:num_pca_comps] + + self.np_left_hand_components = left_hand_components + self.np_right_hand_components = right_hand_components + if self.use_pca: + self.register_buffer( + 'left_hand_components', + torch.tensor(left_hand_components, dtype=dtype)) + self.register_buffer( + 'right_hand_components', + torch.tensor(right_hand_components, dtype=dtype)) + + if self.flat_hand_mean: + left_hand_mean = np.zeros_like(data_struct.hands_meanl) + else: + left_hand_mean = data_struct.hands_meanl + + if self.flat_hand_mean: + right_hand_mean = np.zeros_like(data_struct.hands_meanr) + else: + right_hand_mean = data_struct.hands_meanr + + self.register_buffer('left_hand_mean', + to_tensor(left_hand_mean, dtype=self.dtype)) + self.register_buffer('right_hand_mean', + to_tensor(right_hand_mean, dtype=self.dtype)) + + # Create the buffers for the pose of the left hand + hand_pose_dim = num_pca_comps if use_pca else 3 * self.NUM_HAND_JOINTS + if create_left_hand_pose: + if left_hand_pose is None: + default_lhand_pose = torch.zeros([batch_size, hand_pose_dim], + dtype=dtype) + else: + default_lhand_pose = torch.tensor(left_hand_pose, dtype=dtype) + + left_hand_pose_param = nn.Parameter(default_lhand_pose, + requires_grad=True) + self.register_parameter('left_hand_pose', + left_hand_pose_param) + + if create_right_hand_pose: + if right_hand_pose is None: + default_rhand_pose = torch.zeros([batch_size, hand_pose_dim], + dtype=dtype) + else: + default_rhand_pose = torch.tensor(right_hand_pose, dtype=dtype) + + right_hand_pose_param = nn.Parameter(default_rhand_pose, + requires_grad=True) + self.register_parameter('right_hand_pose', + right_hand_pose_param) + + # Create the buffer for the mean pose. + pose_mean_tensor = self.create_mean_pose( + data_struct, flat_hand_mean=flat_hand_mean) + if not torch.is_tensor(pose_mean_tensor): + pose_mean_tensor = torch.tensor(pose_mean_tensor, dtype=dtype) + self.register_buffer('pose_mean', pose_mean_tensor) + + def create_mean_pose(self, data_struct, flat_hand_mean=False): + # Create the array for the mean pose. If flat_hand is false, then use + # the mean that is given by the data, rather than the flat open hand + global_orient_mean = torch.zeros([3], dtype=self.dtype) + body_pose_mean = torch.zeros([self.NUM_BODY_JOINTS * 3], + dtype=self.dtype) + + pose_mean = torch.cat([global_orient_mean, body_pose_mean, + self.left_hand_mean, + self.right_hand_mean], dim=0) + return pose_mean + + def name(self) -> str: + return 'SMPL+H' + + def extra_repr(self): + msg = super(SMPLH, self).extra_repr() + msg = [msg] + if self.use_pca: + msg.append(f'Number of PCA components: {self.num_pca_comps}') + msg.append(f'Flat hand mean: {self.flat_hand_mean}') + return '\n'.join(msg) + + def forward( + self, + betas: Optional[Tensor] = None, + global_orient: Optional[Tensor] = None, + body_pose: Optional[Tensor] = None, + left_hand_pose: Optional[Tensor] = None, + right_hand_pose: Optional[Tensor] = None, + transl: Optional[Tensor] = None, + return_verts: bool = True, + return_full_pose: bool = False, + pose2rot: bool = True + ) -> SMPLHOutput: + ''' + ''' + + # If no shape and pose parameters are passed along, then use the + # ones from the module + global_orient = (global_orient if global_orient is not None else + self.global_orient) + body_pose = body_pose if body_pose is not None else self.body_pose + betas = betas if betas is not None else self.betas + left_hand_pose = (left_hand_pose if left_hand_pose is not None else + self.left_hand_pose) + right_hand_pose = (right_hand_pose if right_hand_pose is not None else + self.right_hand_pose) + + apply_trans = transl is not None or hasattr(self, 'transl') + if transl is None: + if hasattr(self, 'transl'): + transl = self.transl + + if self.use_pca: + left_hand_pose = torch.einsum( + 'bi,ij->bj', [left_hand_pose, self.left_hand_components]) + right_hand_pose = torch.einsum( + 'bi,ij->bj', [right_hand_pose, self.right_hand_components]) + + full_pose = torch.cat([global_orient, body_pose, + left_hand_pose, + right_hand_pose], dim=1) + full_pose += self.pose_mean + + vertices, joints = lbs(betas, full_pose, self.v_template, + self.shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, pose2rot=pose2rot) + + # Add any extra joints that might be needed + joints = self.vertex_joint_selector(vertices, joints) + if self.joint_mapper is not None: + joints = self.joint_mapper(joints) + + if apply_trans: + joints += transl.unsqueeze(dim=1) + vertices += transl.unsqueeze(dim=1) + + output = SMPLHOutput(vertices=vertices if return_verts else None, + faces=self.faces, + joints=joints, + betas=betas, + global_orient=global_orient, + body_pose=body_pose, + left_hand_pose=left_hand_pose, + right_hand_pose=right_hand_pose, + full_pose=full_pose if return_full_pose else None) + + return output + + +class SMPLHLayer(SMPLH): + + def __init__( + self, *args + ) -> None: + ''' SMPL+H as a layer model constructor + ''' + super(SMPLHLayer, self).__init__( + create_global_orient=False, + create_body_pose=False, + create_left_hand_pose=False, + create_right_hand_pose=False, + create_betas=False, + create_transl=False, + *args) + + def forward( + self, + betas: Optional[Tensor] = None, + global_orient: Optional[Tensor] = None, + body_pose: Optional[Tensor] = None, + left_hand_pose: Optional[Tensor] = None, + right_hand_pose: Optional[Tensor] = None, + transl: Optional[Tensor] = None, + return_verts: bool = True, + return_full_pose: bool = False, + pose2rot: bool = True + ) -> SMPLHOutput: + ''' Forward pass for the SMPL+H model + + Parameters + ---------- + global_orient: torch.tensor, optional, shape Bx3x3 + Global rotation of the body. Useful if someone wishes to + predicts this with an external model. It is expected to be in + rotation matrix format. (default=None) + betas: torch.tensor, optional, shape BxN_b + Shape parameters. For example, it can used if shape parameters + `betas` are predicted from some external model. + (default=None) + body_pose: torch.tensor, optional, shape BxJx3x3 + If given, ignore the member variable `body_pose` and use it + instead. For example, it can used if someone predicts the + pose of the body joints are predicted from some external model. + It should be a tensor that contains joint rotations in + rotation matrix format. (default=None) + left_hand_pose: torch.tensor, optional, shape Bx15x3x3 + If given, contains the pose of the left hand. + It should be a tensor that contains joint rotations in + rotation matrix format. (default=None) + right_hand_pose: torch.tensor, optional, shape Bx15x3x3 + If given, contains the pose of the right hand. + It should be a tensor that contains joint rotations in + rotation matrix format. (default=None) + transl: torch.tensor, optional, shape Bx3 + Translation vector of the body. + For example, it can used if the translation + `transl` is predicted from some external model. + (default=None) + return_verts: bool, optional + Return the vertices. (default=True) + return_full_pose: bool, optional + Returns the full axis-angle pose vector (default=False) + + Returns + ------- + ''' + model_vars = [betas, global_orient, body_pose, transl, left_hand_pose, + right_hand_pose] + batch_size = 1 + for var in model_vars: + if var is None: + continue + batch_size = max(batch_size, len(var)) + device, dtype = self.shapedirs.device, self.shapedirs.dtype + if global_orient is None: + global_orient = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous() + if body_pose is None: + body_pose = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, 21, -1, -1).contiguous() + if left_hand_pose is None: + left_hand_pose = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, 15, -1, -1).contiguous() + if right_hand_pose is None: + right_hand_pose = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, 15, -1, -1).contiguous() + if betas is None: + betas = torch.zeros([batch_size, self.num_betas], + dtype=dtype, device=device) + if transl is None: + transl = torch.zeros([batch_size, 3], dtype=dtype, device=device) + + # Concatenate all pose vectors + full_pose = torch.cat( + [global_orient.reshape(-1, 1, 3, 3), + body_pose.reshape(-1, self.NUM_BODY_JOINTS, 3, 3), + left_hand_pose.reshape(-1, self.NUM_HAND_JOINTS, 3, 3), + right_hand_pose.reshape(-1, self.NUM_HAND_JOINTS, 3, 3)], + dim=1) + + vertices, joints = lbs(betas, full_pose, self.v_template, + self.shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, pose2rot=False) + + # Add any extra joints that might be needed + joints = self.vertex_joint_selector(vertices, joints) + if self.joint_mapper is not None: + joints = self.joint_mapper(joints) + + if transl is not None: + joints += transl.unsqueeze(dim=1) + vertices += transl.unsqueeze(dim=1) + + output = SMPLHOutput(vertices=vertices if return_verts else None, + joints=joints, + betas=betas, + global_orient=global_orient, + body_pose=body_pose, + left_hand_pose=left_hand_pose, + right_hand_pose=right_hand_pose, + full_pose=full_pose if return_full_pose else None) + + return output + + +class SMPLX(SMPLH): + ''' + SMPL-X (SMPL eXpressive) is a unified body model, with shape parameters + trained jointly for the face, hands and body. + SMPL-X uses standard vertex based linear blend skinning with learned + corrective blend shapes, has N=10475 vertices and K=54 joints, + which includes joints for the neck, jaw, eyeballs and fingers. + ''' + + NUM_BODY_JOINTS = SMPLH.NUM_BODY_JOINTS + NUM_HAND_JOINTS = 15 + NUM_FACE_JOINTS = 3 + NUM_JOINTS = NUM_BODY_JOINTS + 2 * NUM_HAND_JOINTS + NUM_FACE_JOINTS + EXPRESSION_SPACE_DIM = 100 + NECK_IDX = 12 + + def __init__( + self, model_path: str, + num_expression_coeffs: int = 10, + create_expression: bool = True, + expression: Optional[Tensor] = None, + create_jaw_pose: bool = True, + jaw_pose: Optional[Tensor] = None, + create_leye_pose: bool = True, + leye_pose: Optional[Tensor] = None, + create_reye_pose=True, + reye_pose: Optional[Tensor] = None, + use_face_contour: bool = False, + batch_size: int = 1, + gender: str = 'neutral', + dtype=torch.float32, + ext: str = 'npz' + ) -> None: + ''' SMPLX model constructor + + Parameters + ---------- + model_path: str + The path to the folder or to the file where the model + parameters are stored + num_expression_coeffs: int, optional + Number of expression components to use + (default = 10). + create_expression: bool, optional + Flag for creating a member variable for the expression space + (default = True). + expression: torch.tensor, optional, Bx10 + The default value for the expression member variable. + (default = None) + create_jaw_pose: bool, optional + Flag for creating a member variable for the jaw pose. + (default = False) + jaw_pose: torch.tensor, optional, Bx3 + The default value for the jaw pose variable. + (default = None) + create_leye_pose: bool, optional + Flag for creating a member variable for the left eye pose. + (default = False) + leye_pose: torch.tensor, optional, Bx10 + The default value for the left eye pose variable. + (default = None) + create_reye_pose: bool, optional + Flag for creating a member variable for the right eye pose. + (default = False) + reye_pose: torch.tensor, optional, Bx10 + The default value for the right eye pose variable. + (default = None) + use_face_contour: bool, optional + Whether to compute the keypoints that form the facial contour + batch_size: int, optional + The batch size used for creating the member variables + gender: str, optional + Which gender to load + dtype: torch.dtype + The data type for the created variables + ''' + + # Load the model + if osp.isdir(model_path): + model_fn = 'SMPLX_{}.