def test_map_semantic_img_fast_uint16_stress_test(): """ Test fast method on large example. Map 60,000 classes to a different 60,000 classes with a fast array conversion. Should take less than 1 millisecond. """ semantic_img = np.array(range(60000)).reshape(3000, 20).astype(np.uint16) # will expect uint16 back since 60000 < 65535, which is uint16 max gt_mapped_img1 = np.array(range(60000)).reshape(3000, 20).astype( np.uint16) + 1 label_mapping = {i: i + 1 for i in range(60000)} label_mapping_copy = copy.deepcopy(label_mapping) label_mapping_arr = form_label_mapping_array(label_mapping) dict_is_equal(label_mapping, label_mapping_copy) assert label_mapping == label_mapping_copy start = time.time() mapped_img = map_semantic_img_fast(semantic_img, label_mapping_arr) end = time.time() print(f'Took {end-start} sec.') gt_mapped_img2 = map_semantic_img_slow(semantic_img, label_mapping) assert np.allclose(gt_mapped_img1, mapped_img) assert np.allclose(gt_mapped_img2, mapped_img) assert gt_mapped_img1.dtype == mapped_img.dtype assert gt_mapped_img2.dtype == mapped_img.dtype
def test_map_semantic_img_fast_dontwidenvalue(): """ Test fast method on simple conversion from 2x3 grayscale -> 2x3 grayscale. """ semantic_img = np.array([[300, 301, 302], [300, 301, 302]], dtype=np.uint16) label_mapping = {300: 0, 301: 1, 302: 2} label_mapping_arr = form_label_mapping_array(label_mapping) mapped_img = map_semantic_img_fast(semantic_img, label_mapping_arr) # Expect uint8 since max class index in values <= 255, so uint16 unnecessary gt_mapped_img1 = np.array([[0, 1, 2], [0, 1, 2]], dtype=np.uint8) assert np.allclose(gt_mapped_img1, mapped_img) assert mapped_img.dtype == np.uint8
def test_map_semantic_img_fast_widenvalue(): """ Test fast method on simple conversion from 2x3 grayscale -> 2x3 grayscale. """ semantic_img = np.array([[255, 255, 255], [255, 255, 255]], dtype=np.uint8) label_mapping = {255: 256} label_mapping_arr = form_label_mapping_array(label_mapping) mapped_img = map_semantic_img_fast(semantic_img, label_mapping_arr) # Expect uint8 since max class index <= 255, so uint16 unnecessary gt_mapped_img1 = np.array([[256, 256, 256], [256, 256, 256]], dtype=np.uint16) assert np.allclose(gt_mapped_img1, mapped_img) assert mapped_img.dtype == np.uint16
def test_map_semantic_img_fast(): """ Test fast method on simple conversion from 2x3 grayscale -> 2x3 grayscale. """ semantic_img = np.array([[254, 0, 1], [7, 8, 9]], dtype=np.uint8) label_mapping = {254: 253, 0: 255, 1: 0, 7: 6, 8: 7, 9: 8} label_mapping_arr = form_label_mapping_array(label_mapping) mapped_img = map_semantic_img_fast(semantic_img, label_mapping_arr) # Expect uint8 since max class index <= 255, so uint16 unnecessary gt_mapped_img1 = np.array([[253, 255, 0], [6, 7, 8]], dtype=np.uint8) gt_mapped_img2 = map_semantic_img_slow(semantic_img, label_mapping) assert np.allclose(gt_mapped_img1, mapped_img) assert np.allclose(gt_mapped_img2, mapped_img) assert gt_mapped_img1.dtype == mapped_img.dtype assert gt_mapped_img2.dtype == mapped_img.dtype
def relabel_pair(old_dataroot: str, new_dataroot: str, orig_pair: Tuple[str, str], remapped_pair: Tuple[str, str], label_mapping_arr: np.ndarray, dataset_colors: Optional[np.ndarray] = None): """ No need to copy the RGB files again. We just update the label file paths. Args: - old_dataroot: - new_dataroot: - orig_pair: Tuple containing relative path to RGB image and label image - remapped_pair: Tuple containing relative path to RGB image and label image - label_mapping_arr: - dataset_colors: Returns: - None """ _, orig_rel_label_fpath = orig_pair _, remapped_rel_label_fpath = remapped_pair old_label_fpath = f'{old_dataroot}/{orig_rel_label_fpath}' if dataset_colors is None: label_img = imageio.imread(old_label_fpath) else: # remap from RGB encoded labels to 1-channel class indices label_img_rgb = cv2_imread_rgb(old_label_fpath) label_img = rgb_img_to_obj_cls_img(label_img_rgb, dataset_colors) remapped_img = map_semantic_img_fast(label_img, label_mapping_arr) new_label_fpath = f'{new_dataroot}/{remapped_rel_label_fpath}' create_leading_fpath_dirs(new_label_fpath) imageio.imwrite(new_label_fpath, remapped_img)