import argparse import os # limit the number of cpus used by high performance libraries # os.environ["OMP_NUM_THREADS"] = "8" # os.environ["OPENBLAS_NUM_THREADS"] = "8" # os.environ["MKL_NUM_THREADS"] = "8" # os.environ["VECLIB_MAXIMUM_THREADS"] = "8" # os.environ["NUMEXPR_NUM_THREADS"] = "8" import platform import sys import numpy as np from pathlib import Path import torch import torch.backends.cudnn as cudnn from numpy import random from time import time 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 / 'yolov9') not in sys.path: sys.path.append(str(ROOT / 'yolov9')) # add yolov5 ROOT to PATH if str(ROOT / 'strong_sort') not in sys.path: sys.path.append(str(ROOT / 'strong_sort')) # add strong_sort ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from models.experimental import attempt_load from models.common import DetectMultiBackend from utils.dataloaders import LoadImages, LoadStreams, LoadScreenshots from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh) from utils.torch_utils import select_device, time_sync, smart_inference_mode from utils.plots import Annotator, colors, save_one_box from strong_sort.utils.parser import get_config from strong_sort.strong_sort import StrongSORT VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes def plot_one_box(x, img, color=None, label=None, line_thickness=3): # Plots one bounding box on image img tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness #color = color or [random.randint(0, 255) for _ in range(3)] c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) if label: tf = max(tl - 1, 1) # font thickness t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) @smart_inference_mode() def run_strongsort( source='0', data = ROOT / 'data/coco.yaml', # data.yaml path yolo_weights=WEIGHTS / 'yolo.pt', # model.pt path(s), strong_sort_weights=WEIGHTS / 'osnet_x0_25_msmt17.pt', # model.pt path, config_strongsort=ROOT / 'strong_sort/configs/strong_sort.yaml', 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 view_img=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 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=3, # bounding box thickness (pixels) hide_labels=False, # hide labels hide_conf=False, # hide confidences half=False, # use FP16 half-precision inference dnn=False, # use OpenCV DNN for ONNX inference vid_stride=1, # video frame-rate stride ): 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) screenshot = source.lower().startswith('screen') if is_url and is_file: source = check_file(source) # download # Directories if not isinstance(yolo_weights, list): # single yolo model exp_name = Path(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 yolo_weights = Path(yolo_weights[0]) else: # multiple models after --yolo_weights exp_name = 'ensemble' exp_name = name if name else exp_name + "_" + Path(strong_sort_weights).stem save_dir = increment_path(Path(project) / exp_name, exist_ok=exist_ok) # increment run save_dir = Path(save_dir) (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=data, fp16=half) stride, names, pt = model.stride, model.names, model.pt imgsz = check_img_size(imgsz, s=stride) # check image size # Dataloader # Dataloader bs = 1 # batch_size if webcam: view_img = check_imshow(warn=True) dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) bs = len(dataset) elif screenshot: dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) else: dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) vid_path, vid_writer,txt_path = [None] * bs, [None] * bs, [None] * bs # initialize StrongSORT cfg = get_config() cfg.merge_from_file(config_strongsort) # Create as many strong sort instances as there are video sources strongsort_list = [] for i in range(bs): strongsort_list.append( StrongSORT( strong_sort_weights, device, half, max_dist=cfg.STRONGSORT.MAX_DIST, max_iou_distance=cfg.STRONGSORT.MAX_IOU_DISTANCE, max_age=cfg.STRONGSORT.MAX_AGE, n_init=cfg.STRONGSORT.N_INIT, nn_budget=cfg.STRONGSORT.NN_BUDGET, mc_lambda=cfg.STRONGSORT.MC_LAMBDA, ema_alpha=cfg.STRONGSORT.EMA_ALPHA, ) ) strongsort_list[i].model.warmup() outputs = [None] * bs colors = [[0, 0, 255], [0,148,255], [0, 255, 10], [250, 247, 0], [235,0,255]] # Run tracking model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup seen, windows, dt,sdt = 0, [], (Profile(), Profile(), Profile(), Profile()),[0.0, 0.0, 0.0, 0.0] curr_frames, prev_frames = [None] * bs, [None] * bs for frame_idx, (path, im, im0s, vid_cap, s) in enumerate(dataset): # s = '' t1 = time_sync() with dt[0]: im = torch.from_numpy(im).to(model.device) im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 im /= 255 # 0 - 255 to 0.0 - 1.0 if len(im.shape) == 3: im = im[None] # expand for batch dim t2 = time_sync() sdt[0] += t2 - t1 # Inference with dt[1]: visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False pred = model(im, augment=augment, visualize=visualize) # pred = pred[0][1] t3 = time_sync() sdt[1] += t3 - t2 # Apply NMS with dt[2]: pred = pred[0][1] if