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Zero
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import numpy as np
import torch
import sys
import cv2
import gdown
from os.path import exists as file_exists, join
import torchvision.transforms as transforms
from .sort.nn_matching import NearestNeighborDistanceMetric
from .sort.detection import Detection
from .sort.tracker import Tracker
# from .deep.reid_model_factory import show_downloadeable_models, get_model_url, get_model_name
from torchreid.reid.utils import FeatureExtractor
from torchreid.reid.utils.tools import download_url
from .reid_multibackend import ReIDDetectMultiBackend
__all__ = ['StrongSORT']
class StrongSORT(object):
def __init__(self,
model_weights,
device,
fp16,
max_dist=0.2,
max_iou_distance=0.7,
max_age=70, n_init=3,
nn_budget=100,
mc_lambda=0.995,
ema_alpha=0.9
):
self.model = ReIDDetectMultiBackend(weights=model_weights, device=device, fp16=fp16)
self.max_dist = max_dist
metric = NearestNeighborDistanceMetric(
"cosine", self.max_dist, nn_budget)
self.tracker = Tracker(
metric, max_iou_distance=max_iou_distance, max_age=max_age, n_init=n_init)
def update(self, bbox_xywh, confidences, classes, ori_img):
self.height, self.width = ori_img.shape[:2]
# generate detections
features = self._get_features(bbox_xywh, ori_img)
bbox_tlwh = self._xywh_to_tlwh(bbox_xywh)
detections = [Detection(bbox_tlwh[i], conf, features[i]) for i, conf in enumerate(
confidences)]
# run on non-maximum supression
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
# update tracker
self.tracker.predict()
self.tracker.update(detections, classes, confidences)
# output bbox identities
outputs = []
for track in self.tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 1:
continue
box = track.to_tlwh()
x1, y1, x2, y2 = self._tlwh_to_xyxy(box)
track_id = track.track_id
class_id = track.class_id
conf = track.conf
outputs.append(np.array([x1, y1, x2, y2, track_id, class_id, conf]))
if len(outputs) > 0:
outputs = np.stack(outputs, axis=0)
return outputs
"""
TODO:
Convert bbox from xc_yc_w_h to xtl_ytl_w_h
Thanks JieChen91@github.com for reporting this bug!
"""
@staticmethod
def _xywh_to_tlwh(bbox_xywh):
if isinstance(bbox_xywh, np.ndarray):
bbox_tlwh = bbox_xywh.copy()
elif isinstance(bbox_xywh, torch.Tensor):
bbox_tlwh = bbox_xywh.clone()
bbox_tlwh[:, 0] = bbox_xywh[:, 0] - bbox_xywh[:, 2] / 2.
bbox_tlwh[:, 1] = bbox_xywh[:, 1] - bbox_xywh[:, 3] / 2.
return bbox_tlwh
def _xywh_to_xyxy(self, bbox_xywh):
x, y, w, h = bbox_xywh
x1 = max(int(x - w / 2), 0)
x2 = min(int(x + w / 2), self.width - 1)
y1 = max(int(y - h / 2), 0)
y2 = min(int(y + h / 2), self.height - 1)
return x1, y1, x2, y2
def _tlwh_to_xyxy(self, bbox_tlwh):
"""
TODO:
Convert bbox from xtl_ytl_w_h to xc_yc_w_h
Thanks JieChen91@github.com for reporting this bug!
"""
x, y, w, h = bbox_tlwh
x1 = max(int(x), 0)
x2 = min(int(x+w), self.width - 1)
y1 = max(int(y), 0)
y2 = min(int(y+h), self.height - 1)
return x1, y1, x2, y2
def increment_ages(self):
self.tracker.increment_ages()
def _xyxy_to_tlwh(self, bbox_xyxy):
x1, y1, x2, y2 = bbox_xyxy
t = x1
l = y1
w = int(x2 - x1)
h = int(y2 - y1)
return t, l, w, h
def _get_features(self, bbox_xywh, ori_img):
im_crops = []
for box in bbox_xywh:
x1, y1, x2, y2 = self._xywh_to_xyxy(box)
im = ori_img[y1:y2, x1:x2]
im_crops.append(im)
if im_crops:
features = self.model(im_crops)
else:
features = np.array([])
return features
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