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| import argparse | |
| import cv2 | |
| 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 platform | |
| 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 / 'yolov8') not in sys.path: | |
| sys.path.append(str(ROOT / 'yolov8')) # add yolov5 ROOT to PATH | |
| if str(ROOT / 'trackers' / 'strongsort') not in sys.path: | |
| sys.path.append(str(ROOT / 'trackers' / 'strongsort')) # add strong_sort ROOT to PATH | |
| ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative | |
| import logging | |
| from ultralytics.nn.autobackend import AutoBackend | |
| from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadStreams | |
| from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS | |
| from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, SETTINGS, callbacks, colorstr, ops | |
| from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_imshow, print_args, check_requirements | |
| from ultralytics.yolo.utils.files import increment_path | |
| from ultralytics.yolo.utils.torch_utils import select_device | |
| from ultralytics.yolo.utils.ops import Profile, non_max_suppression, scale_boxes, process_mask, process_mask_native | |
| from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box | |
| from trackers.multi_tracker_zoo import create_tracker | |
| 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', | |
| tracking_config=None, | |
| 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_trajectories=False, # save trajectories for each track | |
| 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 | |
| project=ROOT ,# save results to project/name | |
| name='exp', # save results to project/name | |
| exist_ok=True, # existing project/name ok, do not increment | |
| line_thickness=2, # 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 | |
| retina_masks=False, | |
| ): | |
| #print the inputs | |
| print(f"model used : {yolo_weights}, tracking method : {tracking_method}") | |
| 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) | |
| is_seg = '-seg' in str(yolo_weights) | |
| model = AutoBackend(yolo_weights, device=device, dnn=dnn, fp16=half) | |
| stride, names, pt = model.stride, model.names, model.pt | |
| imgsz = check_imgsz(imgsz, stride=stride) # check image size | |
| # Dataloader | |
| bs = 1 | |
| if webcam: | |
| show_vid = check_imshow(warn=True) | |
| dataset = LoadStreams( | |
| source, | |
| imgsz=imgsz, | |
| stride=stride, | |
| auto=pt, | |
| transforms=getattr(model.model, 'transforms', None), | |
| vid_stride=vid_stride | |
| ) | |
| bs = len(dataset) | |
| else: | |
| dataset = LoadImages( | |
| source, | |
| imgsz=imgsz, | |
| stride=stride, | |
| auto=pt, | |
| transforms=getattr(model.model, 'transforms', None), | |
| vid_stride=vid_stride | |
| ) | |
| vid_path, vid_writer, txt_path = [None] * bs, [None] * bs, [None] * bs | |
| model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup | |
| # Create as many strong sort instances as there are video sources | |
| tracker_list = [] | |
| for i in range(bs): | |
| tracker = create_tracker(tracking_method, tracking_config, 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] * bs | |
| # Run tracking | |
| #model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup | |
| seen, windows, dt = 0, [], (Profile(), Profile(), Profile(), Profile()) | |
| curr_frames, prev_frames = [None] * bs, [None] * bs | |
| for frame_idx, batch in enumerate(dataset): | |
| path, im, im0s, vid_cap, s = batch | |
| visualize = increment_path(save_dir / Path(path[0]).stem, mkdir=True) if visualize else False | |
| with dt[0]: | |
| 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 | |
| # Inference | |
| with dt[1]: | |
| preds = model(im, augment=augment, visualize=visualize) | |
| # Apply NMS | |
| with dt[2]: | |
| if is_seg: | |
| masks = [] | |
| p = non_max_suppression(preds[0], conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det, nm=32) | |
| proto = preds[1][-1] | |
| else: | |
| p = non_max_suppression(preds, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) | |
| # Process detections | |
| filename = 'out.mp4' | |
| for i, det in enumerate(p): # detections per image | |
| seen += 1 | |
| if webcam: # bs >= 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 / filename) # 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 / filename) # im.jpg, vid.mp4, ... | |
| LOGGER.info(f"p.name is {p.name}, save_path value is {save_path}") | |
| # 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): | |
| if is_seg: | |
| shape = im0.shape | |
| # scale bbox first the crop masks | |
| if retina_masks: | |
| det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], shape).round() # rescale boxes to im0 size | |
| masks.append(process_mask_native(proto[i], det[:, 6:], det[:, :4], im0.shape[:2])) # HWC | |
| else: | |
| masks.append(process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True)) # HWC | |
| det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], shape).round() # rescale boxes to im0 size | |
| else: | |
| det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size | |
| # Print results | |
| for c in det[:, 5].unique(): | |
