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 @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', 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