# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license """ Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc. Usage - sources: $ python detect.py --weights yolov5s.pt --source 0 # webcam img.jpg # image vid.mp4 # video screen # screenshot path/ # directory list.txt # list of images list.streams # list of streams 'path/*.jpg' # glob 'https://youtu.be/Zgi9g1ksQHc' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream Usage - formats: $ python detect.py --weights yolov5s.pt # PyTorch yolov5s.torchscript # TorchScript yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn yolov5s_openvino_model # OpenVINO yolov5s.engine # TensorRT yolov5s.mlmodel # CoreML (macOS-only) yolov5s_saved_model # TensorFlow SavedModel yolov5s.pb # TensorFlow GraphDef yolov5s.tflite # TensorFlow Lite yolov5s_edgetpu.tflite # TensorFlow Edge TPU yolov5s_paddle_model # PaddlePaddle """ import argparse import csv import os import platform import sys from pathlib import Path import glob import numpy as np import pickle import torch FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # YOLOv5 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH sys.path.append((str(ROOT)+'\\tracking\\utils')) ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from models.common import DetectMultiBackend from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams 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, smart_inference_mode '''集成跟踪模块，输出跟踪结果文件 .npy''' # from ultralytics.engine.results import Boxes # Results # from ultralytics.utils import IterableSimpleNamespace, yaml_load from tracking.utils.plotting import Annotator, colors from tracking.utils import Boxes, IterableSimpleNamespace, yaml_load, boxes_add_fid from tracking.trackers import BOTSORT, BYTETracker from tracking.utils.showtrack import drawtracks from hands.hand_inference import hand_pose from tracking.dotrack.dotracks_back import doBackTracks from tracking.dotrack.dotracks_front import doFrontTracks from tracking.trackers.reid.reid_interface import ReIDInterface from tracking.trackers.reid.config import config as ReIDConfig ReIDEncoder = ReIDInterface(ReIDConfig) # tracker_yaml = r"./tracking/trackers/cfg/botsort.yaml" def inference_image(image, detections): H, W, _ = np.shape(image) imgs = [] batch_patches = [] patches = [] for d in range(np.size(detections, 0)): tlbr = detections[d, :4].astype(np.int_) tlbr[0] = max(0, tlbr[0]) tlbr[1] = max(0, tlbr[1]) tlbr[2] = min(W - 1, tlbr[2]) tlbr[3] = min(H - 1, tlbr[3]) img1 = image[tlbr[1]:tlbr[3], tlbr[0]:tlbr[2], :] img = img1[:, :, ::-1].copy() # the model expects RGB inputs patch = ReIDEncoder.transform(img) imgs.append(img1) # patch = patch.to(device=self.device).half() if str(ReIDEncoder.device) != "cpu": patch = patch.to(device=ReIDEncoder.device).half() else: patch = patch.to(device=ReIDEncoder.device) patches.append(patch) if (d + 1) % ReIDEncoder.batch_size == 0: patches = torch.stack(patches, dim=0) batch_patches.append(patches) patches = [] if len(patches): patches = torch.stack(patches, dim=0) batch_patches.append(patches) features = np.zeros((0, ReIDEncoder.embedding_size)) for patches in batch_patches: pred = ReIDEncoder.model(patches) pred[torch.isinf(pred)] = 1.0 feat = pred.cpu().data.numpy() features = np.vstack((features, feat)) return imgs, features def init_trackers(tracker_yaml = None, bs=1): """ Initialize trackers for object tracking during prediction. """ # tracker_yaml = r"./tracking/trackers/cfg/botsort.yaml" TRACKER_MAP = {'bytetrack': BYTETracker, 'botsort': BOTSORT} cfg = IterableSimpleNamespace(**yaml_load(tracker_yaml)) trackers = [] for _ in range(bs): tracker = TRACKER_MAP[cfg.tracker_type](args=cfg, frame_rate=30) trackers.append(tracker) return trackers def detect_start_end(bboxes, features_dict, filename): boxes = np.empty(shape=(0, 9), dtype = np.float64) if filename.find("back") >= 0: vts = doBackTracks(bboxes, features_dict) vts.classify() # vtx = [t for t in vts.tracks if t.cls != 0] # for track in vtx: for track in vts.Residual: if track.moving_index.size: boxes = np.concatenate((boxes, track.moving_index), axis=0) elif filename.find("front") >= 0: vts = doFrontTracks(bboxes, features_dict) vts.classify() # vtx = [t for t in vts.tracks if t.cls != 0] # for track in vtx: for track in vts.Residual: for start, end in track.dynamic_y2: boxes = np.concatenate((boxes, track.boxes[start:end+1, :]), axis=0) for start, end in track.dynamic_y1: boxes = np.concatenate((boxes, track.boxes[start:end+1, :]), axis=0) if boxes.size > 0: start = np.min(boxes[:, 7]) end = np.max(boxes[:, 7]) else: start, end = 0, 0 return start, end @smart_inference_mode() def run( weights=ROOT / 'yolov5s.pt', # model path or triton URL source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) project=ROOT / 'runs/detect', # save results to project/name name='exp', # save results to project/name tracker_yaml = "./tracking/trackers/cfg/botsort.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_csv=False, # save results in CSV format 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 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 confidencesL half=False, # use FP16 half-precision inference dnn=False, # use OpenCV DNN for ONNX inference vid_stride=1, # video frame-rate stride data=ROOT / 'data/coco128.yaml', # dataset.yaml path ): source = str(source) # filename = os.path.split(source)[-1] save_img = not nosave and not source.endswith('.txt') # save inference images is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file) screenshot = source.lower().startswith('screen') if is_url and is_file: source = check_file(source) # download save_dir = Path(project) / Path(source).stem if save_dir.exists(): print(Path(source).stem) # return save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir else: save_dir.mkdir(parents=True, exist_ok=True) # Load model device = select_device(device) model = DetectMultiBackend(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 bs = 1 # batch_size dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) vid_path, vid_writer = [None] * bs, [None] * bs # Run inference model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup seen, dt = 0, (Profile(), Profile(), Profile()) tracker = init_trackers(tracker_yaml, bs)[0] handpose = hand_pose() handlocals_dict = {} boxes_and_imgs = [] BoxesFeats = [] track_boxes = np.empty((0, 9), dtype = np.float32) det_boxes = np.empty((0, 9), dtype = np.float32) DetBoxes = np.empty((0, 6), dtype = np.float32) TrackerBoxes = np.empty((0, 9), dtype = np.float32) TrackerFeats = np.empty((0, 256), dtype = np.float32) images = [] features_dict = {} TracksDict = {} for path, im, im0s, vid_cap, s in dataset: if save_img and 'imgshow' not in locals().keys(): imgshow = im0s.copy() ## ============================= tracking 功能只处理视频，writed by WQG if dataset.mode == 'image': continue 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 # 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) # NMS with dt[2]: pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) # Process predictions for i, det in enumerate(pred): # per image seen += 1 if webcam: # batch_size >= 1 p, im0, frame = path[i], im0s[i].copy(), dataset.count s += f'{i}: ' else: p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) images.append((dataset.frame, im0)) p = Path(p) # to Path save_path = str(save_dir / p.name) # im.jpg s += '%gx%g ' % im.shape[2:] # print string # im0_ant = im0.copy() annotator = Annotator(im0.copy(), line_width=line_thickness, example=str(names)) nd = len(det) if nd: # Rescale boxes from img_size to im0 size det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() det = det.cpu().numpy() det = np.concatenate([det[:, :4], np.arange(nd).reshape(-1, 1), det[:, 4:]], axis=-1) DetBoxes = np.concatenate([DetBoxes, det[:, :6]], axis=0) ## ============================================================ 前后帧相同 boxes 的特征赋值 # def static_estimate(box1, box2, TH1=8, TH2=12): # dij_abs = max(np.abs(box1 - box2)) # dij_euc = max([np.linalg.norm((box1[:2] - box2[:2])), # np.linalg.norm((box1[2:4] - box2[2:4])) # ]) # if dij_abs < TH1 and dij_euc < TH2: # return True # else: # return False # nw = 3 # 向前递推检查的窗口大小 # nf = len(BoxesFeats) # 已经检测+特征提取的帧数 # feat_curr = [None] * nd # nd: 当前帧检测出的boxes数 # for ii in range(nd): # box = det[ii, :4] # kk=1 # feat = None # while kk <= nw and nf>=kk: # ki = -1 * kk # boxes_ = BoxesFeats[ki][0] # feats_ = BoxesFeats[ki][1] # flag = [jj for jj in range(len(boxes_)) if static_estimate(box, boxes_[jj, :4])] # if len(flag) == 1: # feat = feats_[flag[0]] # break # kk += 1 # if feat is not None: # feat_curr[ii] = feat ## ================================================================ writed by WQG '''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index] 0 1 2 3 4 5 6 7 8 这里，frame_index 也可以用视频的 帧ID 代替, box_index 保持不变 ''' det_tracking = Boxes(det, im0.shape).cpu().numpy() tracks = tracker.update(det_tracking, im0) if len(tracks) == 0: continue tracks[:, 7] = dataset.frame '''================== 1. 存储 dets/subimgs/features Dict =============''' imgs, features = inference_image(im0, tracks) TrackerFeats = np.concatenate([TrackerFeats, features], axis=0) imgdict = {} boxdict = {} featdict = {} for ii, bid in enumerate(tracks[:, 8]): imgdict.update({int(bid): imgs[ii]}) # [f"img_{int(bid)}"] = imgs[i] boxdict.update({int(bid): tracks[ii, :]}) # [f"box_{int(bid)}"] = tracks[i, :] featdict.update({int(bid): features[ii, :]}) # [f"feat_{int(bid)}"] = features[i, :] TracksDict[f"frame_{int(dataset.frame)}"] = {"imgs":imgdict, "boxes":boxdict, "feats":featdict} track_boxes = np.concatenate([track_boxes, tracks], axis=0) '''================== 2. 提取手势位置 ===================''' # idx_0 = tracks[:, 6].astype(np.int_) == 0 # hn = 0 # for j, index in enumerate(idx_0): # if index: # track = tracks[j, :] # hand_local, imgshow = handpose.get_hand_local(track, im0) # handlocals_dict.update({int(track[7]): {int(track[8]): hand_local}}) # # '''yoloV5和手势检测的召回率并不一直，用hand_local代替tracks中手部的(x1, y1, x2, y2)，会使得两种坐标方式混淆''' # # if hand_local: tracks[j, :4] = hand_local # hn += 1 # cv2.imwrite(f"D:\DeepLearning\yolov5\hands\images\{Path(source).stem}_{int(track[7])}_{hn}.png", imgshow) for *xyxy, id, conf, cls, fid, bid in reversed(tracks): name = ('' if id==-1 else f'id:{int(id)} ') + names[int(cls)] label = None if hide_labels else (name if hide_conf else f'{name} {conf:.2f}') if id >=0 and cls==0: color = colors(int(cls), True) elif id >=0 and cls!=0: color = colors(int(id), True) else: color = colors(19, True) # 19为调色板的最后一个元素 annotator.box_label(xyxy, label, color=color) # Save results (image and video with tracking) im0 = annotator.result() save_path_img, ext = os.path.splitext(save_path) if save_img: if dataset.mode == 'image': imgpath = save_path_img + f"_{dataset}.png" else: imgpath = save_path_img + f"_{dataset.frame}.png" cv2.imwrite(Path(imgpath), im0) 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) # Print time (inference-only) LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") ## ======================================================================== written by WQG ## track_boxes: Array, [x1, y1, x2, y2, track_id, score, cls, frame_index, box_id] '''上面保存了检测结果是视频和图像，以下还保存五种类型的数据''' filename = os.path.split(save_path_img)[-1] file, ext = os.path.splitext(filename) mfid = np.max(track_boxes[:, 7]) start, end = detect_start_end(track_boxes, features_dict, filename) if start == end: return if start > 5: start = start - 5 else: