import os import cv2 import time import torch import argparse from pathlib import Path from numpy import random from random import randint import torch.backends.cudnn as cudnn import os os.environ['KMP_DUPLICATE_LIB_OK']='TRUE' from models.experimental import attempt_load from utils.datasets import LoadStreams, LoadImages from utils.general import check_img_size, check_requirements, \ check_imshow, non_max_suppression, apply_classifier, \ scale_coords, xyxy2xywh, strip_optimizer, set_logging, \ increment_path from utils.plots import plot_one_box from utils.torch_utils import select_device, load_classifier, \ time_synchronized, TracedModel from utils.download_weights import download #For SORT tracking import skimage from sort import * #............................... Bounding Boxes Drawing ............................ """Function to Draw Bounding boxes""" def draw_boxes(img, bbox, identities=None, categories=None, names=None, save_with_object_id=False, path=None,offset=(0, 0)): for i, box in enumerate(bbox): x1, y1, x2, y2 = [int(i) for i in box] x1 += offset[0] x2 += offset[0] y1 += offset[1] y2 += offset[1] cat = int(categories[i]) if categories is not None else 0 id = int(identities[i]) if identities is not None else 0 data = (int((box[0]+box[2])/2),(int((box[1]+box[3])/2))) label = str(id) + ":"+ names[cat] (w, h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 1) cv2.rectangle(img, (x1, y1), (x2, y2), (255,0,20), 2) cv2.rectangle(img, (x1, y1 - 20), (x1 + w, y1), (255,144,30), -1) cv2.putText(img, label, (x1, y1 - 5),cv2.FONT_HERSHEY_SIMPLEX, 0.6, [255, 255, 255], 1) # cv2.circle(img, data, 6, color,-1) #centroid of box txt_str = "" if save_with_object_id: txt_str += "%i %i %f %f %f %f %f %f" % ( id, cat, int(box[0])/img.shape[1], int(box[1])/img.shape[0] , int(box[2])/img.shape[1], int(box[3])/img.shape[0] ,int(box[0] + (box[2] * 0.5))/img.shape[1] , int(box[1] + ( box[3]* 0.5))/img.shape[0]) txt_str += "\n" with open(path + '.txt', 'a') as f: f.write(txt_str) return img #.............................................................................. def detect(save_img=False): source, weights, view_img, save_txt, imgsz, trace, colored_trk, save_bbox_dim, save_with_object_id= opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, opt.no_trace, opt.colored_trk, opt.save_bbox_dim, opt.save_with_object_id save_img = not opt.nosave and not source.endswith('.txt') # save inference images webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( ('rtsp://', 'rtmp://', 'http://', 'https://')) # opt.no_trace 这里控制是否转模型： no opt.no_trace #.... Initialize SORT .... #......................... sort_max_age = 2 # sort_min_hits = 2 sort_min_hits = 3 sort_iou_thresh = 0.2 # sort_iou_thresh = 0.1 sort_tracker = Sort(max_age=sort_max_age, min_hits=sort_min_hits, iou_threshold=sort_iou_thresh) #......................... #........Rand Color for every trk....... rand_color_list = [] for i in range(0,5005): r = randint(0, 255) g = randint(0, 255) b = randint(0, 255) rand_color = (r, g, b) rand_color_list.append(rand_color) #...................................... # Directories save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run (save_dir / 'labels' if save_txt or save_with_object_id else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Initialize set_logging() device = select_device(opt.device) half = device.type != 'cpu' # half precision only supported on CUDA # Load model model = attempt_load(weights, map_location=device) # load FP32 model stride = int(model.stride.max()) # model stride imgsz = check_img_size(imgsz, s=stride) # check img_size if trace: model = TracedModel(model, device, opt.img_size) if half: model.half() # to FP16 # Second-stage classifier classify = False if classify: modelc = load_classifier(name='resnet101', n=2) # initialize modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval() # Set Dataloader vid_path, vid_writer = None, None if webcam: view_img = check_imshow() cudnn.benchmark = True # set True to speed up constant image size inference dataset = LoadStreams(source, img_size=imgsz, stride=stride) else: dataset = LoadImages(source, img_size=imgsz, stride=stride) # Get names and colors names = model.module.names if hasattr(model, 'module') else model.names colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] # Run inference if device.type != 'cpu': model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once old_img_w = old_img_h = imgsz old_img_b = 1 t0 = time.time() for path, img, im0s, vid_cap in dataset: img = torch.from_numpy(img).to(device) img = img.half() if half else img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 if img.ndimension() == 3: img = img.unsqueeze(0) # Warmup if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]): old_img_b = img.shape[0] old_img_h = img.shape[2] old_img_w = img.shape[3] for i in range(3): model(img, augment=opt.augment)[0] # Inference t1 = time_synchronized() pred = model(img, augment=opt.augment)[0] t2 = time_synchronized() # Apply NMS pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) t3 = time_synchronized() # Apply Classifier if classify: pred = apply_classifier(pred, modelc, img, im0s) # 增加去除非需要追踪和检测类别的过滤器 # Process detections for i, det in enumerate(pred): # detections per image if webcam: # batch_size >= 1 p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count else: p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) p = Path(p) # to Path save_path = str(save_dir / p.name) # img.jpg txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh if len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() det_boxes = [] for *x, conf, cls_id in det: lbl = names[int(cls_id)] # if lbl not in ['freighter']: #只输出boat这个标签的label、坐标及置信度 # continue # pass x1, y1 = float(x[0]), float(x[1]) x2, y2 = float(x[2]), float(x[3]) conf=float(conf.cpu().numpy()) cls_id=float(cls_id) det_boxes.append( (x1, y1, x2, y2, conf,cls_id)) # det_boxes.numpy() ###这里有结果 # 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 #s '2 airplanes, 1 kite, ' #在这里增加设置调用追踪器的频率 #..................USE TRACK FUNCTION.................... #pass an empty array to sort dets_to_sort = np.empty((0,6)) # NOTE: We send in detected object class too for x1,y1,x2,y2,conf,detclass in det_boxes: dets_to_sort = np.vstack((dets_to_sort, np.array([x1, y1, x2, y2, conf, detclass]))) # Run SORT tracked_dets = sort_tracker.update(dets_to_sort) tracks =sort_tracker.getTrackers() txt_str = "" #loop over tracks for track in tracks: # color = compute_color_for_labels(id) #draw colored tracks if colored_trk: [cv2.line(im0, (int(track.centroidarr[i][0]), int(track.centroidarr[i][1])), (int(track.centroidarr[i+1][0]), int(track.centroidarr[i+1][1])), rand_color_list[track.id], thickness=2) for i,_ in enumerate(track.centroidarr) if i < len(track.centroidarr)-1 ] #draw same color tracks else: [cv2.line(im0, (int(track.centroidarr[i][0]), int(track.centroidarr[i][1])), (int(track.centroidarr[i+1][0]), int(track.centroidarr[i+1][1])), (255,0,0), thickness=2) for i,_ in enumerate(track.centroidarr) if i < len(track.centroidarr)-1 ] if save_txt and not save_with_object_id: # Normalize coordinates txt_str += "%i %i %f %f" % (track.id, track.detclass, track.centroidarr[-1][0] / im0.shape[1], track.centroidarr[-1][1] / im0.shape[0]) if save_bbox_dim: txt_str += " %f %f" % (np.abs(track.bbox_history[-1][0] - track.bbox_history[-1][2]) / im0.shape[0], np.abs(track.bbox_history[-1][1] - track.bbox_history[-1][3]) / im0.shape[1]) txt_str += "\n" if save_txt and not save_with_object_id: with open(txt_path + '.txt', 'a') as f: f.write(txt_str) # draw boxes for visualization if len(tracked_dets)>0: bbox_xyxy = tracked_dets[:,:4] identities = tracked_dets[:, 8] categories = tracked_dets[:, 4] draw_boxes(im0, bbox_xyxy, identities, categories, names, save_with_object_id, txt_path) #........................................................ # Print time (inference + NMS) t4 = time_synchronized() print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS,, ({(1E3 * (t4 - t3)):.1f}ms) Track') # print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS') # Stream results if view_img: cv2.imshow(str(p), im0) if cv2.waitKey(1) == ord('q'): # q to quit cv2.destroyAllWindows() raise StopIteration # Save results (image with detections) if save_img: if dataset.mode == 'image': cv2.imwrite(save_path, im0) print(f" The image with the result is saved in: {save_path}") else: # 'video' or 'stream' if vid_path != save_path: # new video vid_path = save_path if isinstance(vid_writer, cv2.VideoWriter): vid_writer.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 += '.mp4' vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) vid_writer.write(im0) if save_txt or save_img or save_with_object_id: s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' #print(f"Results saved to {save_dir}{s}") # print(f'总耗时. ({time.time() - t0:.3f}s)') print('总耗时', time.time() - t0) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+', type=str, default='weights/freighter20230113.pt', help='model.pt path(s)') # parser.add_argument('--weights', nargs='+', type=str, default='weights/best_vehicle20230210.pt', help='model.pt path(s)') # parser.add_argument('--weights', nargs='+', type=str, default='weights/pedestrian20230210.pt', help='model.pt path(s)') parser.add_argument('--download', action='store_true', help='download model weights automatically') parser.add_argument('--no-download', dest='download', action='store_false',help='not download model weights if already exist') # parser.add_argument('--source', type=str, default='inference/video3', help='source') # file/folder, 0 for webcam parser.add_argument('--source', type=str, default=r'D:\TH\8_track\yolov5_sort\inference\video', help='source') # file/folder, 0 for webcam parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') 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='display 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('--nosave', action='store_true', help='do not save images/videos') parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 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('--update', action='store_true', help='update all models') parser.add_argument('--project', default='runs/detect', help='save results to project/name') parser.add_argument('--name', default='object_tracking', 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('--no-trace', action='store_true', help='don`t trace model') parser.add_argument('--colored-trk', action='store_true', help='assign different color to every track') parser.add_argument('--save-bbox-dim', action='store_true', help='save bounding box dimensions with --save-txt tracks') parser.add_argument('--save-with-object-id', action='store_true', help='save results with object id to *.txt') parser.set_defaults(download=True) opt = parser.parse_args() print(opt) #check_requirements(exclude=('pycocotools', 'thop')) if opt.download and not os.path.exists(str(opt.weights)): print('Model weights not found. Attempting to download now...') download('./') with torch.no_grad(): if opt.update: # update all models (to fix SourceChangeWarning) for opt.weights in ['weights/yolov7.pt']: detect() strip_optimizer(opt.weights) else: detect() # t7 = time.time() # # print('总耗时', t7 - t1) # print("读二值图像耗时:%s 形成轮廓耗时:%s 等距离缩放耗时:%s 读取原图：%s 绘制多段线: %s 保存图像耗时:%s" % ( # t2 - t1, t3 - t2, t4 - t3, t5 - t4, t6 - t5, t7 - t6))