isinstance(pred[0], list) else pred[0] # single model or ensemble pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) sdt[2] += time_sync() - t3 # Second-stage classifier (optional) # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) # Process detections for i, det in enumerate(pred): # 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 txt_file_name = p.stem + f'_{i}' # Unique text file name # save_path = str(save_dir / p.name) + str(i) # im.jpg, vid.mp4, ... save_path = str(save_dir / p.stem) + f'_{i}' # Unique video file name 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 gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh imc = im0.copy() if save_crop else im0 # for save_crop annotator = Annotator(im0, line_width=line_thickness, example=str(names)) if cfg.STRONGSORT.ECC: # camera motion compensation strongsort_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() # 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 xywhs = xyxy2xywh(det[:, 0:4]) confs = det[:, 4] clss = det[:, 5] # pass detections to strongsort t4 = time_sync() outputs[i] = strongsort_list[i].update(xywhs.cpu(), confs.cpu(), clss.cpu(), im0) t5 = time_sync() sdt[3] += t5 - t4 # Write results for j, (output, conf) in enumerate(zip(outputs[i], confs)): xyxy = output[0:4] id = output[4] cls = output[5] # for *xyxy, conf, cls in reversed(det): if save_txt: # Write to file xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh # line = (id , cls, *xywh, conf) if save_conf else (cls, *xywh) # label format line = ( int(p.stem), frame_idx, id , cls, *xywh, conf) if save_conf else ( p.stem, frame_idx, cls, *xywh) # label format with open(txt_path + '.txt', 'a') as file: file.write(('%g ' * len(line) + '\n') % line) if save_img or save_crop or view_img: # Add bbox to image c = int(cls) # integer class label = None if hide_labels else ( str(id) + ' ' + names[c] if hide_conf else f' { id } {names[c]} {conf:.2f}') plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=2) if save_crop: save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) # # draw boxes for visualization # if len(outputs[i]) > 0: # for j, (output, conf) in enumerate(zip(outputs[i], confs)): # 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] # # format video_name frame id xmin ymin width height score class # with open(txt_path + '.txt', 'a') as file: # file.write(f'{p.stem} {frame_idx} {id} {bbox_left} {bbox_top} {bbox_w} {bbox_h} {conf:.2f} {cls}\n') # if save_img or save_crop or view_img: # Add bbox to image # c = int(cls) # integer class # id = int(id) # integer id # label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') # plot_one_box(bboxes, im0, label=label, color=colors[int(cls)], line_thickness=2) # 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) print(f'{s}Done. YOLO:({t3 - t2:.3f}s), StrongSORT:({t5 - t4:.3f}s)') else: strongsort_list[i].increment_ages() print('No detections') # Stream results im0 = annotator.result() if view_img: 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) cv2.waitKey(1) # 1 millisecond # Save results (image with detections) if save_img: if dataset.mode == 'image': cv2.imwrite(save_path, im0) else: # 'video' or 'stream' 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('m','p','4','v'), fps, (w, h)) vid_writer[i].write(im0) prev_frames[i] = curr_frames[i] # Print time (inference-only) LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") # Print results LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape, %.1fms StrongSORT' % tuple(1E3 * x / seen for x in sdt)) if save_txt or save_img: s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") if update: strip_optimizer(yolo_weights[0]) # update model (to fix SourceChangeWarning) return save_path def parse_opt(): parser = argparse.ArgumentParser() parser.add_argument('--yolo-weights', nargs='+', type=str, default=WEIGHTS / 'yolov9.pt', help='model.pt path(s)') parser.add_argument('--strong-sort-weights', type=str, default=WEIGHTS / 'osnet_x0_25_msmt17.pt') parser.add_argument('--config-strongsort', type=str, default='strong_sort/configs/strong_sort.yaml') parser.add_argument('--source', type=str, default='0', help='file/dir/URL/glob, 0 for webcam') parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') 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('--view-img', action='store_true', help='show 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('--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 / '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=3, 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('--half', action='store_true', help='use FP16 half-precision inference') parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') opt = parser.parse_args() opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand return opt def main(opt): # check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) run_strongsort(**vars(opt)) if __name__ == "__main__": opt = parse_opt() main(opt)