| n = (det[:, 5] == c).sum() # detections per class | |
| s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string | |
| # pass detections to strongsort | |
| with dt[3]: | |
| outputs[i] = tracker_list[i].update(det.cpu(), im0) | |
| # draw boxes for visualization | |
| if len(outputs[i]) > 0: | |
| if is_seg: | |
| # Mask plotting | |
| annotator.masks( | |
| masks[i], | |
| colors=[colors(x, True) for x in det[:, 5]], | |
| im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() / | |
| 255 if retina_masks else im[i] | |
| ) | |
| for j, (output) in enumerate(outputs[i]): | |
| bbox = output[0:4] | |
| id = output[4] | |
| cls = output[5] | |
| conf = output[6] | |
| 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)) | |
| if save_vid or save_crop or show_vid: # Add bbox/seg 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}')) | |
| color = colors(c, True) | |
| annotator.box_label(bbox, label, color=color) | |
| if save_trajectories and tracking_method == 'strongsort': | |
| q = output[7] | |
| tracker_list[i].trajectory(im0, q, color=color) | |
| if save_crop: | |
| txt_file_name = txt_file_name if (isinstance(path, list) and len(path) > 1) else '' | |
| save_one_box(np.array(bbox, dtype=np.int16), imc, file=save_dir / 'crops' / txt_file_name / names[c] / f'{id}' / f'{p.stem}.jpg', BGR=True) | |
| else: | |
| pass | |
| #tracker_list[i].tracker.pred_n_update_all_tracks() | |
| # Stream results | |
| im0 = annotator.result() | |
| if show_vid: | |
| if platform.system() == 'Linux' and p not in windows: | |
| windows.append(p) | |
| cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) | |
| cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) | |
| cv2.imshow(str(p), im0) | |
| if cv2.waitKey(1) == ord('q'): # 1 millisecond | |
| exit() | |
| # Save results (image with detections) | |
| if save_vid: | |
| LOGGER.info(f"vid_path, save_path {vid_path[i]}{save_path}") | |
| 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 | |
| LOGGER.info(f"test Results saved to {colorstr('bold', save_path)}") | |
| 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 total time (preprocessing + inference + NMS + tracking) | |
| LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{sum([dt.dt for dt in dt if hasattr(dt, 'dt')]) * 1E3:.1f}ms") | |
| # Print results | |
| t = tuple(x.t / 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 / 'tracks').glob('*.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 / 'yolov8s-seg.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('--tracking-config', type=Path, default=None) | |
| #parser.add_argument('--source', type=str, default='0', 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') | |
| 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-trajectories', action='store_true', help='save trajectories for each track') | |
| parser.add_argument('--save-vid', action='store_true',default=True, help='save video tracking results') | |
| parser.add_argument('--nosave', action='store_true', help='do not save images/videos') | |
| # class 0 is person, 1 is bycicle, 2 is car... 79 is oven | |
| 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 , help='save results to project/name') | |
| parser.add_argument('--name', default='exp', help='save results to ROOT') | |
| parser.add_argument('--exist-ok', default='True', action='store_true', help='existing project/name ok, do not increment') | |
| parser.add_argument('--line-thickness', default=2, 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=False, 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') | |
| parser.add_argument('--retina-masks', action='store_true', help='whether to plot masks in native resolution') | |
| #opt = parser.parse_args() | |
| #opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand | |
| #opt.tracking_config = ROOT / 'trackers' / opt.tracking_method / 'configs' / (opt.tracking_method + '.yaml') | |
| #print_args(vars(opt)) | |
| #return opt | |
| return parser | |
| def main(opt): | |
| check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) | |
| run(**vars(opt)) | |
| #if __name__ == "__main__": | |
| # opt = parse_opt() | |
| # main(opt) | |
| def MOT(yoloweights, trackingmethod, sourceVideo): | |
| parser = parse_opt() | |
| parser.add_argument('--yolo-weights', nargs='+', type=Path, default= yoloweights, help='model.pt path(s)') | |
| parser.add_argument('--tracking-method', type=str, default= trackingmethod, help='strongsort, ocsort, bytetrack') | |
| parser.add_argument('--source', type=str, default=sourceVideo, help='file/dir/URL/glob, 0 for webcam') | |
| opt = parser.parse_args() | |
| opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand | |
| opt.tracking_config = ROOT / 'trackers' / opt.tracking_method / 'configs' / (opt.tracking_method + '.yaml') | |
| print_args(vars(opt)) | |
| main(opt) | |
| save_dir = increment_path('exp', exist_ok=True) | |
| input = os.path.join(save_dir,'out.mp4') | |
| outpath = 'output.mp4' #'output/'+ 'output.mp4' | |
| if os.path.exists(outpath): | |
| os.remove(outpath) | |
| command = f"ffmpeg -i {input} -vf fps=30 -vcodec libx264 {outpath}" | |
| print(command) | |
| os.system(command) | |
| return outpath |