python detect.py，,python det
python detect.py，,python det
python detect.py
import argparsefrom sys import platformfrom models import * # set ONNX_EXPORT in models.pyfrom utils.datasets import *from utils.utils import *def detect(save_txt=False, save_img=False): img_size = (320, 192) if ONNX_EXPORT else opt.img_size # (320, 192) or (416, 256) or (608, 352) for (height, width) out, source, weights, half, view_img = opt.output, opt.source, opt.weights, opt.half, opt.view_img webcam = source == ‘0‘ or source.startswith(‘rtsp‘) or source.startswith(‘http‘) or source.endswith(‘.txt‘) # Initialize device = torch_utils.select_device(device=‘cpu‘ if ONNX_EXPORT else opt.device) if os.path.exists(out): shutil.rmtree(out) # delete output folder os.makedirs(out) # make new output folder # Initialize model model = Darknet(opt.cfg, img_size) # Load weights attempt_download(weights) if weights.endswith(‘.pt‘): # pytorch format model.load_state_dict(torch.load(weights, map_location=device)[‘model‘]) else: # darknet format _ = load_darknet_weights(model, weights) # Second-stage classifier classify = False if classify: modelc = torch_utils.load_classifier(name=‘resnet101‘, n=2) # initialize modelc.load_state_dict(torch.load(‘weights/resnet101.pt‘, map_location=device)[‘model‘]) # load weights modelc.to(device).eval() # Fuse Conv2d + BatchNorm2d layers # model.fuse() # Eval mode model.to(device).eval() # Export mode if ONNX_EXPORT: img = torch.zeros((1, 3) + img_size) # (1, 3, 320, 192) torch.onnx.export(model, img, ‘weights/export.onnx‘, verbose=False, opset_version=10) # Validate exported model import onnx model = onnx.load(‘weights/export.onnx‘) # Load the ONNX model onnx.checker.check_model(model) # Check that the IR is well formed print(onnx.helper.printable_graph(model.graph)) # Print a human readable representation of the graph return # Half precision half = half and device.type != ‘cpu‘ # half precision only supported on CUDA if half: model.half() # Set Dataloader vid_path, vid_writer = None, None if webcam: view_img = True torch.backends.cudnn.benchmark = True # set True to speed up constant image size inference dataset = LoadStreams(source, img_size=img_size, half=half) else: save_img = True dataset = LoadImages(source, img_size=img_size, half=half) # Get classes and colors classes = load_classes(parse_data_cfg(opt.data)[‘names‘]) colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(classes))] # Run inference t0 = time.time() for path, img, im0s, vid_cap in dataset: t = time.time() # Get detections img = torch.from_numpy(img).to(device) if img.ndimension() == 3: img = img.unsqueeze(0) pred = model(img)[0] if opt.half: pred = pred.float() # Apply NMS pred = non_max_suppression(pred, opt.conf_thres, opt.nms_thres) # Apply 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 = path[i], ‘%g: ‘ % i, im0s[i] else: p, s, im0 = path, ‘‘, im0s save_path = str(Path(out) / Path(p).name) s += ‘%gx%g ‘ % img.shape[2:] # print string if det is not None and 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 += ‘%g %ss, ‘ % (n, classes[int(c)]) # add to string # Write results for *xyxy, conf, _, cls in det: if save_txt: # Write to file with open(save_path + ‘.txt‘, ‘a‘) as file: file.write((‘%g ‘ * 6 + ‘\n‘) % (*xyxy, cls, conf)) if save_img or view_img: # Add bbox to image label = ‘%s %.2f‘ % (classes[int(cls)], conf) plot_one_box(xyxy, im0, label=label, color=colors[int(cls)]) print(‘%sDone. (%.3fs)‘ % (s, time.time() - t)) # Stream results if view_img: cv2.imshow(p, im0) # Save results (image with detections) if save_img: if dataset.mode == ‘images‘: cv2.imwrite(save_path, im0) else: if vid_path != save_path: # new video vid_path = save_path if isinstance(vid_writer, cv2.VideoWriter): vid_writer.release() # release previous video writer 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)) vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*opt.fourcc), fps, (w, h)) vid_writer.write(im0) if save_txt or save_img: print(‘Results saved to %s‘ % os.getcwd() + os.sep + out) if platform == ‘darwin‘: # MacOS os.system(‘open ‘ + out + ‘ ‘ + save_path) print(‘Done. (%.3fs)‘ % (time.time() - t0))if __name__ == ‘__main__‘: parser = argparse.ArgumentParser() parser.add_argument(‘--cfg‘, type=str, default=‘cfg/yolov3-spp.cfg‘, help=‘cfg file path‘) parser.add_argument(‘--data‘, type=str, default=‘data/coco.data‘, help=‘coco.data file path‘) parser.add_argument(‘--weights‘, type=str, default=‘weights/yolov3-spp.weights‘, help=‘path to weights file‘) parser.add_argument(‘--source‘, type=str, default=‘data/samples‘, help=‘source‘) # input file/folder, 0 for webcam parser.add_argument(‘--output‘, type=str, default=‘output‘, help=‘output folder‘) # output folder parser.add_argument(‘--img-size‘, type=int, default=416, help=‘inference size (pixels)‘) parser.add_argument(‘--conf-thres‘, type=float, default=0.3, help=‘object confidence threshold‘) parser.add_argument(‘--nms-thres‘, type=float, default=0.5, help=‘iou threshold for non-maximum suppression‘) parser.add_argument(‘--fourcc‘, type=str, default=‘mp4v‘, help=‘output video codec (verify ffmpeg support)‘) parser.add_argument(‘--half‘, action=‘store_true‘, help=‘half precision FP16 inference‘) parser.add_argument(‘--device‘, default=‘‘, help=‘device id (i.e. 0 or 0,1) or cpu‘) parser.add_argument(‘--view-img‘, action=‘store_true‘, help=‘display results‘) opt = parser.parse_args() print(opt) with torch.no_grad(): detect()
python detect.py
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