start = 0 if mfid - end > 5: end = end + 5 # img_path = Path(f'./runs/images/{file}/') img_path = savepath / f'{file}' if not img_path.exists(): img_path.mkdir(parents=True, exist_ok=True) '''抽帧间隔数''' Interval = 3 for i, img in images: if i >= start and i % Interval == 0: imgpath = img_path / f'{file}_{int(i)}.png' cv2.imwrite(Path(imgpath), img) if i == end: break '''======================== 3. save hand_local data ==================''' # handlocal_dir = Path('./tracking/data/handlocal/') # if not handlocal_dir.exists(): # handlocal_dir.mkdir(parents=True, exist_ok=True) # handlocal_path = handlocal_dir.joinpath(f'{filename}.pkl') # with open(handlocal_path, 'wb') as file: # pickle.dump(handlocals_dict, file) # 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 per image at shape {(1, 3, *imgsz)}' % t) 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(weights[0]) # update model (to fix SourceChangeWarning) def parse_opt(): modelpath = ROOT / 'ckpts/best_yolov5m_250000.pt' # 'ckpts/best_15000_0908.pt', 'ckpts/yolov5s.pt', 'ckpts/best_20000_cls30.pt' '''datapath为视频文件目录或视频文件''' datapath = r"D:/datasets/ym/videos/标记视频/" # ROOT/'data/videos', ROOT/'data/images' images # datapath = r"D:\datasets\ym\highvalue\videos" # datapath = r"D:/dcheng/videos/" # modelpath = ROOT / 'ckpts/yolov5s.pt' parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+', type=str, default=modelpath, help='model path or triton URL') # 'yolov5s.pt', best_15000_0908.pt parser.add_argument('--source', type=str, default=datapath, help='file/dir/URL/glob/screen/0(webcam)') # images, videos 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=0.25, help='confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.45, 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-csv', action='store_true', help='save results in CSV format') 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') 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/detect', 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('--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 find_files_in_nested_dirs(root_dir): all_files = [] extensions = ['.mp4'] for dirpath, dirnames, filenames in os.walk(root_dir): for filename in filenames: file, ext = os.path.splitext(filename) if ext in extensions: all_files.append(os.path.join(dirpath, filename)) return all_files print('=======') def main(opt): check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) p = r"D:\datasets\ym\永辉测试数据_202404\20240402" optdict = vars(opt) files = [] k = 0 if os.path.isdir(p): files.extend(sorted(glob.glob(os.path.join(p, '*.*')))) for file in files: optdict["source"] = file run(**optdict) k += 1 if k == 2: break elif os.path.isfile(p): run(**vars(opt)) def main_loop(opt): check_requirements(ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) optdict = vars(opt) # p = r"D:\datasets\ym\永辉测试数据_比对" p = r"D:\datasets\ym\广告板遮挡测试\8" # p = r"D:\datasets\ym\videos\标记视频" # p = r"D:\datasets\ym\实验室测试" # p = r"D:\datasets\ym\永辉双摄视频\新建文件夹" k = 0 if os.path.isdir(p): files = find_files_in_nested_dirs(p) # files = [r"D:\datasets\ym\广告板遮挡测试\8\6926636301004_20240508-175300_back_addGood_70f754088050_215_17327712807.mp4", # r"D:\datasets\ym\videos\标记视频\test_20240402-173935_6920152400975_back_174037372.mp4", # r"D:\datasets\ym\videos\标记视频\test_20240402-173935_6920152400975_front_174037379.mp4", # r"D:\datasets\ym\广告板遮挡测试\8\2500441577966_20240508-175946_front_addGood_70f75407b7ae_155_17788571404.mp4" # ] files = [r"D:\datasets\ym\广告板遮挡测试\8\6907149227609_20240508-174733_back_returnGood_70f754088050_425_17327712807.mp4"] for file in files: optdict["source"] = file run(**optdict) k += 1 if k == 1: break elif os.path.isfile(p): optdict["source"] = p run(**vars(opt)) if __name__ == '__main__': opt = parse_opt() # main(opt) savepath = ROOT / 'runs/images' main_loop(opt)