def test_random_horizontal_flip_with_bbox_op_coco_c(plot_vis=False): """ Prints images and bboxes side by side with and without RandomHorizontalFlipWithBBox Op applied, Testing with COCO dataset """ logger.info("test_random_horizontal_flip_with_bbox_op_coco_c") dataCoco1 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection", decode=True, shuffle=False) dataCoco2 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection", decode=True, shuffle=False) test_op = c_vision.RandomHorizontalFlipWithBBox(1) dataCoco2 = dataCoco2.map(input_columns=["image", "bbox"], output_columns=["image", "bbox"], columns_order=["image", "bbox"], operations=[test_op]) unaugSamp, augSamp = [], [] for unAug, Aug in zip(dataCoco1.create_dict_iterator(), dataCoco2.create_dict_iterator()): unaugSamp.append(unAug) augSamp.append(Aug) if plot_vis: visualize_with_bounding_boxes(unaugSamp, augSamp, "bbox")
def test_random_horizontal_flip_with_bbox_invalid_bounds_c(): """ Test RandomHorizontalFlipWithBBox op with invalid bounding boxes """ logger.info("test_random_horizontal_bbox_invalid_bounds_c") test_op = c_vision.RandomHorizontalFlipWithBBox(1) dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.WidthOverflow, "bounding boxes is out of bounds of the image") dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.HeightOverflow, "bounding boxes is out of bounds of the image") dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.NegativeXY, "min_x") dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.WrongShape, "4 features")
def test_random_horizontal_flip_with_bbox_op_c(plot_vis=False): """ Prints images and bboxes side by side with and without RandomHorizontalFlipWithBBox Op applied """ logger.info("test_random_horizontal_flip_with_bbox_op_c") # Load dataset dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) test_op = c_vision.RandomHorizontalFlipWithBBox(1) dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"], output_columns=["image", "bbox"], columns_order=["image", "bbox"], operations=[test_op]) unaugSamp, augSamp = [], [] for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()): unaugSamp.append(unAug) augSamp.append(Aug) if plot_vis: visualize_with_bounding_boxes(unaugSamp, augSamp)
def test_random_horizontal_flip_with_bbox_invalid_prob_c(): """ Test RandomHorizontalFlipWithBBox op with invalid input probability """ logger.info("test_random_horizontal_bbox_invalid_prob_c") dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) try: # Note: Valid range of prob should be [0.0, 1.0] test_op = c_vision.RandomHorizontalFlipWithBBox(1.5) # map to apply ops dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"], output_columns=["image", "annotation"], columns_order=["image", "annotation"], operations=[test_op ]) # Add column for "annotation" except ValueError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert "Input prob is not within the required interval of (0.0 to 1.0)." in str( error)
def check_bad_box(data, box_type, expected_error): """ :param data: de object detection pipeline :param box_type: type of bad box :param expected_error: error expected to get due to bad box :return: None """ # DEFINE TEST OP HERE -- (PROB 1 IN CASE OF RANDOM) try: test_op = c_vision.RandomHorizontalFlipWithBBox(1) data = data.map(input_columns=["annotation"], output_columns=["annotation"], operations=fix_annotate) # map to use width overflow data = data.map(input_columns=["image", "annotation"], output_columns=["image", "annotation"], columns_order=["image", "annotation"], operations=lambda img, bboxes: add_bad_annotation(img, bboxes, box_type)) # map to apply ops data = data.map(input_columns=["image", "annotation"], output_columns=["image", "annotation"], columns_order=["image", "annotation"], operations=[test_op]) # Add column for "annotation" for _, _ in enumerate(data.create_dict_iterator()): break except RuntimeError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert expected_error in str(error)
def test_random_horizontal_bbox_valid_prob_c(plot=False): """ Test RandomHorizontalFlipWithBBox op Prints images side by side with and without Aug applied + bboxes to compare and test """ logger.info("test_random_horizontal_bbox_valid_prob_c") data_voc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) # DEFINE TEST OP HERE -- (PROB 1 IN CASE OF RANDOM) test_op = c_vision.RandomHorizontalFlipWithBBox(0.3) # maps to fix annotations to minddata standard data_voc1 = data_voc1.map(input_columns=["annotation"], output_columns=["annotation"], operations=fix_annotate) data_voc2 = data_voc2.map(input_columns=["annotation"], output_columns=["annotation"], operations=fix_annotate) # map to apply ops data_voc2 = data_voc2.map(input_columns=["image", "annotation"], output_columns=["image", "annotation"], columns_order=["image", "annotation"], operations=[test_op]) # Add column for "annotation" if plot: visualize(data_voc1, data_voc2)
def test_random_horizontal_flip_with_bbox_valid_edge_c(plot_vis=False): """ Test RandomHorizontalFlipWithBBox op (testing with valid edge case, box covering full image). Prints images side by side with and without Aug applied + bboxes to compare and test """ logger.info("test_horizontal_flip_with_bbox_valid_edge_c") dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) test_op = c_vision.RandomHorizontalFlipWithBBox(1) # maps to fix annotations to minddata standard dataVoc1 = dataVoc1.map(input_columns=["annotation"], output_columns=["annotation"], operations=fix_annotate) dataVoc2 = dataVoc2.map(input_columns=["annotation"], output_columns=["annotation"], operations=fix_annotate) # map to apply ops # Add column for "annotation" dataVoc1 = dataVoc1.map( input_columns=["image", "annotation"], output_columns=["image", "annotation"], columns_order=["image", "annotation"], operations=lambda img, bbox: (img, np.array([[0, 0, img.shape[1], img.shape[0], 0, 0, 0]]).astype( np.uint32))) dataVoc2 = dataVoc2.map( input_columns=["image", "annotation"], output_columns=["image", "annotation"], columns_order=["image", "annotation"], operations=lambda img, bbox: (img, np.array([[0, 0, img.shape[1], img.shape[0], 0, 0, 0]]).astype( np.uint32))) dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"], output_columns=["image", "annotation"], columns_order=["image", "annotation"], operations=[test_op]) unaugSamp, augSamp = [], [] for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()): unaugSamp.append(unAug) augSamp.append(Aug) if plot_vis: visualize_with_bounding_boxes(unaugSamp, augSamp)
def test_random_horizontal_bbox_with_bbox_valid_rand_c(plot_vis=False): """ Uses a valid non-default input, expect to pass Prints images side by side with and without Aug applied + bboxes to compare and test """ logger.info("test_random_horizontal_bbox_valid_rand_c") original_seed = config_get_set_seed(1) original_num_parallel_workers = config_get_set_num_parallel_workers(1) # Load dataset dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) test_op = c_vision.RandomHorizontalFlipWithBBox(0.6) # maps to fix annotations to minddata standard dataVoc1 = dataVoc1.map(input_columns=["annotation"], output_columns=["annotation"], operations=fix_annotate) dataVoc2 = dataVoc2.map(input_columns=["annotation"], output_columns=["annotation"], operations=fix_annotate) # map to apply ops dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"], output_columns=["image", "annotation"], columns_order=["image", "annotation"], operations=[test_op]) filename = "random_horizontal_flip_with_bbox_01_c_result.npz" save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN) unaugSamp, augSamp = [], [] for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()): unaugSamp.append(unAug) augSamp.append(Aug) if plot_vis: visualize_with_bounding_boxes(unaugSamp, augSamp) # Restore config setting ds.config.set_seed(original_seed) ds.config.set_num_parallel_workers(original_num_parallel_workers)