import argparse import os # limit the number of cpus used by high performance libraries os.environ["OMP_NUM_THREADS"] = "1" os.environ["OPENBLAS_NUM_THREADS"] = "1" os.environ["MKL_NUM_THREADS"] = "1" os.environ["VECLIB_MAXIMUM_THREADS"] = "1" os.environ["NUMEXPR_NUM_THREADS"] = "1" import sys import numpy as np from pathlib import Path import torch import torch.backends.cudnn as cudnn FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # yolov5 strongsort root directory WEIGHTS = ROOT / 'weights' if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH if str(ROOT / 'yolov5') not in sys.path: sys.path.append(str(ROOT / 'yolov5')) # add yolov5 ROOT to PATH if str(ROOT / 'trackers' / 'strong_sort') not in sys.path: sys.path.append(str(ROOT / 'trackers' / 'strong_sort')) # add strong_sort ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative import logging from yolov5.models.common import DetectMultiBackend from yolov5.utils.dataloaders import VID_FORMATS, LoadImages, LoadStreams from yolov5.utils.general import (LOGGER, check_img_size, non_max_suppression, scale_boxes, check_requirements, cv2, check_imshow, xyxy2xywh, increment_path, strip_optimizer, colorstr, print_args, check_file) from yolov5.utils.torch_utils import select_device, time_sync from yolov5.utils.plots import Annotator, colors, save_one_box from trackers.multi_tracker_zoo import create_tracker # remove duplicated stream handler to avoid duplicated logging #logging.getLogger().removeHandler(logging.getLogger().handlers[0]) @torch.no_grad() def run( source='0', yolo_weights=WEIGHTS / 'yolov5m.pt', # model.pt path(s), reid_weights=WEIGHTS / 'osnet_x0_25_msmt17.pt', # model.pt path, tracking_method='strongsort', imgsz=(640, 640), # inference size (height, width) conf_thres=0.25, # confidence threshold iou_thres=0.45, # NMS IOU threshold max_det=1000, # maximum detections per image device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu show_vid=False, # show results save_txt=False, # save results to *.txt save_conf=False, # save confidences in --save-txt labels save_crop=False, # save cropped prediction boxes save_vid=True, # save confidences in --save-txt labels nosave=False, # do not save images/videos classes=None, # filter by class: --class 0, or --class 0 2 3 agnostic_nms=False, # class-agnostic NMS augment=False, # augmented inference visualize=False, # visualize features update=False, # update all models project=ROOT / 'runs/track', # save results to project/name name='exp', # save results to project/name exist_ok=False, # existing project/name ok, do not increment line_thickness=1, # bounding box thickness (pixels) hide_labels=False, # hide labels hide_conf=False, # hide confidences hide_class=False, # hide IDs half=False, # use FP16 half-precision inference dnn=False, # use OpenCV DNN for ONNX inference vid_stride=1, # video frame-rate stride ): save_txt = True source = str(source) save_img = not nosave and not source.endswith('.txt') # save inference images is_file = Path(source).suffix[1:] in (VID_FORMATS) is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file) if is_url and is_file: source = check_file(source) # download # Directories if not isinstance(yolo_weights, list): # single yolo model exp_name = yolo_weights.stem elif type(yolo_weights) is list and len(yolo_weights) == 1: # single models after --yolo_weights exp_name = Path(yolo_weights[0]).stem else: # multiple models after --yolo_weights exp_name = 'ensemble' exp_name = name if name else exp_name + "_" + reid_weights.stem save_dir = increment_path(Path(project) / exp_name, exist_ok=exist_ok) # increment run (save_dir / 'tracks' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Load model device = select_device(device) model = DetectMultiBackend(yolo_weights, device=device, dnn=dnn, data=None, fp16=half) stride, names, pt = model.stride, model.names, model.pt imgsz = check_img_size(imgsz, s=stride) # check image size # Dataloader if webcam: show_vid = check_imshow() dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) nr_sources = len(dataset) else: dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt) nr_sources = 1 vid_path, vid_writer, txt_path = [None] * nr_sources, [None] * nr_sources, [None] * nr_sources # Create as many strong sort instances as there are video sources tracker_list = [] for i in range(nr_sources): tracker = create_tracker(tracking_method, reid_weights, device, half) tracker_list.append(tracker, ) if hasattr(tracker_list[i], 'model'): if hasattr(tracker_list[i].model, 'warmup'): tracker_list[i].model.warmup() outputs = [None] * nr_sources # Run tracking #model.warmup(imgsz=(1 if pt else nr_sources, 3, *imgsz)) # warmup dt, seen = [0.0, 0.0, 0.0, 0.0], 0 curr_frames, prev_frames = [None] * nr_sources, [None] * nr_sources for frame_idx, (path, im, im0s, vid_cap, s) in enumerate(dataset): t1 = time_sync() im = torch.from_numpy(im).to(device) im = im.half() if half else im.float() # uint8 to fp16/32 im /= 255.0 # 0 - 255 to 0.0 - 1.0 if len(im.shape) == 3: im = im[None] # expand for batch dim t2 = time_sync() dt[0] += t2 - t1 # Inference visualize = increment_path(save_dir / Path(path[0]).stem, mkdir=True) if visualize else False pred = model(im, augment=augment, visualize=visualize) t3 = time_sync() dt[1] += t3 - t2 # Apply NMS pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) dt[2] += time_sync() - t3 # Process detections for i, det in enumerate(pred): # detections per image seen += 1 if webcam: # nr_sources >= 1 p, im0, _ = path[i], im0s[i].copy(), dataset.count p = Path(p) # to Path s += f'{i}: ' txt_file_name = p.name save_path = str(save_dir / p.name) # im.jpg, vid.mp4, ... else: p, im0, _ = path, im0s.copy(), getattr(dataset, 'frame', 0) p = Path(p) # to Path # video file if