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| 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 | |
| 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://')) | |
| # Directories | |
| save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run | |
| (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() | |
| 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 | |
| # Write results | |
| 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 = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format | |
| with open(txt_path + '.txt', 'a') as f: | |
| f.write(('%g ' * len(line)).rstrip() % line + '\n') | |
| if save_img or view_img: # Add bbox to image | |
| label = f'{names[int(cls)]} {conf:.2f}' | |
| plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1) | |
| # Print time (inference + NMS) | |
| 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) | |
| 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') | |
| opt = parser.parse_args() | |
| print(opt) | |
| #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() | |