{ext}'.format(gender.upper(), ext=ext) + smplx_path = os.path.join(model_path, model_fn) + else: + smplx_path = model_path + assert osp.exists(smplx_path), 'Path {} does not exist!'.format( + smplx_path) + + if ext == 'pkl': + with open(smplx_path, 'rb') as smplx_file: + model_data = pickle.load(smplx_file, encoding='latin1') + elif ext == 'npz': + model_data = np.load(smplx_path, allow_pickle=True) + else: + raise ValueError('Unknown extension: {}'.format(ext)) + + data_struct = Struct(**model_data) + + super(SMPLX, self).__init__( + model_path=model_path, + data_struct=data_struct, + dtype=dtype, + batch_size=batch_size, + vertex_ids=VERTEX_IDS['smplx'], + gender=gender, ext=ext + ) + + lmk_faces_idx = data_struct.lmk_faces_idx + self.register_buffer('lmk_faces_idx', + torch.tensor(lmk_faces_idx, dtype=torch.long)) + lmk_bary_coords = data_struct.lmk_bary_coords + self.register_buffer('lmk_bary_coords', + torch.tensor(lmk_bary_coords, dtype=dtype)) + + self.use_face_contour = use_face_contour + if self.use_face_contour: + dynamic_lmk_faces_idx = data_struct.dynamic_lmk_faces_idx + dynamic_lmk_faces_idx = torch.tensor( + dynamic_lmk_faces_idx, + dtype=torch.long) + self.register_buffer('dynamic_lmk_faces_idx', + dynamic_lmk_faces_idx) + + dynamic_lmk_bary_coords = data_struct.dynamic_lmk_bary_coords + dynamic_lmk_bary_coords = torch.tensor( + dynamic_lmk_bary_coords, dtype=dtype) + self.register_buffer('dynamic_lmk_bary_coords', + dynamic_lmk_bary_coords) + + neck_kin_chain = find_joint_kin_chain(self.NECK_IDX, self.parents) + self.register_buffer( + 'neck_kin_chain', + torch.tensor(neck_kin_chain, dtype=torch.long)) + + if create_jaw_pose: + if jaw_pose is None: + default_jaw_pose = torch.zeros([batch_size, 3], dtype=dtype) + else: + default_jaw_pose = torch.tensor(jaw_pose, dtype=dtype) + jaw_pose_param = nn.Parameter(default_jaw_pose, + requires_grad=True) + self.register_parameter('jaw_pose', jaw_pose_param) + + if create_leye_pose: + if leye_pose is None: + default_leye_pose = torch.zeros([batch_size, 3], dtype=dtype) + else: + default_leye_pose = torch.tensor(leye_pose, dtype=dtype) + leye_pose_param = nn.Parameter(default_leye_pose, + requires_grad=True) + self.register_parameter('leye_pose', leye_pose_param) + + if create_reye_pose: + if reye_pose is None: + default_reye_pose = torch.zeros([batch_size, 3], dtype=dtype) + else: + default_reye_pose = torch.tensor(reye_pose, dtype=dtype) + reye_pose_param = nn.Parameter(default_reye_pose, + requires_grad=True) + self.register_parameter('reye_pose', reye_pose_param) + + shapedirs = data_struct.shapedirs + if len(shapedirs.shape) < 3: + shapedirs = shapedirs[:, :, None] + if (shapedirs.shape[-1] < self.SHAPE_SPACE_DIM + + self.EXPRESSION_SPACE_DIM): + print(f'WARNING: You are using a {self.name()} model, with only' + ' 10 shape and 10 expression coefficients.') + expr_start_idx = 10 + expr_end_idx = 20 + num_expression_coeffs = min(num_expression_coeffs, 10) + else: + expr_start_idx = self.SHAPE_SPACE_DIM + expr_end_idx = self.SHAPE_SPACE_DIM + num_expression_coeffs + num_expression_coeffs = min( + num_expression_coeffs, self.EXPRESSION_SPACE_DIM) + + self._num_expression_coeffs = num_expression_coeffs + + expr_dirs = shapedirs[:, :, expr_start_idx:expr_end_idx] + self.register_buffer( + 'expr_dirs', to_tensor(to_np(expr_dirs), dtype=dtype)) + + if create_expression: + if expression is None: + default_expression = torch.zeros( + [batch_size, self.num_expression_coeffs], dtype=dtype) + else: + default_expression = torch.tensor(expression, dtype=dtype) + expression_param = nn.Parameter(default_expression, + requires_grad=True) + self.register_parameter('expression', expression_param) + + def name(self) -> str: + return 'SMPL-X' + + @property + def num_expression_coeffs(self): + return self._num_expression_coeffs + + def create_mean_pose(self, data_struct, flat_hand_mean=False): + # Create the array for the mean pose. If flat_hand is false, then use + # the mean that is given by the data, rather than the flat open hand + global_orient_mean = torch.zeros([3], dtype=self.dtype) + body_pose_mean = torch.zeros([self.NUM_BODY_JOINTS * 3], + dtype=self.dtype) + jaw_pose_mean = torch.zeros([3], dtype=self.dtype) + leye_pose_mean = torch.zeros([3], dtype=self.dtype) + reye_pose_mean = torch.zeros([3], dtype=self.dtype) + + pose_mean = np.concatenate([global_orient_mean, body_pose_mean, + jaw_pose_mean, + leye_pose_mean, reye_pose_mean, + self.left_hand_mean, self.right_hand_mean], + axis=0) + + return pose_mean + + def extra_repr(self): + msg = super(SMPLX, self).extra_repr() + msg = [ + msg, + f'Number of Expression Coefficients: {self.num_expression_coeffs}' + ] + return '\n'.join(msg) + + def forward( + self, + betas: Optional[Tensor] = None, + global_orient: Optional[Tensor] = None, + body_pose: Optional[Tensor] = None, + left_hand_pose: Optional[Tensor] = None, + right_hand_pose: Optional[Tensor] = None, + transl: Optional[Tensor] = None, + expression: Optional[Tensor] = None, + jaw_pose: Optional[Tensor] = None, + leye_pose: Optional[Tensor] = None, + reye_pose: Optional[Tensor] = None, + return_verts: bool = True, + return_full_pose: bool = False, + pose2rot: bool = True + ) -> SMPLXOutput: + ''' + Forward pass for the SMPLX model + + Parameters + ---------- + global_orient: torch.tensor, optional, shape Bx3 + If given, ignore the member variable and use it as the global + rotation of the body. Useful if someone wishes to predicts this + with an external model. (default=None) + betas: torch.tensor, optional, shape BxN_b + If given, ignore the member variable `betas` and use it + instead. For example, it can used if shape parameters + `betas` are predicted from some external model. + (default=None) + expression: torch.tensor, optional, shape BxN_e + If given, ignore the member variable `expression` and use it + instead. For example, it can used if expression parameters + `expression` are predicted from some external model. + body_pose: torch.tensor, optional, shape Bx(J*3) + If given, ignore the member variable `body_pose` and use it + instead. For example, it can used if someone predicts the + pose of the body joints are predicted from some external model. + It should be a tensor that contains joint rotations in + axis-angle format. (default=None) + left_hand_pose: torch.tensor, optional, shape BxP + If given, ignore the member variable `left_hand_pose` and + use this instead. It should either contain PCA coefficients or + joint rotations in axis-angle format. + right_hand_pose: torch.tensor, optional, shape BxP + If given, ignore the member variable `right_hand_pose` and + use this instead. It should either contain PCA coefficients or + joint rotations in axis-angle format. + jaw_pose: torch.tensor, optional, shape Bx3 + If given, ignore the member variable `jaw_pose` and + use this instead. It should either joint rotations in + axis-angle format. + transl: torch.tensor, optional, shape Bx3 + If given, ignore the member variable `transl` and use it + instead. For example, it can used if the translation + `transl` is predicted from some external model. + (default=None) + return_verts: bool, optional + Return the vertices. (default=True) + return_full_pose: bool, optional + Returns the full axis-angle pose vector (default=False) + + Returns + ------- + output: ModelOutput + A named tuple of type `ModelOutput` + ''' + + # If no shape and pose parameters are passed along, then use the + # ones from the module + global_orient = (global_orient if global_orient is not None else + self.global_orient) + body_pose = body_pose if body_pose is not None else self.body_pose + betas = betas if betas is not None else self.betas + + left_hand_pose = (left_hand_pose if left_hand_pose is not None else + self.left_hand_pose) + right_hand_pose = (right_hand_pose if right_hand_pose is not None else + self.right_hand_pose) + jaw_pose = jaw_pose if jaw_pose is not None else self.jaw_pose + leye_pose = leye_pose if leye_pose is not None else self.leye_pose + reye_pose = reye_pose if reye_pose is not None else self.reye_pose + expression = expression if expression is not None else self.expression + + apply_trans = transl is not None or hasattr(self, 'transl') + if transl is None: + if hasattr(self, 'transl'): + transl = self.transl + + if self.use_pca: + left_hand_pose = torch.einsum( + 'bi,ij->bj', [left_hand_pose, self.left_hand_components]) + right_hand_pose = torch.einsum( + 'bi,ij->bj', [right_hand_pose, self.right_hand_components]) + + full_pose = torch.cat([global_orient, body_pose, + jaw_pose, leye_pose, reye_pose, + left_hand_pose, + right_hand_pose], dim=1) + + # Add the mean pose of the model. Does not affect the body, only the + # hands when flat_hand_mean == False + full_pose += self.pose_mean + + batch_size = max(betas.shape[0], global_orient.shape[0], + body_pose.shape[0]) + # Concatenate the shape and expression coefficients + scale = int(batch_size / betas.shape[0]) + if scale > 1: + betas = betas.expand(scale, -1) + shape_components = torch.cat([betas, expression], dim=-1) + + shapedirs = torch.cat([self.shapedirs, self.expr_dirs], dim=-1) + + vertices, joints = lbs(shape_components, full_pose, self.v_template, + shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, pose2rot=pose2rot, + ) + + lmk_faces_idx = self.lmk_faces_idx.unsqueeze( + dim=0).expand(batch_size, -1).contiguous() + lmk_bary_coords = self.lmk_bary_coords.unsqueeze(dim=0).repeat( + self.batch_size, 1, 1) + if self.use_face_contour: + lmk_idx_and_bcoords = find_dynamic_lmk_idx_and_bcoords( + vertices, full_pose, self.dynamic_lmk_faces_idx, + self.dynamic_lmk_bary_coords, + self.neck_kin_chain, + pose2rot=True, + ) + dyn_lmk_faces_idx, dyn_lmk_bary_coords = lmk_idx_and_bcoords + + lmk_faces_idx = torch.cat([lmk_faces_idx, + dyn_lmk_faces_idx], 1) + lmk_bary_coords = torch.cat( + [lmk_bary_coords.expand(batch_size, -1, -1), + dyn_lmk_bary_coords], 1) + + landmarks = vertices2landmarks(vertices, self.faces_tensor, + lmk_faces_idx, + lmk_bary_coords) + + # Add any extra joints that might be needed + joints = self.vertex_joint_selector(vertices, joints) + # Add the landmarks to the joints + joints = torch.cat([joints, landmarks], dim=1) + # Map the joints to the current dataset + + if self.joint_mapper is not None: + joints = self.joint_mapper(joints=joints, vertices=vertices) + + if apply_trans: + joints += transl.unsqueeze(dim=1) + vertices += transl.unsqueeze(dim=1) + + output = SMPLXOutput(vertices=vertices if return_verts else None, + faces=self.faces, + joints=joints, + betas=betas, + expression=expression, + global_orient=global_orient, + body_pose=body_pose, + left_hand_pose=left_hand_pose, + right_hand_pose=right_hand_pose, + jaw_pose=jaw_pose, + full_pose=full_pose if return_full_pose else None) + return output + + +class SMPLXLayer(SMPLX): + def __init__( + self, + *args + ) -> None: + # Just create a SMPLX module without any member variables + super(SMPLXLayer, self).__init__( + create_global_orient=False, + create_body_pose=False, + create_left_hand_pose=False, + create_right_hand_pose=False, + create_jaw_pose=False, + create_leye_pose=False, + create_reye_pose=False, + create_betas=False, + create_expression=False, + create_transl=False, + *args, + ) + + def forward( + self, + betas: Optional[Tensor] = None, + global_orient: Optional[Tensor] = None, + body_pose: Optional[Tensor] = None, + left_hand_pose: Optional[Tensor] = None, + right_hand_pose: Optional[Tensor] = None, + transl: Optional[Tensor] = None, + expression: Optional[Tensor] = None, + jaw_pose: Optional[Tensor] = None, + leye_pose: Optional[Tensor] = None, + reye_pose: Optional[Tensor] = None, + return_verts: bool = True, + return_full_pose: bool = False + ) -> SMPLXOutput: + ''' + Forward pass for the SMPLX model + + Parameters + ---------- + global_orient: torch.tensor, optional, shape Bx3x3 + If given, ignore the member variable and use it as the global + rotation of the body. Useful if someone wishes to predicts this + with an external model. It is expected to be in rotation matrix + format. (default=None) + betas: torch.tensor, optional, shape BxN_b + If given, ignore the member variable `betas` and use it + instead. For example, it can used if shape parameters + `betas` are predicted from some external model. + (default=None) + expression: torch.tensor, optional, shape BxN_e + Expression coefficients. + For example, it can used if expression parameters + `expression` are predicted from some external model. + body_pose: torch.tensor, optional, shape BxJx3x3 + If given, ignore the member variable `body_pose` and use it + instead. For example, it can used if someone predicts the + pose of the body joints are predicted from some external model. + It should be a tensor that contains joint rotations in + rotation matrix format. (default=None) + left_hand_pose: torch.tensor, optional, shape Bx15x3x3 + If given, contains the pose of the left hand. + It should be a tensor that contains joint rotations in + rotation matrix format. (default=None) + right_hand_pose: torch.tensor, optional, shape Bx15x3x3 + If given, contains the pose of the right hand. + It should be a tensor that contains joint rotations in + rotation matrix format. (default=None) + jaw_pose: torch.tensor, optional, shape Bx3x3 + Jaw pose. It should either joint rotations in + rotation matrix format. + transl: torch.tensor, optional, shape Bx3 + Translation vector of the body. + For example, it can used if the translation + `transl` is predicted from some external model. + (default=None) + return_verts: bool, optional + Return the vertices. (default=True) + return_full_pose: bool, optional + Returns the full pose vector (default=False) + Returns + ------- + output: ModelOutput + A data class that contains the posed vertices and joints + ''' + device, dtype = self.shapedirs.device, self.shapedirs.dtype + + model_vars = [betas, global_orient, body_pose, transl, + expression, left_hand_pose, right_hand_pose, jaw_pose] + batch_size = 1 + for var in model_vars: + if var is None: + continue + batch_size = max(batch_size, len(var)) + + if global_orient is None: + global_orient = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous() + if body_pose is None: + body_pose = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand( + batch_size, self.NUM_BODY_JOINTS, -1, -1).contiguous() + if left_hand_pose is None: + left_hand_pose = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, 15, -1, -1).contiguous() + if right_hand_pose is None: + right_hand_pose = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, 15, -1, -1).contiguous() + if jaw_pose is None: + jaw_pose = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous() + if leye_pose is None: + leye_pose = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous() + if reye_pose is None: + reye_pose = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous() + if expression is None: + expression = torch.zeros([batch_size, self.num_expression_coeffs], + dtype=dtype, device=device) + if betas is None: + betas = torch.zeros([batch_size, self.num_betas], + dtype=dtype, device=device) + if transl is None: + transl = torch.zeros([batch_size, 3], dtype=dtype, device=device) + + # Concatenate all pose vectors + full_pose = torch.cat( + [global_orient.reshape(-1, 1, 3, 3), + body_pose.reshape(-1, self.NUM_BODY_JOINTS, 3, 3), + jaw_pose.reshape(-1, 1, 3, 3), + leye_pose.reshape(-1, 1, 3, 3), + reye_pose.reshape(-1, 1, 3, 3), + left_hand_pose.reshape(-1, self.NUM_HAND_JOINTS, 3, 3), + right_hand_pose.reshape(-1, self.NUM_HAND_JOINTS, 3, 3)], + dim=1) + shape_components = torch.cat([betas, expression], dim=-1) + + shapedirs = torch.cat([self.shapedirs, self.expr_dirs], dim=-1) + + vertices, joints = lbs(shape_components, full_pose, self.v_template, + shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, pose2rot=False) + + lmk_faces_idx = self.lmk_faces_idx.unsqueeze( + dim=0).expand(batch_size, -1).contiguous() + lmk_bary_coords = self.lmk_bary_coords.unsqueeze(dim=0).repeat( + batch_size, 1, 1) + if self.use_face_contour: + lmk_idx_and_bcoords = find_dynamic_lmk_idx_and_bcoords( + vertices, full_pose, + self.dynamic_lmk_faces_idx, + self.dynamic_lmk_bary_coords, + self.neck_kin_chain, + pose2rot=False, + ) + dyn_lmk_faces_idx, dyn_lmk_bary_coords = lmk_idx_and_bcoords + + lmk_faces_idx = torch.cat([lmk_faces_idx, dyn_lmk_faces_idx], 1) + lmk_bary_coords = torch.cat( + [lmk_bary_coords.expand(batch_size, -1, -1), + dyn_lmk_bary_coords], 1) + + landmarks = vertices2landmarks(vertices, self.faces_tensor, + lmk_faces_idx, + lmk_bary_coords) + + # Add any extra joints that might be needed + joints = self.vertex_joint_selector(vertices, joints) + # Add the landmarks to the joints + joints = torch.cat([joints, landmarks], dim=1) + # Map the joints to the current dataset + + if self.joint_mapper is not None: + joints = self.joint_mapper(joints=joints, vertices=vertices) + + if transl is not None: + joints += transl.unsqueeze(dim=1) + vertices += transl.unsqueeze(dim=1) + + output = SMPLXOutput(vertices=vertices if return_verts else None, + joints=joints, + betas=betas, + expression=expression, + global_orient=global_orient, + body_pose=body_pose, + left_hand_pose=left_hand_pose, + right_hand_pose=right_hand_pose, + jaw_pose=jaw_pose, + transl=transl, + full_pose=full_pose if return_full_pose else None) + return output + + +class MANO(SMPL): + # The hand joints are replaced by MANO + NUM_BODY_JOINTS = 1 + NUM_HAND_JOINTS = 15 + NUM_JOINTS = NUM_BODY_JOINTS + NUM_HAND_JOINTS + + def __init__( + self, + model_path: str, + is_rhand: bool = True, + data_struct: Optional[Struct] = None, + create_hand_pose: bool = True, + hand_pose: Optional[Tensor] = None, + use_pca: bool = True, + num_pca_comps: int = 6, + flat_hand_mean: bool = False, + batch_size: int = 1, + dtype=torch.float32, + vertex_ids=None, + use_compressed: bool = True, + ext: str = 'pkl' + ) -> None: + ''' MANO model constructor + + Parameters + ---------- + model_path: str + The path to the folder or to the file where the model + parameters are stored + data_struct: Strct + A struct object. If given, then the parameters of the model are + read from the object. Otherwise, the model tries to read the + parameters from the given `model_path`. (default = None) + create_hand_pose: bool, optional + Flag for creating a member variable for the pose of the right + hand. (default = True) + hand_pose: torch.tensor, optional, BxP + The default value for the right hand pose member variable. + (default = None) + num_pca_comps: int, optional + The number of PCA components to use for each hand. + (default = 6) + flat_hand_mean: bool, optional + If False, then the pose of the hand is initialized to False. + batch_size: int, optional + The batch size used for creating the member variables + dtype: torch.dtype, optional + The data type for the created variables + vertex_ids: dict, optional + A dictionary containing the indices of the extra vertices that + will be selected + ''' + + self.num_pca_comps = num_pca_comps + self.is_rhand = is_rhand + # If no data structure is passed, then load the data from the given + # model folder + if data_struct is None: + # Load the model + if osp.isdir(model_path): + model_fn = 'MANO_{}.{ext}'.format( + 'RIGHT' if is_rhand else 'LEFT', ext=ext) + mano_path = os.path.join(model_path, model_fn) + else: + mano_path = model_path + self.is_rhand = True if 'RIGHT' in os.path.basename( + model_path) else False + assert osp.exists(mano_path), 'Path {} does not exist!'