| | |
|---|
| | | default=False, type=bool) |
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| | | parser.add_argument('--dataset', dest='dataset', help='Dataset type.', default='AFLW2000', type=str) |
|---|
| | | parser.add_argument('--min_yaw', dest='min_yaw', type=float) |
|---|
| | | parser.add_argument('--max_yaw', dest='max_yaw', type=float) |
|---|
| | | |
|---|
| | | args = parser.parse_args() |
|---|
| | | |
|---|
| | |
|---|
| | | roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu() * 3 - 99 |
|---|
| | | |
|---|
| | | # Mean absolute error |
|---|
| | | if args.min_yaw <= label_yaw[0]: |
|---|
| | | if args.min_yaw <= abs(label_pitch[0]) and args.max_yaw >= abs(label_pitch[0]): |
|---|
| | | yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw)) |
|---|
| | | pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch)) |
|---|
| | | roll_error += torch.sum(torch.abs(roll_predicted - label_roll)) |
|---|
| | | total += 1 |
|---|
| | | |
|---|
| | | # Save images with pose cube. |
|---|
| | | # TODO: fix for larger batch size |
|---|
| | | if args.save_viz: |
|---|
| | | name = name[0] |
|---|
| | | if args.dataset == 'BIWI': |
|---|
| | | cv2_img = cv2.imread(os.path.join(args.data_dir, name + '_rgb.png')) |
|---|
| | | else: |
|---|
| | | cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg')) |
|---|
| | | if args.batch_size == 1: |
|---|
| | | error_string = 'y %.2f, p %.2f, r %.2f' % (torch.sum(torch.abs(yaw_predicted - label_yaw)), torch.sum(torch.abs(pitch_predicted - label_pitch)), torch.sum(torch.abs(roll_predicted - label_roll))) |
|---|
| | | cv2.putText(cv2_img, error_string, (30, cv2_img.shape[0]- 30), fontFace=1, fontScale=1, color=(0,0,255), thickness=1) |
|---|
| | | utils.plot_pose_cube(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0]) |
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| | | cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img) |
|---|
| | | if args.save_viz: |
|---|
| | | name = name[0] |
|---|
| | | if args.dataset == 'BIWI': |
|---|
| | | cv2_img = cv2.imread(os.path.join(args.data_dir, name + '_rgb.png')) |
|---|
| | | else: |
|---|
| | | cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg')) |
|---|
| | | if args.batch_size == 1: |
|---|
| | | error_string = 'y %.2f, p %.2f, r %.2f' % (torch.sum(torch.abs(yaw_predicted - label_yaw)), torch.sum(torch.abs(pitch_predicted - label_pitch)), torch.sum(torch.abs(roll_predicted - label_roll))) |
|---|
| | | cv2.putText(cv2_img, error_string, (30, cv2_img.shape[0]- 30), fontFace=1, fontScale=1, color=(0,0,255), thickness=1) |
|---|
| | | utils.plot_pose_cube(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0]) |
|---|
| | | cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img) |
|---|
| | | |
|---|
| | | print('Test error in degrees of the model on the ' + str(total) + |
|---|
| | | ' test images. Yaw: %.4f, Pitch: %.4f, Roll: %.4f' % (yaw_error / total, |
|---|
