import argparse import time from pathlib import Path import cv2 import torch import torch.backends.cudnn as cudnn from numpy import random 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 sort import * """Function to Draw Bounding boxes""" def draw_boxes(img, bbox, identities=None, categories=None, confidences = None, names=None, colors = None): for i, box in enumerate(bbox): x1, y1, x2, y2 = [int(i) for i in box] tl = opt.thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness cat = int(categories[i]) if categories is not None else 0 id = int(identities[i]) if identities is not None else 0 # conf = confidences[i] if confidences is not None else 0 color = colors[cat] if not opt.nobbox: cv2.rectangle(img, (x1, y1), (x2, y2), color, tl) if not opt.nolabel: label = str(id) + ":"+ names[cat] if identities is not None else f'{names[cat]} {confidences[i]:.2f}' tf = max(tl - 1, 1) # font thickness t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] c2 = x1 + t_size[0], y1 - t_size[1] - 3 cv2.rectangle(img, (x1, y1), c2, color, -1, cv2.LINE_AA) # filled cv2.putText(img, label, (x1, y1 - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) return img def detect(save_img=False): source, weights, view_img, save_txt, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, not opt.no_trace 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://')) save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run if not opt.nosave: (save_dir / 'labels' if save_txt 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() ################################### startTime = 0 ################################### 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) # 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() # 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 dets_to_sort = np.empty((0,6)) # NOTE: We send in detected object class too for x1,y1,x2,y2,conf,detclass in det.cpu().detach().numpy(): dets_to_sort = np.vstack((dets_to_sort, np.array([x1, y1, x2, y2, conf, detclass]))) if opt.track: tracked_dets = sort_tracker.update(dets_to_sort, opt.unique_track_color) tracks =sort_tracker.getTrackers() # draw boxes for visualization if len(tracked_dets)>0: bbox_xyxy = tracked_dets[:,:4] identities = tracked_dets[:, 8] categories = tracked_dets[:, 4] confidences = None if opt.show_track: #loop over tracks for t, track in enumerate(tracks): track_color = colors[int(track.detclass)] if not opt.unique_track_color else sort_tracker.color_list[t] [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])), track_color, thickness=opt.thickness) for i,_ in enumerate(track.centroidarr) if i < len(track.centroidarr)-1 ] else: bbox_xyxy = dets_to_sort[:,:4] identities = None categories = dets_to_sort[:, 5] confidences = dets_to_sort[:, 4] im0 = draw_boxes(im0, bbox_xyxy, identities, categories, confidences, names, colors) # Print time (inference + NMS) print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS') # Stream results ###################################################### if dataset.mode != 'image' and opt.show_fps: currentTime = time.time() fps = 1/(currentTime - startTime) startTime = currentTime cv2.putText(im0, "FPS: " + str(int(fps)), (20, 70), cv2.FONT_HERSHEY_PLAIN, 2, (0,255,0),2) ####################################################### if view_img: 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) 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: 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'Done. ({time.time() - t0:.3f}s)') if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+', type=str, default='yolov7.pt', help='model.pt path(s)') parser.add_argument('--source', type=str, default='inference/images', 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='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('--no-trace', action='store_true', help='don`t trace model') parser.add_argument('--track', action='store_true', help='run tracking') parser.add_argument('--show-track', action='store_true', help='show tracked path') parser.add_argument('--show-fps', action='store_true', help='show fps') parser.add_argument('--thickness', type=int, default=2, help='bounding box and font size thickness') parser.add_argument('--seed', type=int, default=1, help='random seed to control bbox colors') parser.add_argument('--nobbox', action='store_true', help='don`t show bounding box') parser.add_argument('--nolabel', action='store_true', help='don`t show label') parser.add_argument('--unique-track-color', action='store_true', help='show each track in unique color') opt = parser.parse_args() print(opt) np.random.seed(opt.seed) sort_tracker = Sort(max_age=5, min_hits=2, iou_threshold=0.2) #check_requirements(exclude=('pycocotools', 'thop')) with torch.no_grad(): if opt.update: # update all models (to fix SourceChangeWarning) for opt.weights in ['yolov7.pt']: detect() strip_optimizer(opt.weights) else: detect()