avakh
5 years ago
4 changed files with 225 additions and 426 deletions
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BINtests/ut/data/dataset/golden/random_resize_with_bbox_op_01_c_result.npz
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BINtests/ut/data/dataset/golden/resize_with_bbox_op_01_c_result.npz
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+126 -197tests/ut/python/dataset/test_random_resize_with_bbox.py
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+99 -229tests/ut/python/dataset/test_resize_with_bbox.py
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tests/ut/data/dataset/golden/random_resize_with_bbox_op_01_c_result.npz
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tests/ut/data/dataset/golden/resize_with_bbox_op_01_c_result.npz
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tests/ut/python/dataset/test_random_resize_with_bbox.py
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| @@ -15,251 +15,180 @@ | |||||
| """ | """ | ||||
| Testing the random resize with bounding boxes op in DE | Testing the random resize with bounding boxes op in DE | ||||
| """ | """ | ||||
| from enum import Enum | |||||
| import matplotlib.pyplot as plt | |||||
| import matplotlib.patches as patches | |||||
| import numpy as np | import numpy as np | ||||
| import mindspore.dataset as ds | import mindspore.dataset as ds | ||||
| from mindspore import log as logger | |||||
| import mindspore.dataset.transforms.vision.c_transforms as c_vision | import mindspore.dataset.transforms.vision.c_transforms as c_vision | ||||
| from mindspore import log as logger | |||||
| from util import visualize_with_bounding_boxes, InvalidBBoxType, check_bad_bbox, \ | |||||
| config_get_set_seed, config_get_set_num_parallel_workers, save_and_check_md5 | |||||
| GENERATE_GOLDEN = False | GENERATE_GOLDEN = False | ||||
| DATA_DIR = "../data/dataset/testVOC2012" | |||||
| DATA_DIR = "../data/dataset/testVOC2012_2" | |||||
| def fix_annotate(bboxes): | def fix_annotate(bboxes): | ||||
| """ | """ | ||||
| Fix annotations to format followed by mindspore. | |||||
| :param bboxes: in [label, x_min, y_min, w, h, truncate, difficult] format | :param bboxes: in [label, x_min, y_min, w, h, truncate, difficult] format | ||||
| :return: annotation in [x_min, y_min, w, h, label, truncate, difficult] format | :return: annotation in [x_min, y_min, w, h, label, truncate, difficult] format | ||||
| """ | """ | ||||
| for bbox in bboxes: | |||||
| tmp = bbox[0] | |||||
| bbox[0] = bbox[1] | |||||
| bbox[1] = bbox[2] | |||||
| bbox[2] = bbox[3] | |||||
| bbox[3] = bbox[4] | |||||
| bbox[4] = tmp | |||||
| for (i, box) in enumerate(bboxes): | |||||
| bboxes[i] = np.roll(box, -1) | |||||
| return bboxes | return bboxes | ||||
| class BoxType(Enum): | |||||
| def test_random_resize_with_bbox_op_rand_c(plot_vis=False): | |||||
| """ | """ | ||||
| Defines box types for test cases | |||||
| Prints images and bboxes side by side with and without RandomResizeWithBBox Op applied, | |||||
| tests with MD5 check, expected to pass | |||||
| """ | """ | ||||
| WidthOverflow = 1 | |||||
| HeightOverflow = 2 | |||||
| NegativeXY = 3 | |||||
| OnEdge = 4 | |||||
| WrongShape = 5 | |||||
| logger.info("test_random_resize_with_bbox_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) | |||||
| class AddBadAnnotation: # pylint: disable=too-few-public-methods | |||||
| """ | |||||
| Used to add erroneous bounding boxes to object detection pipelines. | |||||
| Usage: | |||||
| >>> # Adds a box that covers the whole image. Good for testing edge cases | |||||
| >>> de = de.map(input_columns=["image", "annotation"], | |||||
| >>> output_columns=["image", "annotation"], | |||||
