zoom_diff_range = list(np.arange(0.2, 0.257, 0.001)) + list( np.arange(0.257, 0.2, -0.005)) # iterate through all files for idx, pc_filename in enumerate(pc_filenames): # read point-cloud velo_pc = read_velo_bin(pc_filename) # read corresponding image fname, file_ext = os.path.splitext(pc_filename) fname = fname.split('/')[-1] img_fname = os.path.join(args.img_dir, fname + '.png') img_bgr = cv2.imread(img_fname) img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB) # perform prediction pred_tuple, dt = trainer.predict(velo_pc, img_rgb) if args.dense_depth: label_dict, dense_depth = pred_tuple # tracking label_tracked_dict = [] if TRACKING == True: # get detection list for mot_tracker mot_det = [] mot_other_info = [] for label_ in label_dict: det = [ label_['h'], label_['w'], label_['l'], label_['x'], label_['y'], label_['z'], label_['yaw'] ] other_info = (label_['class'], label_['conf'])
train_depth_only=args.train_depth_only, train_obj_only=args.train_obj_only) # show 100 samples cv2.namedWindow('VR3Dense', cv2.WINDOW_NORMAL) cv2.resizeWindow('VR3Dense', 800, 1440) for i in range(100): sample = trainer.dataset[i] ## get true labels visualization pc_bbox_img_true = draw_point_cloud_w_bbox(sample['cloud'], sample['label_dict'], \ xlim=trainer.xlim, ylim=trainer.ylim, zlim=trainer.zlim) pc_bbox_img_true_bgr = cv2.cvtColor(pc_bbox_img_true, cv2.COLOR_RGB2BGR) ## get predicted labels visualization # perform prediction pred_tuple, dt = trainer.predict(sample['cloud'], sample['left_image']) if args.dense_depth: label_dict, dense_depth = pred_tuple # get visualization pc_bbox_img_pred = draw_point_cloud_w_bbox(sample['cloud'], label_dict, \ xlim=trainer.xlim, ylim=trainer.ylim, zlim=trainer.zlim) pc_bbox_img_pred_bgr = cv2.cvtColor(pc_bbox_img_pred, cv2.COLOR_RGB2BGR) # visualization image img_viz = cv2.vconcat([pc_bbox_img_true_bgr, pc_bbox_img_pred_bgr]) cv2.line( img_viz, (0, pc_bbox_img_true_bgr.shape[0]), (pc_bbox_img_true_bgr.shape[1] - 1, pc_bbox_img_true_bgr.shape[0]), color=(255, 255, 255),