.format( + mano_path) + + if ext == 'pkl': + with open(mano_path, 'rb') as mano_file: + model_data = pickle.load(mano_file, encoding='latin1') + elif ext == 'npz': + model_data = np.load(mano_path, allow_pickle=True) + else: + raise ValueError('Unknown extension: {}'.format(ext)) + data_struct = Struct(**model_data) + + if vertex_ids is None: + vertex_ids = VERTEX_IDS['smplh'] + + super(MANO, self).__init__( + model_path=model_path, data_struct=data_struct, + batch_size=batch_size, vertex_ids=vertex_ids, + use_compressed=use_compressed, dtype=dtype, ext=ext) + + # add only MANO tips to the extra joints + self.vertex_joint_selector.extra_joints_idxs = to_tensor( + list(VERTEX_IDS['mano'].values()), dtype=torch.long) + + self.use_pca = use_pca + self.num_pca_comps = num_pca_comps + if self.num_pca_comps == 45: + self.use_pca = False + self.flat_hand_mean = flat_hand_mean + + hand_components = data_struct.hands_components[:num_pca_comps] + + self.np_hand_components = hand_components + + if self.use_pca: + self.register_buffer( + 'hand_components', + torch.tensor(hand_components, dtype=dtype)) + + if self.flat_hand_mean: + hand_mean = np.zeros_like(data_struct.hands_mean) + else: + hand_mean = data_struct.hands_mean + + self.register_buffer('hand_mean', + to_tensor(hand_mean, dtype=self.dtype)) + + # Create the buffers for the pose of the left hand + hand_pose_dim = num_pca_comps if use_pca else 3 * self.NUM_HAND_JOINTS + if create_hand_pose: + if hand_pose is None: + default_hand_pose = torch.zeros([batch_size, hand_pose_dim], + dtype=dtype) + else: + default_hand_pose = torch.tensor(hand_pose, dtype=dtype) + + hand_pose_param = nn.Parameter(default_hand_pose, + requires_grad=True) + self.register_parameter('hand_pose', + hand_pose_param) + + # Create the buffer for the mean pose. + pose_mean = self.create_mean_pose( + data_struct, flat_hand_mean=flat_hand_mean) + pose_mean_tensor = pose_mean.clone().to(dtype) + # pose_mean_tensor = torch.tensor(pose_mean, dtype=dtype) + self.register_buffer('pose_mean', pose_mean_tensor) + + def name(self) -> str: + return 'MANO' + + def create_mean_pose(self, data_struct, flat_hand_mean=False): + # Create the array for the mean pose. If flat_hand is false, then use + # the mean that is given by the data, rather than the flat open hand + global_orient_mean = torch.zeros([3], dtype=self.dtype) + pose_mean = torch.cat([global_orient_mean, self.hand_mean], dim=0) + return pose_mean + + def extra_repr(self): + msg = [super(MANO, self).extra_repr()] + if self.use_pca: + msg.append(f'Number of PCA components: {self.num_pca_comps}') + msg.append(f'Flat hand mean: {self.flat_hand_mean}') + return '\n'.join(msg) + + def forward( + self, + betas: Optional[Tensor] = None, + global_orient: Optional[Tensor] = None, + hand_pose: Optional[Tensor] = None, + transl: Optional[Tensor] = None, + return_verts: bool = True, + return_full_pose: bool = False + ) -> MANOOutput: + ''' Forward pass for the MANO model + ''' + # If no shape and pose parameters are passed along, then use the + # ones from the module + global_orient = (global_orient if global_orient is not None else + self.global_orient) + betas = betas if betas is not None else self.betas + hand_pose = (hand_pose if hand_pose is not None else + self.hand_pose) + + apply_trans = transl is not None or hasattr(self, 'transl') + if transl is None: + if hasattr(self, 'transl'): + transl = self.transl + + if self.use_pca: + hand_pose = torch.einsum( + 'bi,ij->bj', [hand_pose, self.hand_components]) + + full_pose = torch.cat([global_orient, hand_pose], dim=1) + full_pose += self.pose_mean + + vertices, joints = lbs(betas, full_pose, self.v_template, + self.shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, pose2rot=True, + ) + + # # Add pre-selected extra joints that might be needed + # joints = self.vertex_joint_selector(vertices, joints) + + if self.joint_mapper is not None: + joints = self.joint_mapper(joints) + + if apply_trans: + joints = joints + transl.unsqueeze(dim=1) + vertices = vertices + transl.unsqueeze(dim=1) + + output = MANOOutput(vertices=vertices if return_verts else None, + joints=joints if return_verts else None, + betas=betas, + global_orient=global_orient, + hand_pose=hand_pose, + full_pose=full_pose if return_full_pose else None) + + return output + + +class MANOLayer(MANO): + def __init__(self, *args) -> None: + ''' MANO as a layer model constructor + ''' + super(MANOLayer, self).__init__( + create_global_orient=False, + create_hand_pose=False, + create_betas=False, + create_transl=False, + *args) + + def name(self) -> str: + return 'MANO' + + def forward( + self, + betas: Optional[Tensor] = None, + global_orient: Optional[Tensor] = None, + hand_pose: Optional[Tensor] = None, + transl: Optional[Tensor] = None, + return_verts: bool = True, + return_full_pose: bool = False + ) -> MANOOutput: + ''' Forward pass for the MANO model + ''' + device, dtype = self.shapedirs.device, self.shapedirs.dtype + if global_orient is None: + batch_size = 1 + global_orient = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous() + else: + batch_size = global_orient.shape[0] + if hand_pose is None: + hand_pose = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, 15, -1, -1).contiguous() + if betas is None: + betas = torch.zeros( + [batch_size, self.num_betas], dtype=dtype, device=device) + if transl is None: + transl = torch.zeros([batch_size, 3], dtype=dtype, device=device) + + full_pose = torch.cat([global_orient, hand_pose], dim=1) + vertices, joints = lbs(betas, full_pose, self.v_template, + self.shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, pose2rot=False) + + if self.joint_mapper is not None: + joints = self.joint_mapper(joints) + + if transl is not None: + joints = joints + transl.unsqueeze(dim=1) + vertices = vertices + transl.unsqueeze(dim=1) + + output = MANOOutput( + vertices=vertices if return_verts else None, + joints=joints if return_verts else None, + betas=betas, + global_orient=global_orient, + hand_pose=hand_pose, + full_pose=full_pose if return_full_pose else None) + + return output + + +class FLAME(SMPL): + NUM_JOINTS = 5 + SHAPE_SPACE_DIM = 300 + EXPRESSION_SPACE_DIM = 100 + NECK_IDX = 0 + + def __init__( + self, + model_path: str, + data_struct=None, + num_expression_coeffs=10, + create_expression: bool = True, + expression: Optional[Tensor] = None, + create_neck_pose: bool = True, + neck_pose: Optional[Tensor] = None, + create_jaw_pose: bool = True, + jaw_pose: Optional[Tensor] = None, + create_leye_pose: bool = True, + leye_pose: Optional[Tensor] = None, + create_reye_pose=True, + reye_pose: Optional[Tensor] = None, + use_face_contour=False, + batch_size: int = 1, + gender: str = 'neutral', + dtype: torch.dtype = torch.float32, + ext='pkl' + ) -> None: + ''' FLAME model constructor + + Parameters + ---------- + model_path: str + The path to the folder or to the file where the model + parameters are stored + num_expression_coeffs: int, optional + Number of expression components to use + (default = 10). + create_expression: bool, optional + Flag for creating a member variable for the expression space + (default = True). + expression: torch.tensor, optional, Bx10 + The default value for the expression member variable. + (default = None) + create_neck_pose: bool, optional + Flag for creating a member variable for the neck pose. + (default = False) + neck_pose: torch.tensor, optional, Bx3 + The default value for the neck pose variable. + (default = None) + create_jaw_pose: bool, optional + Flag for creating a member variable for the jaw pose. + (default = False) + jaw_pose: torch.tensor, optional, Bx3 + The default value for the jaw pose variable. + (default = None) + create_leye_pose: bool, optional + Flag for creating a member variable for the left eye pose. + (default = False) + leye_pose: torch.tensor, optional, Bx10 + The default value for the left eye pose variable. + (default = None) + create_reye_pose: bool, optional + Flag for creating a member variable for the right eye pose. + (default = False) + reye_pose: torch.tensor, optional, Bx10 + The default value for the right eye pose variable. + (default = None) + use_face_contour: bool, optional + Whether to compute the keypoints that form the facial contour + batch_size: int, optional + The batch size used for creating the member variables + gender: str, optional + Which gender to load + dtype: torch.dtype + The data type for the created variables + ''' + model_fn = f'FLAME_{gender.upper()}.{ext}' + flame_path = os.path.join(model_path, model_fn) + assert osp.exists(flame_path), 'Path {} does not exist!'.format( + flame_path) + if ext == 'npz': + file_data = np.load(flame_path, allow_pickle=True) + elif ext == 'pkl': + with open(flame_path, 'rb') as smpl_file: + file_data = pickle.load(smpl_file, encoding='latin1') + else: + raise ValueError('Unknown extension: {}'.format(ext)) + data_struct = Struct(**file_data) + + super(FLAME, self).