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| import os | |
| import yaml | |
| import numpy as np | |
| from matplotlib import cm | |
| import gradio as gr | |
| import deeplabcut | |
| import dlclibrary | |
| import transformers | |
| from PIL import Image | |
| import requests | |
| from viz_utils import save_results_as_json, draw_keypoints_on_image, draw_bbox_w_text, save_results_only_dlc | |
| from detection_utils import predict_md, crop_animal_detections | |
| from ui_utils import gradio_inputs_for_MD_DLC, gradio_outputs_for_MD_DLC, gradio_description_and_examples | |
| from deeplabcut.utils import auxiliaryfunctions | |
| from dlclibrary.dlcmodelzoo.modelzoo_download import ( | |
| download_huggingface_model, | |
| MODELOPTIONS, | |
| ) | |
| # TESTING (passes) download the SuperAnimal models: | |
| #model = 'superanimal_topviewmouse' | |
| #train_dir = 'DLC_models/sa-tvm' | |
| #download_huggingface_model(model, train_dir) | |
| # grab demo data cooco cat: | |
| url = "http://images.cocodataset.org/val2017/000000039769.jpg" | |
| image = Image.open(requests.get(url, stream=True).raw) | |
| # megadetector and dlc model look up | |
| MD_models_dict = {'md_v5a': "MD_models/md_v5a.0.0.pt", # | |
| 'md_v5b': "MD_models/md_v5b.0.0.pt"} | |
| # DLC models target dirs | |
| DLC_models_dict = {'superanimal_topviewmouse': "DLC_models/sa-tvm", | |
| 'superanimal_quadruped': "DLC_models/sa-q", | |
| 'full_human': "DLC_models/DLC_human_dancing/"} | |
| ##################################################### | |
| def predict_pipeline(img_input, | |
| mega_model_input, | |
| dlc_model_input_str, | |
| flag_dlc_only, | |
| flag_show_str_labels, | |
| bbox_likelihood_th, | |
| kpts_likelihood_th, | |
| font_style, | |
| font_size, | |
| keypt_color, | |
| marker_size, | |
| ): | |
| if not flag_dlc_only: | |
| ############################################################ | |
| # ### Run Megadetector | |
| md_results = predict_md(img_input, | |
| MD_models_dict[mega_model_input], #mega_model_input, | |
| size=640) #Image.fromarray(results.imgs[0]) | |
| ################################################################ | |
| # Obtain animal crops for bboxes with confidence above th | |
| list_crops = crop_animal_detections(img_input, | |
| md_results, | |
| bbox_likelihood_th) | |
| ############################################################ | |
| ## Get DLC model and label map | |
| # If model is found: do not download (previous execution is likely within same day) | |
| # TODO: can we ask the user whether to reload dlc model if a directory is found? | |
| if os.path.isdir(DLC_models_dict[dlc_model_input_str]) and \ | |
| len(os.listdir(DLC_models_dict[dlc_model_input_str])) > 0: | |
| path_to_DLCmodel = DLC_models_dict[dlc_model_input_str] | |
| else: | |
| path_to_DLCmodel = download_huggingface_model(dlc_model_input_str, | |
| DLC_models_dict[dlc_model_input_str]) | |
| # extract map label ids to strings | |
| pose_cfg_path = os.path.join(DLC_models_dict[dlc_model_input_str], | |
| 'pose_cfg.yaml') | |
| with open(pose_cfg_path, "r") as stream: | |
| pose_cfg_dict = yaml.safe_load(stream) | |
| map_label_id_to_str = dict([(k,v) for k,v in zip([el[0] for el in pose_cfg_dict['all_joints']], # pose_cfg_dict['all_joints'] is a list of one-element lists, | |
| pose_cfg_dict['all_joints_names'])]) | |
| ######################################################### | |
| # Define user interface and launch | |
| inputs = gradio_inputs_for_MD_DLC(list(MD_models_dict.keys()), | |
| list(DLC_models_dict.keys())) | |
| outputs = gradio_outputs_for_MD_DLC() | |
| [gr_title, | |
| gr_description, | |
| examples] = gradio_description_and_examples() | |
| # launch | |
| demo = gr.Interface(predict_pipeline, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title=gr_title, | |
| description=gr_description, | |
| examples=examples, | |
| theme="huggingface") | |
| demo.launch(enable_queue=True, share=True) | |