| >>> operations=AddBadAnnotation(BoxType.OnEdge)) | |||||
| """ | |||||
| dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", | |||||
| decode=True, shuffle=False) | |||||
| def __init__(self, box_type): | |||||
| self.box_type = box_type | |||||
| def __call__(self, img, bboxes): | |||||
| """ | |||||
| Used to generate erroneous bounding box examples on given img. | |||||
| :param img: image where the bounding boxes are. | |||||
| :param bboxes: in [x_min, y_min, w, h, label, truncate, difficult] format | |||||
| :return: bboxes with bad examples added | |||||
| """ | |||||
| height = img.shape[0] | |||||
| width = img.shape[1] | |||||
| if self.box_type == BoxType.WidthOverflow: | |||||
| # use box that overflows on width | |||||
| return img, np.array([[0, 0, width + 1, height - 1, 0, 0, 0]]).astype(np.uint32) | |||||
| if self.box_type == BoxType.HeightOverflow: | |||||
| # use box that overflows on height | |||||
| return img, np.array([[0, 0, width - 1, height + 1, 0, 0, 0]]).astype(np.uint32) | |||||
| if self.box_type == BoxType.NegativeXY: | |||||
| # use box with negative xy | |||||
| return img, np.array([[-10, -10, width - 1, height - 1, 0, 0, 0]]).astype(np.uint32) | |||||
| if self.box_type == BoxType.OnEdge: | |||||
| # use box that covers the whole image | |||||
| return img, np.array([[0, 0, width - 1, height - 1, 0, 0, 0]]).astype(np.uint32) | |||||
| if self.box_type == BoxType.WrongShape: | |||||
| # use box that covers the whole image | |||||
| return img, np.array([[0, 0, width - 1]]).astype(np.uint32) | |||||
| return img, bboxes | |||||
| def check_bad_box(data, box_type, expected_error): | |||||
| try: | |||||
| test_op = c_vision.RandomResizeWithBBox(100) # DEFINE TEST OP HERE -- (PROB 1 IN CASE OF RANDOM) | |||||
| 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=AddBadAnnotation(box_type)) # Add column for "annotation" | |||||
| # 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 e: | |||||
| logger.info("Got an exception in DE: {}".format(str(e))) | |||||
| assert expected_error in str(e) | |||||
| def add_bounding_boxes(axis, bboxes): | |||||
| """ | |||||
| :param axis: axis to modify | |||||
| :param bboxes: bounding boxes to draw on the axis | |||||
| :return: None | |||||
| """ | |||||
| for bbox in bboxes: | |||||
| rect = patches.Rectangle((bbox[0], bbox[1]), | |||||
| bbox[2], bbox[3], | |||||
| linewidth=1, edgecolor='r', facecolor='none') | |||||
| # Add the patch to the Axes | |||||
| axis.add_patch(rect) | |||||
| def visualize(unaugmented_data, augment_data): | |||||
| for idx, (un_aug_item, aug_item) in \ | |||||
| enumerate(zip(unaugmented_data.create_dict_iterator(), augment_data.create_dict_iterator())): | |||||
| axis = plt.subplot(141) | |||||
| plt.imshow(un_aug_item["image"]) | |||||
| add_bounding_boxes(axis, un_aug_item["annotation"]) # add Orig BBoxes | |||||
| plt.title("Original" + str(idx + 1)) | |||||
| logger.info("Original ", str(idx + 1), " :", un_aug_item["annotation"]) | |||||
| axis = plt.subplot(142) | |||||
| plt.imshow(aug_item["image"]) | |||||
| add_bounding_boxes(axis, aug_item["annotation"]) # add AugBBoxes | |||||
| plt.title("Augmented" + str(idx + 1)) | |||||
| logger.info("Augmented ", str(idx + 1), " ", aug_item["annotation"], "\n") | |||||
| plt.show() | |||||
| def test_random_resize_with_bbox_op(plot=False): | |||||
| test_op = c_vision.RandomResizeWithBBox(200) | |||||
| 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_resize_with_bbox_op_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) | |||||
| def test_random_resize_with_bbox_op_edge_c(plot_vis=False): | |||||
| """ | """ | ||||
| Test random_resize_with_bbox_op | |||||
