import argparse import time from sys import platform from models import * from utils.datasets import * from utils.utils import * def detect(cfg, data, weights, images='data/samples', # input folder output='output', # output folder fourcc='mp4v', # video codec img_size=416, conf_thres=0.5, nms_thres=0.5, save_txt=False, save_images=True): # Initialize device = torch_utils.select_device(force_cpu=ONNX_EXPORT) torch.backends.cudnn.benchmark = False # set False for reproducible results if os.path.exists(output): shutil.rmtree(output) # delete output folder os.makedirs(output) # make new output folder # Initialize model if ONNX_EXPORT: s = (320, 192) # (320, 192) or (416, 256) or (608, 352) onnx model image size (height, width) model = Darknet(cfg, s) else: model = Darknet(cfg, img_size) # Load 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) # Fuse Conv2d + BatchNorm2d layers # model.fuse() # Eval mode model.to(device).eval() # Export mode if ONNX_EXPORT: img = torch.zeros((1, 3, s[0], s[1])) torch.onnx.export(model, img, 'weights/export.onnx', verbose=True) return # Half precision opt.half = opt.half and device.type != 'cpu' # half precision only supported on CUDA if opt.half: model.half() # Set Dataloader vid_path, vid_writer = None, None if opt.webcam: save_images = False dataloader = LoadWebcam(img_size=img_size, half=opt.half) else: dataloader = LoadImages(images, img_size=img_size, half=opt.half) # Get classes and colors classes = load_classes(parse_data_cfg(data)['names']) colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(classes))] # Run inference t0 = time.time() for i, (path, img, im0, vid_cap) in enumerate(dataloader): t = time.time() save_path = str(Path(output) / Path(path).name) # Get detections img = torch.from_numpy(img).unsqueeze(0).to(device) pred, _ = model(img) det = non_max_suppression(pred.float(), conf_thres, nms_thres)[0] if det is not None and len(det) > 0: # Rescale boxes from 416 to true image size det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() # Print results to screen print('%gx%g ' % img.shape[2:], end='') # print image size for c in det[:, -1].unique(): n = (det[:, -1] == c).sum() print('%g %ss' % (n, classes[int(c)]), end=', ') # Draw bounding boxes and labels of detections for *xyxy, conf, cls_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)) # Add bbox to the image label = '%s %.2f' % (classes[int(cls)], conf) plot_one_box(xyxy, im0, label=label, color=colors[int(cls)]) print('Done. (%.3fs)' % (time.time() - t)) if opt.webcam: # Show live webcam cv2.imshow(weights, im0) if save_images: # Save image with detections if dataloader.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) width = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (width, height)) vid_writer.write(im0) if save_images: print('Results saved to %s' % os.getcwd() + os.sep + output) if platform == 'darwin': # macos os.system('open ' + output + ' ' + 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('--images', type=str, default='data/samples', help='path to images') 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='fourcc output video codec (verify ffmpeg support)') parser.add_argument('--output', type=str, default='output', help='specifies the output path for images and videos') parser.add_argument('--half', action='store_true', help='half precision FP16 inference') parser.add_argument('--webcam', action='store_true', help='use webcam') opt = parser.parse_args() print(opt) with torch.no_grad(): detect(opt.cfg, opt.data, opt.weights, images=opt.images, img_size=opt.img_size, conf_thres=opt.conf_thres, nms_thres=opt.nms_thres, fourcc=opt.fourcc, output=opt.output)