import argparse import time from models import * from utils.datasets import * from utils.utils import * cuda = torch.cuda.is_available() device = torch.device('cuda:0' if cuda else 'cpu') f_path = os.path.dirname(os.path.realpath(__file__)) + '/' parser = argparse.ArgumentParser() # Get data configuration parser.add_argument('-image_folder', type=str, default='data/samples', help='path to images') parser.add_argument('-output_folder', type=str, default='output', help='path to outputs') parser.add_argument('-plot_flag', type=bool, default=True) parser.add_argument('-txt_out', type=bool, default=False) parser.add_argument('-cfg', type=str, default=f_path + 'cfg/yolov3.cfg', help='cfg file path') parser.add_argument('-class_path', type=str, default=f_path + 'data/coco.names', help='path to class label file') parser.add_argument('-conf_thres', type=float, default=0.50, help='object confidence threshold') parser.add_argument('-nms_thres', type=float, default=0.45, help='iou threshold for non-maximum suppression') parser.add_argument('-batch_size', type=int, default=1, help='size of the batches') parser.add_argument('-img_size', type=int, default=32 * 13, help='size of each image dimension') opt = parser.parse_args() print(opt) def main(opt): os.system('rm -rf ' + opt.output_folder) os.makedirs(opt.output_folder, exist_ok=True) # Load model model = Darknet(opt.cfg, opt.img_size) weights_path = f_path + 'weights/yolov3.pt' if weights_path.endswith('.weights'): # saved in darknet format load_weights(model, weights_path) else: # endswith('.pt'), saved in pytorch format if weights_path.endswith('weights/yolov3.pt') and not os.path.isfile(weights_path): os.system('wget https://storage.googleapis.com/ultralytics/yolov3.pt -O ' + weights_path) checkpoint = torch.load(weights_path, map_location='cpu') model.load_state_dict(checkpoint['model']) del checkpoint # current = model.state_dict() # saved = checkpoint['model'] # # 1. filter out unnecessary keys # saved = {k: v for k, v in saved.items() if ((k in current) and (current[k].shape == v.shape))} # # 2. overwrite entries in the existing state dict # current.update(saved) # # 3. load the new state dict # model.load_state_dict(current) # model.to(device).eval() # del checkpoint, current, saved model.to(device).eval() # Set Dataloader classes = load_classes(opt.class_path) # Extracts class labels from file dataloader = load_images(opt.image_folder, batch_size=opt.batch_size, img_size=opt.img_size) imgs = [] # Stores image paths img_detections = [] # Stores detections for each image index prev_time = time.time() for i, (img_paths, img) in enumerate(dataloader): print('%g/%g' % (i + 1, len(dataloader)), end=' ') # Get detections with torch.no_grad(): pred = model(torch.from_numpy(img).unsqueeze(0).to(device)) pred = pred[pred[:, :, 4] > opt.conf_thres] if len(pred) > 0: detections = non_max_suppression(pred.unsqueeze(0), opt.conf_thres, opt.nms_thres) img_detections.extend(detections) imgs.extend(img_paths) print('Batch %d... Done. (%.3fs)' % (i, time.time() - prev_time)) prev_time = time.time() # Bounding-box colors color_list = [[random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)] for _ in range(len(classes))] if len(img_detections) == 0: return # Iterate through images and save plot of detections for img_i, (path, detections) in enumerate(zip(imgs, img_detections)): print("image %g: '%s'" % (img_i, path)) if opt.plot_flag: img = cv2.imread(path) # The amount of padding that was added pad_x = max(img.shape[0] - img.shape[1], 0) * (opt.img_size / max(img.shape)) pad_y = max(img.shape[1] - img.shape[0], 0) * (opt.img_size / max(img.shape)) # Image height and width after padding is removed unpad_h = opt.img_size - pad_y unpad_w = opt.img_size - pad_x # Draw bounding boxes and labels of detections if detections is not None: unique_classes = detections[:, -1].cpu().unique() bbox_colors = random.sample(color_list, len(unique_classes)) # write results to .txt file results_img_path = os.path.join(opt.output_folder, path.split('/')[-1]) results_txt_path = results_img_path + '.txt' if os.path.isfile(results_txt_path): os.remove(results_txt_path) for i in unique_classes: n = (detections[:, -1].cpu() == i).sum() print('%g %ss' % (n, classes[int(i)])) for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections: # Rescale coordinates to original dimensions box_h = ((y2 - y1) / unpad_h) * img.shape[0] box_w = ((x2 - x1) / unpad_w) * img.shape[1] y1 = (((y1 - pad_y // 2) / unpad_h) * img.shape[0]).round().item() x1 = (((x1 - pad_x // 2) / unpad_w) * img.shape[1]).round().item() x2 = (x1 + box_w).round().item() y2 = (y1 + box_h).round().item() x1, y1, x2, y2 = max(x1, 0), max(y1, 0), max(x2, 0), max(y2, 0) # write to file if opt.txt_out: with open(results_txt_path, 'a') as file: file.write(('%g %g %g %g %g %g \n') % (x1, y1, x2, y2, cls_pred, cls_conf * conf)) if opt.plot_flag: # Add the bbox to the plot label = '%s %.2f' % (classes[int(cls_pred)], conf) color = bbox_colors[int(np.where(unique_classes == int(cls_pred))[0])] plot_one_box([x1, y1, x2, y2], img, label=label, color=color) if opt.plot_flag: # Save generated image with detections cv2.imwrite(results_img_path.replace('.bmp', '.jpg').replace('.tif', '.jpg'), img) if platform == 'darwin': # MacOS (local) os.system('open ' + opt.output_folder) if __name__ == '__main__': torch.cuda.empty_cache() main(opt)