source.endswith(VID_FORMATS): txt_file_name = p.stem save_path = str(save_dir / p.name) # im.jpg, vid.mp4, ... # folder with imgs else: txt_file_name = p.parent.name # get folder name containing current img save_path = str(save_dir / p.parent.name) # im.jpg, vid.mp4, ... curr_frames[i] = im0 txt_path = str(save_dir / 'tracks' / txt_file_name) # im.txt s += '%gx%g ' % im.shape[2:] # print string imc = im0.copy() if save_crop else im0 # for save_crop annotator = Annotator(im0, line_width=line_thickness, example=str(names)) if hasattr(tracker_list[i], 'tracker') and hasattr(tracker_list[i].tracker, 'camera_update'): if prev_frames[i] is not None and curr_frames[i] is not None: # camera motion compensation tracker_list[i].tracker.camera_update(prev_frames[i], curr_frames[i]) if det is not None and len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # xyxy # Print results for c in det[:, -1].unique(): n = (det[:, -1] == c).sum() # detections per class s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string # pass detections to strongsort t4 = time_sync() outputs[i] = tracker_list[i].update(det.cpu(), im0) t5 = time_sync() dt[3] += t5 - t4 # draw boxes for visualization if len(outputs[i]) > 0: for j, (output, conf) in enumerate(zip(outputs[i], det[:, 4])): bboxes = output[0:4] id = output[4] cls = output[5] if save_txt: # to MOT format bbox_left = output[0] bbox_top = output[1] bbox_w = output[2] - output[0] bbox_h = output[3] - output[1] # Write MOT compliant results to file with open(txt_path + '.txt', 'a') as f: f.write(('%g ' * 10 + '\n') % (frame_idx + 1, id, bbox_left, # MOT format bbox_top, bbox_w, bbox_h, -1, -1, -1, i)) save_vid=True if save_vid or save_crop or show_vid: # Add bbox to image c = int(cls) # integer class id = int(id) # integer id label = None if hide_labels else (f'{id} {names[c]}' if hide_conf else \ (f'{id} {conf:.2f}' if hide_class else f'{id} {names[c]} {conf:.2f}')) annotator.box_label(bboxes, label, color=colors(c, True)) if save_crop: txt_file_name = txt_file_name if (isinstance(path, list) and len(path) > 1) else '' save_one_box(bboxes, imc, file=save_dir / 'crops' / txt_file_name / names[c] / f'{id}' / f'{p.stem}.jpg', BGR=True) LOGGER.info(f'{s}Done. yolo:({t3 - t2:.3f}s), {tracking_method}:({t5 - t4:.3f}s)') else: #strongsort_list[i].increment_ages() LOGGER.info('No detections') # Stream results im0 = annotator.result() if show_vid: cv2.imshow(str(p), im0) cv2.waitKey(1) # 1 millisecond # Save results (image with detections) if save_vid: if vid_path[i] != save_path: # new video vid_path[i] = save_path if isinstance(vid_writer[i], cv2.VideoWriter): vid_writer[i].release() # release previous video writer if vid_cap: # video fps = vid_cap.get(cv2.CAP_PROP_FPS) w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) else: # stream fps, w, h = 30, im0.shape[1], im0.shape[0] save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) vid_writer[i].write(im0) prev_frames[i] = curr_frames[i] # Print results t = tuple(x / seen * 1E3 for x in dt) # speeds per image LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS, %.1fms {tracking_method} update per image at shape {(1, 3, *imgsz)}' % t) if save_txt or save_vid: s = f"\n{len(list(save_dir.glob('tracks/*.txt')))} tracks saved to {save_dir / 'tracks'}" if save_txt else '' LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") if update: strip_optimizer(yolo_weights) # update model (to fix SourceChangeWarning) def parse_opt(): parser = argparse.ArgumentParser() parser.add_argument('--yolo-weights', nargs='+', type=Path, default=WEIGHTS / 'best2.pt', help='model.pt path(s)') parser.add_argument('--reid-weights', type=Path, default=WEIGHTS / 'osnet_x0_25_msmt17.pt') parser.add_argument('--tracking-method', type=str, default='bytetrack', help='strongsort, ocsort, bytetrack') parser.add_argument('--source', type=str, default=r'video', help='file/dir/URL/glob, 0 for webcam') parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') parser.add_argument('--conf-thres', type=float, default=0.5, help='confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.5, help='NMS IoU threshold') #0.5 parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--show-vid', action='store_true', help='display tracking video results') parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') parser.add_argument('--save-vid', action='store_true', help='save video tracking results') parser.add_argument('--nosave', action='store_false', help='do not save images/videos') # class 0 is person, 1 is bycicle, 2 is car... 79 is oven 0 1 2 3 5 7 9 11 10 parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3') parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') parser.add_argument('--augment', action='store_true', help='augmented inference') parser.add_argument('--visualize', action='store_true', help='visualize features') parser.add_argument('--update', action='store_true', help='update all models') parser.add_argument('--project', default=ROOT / 'runs/track', help='save results to project/name') parser.add_argument('--name', default='exp', help='save results to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--line-thickness', default=1, type=int, help='bounding box thickness (pixels)') parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') parser.add_argument('--hide-conf', default=True, action='store_true', help='hide confidences') parser.add_argument('--hide-class', default=False, action='store_true', help='hide IDs') parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') opt = parser.parse_args() opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand print_args(vars(opt)) return opt def main(opt): check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) run(**vars(opt)) if __name__ == "__main__": opt = parse_opt() main(opt)