__init__( + model_path=model_path, + data_struct=data_struct, + dtype=dtype, + batch_size=batch_size, + gender=gender, + ext=ext) + + self.use_face_contour = use_face_contour + + self.vertex_joint_selector.extra_joints_idxs = to_tensor( + [], dtype=torch.long) + + if create_neck_pose: + if neck_pose is None: + default_neck_pose = torch.zeros([batch_size, 3], dtype=dtype) + else: + default_neck_pose = torch.tensor(neck_pose, dtype=dtype) + neck_pose_param = nn.Parameter( + default_neck_pose, requires_grad=True) + self.register_parameter('neck_pose', neck_pose_param) + + if create_jaw_pose: + if jaw_pose is None: + default_jaw_pose = torch.zeros([batch_size, 3], dtype=dtype) + else: + default_jaw_pose = torch.tensor(jaw_pose, dtype=dtype) + jaw_pose_param = nn.Parameter(default_jaw_pose, + requires_grad=True) + self.register_parameter('jaw_pose', jaw_pose_param) + + if create_leye_pose: + if leye_pose is None: + default_leye_pose = torch.zeros([batch_size, 3], dtype=dtype) + else: + default_leye_pose = torch.tensor(leye_pose, dtype=dtype) + leye_pose_param = nn.Parameter(default_leye_pose, + requires_grad=True) + self.register_parameter('leye_pose', leye_pose_param) + + if create_reye_pose: + if reye_pose is None: + default_reye_pose = torch.zeros([batch_size, 3], dtype=dtype) + else: + default_reye_pose = torch.tensor(reye_pose, dtype=dtype) + reye_pose_param = nn.Parameter(default_reye_pose, + requires_grad=True) + self.register_parameter('reye_pose', reye_pose_param) + + shapedirs = data_struct.shapedirs + if len(shapedirs.shape) < 3: + shapedirs = shapedirs[:, :, None] + if (shapedirs.shape[-1] < self.SHAPE_SPACE_DIM + + self.EXPRESSION_SPACE_DIM): + print(f'WARNING: You are using a {self.name()} model, with only' + ' 10 shape and 10 expression coefficients.') + expr_start_idx = 10 + expr_end_idx = 20 + num_expression_coeffs = min(num_expression_coeffs, 10) + else: + expr_start_idx = self.SHAPE_SPACE_DIM + expr_end_idx = self.SHAPE_SPACE_DIM + num_expression_coeffs + num_expression_coeffs = min( + num_expression_coeffs, self.EXPRESSION_SPACE_DIM) + + self._num_expression_coeffs = num_expression_coeffs + + expr_dirs = shapedirs[:, :, expr_start_idx:expr_end_idx] + self.register_buffer( + 'expr_dirs', to_tensor(to_np(expr_dirs), dtype=dtype)) + + if create_expression: + if expression is None: + default_expression = torch.zeros( + [batch_size, self.num_expression_coeffs], dtype=dtype) + else: + default_expression = torch.tensor(expression, dtype=dtype) + expression_param = nn.Parameter(default_expression, + requires_grad=True) + self.register_parameter('expression', expression_param) + + # The pickle file that contains the barycentric coordinates for + # regressing the landmarks + landmark_bcoord_filename = osp.join( + model_path, 'flame_static_embedding.pkl') + + with open(landmark_bcoord_filename, 'rb') as fp: + landmarks_data = pickle.load(fp, encoding='latin1') + + lmk_faces_idx = landmarks_data['lmk_face_idx'].astype(np.int64) + self.register_buffer('lmk_faces_idx', + torch.tensor(lmk_faces_idx, dtype=torch.long)) + lmk_bary_coords = landmarks_data['lmk_b_coords'] + self.register_buffer('lmk_bary_coords', + torch.tensor(lmk_bary_coords, dtype=dtype)) + if self.use_face_contour: + face_contour_path = os.path.join( + model_path, 'flame_dynamic_embedding.npy') + contour_embeddings = np.load(face_contour_path, + allow_pickle=True, + encoding='latin1')[()] + + dynamic_lmk_faces_idx = np.array( + contour_embeddings['lmk_face_idx'], dtype=np.int64) + dynamic_lmk_faces_idx = torch.tensor( + dynamic_lmk_faces_idx, + dtype=torch.long) + self.register_buffer('dynamic_lmk_faces_idx', + dynamic_lmk_faces_idx) + + dynamic_lmk_b_coords = torch.tensor( + contour_embeddings['lmk_b_coords'], dtype=dtype) + self.register_buffer( + 'dynamic_lmk_bary_coords', dynamic_lmk_b_coords) + + neck_kin_chain = find_joint_kin_chain(self.NECK_IDX, self.parents) + self.register_buffer( + 'neck_kin_chain', + torch.tensor(neck_kin_chain, dtype=torch.long)) + + @property + def num_expression_coeffs(self): + return self._num_expression_coeffs + + def name(self) -> str: + return 'FLAME' + + def extra_repr(self): + msg = [ + super(FLAME, self).extra_repr(), + f'Number of Expression Coefficients: {self.num_expression_coeffs}', + f'Use face contour: {self.use_face_contour}', + ] + return '\n'.join(msg) + + def forward( + self, + betas: Optional[Tensor] = None, + global_orient: Optional[Tensor] = None, + neck_pose: Optional[Tensor] = None, + transl: Optional[Tensor] = None, + expression: Optional[Tensor] = None, + jaw_pose: Optional[Tensor] = None, + leye_pose: Optional[Tensor] = None, + reye_pose: Optional[Tensor] = None, + return_verts: bool = True, + return_full_pose: bool = False, + pose2rot: bool = True + ) -> FLAMEOutput: + ''' + Forward pass for the SMPLX model + + Parameters + ---------- + global_orient: torch.tensor, optional, shape Bx3 + If given, ignore the member variable and use it as the global + rotation of the body. Useful if someone wishes to predicts this + with an external model. (default=None) + betas: torch.tensor, optional, shape Bx10 + If given, ignore the member variable `betas` and use it + instead. For example, it can used if shape parameters + `betas` are predicted from some external model. + (default=None) + expression: torch.tensor, optional, shape Bx10 + If given, ignore the member variable `expression` and use it + instead. For example, it can used if expression parameters + `expression` are predicted from some external model. + jaw_pose: torch.tensor, optional, shape Bx3 + If given, ignore the member variable `jaw_pose` and + use this instead. It should either joint rotations in + axis-angle format. + jaw_pose: torch.tensor, optional, shape Bx3 + If given, ignore the member variable `jaw_pose` and + use this instead. It should either joint rotations in + axis-angle format. + transl: torch.tensor, optional, shape Bx3 + If given, ignore the member variable `transl` and use it + instead. For example, it can used if the translation + `transl` is predicted from some external model. + (default=None) + return_verts: bool, optional + Return the vertices. (default=True) + return_full_pose: bool, optional + Returns the full axis-angle pose vector (default=False) + + Returns + ------- + output: ModelOutput + A named tuple of type `ModelOutput` + ''' + + # If no shape and pose parameters are passed along, then use the + # ones from the module + global_orient = (global_orient if global_orient is not None else + self.global_orient) + jaw_pose = jaw_pose if jaw_pose is not None else self.jaw_pose + neck_pose = neck_pose if neck_pose is not None else self.neck_pose + + leye_pose = leye_pose if leye_pose is not None else self.leye_pose + reye_pose = reye_pose if reye_pose is not None else self.reye_pose + + betas = betas if betas is not None else self.betas + expression = expression if expression is not None else self.expression + + apply_trans = transl is not None or hasattr(self, 'transl') + if transl is None: + if hasattr(self, 'transl'): + transl = self.transl + + full_pose = torch.cat( + [global_orient, neck_pose, jaw_pose, leye_pose, reye_pose], dim=1) + + batch_size = max(betas.shape[0], global_orient.shape[0], + jaw_pose.shape[0]) + # Concatenate the shape and expression coefficients + scale = int(batch_size / betas.shape[0]) + if scale > 1: + betas = betas.expand(scale, -1) + shape_components = torch.cat([betas, expression], dim=-1) + shapedirs = torch.cat([self.shapedirs, self.expr_dirs], dim=-1) + + vertices, joints = lbs(shape_components, full_pose, self.v_template, + shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, pose2rot=pose2rot, + ) + + lmk_faces_idx = self.lmk_faces_idx.unsqueeze( + dim=0).expand(batch_size, -1).contiguous() + lmk_bary_coords = self.lmk_bary_coords.unsqueeze(dim=0).repeat( + self.batch_size, 1, 1) + if self.use_face_contour: + lmk_idx_and_bcoords = find_dynamic_lmk_idx_and_bcoords( + vertices, full_pose, self.dynamic_lmk_faces_idx, + self.dynamic_lmk_bary_coords, + self.neck_kin_chain, + pose2rot=True, + ) + dyn_lmk_faces_idx, dyn_lmk_bary_coords = lmk_idx_and_bcoords + lmk_faces_idx = torch.cat([lmk_faces_idx, + dyn_lmk_faces_idx], 1) + lmk_bary_coords = torch.cat( + [lmk_bary_coords.expand(batch_size, -1, -1), + dyn_lmk_bary_coords], 1) + + landmarks = vertices2landmarks(vertices, self.faces_tensor, + lmk_faces_idx, + lmk_bary_coords) + + # Add any extra joints that might be needed + joints = self.vertex_joint_selector(vertices, joints) + # Add the landmarks to the joints + joints = torch.cat([joints, landmarks], dim=1) + + # Map the joints to the current dataset + if self.joint_mapper is not None: + joints = self.joint_mapper(joints=joints, vertices=vertices) + + if apply_trans: + joints += transl.unsqueeze(dim=1) + vertices += transl.unsqueeze(dim=1) + + output = FLAMEOutput(vertices=vertices if return_verts else None, + joints=joints, + betas=betas, + expression=expression, + global_orient=global_orient, + neck_pose=neck_pose, + jaw_pose=jaw_pose, + full_pose=full_pose if return_full_pose else None) + return output + + +class FLAMELayer(FLAME): + def __init__(self, *args) -> None: + ''' FLAME as a layer model constructor ''' + super(FLAMELayer, self).__init__( + create_betas=False, + create_expression=False, + create_global_orient=False, + create_neck_pose=False, + create_jaw_pose=False, + create_leye_pose=False, + create_reye_pose=False, + *args) + + def forward( + self, + betas: Optional[Tensor] = None, + global_orient: Optional[Tensor] = None, + neck_pose: Optional[Tensor] = None, + transl: Optional[Tensor] = None, + expression: Optional[Tensor] = None, + jaw_pose: Optional[Tensor] = None, + leye_pose: Optional[Tensor] = None, + reye_pose: Optional[Tensor] = None, + return_verts: bool = True, + return_full_pose: bool = False, + pose2rot: bool = True + ) -> FLAMEOutput: + ''' + Forward pass for the SMPLX model + + Parameters + ---------- + global_orient: torch.tensor, optional, shape Bx3x3 + Global rotation of the body. Useful if someone wishes to + predicts this with an external model. It is expected to be in + rotation matrix format. (default=None) + betas: torch.tensor, optional, shape BxN_b + Shape parameters. For example, it can used if shape parameters + `betas` are predicted from some external model. + (default=None) + expression: torch.tensor, optional, shape BxN_e + If given, ignore the member variable `expression` and use it + instead. For example, it can used if expression parameters + `expression` are predicted from some external model. + jaw_pose: torch.tensor, optional, shape Bx3x3 + Jaw pose. It should either joint rotations in + rotation matrix format. + transl: torch.tensor, optional, shape Bx3 + Translation vector of the body. + For example, it can used if the translation + `transl` is predicted from some external model. + (default=None) + return_verts: bool, optional + Return the vertices. (default=True) + return_full_pose: bool, optional + Returns the full axis-angle pose vector (default=False) + + Returns + ------- + output: ModelOutput + A named tuple of type `ModelOutput` + ''' + device, dtype = self.shapedirs.device, self.shapedirs.dtype + if global_orient is None: + batch_size = 1 + global_orient = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous() + else: + batch_size = global_orient.shape[0] + if neck_pose is None: + neck_pose = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, 1, -1, -1).contiguous() + if jaw_pose is None: + jaw_pose = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous() + if leye_pose is None: + leye_pose = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous() + if reye_pose is None: + reye_pose = torch.eye(3, device=device, dtype=dtype).view( + 1, 1, 3, 3).expand(batch_size, -1, -1, -1).contiguous() + if betas is None: + betas = torch.zeros([batch_size, self.num_betas], + dtype=dtype, device=device) + if expression is None: + expression = torch.zeros([batch_size, self.num_expression_coeffs], + dtype=dtype, device=device) + if transl is None: + transl = torch.zeros([batch_size, 3], dtype=dtype, device=device) + + full_pose = torch.cat( + [global_orient, neck_pose, jaw_pose, leye_pose, reye_pose], dim=1) + + shape_components = torch.cat([betas, expression], dim=-1) + shapedirs = torch.cat([self.shapedirs, self.expr_dirs], dim=-1) + + vertices, joints = lbs(shape_components, full_pose, self.v_template, + shapedirs, self.posedirs, + self.J_regressor, self.parents, + self.lbs_weights, pose2rot=False, + ) + + lmk_faces_idx = self.lmk_faces_idx.unsqueeze( + dim=0).expand(batch_size, -1).contiguous() + lmk_bary_coords = self.lmk_bary_coords.unsqueeze(dim=0).repeat( + self.batch_size, 1, 1) + if self.use_face_contour: + lmk_idx_and_bcoords = find_dynamic_lmk_idx_and_bcoords( + vertices, full_pose, self.dynamic_lmk_faces_idx, + self.dynamic_lmk_bary_coords, + self.neck_kin_chain, + pose2rot=False, + ) + dyn_lmk_faces_idx, dyn_lmk_bary_coords = lmk_idx_and_bcoords + lmk_faces_idx = torch.cat([lmk_faces_idx, + dyn_lmk_faces_idx], 1) + lmk_bary_coords = torch.cat( + [lmk_bary_coords.expand(batch_size, -1, -1), + dyn_lmk_bary_coords], 1) + + landmarks = vertices2landmarks(vertices, self.faces_tensor, + lmk_faces_idx, + lmk_bary_coords) + + # Add any extra joints that might be needed + joints = self.vertex_joint_selector(vertices, joints) + # Add the landmarks to the joints + joints = torch.cat([joints, landmarks], dim=1) + + # Map the joints to the current dataset + if self.joint_mapper is not None: + joints = self.joint_mapper(joints=joints, vertices=vertices) + + joints += transl.unsqueeze(dim=1) + vertices += transl.unsqueeze(dim=1) + + output = FLAMEOutput(vertices=vertices if return_verts else None, + joints=joints, + betas=betas, + expression=expression, + global_orient=global_orient, + neck_pose=neck_pose, + jaw_pose=jaw_pose, + full_pose=full_pose if return_full_pose else None) + return output + + +def build_layer( + model_path: str, + model_type: str = 'smpl' +) -> Union[SMPLLayer, SMPLHLayer, SMPLXLayer, MANOLayer, FLAMELayer]: + ''' Method for creating a model from a path and a model type + + Parameters + ---------- + model_path: str + Either the path to the model you wish to load or a folder, + where each subfolder contains the differents types, i.e.: + model_path: + | + |-- smpl + |-- SMPL_FEMALE + |-- SMPL_NEUTRAL + |-- SMPL_MALE + |-- smplh + |-- SMPLH_FEMALE + |-- SMPLH_MALE + |-- smplx + |-- SMPLX_FEMALE + |-- SMPLX_NEUTRAL + |-- SMPLX_MALE + |-- mano + |-- MANO RIGHT + |-- MANO LEFT + |-- flame + |-- FLAME_FEMALE + |-- FLAME_MALE + |-- FLAME_NEUTRAL + + model_type: str, optional + When model_path is a folder, then this parameter specifies the + type of model to be loaded + **kwargs: dict + Keyword arguments + + Returns + ------- + body_model: nn.Module + The PyTorch module that implements the corresponding body model + Raises + ------ + ValueError: In case the model type is not one of SMPL, SMPLH, + SMPLX, MANO or FLAME + ''' + + if osp.isdir(model_path): + model_path = os.path.join(model_path, model_type) + else: + model_type = osp.basename(model_path).split('_')[0].lower() + + if model_type.lower() == 'smpl': + return SMPLLayer(model_path) + elif model_type.lower() == 'smplh': + return SMPLHLayer(model_path) + elif model_type.lower() == 'smplx': + return SMPLXLayer(model_path) + elif 'mano' in model_type.lower(): + return MANOLayer(model_path) + elif 'flame' in model_type.lower(): + return FLAMELayer(model_path) + else: + raise ValueError(f'Unknown model type {model_type}, exiting!') + + +def create( + model_path: str, + model_type: str = 'smpl' +) -> Union[SMPL, SMPLH, SMPLX, MANO, FLAME]: + ''' Method for creating a model from a path and a model type + + Parameters + ---------- + model_path: str + Either the path to the model you wish to load or a folder, + where each subfolder contains the differents types, i.e.: + model_path: + | + |-- smpl + |-- SMPL_FEMALE + |-- SMPL_NEUTRAL + |-- SMPL_MALE + |-- smplh + |-- SMPLH_FEMALE + |-- SMPLH_MALE + |-- smplx + |-- SMPLX_FEMALE + |-- SMPLX_NEUTRAL + |-- SMPLX_MALE + |-- mano + |-- MANO RIGHT + |-- MANO LEFT + + model_type: str, optional + When model_path is a folder, then this parameter specifies the + type of model to be loaded + **kwargs: dict + Keyword arguments + + Returns + ------- + body_model: nn.Module + The PyTorch module that implements the corresponding body model + Raises + ------ + ValueError: In case the model type is not one of SMPL, SMPLH, + SMPLX, MANO or FLAME + ''' + + # If it's a folder, assume + if osp.isdir(model_path): + model_path = os.path.join(model_path, model_type) + else: + model_type = osp.basename(model_path).split('_')[0].lower() + + if model_type.lower() == 'smpl': + return SMPL(model_path) + elif model_type.lower() == 'smplh': + return SMPLH(model_path) + elif model_type.lower() == 'smplx': + return SMPLX(model_path) + elif 'mano' in model_type.lower(): + return MANO(model_path) + elif 'flame' in model_type.lower(): + return FLAME(model_path) + else: + raise ValueError(f'Unknown model type {model_type}, exiting!') diff --git a/preprocessing/smplx/joint_names.py b/preprocessing/smplx/joint_names.py new file mode 100644 index 0000000000000000000000000000000000000000..0a3a10f8cef8b50075dc9f680459fc5d596a0013 --- /dev/null +++ b/preprocessing/smplx/joint_names.py @@ -0,0 +1,163 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +JOINT_NAMES = [ + 'pelvis', + 'left_hip', + 'right_hip', + 'spine1', + 'left_knee', + 'right_knee', + 'spine2', + 'left_ankle', + 'right_ankle', + 'spine3', + 'left_foot', + 'right_foot', + 'neck', + 'left_collar', + 'right_collar', + 'head', + 'left_shoulder', + 'right_shoulder', + 'left_elbow', + 'right_elbow', + 'left_wrist', + 'right_wrist', + 'jaw', + 'left_eye_smplhf', + 'right_eye_smplhf', + 'left_index1', + 'left_index2', + 'left_index3', + 'left_middle1', + 'left_middle2', + 'left_middle3', + 'left_pinky1', + 'left_pinky2', + 'left_pinky3', + 'left_ring1', + 'left_ring2', + 'left_ring3', + 'left_thumb1', + 'left_thumb2', + 'left_thumb3', + 'right_index1', + 'right_index2', + 'right_index3', + 'right_middle1', + 'right_middle2', + 'right_middle3', + 'right_pinky1', + 'right_pinky2', + 'right_pinky3', + 'right_ring1', + 'right_ring2', + 'right_ring3', + 'right_thumb1', + 'right_thumb2', + 'right_thumb3', + 'nose', + 'right_eye', + 'left_eye', + 'right_ear', + 'left_ear', + 'left_big_toe', + 'left_small_toe', + 'left_heel', + 'right_big_toe', + 'right_small_toe', + 'right_heel', + 'left_thumb', + 'left_index', + 'left_middle', + 'left_ring', + 'left_pinky', + 'right_thumb', + 'right_index', + 'right_middle', + 'right_ring', + 'right_pinky', + 'right_eye_brow1', + 'right_eye_brow2', + 'right_eye_brow3', + 'right_eye_brow4', + 'right_eye_brow5', + 'left_eye_brow5', + 'left_eye_brow4', + 'left_eye_brow3', + 'left_eye_brow2', + 'left_eye_brow1', + 'nose1', + 'nose2', + 'nose3', + 'nose4', + 'right_nose_2', + 'right_nose_1', + 'nose_middle', + 'left_nose_1', + 'left_nose_2', + 'right_eye1', + 'right_eye2', + 'right_eye3', + 'right_eye4', + 'right_eye5', + 'right_eye6', + 'left_eye4', + 'left_eye3', + 'left_eye2', + 'left_eye1', + 'left_eye6', + 'left_eye5', + 'right_mouth_1', + 'right_mouth_2', + 'right_mouth_3', + 'mouth_top', + 'left_mouth_3', + 'left_mouth_2', + 'left_mouth_1', + 'left_mouth_5', # 59 in OpenPose output + 'left_mouth_4', # 58 in OpenPose output + 'mouth_bottom', + 'right_mouth_4', + 'right_mouth_5', + 'right_lip_1', + 'right_lip_2', + 'lip_top', + 'left_lip_2', + 'left_lip_1', + 'left_lip_3', + 'lip_bottom', + 'right_lip_3', + # Face contour + 'right_contour_1', + 'right_contour_2', + 'right_contour_3', + 'right_contour_4', + 'right_contour_5', + 'right_contour_6', + 'right_contour_7', + 'right_contour_8', + 'contour_middle', + 'left_contour_8', + 'left_contour_7', + 'left_contour_6', + 'left_contour_5', + 'left_contour_4', + 'left_contour_3', + 'left_contour_2', + 'left_contour_1', +] diff --git a/preprocessing/smplx/lbs.py b/preprocessing/smplx/lbs.py new file mode 100644 index 0000000000000000000000000000000000000000..77289a48c3bdb2e8e85b8e8add3ebb198e480d46 --- /dev/null +++ b/preprocessing/smplx/lbs.py @@ -0,0 +1,401 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +from typing import Tuple, List +import numpy as np + +import torch +import torch.nn.functional as F + +from .utils import rot_mat_to_euler, Tensor + + +def find_dynamic_lmk_idx_and_bcoords( + vertices: Tensor, + pose: Tensor, + dynamic_lmk_faces_idx: Tensor, + dynamic_lmk_b_coords: Tensor, + neck_kin_chain: List[int], + pose2rot: bool = True, +) -> Tuple[Tensor, Tensor]: + ''' Compute the faces, barycentric coordinates for the dynamic landmarks + + + To do so, we first compute the rotation of the neck around the y-axis + and then use a pre-computed look-up table to find the faces and the + barycentric coordinates that will be used. + + Special thanks to Soubhik Sanyal (soubhik.sanyal@tuebingen.mpg.de) + for providing the original TensorFlow implementation and for the LUT. + + Parameters + ---------- + vertices: torch.tensor BxVx3, dtype = torch.float32 + The tensor of input vertices + pose: torch.tensor Bx(Jx3), dtype = torch.float32 + The current pose of the body model + dynamic_lmk_faces_idx: torch.tensor L, dtype = torch.long + The look-up table from neck rotation to faces + dynamic_lmk_b_coords: torch.tensor Lx3, dtype = torch.float32 + The look-up table from neck rotation to barycentric coordinates + neck_kin_chain: list + A python list that contains the indices of the joints that form the + kinematic chain of the neck. + dtype: torch.dtype, optional + + Returns + ------- + dyn_lmk_faces_idx: torch.tensor, dtype = torch.long + A tensor of size BxL that contains the indices of the faces that + will be used to compute the current dynamic landmarks. + dyn_lmk_b_coords: torch.tensor, dtype = torch.float32 + A tensor of size BxL that contains the indices of the faces that + will be used to compute the current dynamic landmarks. + ''' + + dtype = vertices.dtype + batch_size = vertices.shape[0] + + if pose2rot: + aa_pose = torch.index_select(pose.view(batch_size, -1, 3), 1, + neck_kin_chain) + rot_mats = batch_rodrigues( + aa_pose.view(-1, 3)).view(batch_size, -1, 3, 3) + else: + rot_mats = torch.index_select( + pose.view(batch_size, -1, 3, 3), 1, neck_kin_chain) + + rel_rot_mat = torch.eye( + 3, device=vertices.device, dtype=dtype).unsqueeze_(dim=0).repeat( + batch_size, 1, 1) + for idx in range(len(neck_kin_chain)): + rel_rot_mat = torch.bmm(rot_mats[:, idx], rel_rot_mat) + + y_rot_angle = torch.round( + torch.clamp(-rot_mat_to_euler(rel_rot_mat) * 180.0 / np.pi, + max=39)).to(dtype=torch.long) + neg_mask = y_rot_angle.lt(0).to(dtype=torch.long) + mask = y_rot_angle.lt(-39).to(dtype=torch.long) + neg_vals = mask * 78 + (1 - mask) * (39 - y_rot_angle) + y_rot_angle = (neg_mask * neg_vals + + (1 - neg_mask) * y_rot_angle) + + dyn_lmk_faces_idx = torch.index_select(dynamic_lmk_faces_idx, + 0, y_rot_angle) + dyn_lmk_b_coords = torch.index_select(dynamic_lmk_b_coords, + 0, y_rot_angle) + + return dyn_lmk_faces_idx, dyn_lmk_b_coords + + +def vertices2landmarks( + vertices: Tensor, + faces: Tensor, + lmk_faces_idx: Tensor, + lmk_bary_coords: Tensor +) -> Tensor: + ''' Calculates landmarks by barycentric interpolation + + Parameters + ---------- + vertices: torch.tensor BxVx3, dtype = torch.float32 + The tensor of input vertices + faces: torch.tensor Fx3, dtype = torch.long + The faces of the mesh + lmk_faces_idx: torch.tensor L, dtype = torch.long + The tensor with the indices of the faces used to calculate the + landmarks. + lmk_bary_coords: torch.tensor Lx3, dtype = torch.float32 + The tensor of barycentric coordinates that are used to interpolate + the landmarks + + Returns + ------- + landmarks: torch.tensor BxLx3, dtype = torch.float32 + The coordinates of the landmarks for each mesh in the batch + ''' + # Extract the indices of the vertices for each face + # BxLx3 + batch_size, num_verts = vertices.shape[:2] + device = vertices.device + + lmk_faces = torch.index_select(faces, 0, lmk_faces_idx.view(-1)).view( + batch_size, -1, 3) + + lmk_faces += torch.arange( + batch_size, dtype=torch.long, device=device).view(-1, 1, 1) * num_verts + + lmk_vertices = vertices.view(-1, 3)[lmk_faces].view( + batch_size, -1, 3, 3) + + landmarks = torch.einsum('blfi,blf->bli', [lmk_vertices, lmk_bary_coords]) + return landmarks + + +def lbs( + betas: Tensor, + pose: Tensor, + v_template: Tensor, + shapedirs: Tensor, + posedirs: Tensor, + J_regressor: Tensor, + parents: Tensor, + lbs_weights: Tensor, + pose2rot: bool = True, +) -> Tuple[Tensor, Tensor]: + ''' Performs Linear Blend Skinning with the given shape and pose parameters + + Parameters + ---------- + betas : torch.tensor BxNB + The tensor of shape parameters + pose : torch.tensor Bx(J + 1) * 3 + The pose parameters in axis-angle format + v_template torch.tensor BxVx3 + The template mesh that will be deformed + shapedirs : torch.tensor 1xNB + The tensor of PCA shape displacements + posedirs : torch.tensor Px(V * 3) + The pose PCA coefficients + J_regressor : torch.tensor JxV + The regressor array that is used to calculate the joints from + the position of the vertices + parents: torch.tensor J + The array that describes the kinematic tree for the model + lbs_weights: torch.tensor N x V x (J + 1) + The linear blend skinning weights that represent how much the + rotation matrix of each part affects each vertex + pose2rot: bool, optional + Flag on whether to convert the input pose tensor to rotation + matrices. The default value is True. If False, then the pose tensor + should already contain rotation matrices and have a size of + Bx(J + 1)x9 + dtype: torch.dtype, optional + + Returns + ------- + verts: torch.tensor BxVx3 + The vertices of the mesh after applying the shape and pose + displacements. + joints: torch.tensor BxJx3 + The joints of the model + ''' + + batch_size = max(betas.shape[0], pose.shape[0]) + device, dtype = betas.device, betas.dtype + + # Add shape contribution + v_shaped = v_template + blend_shapes(betas, shapedirs) + + # Get the joints + # NxJx3 array + J = vertices2joints(J_regressor, v_shaped) + + # 3. Add pose blend shapes + # N x J x 3 x 3 + ident = torch.eye(3, dtype=dtype, device=device) + if pose2rot: + rot_mats = batch_rodrigues(pose.view(-1, 3)).view( + [batch_size, -1, 3, 3]) + + pose_feature = (rot_mats[:, 1:, :, :] - ident).view([batch_size, -1]) + # (N x P) x (P, V * 3) -> N x V x 3 + pose_offsets = torch.matmul( + pose_feature, posedirs).view(batch_size, -1, 3) + else: + pose_feature = pose[:, 1:].view(batch_size, -1, 3, 3) - ident + rot_mats = pose.view(batch_size, -1, 3, 3) + + pose_offsets = torch.matmul(pose_feature.view(batch_size, -1), + posedirs).view(batch_size, -1, 3) + + v_posed = pose_offsets + v_shaped + # 4. Get the global joint location + J_transformed, A = batch_rigid_transform(rot_mats, J, parents, dtype=dtype) + + # 5. Do skinning: + # W is N x V x (J + 1) + W = lbs_weights.unsqueeze(dim=0).expand([batch_size, -1, -1]) + # (N x V x (J + 1)) x (N x (J + 1) x 16) + num_joints = J_regressor.shape[0] + T = torch.matmul(W, A.view(batch_size, num_joints, 16)) \ + .view(batch_size, -1, 4, 4) + + homogen_coord = torch.ones([batch_size, v_posed.shape[1], 1], + dtype=dtype, device=device) + v_posed_homo = torch.cat([v_posed, homogen_coord], dim=2) + v_homo = torch.matmul(T, torch.unsqueeze(v_posed_homo, dim=-1)) + + verts = v_homo[:, :, :3, 0] + + return verts, J_transformed + + +def vertices2joints(J_regressor: Tensor, vertices: Tensor) -> Tensor: + ''' Calculates the 3D joint locations from the vertices + + Parameters + ---------- + J_regressor : torch.tensor JxV + The regressor array that is used to calculate the joints from the + position of the vertices + vertices : torch.tensor BxVx3 + The tensor of mesh vertices + + Returns + ------- + torch.tensor BxJx3 + The location of the joints + ''' + + return torch.einsum('bik,ji->bjk', [vertices, J_regressor]) + + +def blend_shapes(betas: Tensor, shape_disps: Tensor) -> Tensor: + ''' Calculates the per vertex displacement due to the blend shapes + + + Parameters + ---------- + betas : torch.tensor Bx(num_betas) + Blend shape coefficients + shape_disps: torch.tensor Vx3x(num_betas) + Blend shapes + + Returns + ------- + torch.tensor BxVx3 + The per-vertex displacement due to shape deformation + ''' + + # Displacement[b, m, k] = sum_{l} betas[b, l] * shape_disps[m, k, l] + # i.e. Multiply each shape displacement by its corresponding beta and + # then sum them. + blend_shape = torch.einsum('bl,mkl->bmk', [betas, shape_disps]) + return blend_shape + + +def batch_rodrigues( + rot_vecs: Tensor, + epsilon: float = 1e-8, +) -> Tensor: + ''' Calculates the rotation matrices for a batch of rotation vectors + Parameters + ---------- + rot_vecs: torch.tensor Nx3 + array of N axis-angle vectors + Returns + ------- + R: torch.tensor Nx3x3 + The rotation matrices for the given axis-angle parameters + ''' + + batch_size = rot_vecs.shape[0] + device, dtype = rot_vecs.device, rot_vecs.dtype + + angle = torch.norm(rot_vecs + 1e-8, dim=1, keepdim=True) + rot_dir = rot_vecs / angle + + cos = torch.unsqueeze(torch.cos(angle), dim=1) + sin = torch.unsqueeze(torch.sin(angle), dim=1) + + # Bx1 arrays + rx, ry, rz = torch.split(rot_dir, 1, dim=1) + K = torch.zeros((batch_size, 3, 3), dtype=dtype, device=device) + + zeros = torch.zeros((batch_size, 1), dtype=dtype, device=device) + K = torch.cat([zeros, -rz, ry, rz, zeros, -rx, -ry, rx, zeros], dim=1) \ + .view((batch_size, 3, 3)) + + ident = torch.eye(3, dtype=dtype, device=device).unsqueeze(dim=0) + rot_mat = ident + sin * K + (1 - cos) * torch.bmm(K, K) + return rot_mat + + +def transform_mat(R: Tensor, t: Tensor) -> Tensor: + ''' Creates a batch of transformation matrices + Args: + - R: Bx3x3 array of a batch of rotation matrices + - t: Bx3x1 array of a batch of translation vectors + Returns: + - T: Bx4x4 Transformation matrix + ''' + # No padding left or right, only add an extra row + return torch.cat([F.pad(R, [0, 0, 0, 1]), + F.pad(t, [0, 0, 0, 1], value=1)], dim=2) + + +def batch_rigid_transform( + rot_mats: Tensor, + joints: Tensor, + parents: Tensor, + dtype=torch.float32 +) -> Tensor: + """ + Applies a batch of rigid transformations to the joints + + Parameters + ---------- + rot_mats : torch.tensor BxNx3x3 + Tensor of rotation matrices + joints : torch.tensor BxNx3 + Locations of joints + parents : torch.tensor BxN + The kinematic tree of each object + dtype : torch.dtype, optional: + The data type of the created tensors, the default is torch.float32 + + Returns + ------- + posed_joints : torch.tensor BxNx3 + The locations of the joints after applying the pose rotations + rel_transforms : torch.tensor BxNx4x4 + The relative (with respect to the root joint) rigid transformations + for all the joints + """ + + joints = torch.unsqueeze(joints, dim=-1) + + rel_joints = joints.clone() + rel_joints[:, 1:] -= joints[:, parents[1:]] + + transforms_mat = transform_mat( + rot_mats.reshape(-1, 3, 3), + rel_joints.reshape(-1, 3, 1)).reshape(-1, joints.shape[1], 4, 4) + + transform_chain = [transforms_mat[:, 0]] + for i in range(1, parents.shape[0]): + # Subtract the joint location at the rest pose + # No need for rotation, since it's identity when at rest + curr_res = torch.matmul(transform_chain[parents[i]], + transforms_mat[:, i]) + transform_chain.append(curr_res) + + transforms = torch.stack(transform_chain, dim=1) + + # The last column of the transformations contains the posed joints + posed_joints = transforms[:, :, :3, 3] + + joints_homogen = F.pad(joints, [0, 0, 0, 1]) + + rel_transforms = transforms - F.pad( + torch.matmul(transforms, joints_homogen), [3, 0, 0, 0, 0, 0, 0, 0]) + + return posed_joints, rel_transforms diff --git a/preprocessing/smplx/utils.py b/preprocessing/smplx/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..279605fbf4760f79cf2617e07c060fb213a51740 --- /dev/null +++ b/preprocessing/smplx/utils.py @@ -0,0 +1,126 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from typing import NewType, Union, Optional +from dataclasses import dataclass, asdict, fields +import numpy as np +import torch + +Tensor = NewType('Tensor', torch.Tensor) +Array = NewType('Array', np.ndarray) + + +@dataclass +class ModelOutput: + vertices: Optional[Tensor] = None + faces: Optional[Array] = None + joints: Optional[Tensor] = None + full_pose: Optional[Tensor] = None + global_orient: Optional[Tensor] = None + transl: Optional[Tensor] = None + + def __getitem__(self, key): + return getattr(self, key) + + def get(self, key, default=None): + return getattr(self, key, default) + + def __iter__(self): + return self.keys() + + def keys(self): + keys = [t.name for t in fields(self)] + return iter(keys) + + def values(self): + values = [getattr(self, t.name) for t in fields(self)] + return iter(values) + + def items(self): + data = [(t.name, getattr(self, t.name)) for t in fields(self)] + return iter(data) + + +@dataclass +class SMPLOutput(ModelOutput): + betas: Optional[Tensor] = None + body_pose: Optional[Tensor] = None + + +@dataclass +class SMPLHOutput(SMPLOutput): + left_hand_pose: Optional[Tensor] = None + right_hand_pose: Optional[Tensor] = None + transl: Optional[Tensor] = None + + +@dataclass +class SMPLXOutput(SMPLHOutput): + expression: Optional[Tensor] = None + jaw_pose: Optional[Tensor] = None + + +@dataclass +class MANOOutput(ModelOutput): + betas: Optional[Tensor] = None + hand_pose: Optional[Tensor] = None + + +@dataclass +class FLAMEOutput(ModelOutput): + betas: Optional[Tensor] = None + expression: Optional[Tensor] = None + jaw_pose: Optional[Tensor] = None + neck_pose: Optional[Tensor] = None + + +def find_joint_kin_chain(joint_id, kinematic_tree): + kin_chain = [] + curr_idx = joint_id + while curr_idx != -1: + kin_chain.append(curr_idx) + curr_idx = kinematic_tree[curr_idx] + return kin_chain + + +def to_tensor( + array: Union[Array, Tensor], dtype=torch.float32 +) -> Tensor: + if torch.is_tensor(array): + return array + else: + return torch.tensor(array, dtype=dtype) + + +class Struct(object): + def __init__(self, **kwargs): + for key, val in kwargs.items(): + setattr(self, key, val) + + +def to_np(array, dtype=np.float32): + if 'scipy.sparse' in str(type(array)): + array = array.todense() + return np.array(array, dtype=dtype) + + +def rot_mat_to_euler(rot_mats): + # Calculates rotation matrix to euler angles + # Careful for extreme cases of eular angles like [0.0, pi, 0.0] + + sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] + + rot_mats[:, 1, 0] * rot_mats[:, 1, 0]) + return torch.atan2(-rot_mats[:, 2, 0], sy) diff --git a/preprocessing/smplx/vertex_ids.py b/preprocessing/smplx/vertex_ids.py new file mode 100644 index 0000000000000000000000000000000000000000..0e7a4c36700f002da54a9e181eabbd47af2a95bc --- /dev/null +++ b/preprocessing/smplx/vertex_ids.py @@ -0,0 +1,77 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from __future__ import print_function +from __future__ import absolute_import +from __future__ import division + +# Joint name to vertex mapping. SMPL/SMPL-H/SMPL-X vertices that correspond to +# MSCOCO and OpenPose joints +vertex_ids = { + 'smplh': { + 'nose': 332, + 'reye': 6260, + 'leye': 2800, + 'rear': 4071, + 'lear': 583, + 'rthumb': 6191, + 'rindex': 5782, + 'rmiddle': 5905, + 'rring': 6016, + 'rpinky': 6133, + 'lthumb': 2746, + 'lindex': 2319, + 'lmiddle': 2445, + 'lring': 2556, + 'lpinky': 2673, + 'LBigToe': 3216, + 'LSmallToe': 3226, + 'LHeel': 3387, + 'RBigToe': 6617, + 'RSmallToe': 6624, + 'RHeel': 6787 + }, + 'smplx': { + 'nose': 9120, + 'reye': 9929, + 'leye': 9448, + 'rear': 616, + 'lear': 6, + 'rthumb': 8079, + 'rindex': 7669, + 'rmiddle': 7794, + 'rring': 7905, + 'rpinky': 8022, + 'lthumb': 5361, + 'lindex': 4933, + 'lmiddle': 5058, + 'lring': 5169, + 'lpinky': 5286, + 'LBigToe': 5770, + 'LSmallToe': 5780, + 'LHeel': 8846, + 'RBigToe': 8463, + 'RSmallToe': 8474, + 'RHeel': 8635 + }, + 'mano': { + 'thumb': 744, + 'index': 320, + 'middle': 443, + 'ring': 554, + 'pinky': 671, + } +} diff --git a/preprocessing/smplx/vertex_joint_selector.py b/preprocessing/smplx/vertex_joint_selector.py new file mode 100644 index 0000000000000000000000000000000000000000..4b8298bd5e087731f86c1c699703b5219e046c5c --- /dev/null +++ b/preprocessing/smplx/vertex_joint_selector.py @@ -0,0 +1,77 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import numpy as np + +import torch +import torch.nn as nn + +from .utils import to_tensor + + +class VertexJointSelector(nn.Module): + + def __init__(self, vertex_ids=None, + use_hands=True, + use_feet_keypoints=True, **kwargs): + super(VertexJointSelector, self).__init__() + + extra_joints_idxs = [] + + face_keyp_idxs = np.array([ + vertex_ids['nose'], + vertex_ids['reye'], + vertex_ids['leye'], + vertex_ids['rear'], + vertex_ids['lear']], dtype=np.int64) + + extra_joints_idxs = np.concatenate([extra_joints_idxs, + face_keyp_idxs]) + + if use_feet_keypoints: + feet_keyp_idxs = np.array([vertex_ids['LBigToe'], + vertex_ids['LSmallToe'], + vertex_ids['LHeel'], + vertex_ids['RBigToe'], + vertex_ids['RSmallToe'], + vertex_ids['RHeel']], dtype=np.int32) + + extra_joints_idxs = np.concatenate( + [extra_joints_idxs, feet_keyp_idxs]) + + if use_hands: + self.tip_names = ['thumb', 'index', 'middle', 'ring', 'pinky'] + + tips_idxs = [] + for hand_id in ['l', 'r']: + for tip_name in self.tip_names: + tips_idxs.append(vertex_ids[hand_id + tip_name]) + + extra_joints_idxs = np.concatenate( + [extra_joints_idxs, tips_idxs]) + + self.register_buffer('extra_joints_idxs', + to_tensor(extra_joints_idxs, dtype=torch.long)) + + def forward(self, vertices, joints): + extra_joints = torch.index_select(vertices, 1, self.extra_joints_idxs) + joints = torch.cat([joints, extra_joints], dim=1) + + return joints diff --git a/requirement.txt b/requirement.txt new file mode 100644 index 0000000000000000000000000000000000000000..8215136fd010b60480998c24830052a1f812b566 --- /dev/null +++ b/requirement.txt @@ -0,0 +1,11 @@ +cython==0.29.20 +chumpy==0.70 +scikit-image==0.18.1 +torch==1.9.1 +torchvision==0.10.1 +pytorch-lightning==1.5.7 +hydra-core==1.1.1 +wandb==0.13.1 +aitviewer +trimesh +setuptools==59.5.0 \ No newline at end of file diff --git a/setup_env_variables.ps1 b/setup_env_variables.ps1 new file mode 100644 index 0000000000000000000000000000000000000000..d4307c0630d69c7b1339b74a6d9a2242a5fd0c3f --- /dev/null +++ b/setup_env_variables.ps1 @@ -0,0 +1,12 @@ +# This file is used to setup the necessary environment variables, in particular if the code is ruun in a virtual environment +# This is supposed to be ran before the installation and setup of the other libraries and dependencies +# Possible issues that might occur if these variables are not set: +# PyTorch: might be installed by default without GPU support +# TorchVision: might be installed by default without GPU support +# PyTorch3D: might be installed by default without GPU support +# Cuda: might have compatibility issues with newer (or older) versions of Visual Studio +# Kaolin: might have compatibility issues with newer (or older) versions of Visual Studio + +$env:force_cuda = 1 +$env:VSINSTALLDIR = "C:\Program Files (x86)\Microsoft Visual Studio\2019\Community" +$env:VisualStudioVersion = "16.0" diff --git a/setup_kaolin.ps1 b/setup_kaolin.ps1 new file mode 100644 index 0000000000000000000000000000000000000000..75044be3bb1b7bac532e4ab6ef86dfee329672f6 --- /dev/null +++ b/setup_kaolin.ps1 @@ -0,0 +1,9 @@ +# This file is used to install kaolin (in this case version 0.10.0) +# It needs to be noted that kaolin has many requirements on the version of other libraries +# Possible issues of compatibility: cuda and Visual Studio version might need to be selected accordingly +# More details about the installation and requirements can be found at https://kaolin.readthedocs.io/en/v0.10.0/notes/installation.html + +$git clone --recursive https://github.com/NVIDIAGameWorks/kaolin +$cd kaolin +$git checkout v0.10.0 +$python setup.py develop \ No newline at end of file diff --git a/setup_pytorch3d.ps1 b/setup_pytorch3d.ps1 new file mode 100644 index 0000000000000000000000000000000000000000..00636d1936c584f3a72d4dba3819b463474ea2cd --- /dev/null +++ b/setup_pytorch3d.ps1 @@ -0,0 +1,8 @@ +# This file is used to install PyTorch3D +# It needs to be noted that PyTorch3D can be installed in different ways depending on the OS and the necessity of cuda support +# Possible issues of compatibility: cuda and Visual Studio version might need to be selected accordingly +# More details about the installation and requirements can be found at https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md + +$pip install "git+https://github.com/facebookresearch/pytorch3d.git@stable" +$cd pytorch3d +$python setup.py install \ No newline at end of file diff --git a/visualization/vis.py b/visualization/vis.py new file mode 100644 index 0000000000000000000000000000000000000000..a5a6715dc30c1f725b28915ae8caaf470a67f883 --- /dev/null +++ b/visualization/vis.py @@ -0,0 +1,52 @@ +import trimesh +from aitviewer.viewer import Viewer +from aitviewer.renderables.meshes import Meshes, VariableTopologyMeshes +import glob +import argparse + +def vis_dynamic(args): + vertices = [] + faces = [] + vertex_normals = [] + deformed_mesh_paths = sorted(glob.glob(f'{args.path}/*_deformed.ply')) + for deformed_mesh_path in deformed_mesh_paths: + mesh = trimesh.load(deformed_mesh_path, process=False) + # center the human + mesh.vertices = mesh.vertices - mesh.vertices.mean(axis=0) + vertices.append(mesh.vertices) + faces.append(mesh.faces) + vertex_normals.append(mesh.vertex_normals) + + meshes = VariableTopologyMeshes(vertices, + faces, + vertex_normals, + preload=True + ) + + meshes.norm_coloring = True + meshes.flat_shading = True + viewer = Viewer() + viewer.scene.add(meshes) + viewer.scene.origin.enabled = False + viewer.scene.floor.enabled = True + viewer.run() +def vis_static(args): + mesh = trimesh.load(args.path, process=False) + mesh = Meshes(mesh.vertices, mesh.faces, mesh.vertex_normals, name='mesh', flat_shading=True) + mesh.norm_coloring = True + viewer = Viewer() + viewer.scene.add(mesh) + viewer.scene.origin.enabled = False + viewer.scene.floor.enabled = True + viewer.run() +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='3D Visualization') + # static canonical mesh or dynamic sequence + parser.add_argument('--mode', type=str, help='mode: static or dynamic') + # mesh/meshes source + parser.add_argument('--path', type=str, help='path to the file') + args = parser.parse_args() + if args.mode == 'static': + vis_static(args) + elif args.mode == 'dynamic': + vis_dynamic(args) \ No newline at end of file