| Prints images and bboxes side by side with and without RandomresizeWithBBox Op applied, | |||||
| applied on dynamically generated edge case, expected to pass. edge case is when bounding | |||||
| box has dimensions as the image itself. | |||||
| """ | """ | ||||
| logger.info("Test random resize with bbox") | |||||
| logger.info("test_random_resize_with_bbox_op_edge_c") | |||||
| dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", | |||||
| decode=True, shuffle=False) | |||||
| # original images | |||||
| data_original = 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) | |||||
| # augmented images | |||||
| data_augmented = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| test_op = c_vision.RandomResizeWithBBox(500) | |||||
| data_original = data_original.map(input_columns=["annotation"], | |||||
| output_columns=["annotation"], | |||||
| operations=fix_annotate) | |||||
| dataVoc1 = dataVoc1.map(input_columns=["annotation"], | |||||
| output_columns=["annotation"], | |||||
| operations=fix_annotate) | |||||
| dataVoc2 = dataVoc2.map(input_columns=["annotation"], | |||||
| output_columns=["annotation"], | |||||
| operations=fix_annotate) | |||||
| data_augmented = data_augmented.map(input_columns=["annotation"], | |||||
| output_columns=["annotation"], | |||||
| operations=fix_annotate) | |||||
| # maps to convert data into valid edge case data | |||||
| dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"], | |||||
| output_columns=["image", "annotation"], | |||||
| columns_order=["image", "annotation"], | |||||
| operations=[lambda img, bboxes: ( | |||||
| img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))]) | |||||
| # define map operations | |||||
| test_op = c_vision.RandomResizeWithBBox(100) # input value being the target size of resizeOp | |||||
| dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"], | |||||
| output_columns=["image", "annotation"], | |||||
| columns_order=["image", "annotation"], | |||||
| operations=[lambda img, bboxes: ( | |||||
| img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op]) | |||||
| data_augmented = data_augmented.map(input_columns=["image", "annotation"], | |||||
| output_columns=["image", "annotation"], | |||||
| columns_order=["image", "annotation"], operations=[test_op]) | |||||
| if plot: | |||||
| visualize(data_original, data_augmented) | |||||
| unaugSamp, augSamp = [], [] | |||||
| for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()): | |||||
| unaugSamp.append(unAug) | |||||
| augSamp.append(Aug) | |||||
| def test_random_resize_with_bbox_invalid_bounds(): | |||||
| data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| check_bad_box(data_voc2, BoxType.WidthOverflow, "bounding boxes is out of bounds of the image") | |||||
| data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| check_bad_box(data_voc2, BoxType.HeightOverflow, "bounding boxes is out of bounds of the image") | |||||
| data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| check_bad_box(data_voc2, BoxType.NegativeXY, "min_x") | |||||
| data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| check_bad_box(data_voc2, BoxType.WrongShape, "4 features") | |||||
| if plot_vis: | |||||
| visualize_with_bounding_boxes(unaugSamp, augSamp) | |||||
| def test_random_resize_with_bbox_invalid_size(): | |||||
| def test_random_resize_with_bbox_op_invalid_c(): | |||||
| """ | |||||
| Test RandomResizeWithBBox Op on invalid constructor parameters, expected to raise ValueError | |||||
| """ | """ | ||||
| Test random_resize_with_bbox_op | |||||
| """ | |||||
| logger.info("Test random resize with bbox with invalid target size") | |||||
| logger.info("test_random_resize_with_bbox_op_invalid_c") | |||||
