完成目标追踪的开发和测试
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181
utils/deepsort.py
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181
utils/deepsort.py
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import os
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import torch
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from yolov5.models.common import DetectMultiBackend
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from yolov5.utils.torch_utils import select_device
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from yolov5.utils.dataloaders import LoadImages
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from yolov5.utils.general import check_img_size, non_max_suppression, cv2, scale_coords, xyxy2xywh
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from deep_sort.deep_sort import DeepSort
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class VideoTracker(object):
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def __init__(self, weights_pt, data, video_path, save_path, idx_to_class):
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self.video_path = video_path
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self.save_path = save_path
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self.idx_to_class = idx_to_class
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# 选择设备（CPU 或 GPU）
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device = select_device('cpu')
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self.vdo = cv2.VideoCapture()
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self.detector = DetectMultiBackend(weights_pt, device=device, dnn=False, data=data, fp16=False)
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self.deepsort = DeepSort(
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model_path="deep_sort/deep/checkpoint/ckpt.t7", # ReID 模型路径
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max_dist=0.2, # 外观特征匹配阈值（越小越严格）
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max_iou_distance=0.7, # 最大IoU距离阈值
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max_age=70, # 目标最大存活帧数（未匹配时保留的帧数）
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n_init=3 # 初始确认帧数（连续匹配到n_init次后确认跟踪）
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)
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self.class_names = self.detector.class_names
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def __enter__(self):
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self.vdo.open(self.video_path)
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self.im_width = int(self.vdo.get(cv2.CAP_PROP_FRAME_WIDTH))
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self.im_height = int(self.vdo.get(cv2.CAP_PROP_FRAME_HEIGHT))
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assert self.vdo.isOpened()
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if self.save_path:
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os.makedirs(self.args.save_path, exist_ok=True)
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# path of saved video and results
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self.save_video_path = os.path.join(self.save_path, "results.avi")
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self.save_results_path = os.path.join(self.save_path, "results.txt")
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# create video writer
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fourcc = cv2.VideoWriter_fourcc(*'MJPG')
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self.writer = cv2.VideoWriter(self.save_video_path, fourcc, 20, (self.im_width, self.im_height))
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return self
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def __exit__(self, exc_type, exc_value, exc_traceback):
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if exc_type:
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print(exc_type, exc_value, exc_traceback)
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def run(self):
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stride, names, pt = self.model.stride, self.model.names, self.model.pt
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imgsz = check_img_size((640, 640), s=stride) # check image size
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dataset = LoadImages(self.video_path, img_size=imgsz, stride=stride, auto=pt, vid_stride=1)
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bs = len(dataset)
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self.model.warmup(imgsz=(1 if pt or self.model.triton else bs, 3, *imgsz))
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for path, im, im0s, vid_cap, s in dataset:
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im = torch.from_numpy(im).to(self.model.device)
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im = im.half() if self.model.fp16 else im.float() # uint8 to fp16/32
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im /= 255 # 0 - 255 to 0.0 - 1.0
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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if self.model.xml and im.shape[0] > 1:
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ims = torch.chunk(im, im.shape[0], 0)
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# Inference
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if self.model.xml and im.shape[0] > 1:
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pred = None
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for image in ims:
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if pred is None:
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pred = self.model(image, augment=False, visualize=False).unsqueeze(0)
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else:
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pred = torch.cat(
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(pred, self.model(image, augment=False, visualize=False).unsqueeze(0)),
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dim=0
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)
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pred = [pred, None]
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else:
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pred = self.model(im, augment=False, visualize=False)
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# NMS
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pred = non_max_suppression(pred, 0.40, 0.45, None, False, max_det=1000)[0]
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image = im0s[0]
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pred[:, :4] = scale_coords(im.shape[2:], pred[:, :4], image.shape).round()
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# 使用YOLOv5进行检测后得到的pred
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bbox_xywh, cls_conf, cls_ids = yolov5_to_deepsort_format(pred)
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# select person class
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mask = cls_ids == 0
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bbox_xywh = bbox_xywh[mask]
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# bbox dilation just in case bbox too small, delete this line if using a better pedestrian detector
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bbox_xywh[:, 2:] *= 1.2
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cls_conf = cls_conf[mask]
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cls_ids = cls_ids[mask]
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# 调用Deep SORT更新方法
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outputs, _ = self.deepsort.update(bbox_xywh, cls_conf, cls_ids, image)
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count_result = {}
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for key in self.idx_to_class.keys():
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count_result[key] = set()
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# draw boxes for visualization
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if len(outputs) > 0:
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bbox_xyxy = outputs[:, :4] # 这个是检测所在框的坐标的数组
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identities = outputs[:, -1] # 这个是每个元素的计数的数组
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cls = outputs[:, -2] # 这个是标签数组id的数组
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names = [self.idx_to_class[str(label)] for label in cls]
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image = draw_boxes(image, bbox_xyxy, names, identities)
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for i in range(len(cls)):
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count_result[str(cls[i])].add(identities[i])
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def draw_boxes(img, bbox, names=None, identities=None, offset=(0, 0)):
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for i, box in enumerate(bbox):
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x1, y1, x2, y2 = [int(i) for i in box]
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x1 += offset[0]
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x2 += offset[0]
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y1 += offset[1]
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y2 += offset[1]
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# box text and bar
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id = int(identities[i]) if identities is not None else 0
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color = compute_color_for_labels(id)
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label = '{:}{:d}'.format(names[i], id)
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t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0]
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cv2.rectangle(img, (x1, y1), (x2, y2), color, 3)
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cv2.rectangle(img, (x1, y1), (x1 + t_size[0] + 3, y1 + t_size[1] + 4), color, -1)
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cv2.putText(img, label, (x1, y1 + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 2, [255, 255, 255], 2)
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return img
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def compute_color_for_labels(label):
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"""
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Simple function that adds fixed color depending on the class
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"""
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color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette]
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return tuple(color)
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def yolov5_to_deepsort_format(pred):
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"""
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将YOLOv5的预测结果转换为Deep SORT所需的格式
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:param pred: YOLOv5的预测结果
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:return: 转换后的bbox_xywh, confs, class_ids
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"""
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pred[:, :4] = xyxy2xywh(pred[:, :4])
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xywh = pred[:, :4].cpu().numpy()
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conf = pred[:, 4].cpu().numpy()
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cls = pred[:, 5].cpu().numpy()
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return xywh, conf, cls
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if __name__ == "__main__":
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args = parse_args()
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cfg = get_config()
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if args.segment:
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cfg.USE_SEGMENT = True
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else:
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cfg.USE_SEGMENT = False
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if args.mmdet:
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cfg.merge_from_file(args.config_mmdetection)
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cfg.USE_MMDET = True
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else:
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cfg.merge_from_file(args.config_detection)
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cfg.USE_MMDET = False
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cfg.merge_from_file(args.config_deepsort)
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if args.fastreid:
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cfg.merge_from_file(args.config_fastreid)
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cfg.USE_FASTREID = True
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else:
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cfg.USE_FASTREID = False
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with VideoTracker(cfg, args, video_path=args.VIDEO_PATH) as vdo_trk:
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vdo_trk.run()
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