| # original images | |||||
| data = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| try: | |||||
| # zero value for resize | |||||
| c_vision.RandomResizeWithBBox(0) | |||||
| data = data.map(input_columns=["annotation"], | |||||
| output_columns=["annotation"], | |||||
| operations=fix_annotate) | |||||
| except ValueError as err: | |||||
| logger.info("Got an exception in DE: {}".format(str(err))) | |||||
| assert "Input is not" in str(err) | |||||
| # negative target size as input | |||||
| try: | try: | ||||
| test_op = c_vision.RandomResizeWithBBox(-10) # DEFINE TEST OP HERE -- (PROB 1 IN CASE OF RANDOM) | |||||
| # one of the size values is zero | |||||
| c_vision.RandomResizeWithBBox((0, 100)) | |||||
| # 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" | |||||
| except ValueError as err: | |||||
| logger.info("Got an exception in DE: {}".format(str(err))) | |||||
| assert "Input is not" in str(err) | |||||
| for _, _ in enumerate(data.create_dict_iterator()): | |||||
| break | |||||
| try: | |||||
| # negative value for resize | |||||
| c_vision.RandomResizeWithBBox(-10) | |||||
| except ValueError as e: | |||||
| logger.info("Got an exception in DE: {}".format(str(e))) | |||||
| print(e) | |||||
| assert "Input is not" in str(e) | |||||
| except ValueError as err: | |||||
| logger.info("Got an exception in DE: {}".format(str(err))) | |||||
| assert "Input is not" in str(err) | |||||
| # zero target size as input | |||||
| try: | try: | ||||
| test_op = c_vision.RandomResizeWithBBox(0) # DEFINE TEST OP HERE -- (PROB 1 IN CASE OF RANDOM) | |||||
| # invalid input shape | |||||
| c_vision.RandomResizeWithBBox((100, 100, 100)) | |||||
| # 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" | |||||
| except TypeError as err: | |||||
| logger.info("Got an exception in DE: {}".format(str(err))) | |||||
| assert "Size should be" in str(err) | |||||
| for _, _ in enumerate(data.create_dict_iterator()): | |||||
| break | |||||
| except ValueError as e: | |||||
| logger.info("Got an exception in DE: {}".format(str(e))) | |||||
| assert "Input is not" in str(e) | |||||
| # invalid input shape | |||||
| try: | |||||
| test_op = c_vision.RandomResizeWithBBox((10, 10, 10)) # DEFINE TEST OP HERE -- (PROB 1 IN CASE OF RANDOM) | |||||
| # 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" | |||||
| def test_random_resize_with_bbox_op_bad_c(): | |||||
| """ | |||||
| Tests RandomResizeWithBBox Op with invalid bounding boxes, expected to catch multiple errors | |||||
| """ | |||||
| logger.info("test_random_resize_with_bbox_op_bad_c") | |||||
| test_op = c_vision.RandomResizeWithBBox((400, 300)) | |||||
| for _, _ in enumerate(data.create_dict_iterator()): | |||||
| break | |||||
| data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WidthOverflow, "bounding boxes is out of bounds of the image") | |||||
| data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| check_bad_bbox(data_voc2, test_op, InvalidBBoxType.HeightOverflow, "bounding boxes is out of bounds of the image") | |||||
| data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| check_bad_bbox(data_voc2, test_op, InvalidBBoxType.NegativeXY, "min_x") | |||||
| data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WrongShape, "4 features") | |||||
| except TypeError as e: | |||||
| logger.info("Got an exception in DE: {}".format(str(e))) | |||||
| assert "Size should be" in str(e) | |||||
| if __name__ == "__main__": | if __name__ == "__main__": | ||||
| test_random_resize_with_bbox_op(plot=False) | |||||
| test_random_resize_with_bbox_invalid_bounds() | |||||
| test_random_resize_with_bbox_invalid_size() | |||||
| test_random_resize_with_bbox_op_rand_c(plot_vis=False) | |||||
| test_random_resize_with_bbox_op_edge_c(plot_vis=False) | |||||
| test_random_resize_with_bbox_op_invalid_c() | |||||
| test_random_resize_with_bbox_op_bad_c() | |||||
+ 99
- 229
tests/ut/python/dataset/test_resize_with_bbox.py
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| @@ -15,281 +15,151 @@ | |||||
| """ | """ | ||||
| Testing the resize with bounding boxes op in DE | Testing the resize with bounding boxes op in DE | ||||
| """ | """ | ||||
| from enum import Enum | |||||
| import numpy as np | import numpy as np | ||||
| import matplotlib.patches as patches | |||||
| import matplotlib.pyplot as plt | |||||
| import mindspore.dataset as ds | |||||
| import mindspore.dataset.transforms.vision.c_transforms as c_vision | import mindspore.dataset.transforms.vision.c_transforms as c_vision | ||||
| from mindspore import log as logger | from mindspore import log as logger | ||||
| import mindspore.dataset as ds | |||||
| from util import visualize_with_bounding_boxes, InvalidBBoxType, check_bad_bbox, \ | |||||
| save_and_check_md5 | |||||
| GENERATE_GOLDEN = False | GENERATE_GOLDEN = False | ||||
| DATA_DIR = "../data/dataset/testVOC2012" | |||||
| DATA_DIR = "../data/dataset/testVOC2012_2" | |||||
| def fix_annotate(bboxes): | def fix_annotate(bboxes): | ||||
| """ | """ | ||||
| Fix annotations to format followed by mindspore. | |||||
| :param bboxes: in [label, x_min, y_min, w, h, truncate, difficult] format | :param bboxes: in [label, x_min, y_min, w, h, truncate, difficult] format | ||||
| :return: annotation in [x_min, y_min, w, h, label, truncate, difficult] format | :return: annotation in [x_min, y_min, w, h, label, truncate, difficult] format | ||||
| """ | """ | ||||
| for bbox in bboxes: | |||||
| tmp = bbox[0] | |||||
| bbox[0] = bbox[1] | |||||
| bbox[1] = bbox[2] | |||||
| bbox[2] = bbox[3] | |||||
| bbox[3] = bbox[4] | |||||
| bbox[4] = tmp | |||||
| for (i, box) in enumerate(bboxes): | |||||
| bboxes[i] = np.roll(box, -1) | |||||
| return bboxes | return bboxes | ||||
| class BoxType(Enum): | |||||
| def test_resize_with_bbox_op_c(plot_vis=False): | |||||
| """ | """ | ||||
| Defines box types for test cases | |||||
| Prints images and bboxes side by side with and without ResizeWithBBox Op applied, | |||||
| tests with MD5 check, expected to pass | |||||
| """ | """ | ||||
| WidthOverflow = 1 | |||||
| HeightOverflow = 2 | |||||
| NegativeXY = 3 | |||||
| OnEdge = 4 | |||||
| WrongShape = 5 | |||||
| logger.info("test_resize_with_bbox_op_c") | |||||
| # Load dataset | |||||
| dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", | |||||
| decode=True, shuffle=False) | |||||
| class AddBadAnnotation: # pylint: disable=too-few-public-methods | |||||
| """ | |||||
| Used to add erroneous bounding boxes to object detection pipelines. | |||||
| Usage: | |||||
| >>> # Adds a box that covers the whole image. Good for testing edge cases | |||||
| >>> de = de.map(input_columns=["image", "annotation"], | |||||
| >>> output_columns=["image", "annotation"], | |||||
| >>> operations=AddBadAnnotation(BoxType.OnEdge)) | |||||
| """ | |||||
| dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", | |||||
| decode=True, shuffle=False) | |||||
| def __init__(self, box_type): | |||||
| self.box_type = box_type | |||||
| def __call__(self, img, bboxes): | |||||
| """ | |||||
| Used to generate erroneous bounding box examples on given img. | |||||
| :param img: image where the bounding boxes are. | |||||
| :param bboxes: in [x_min, y_min, w, h, label, truncate, difficult] format | |||||
| :return: bboxes with bad examples added | |||||
| """ | |||||
| height = img.shape[0] | |||||
| width = img.shape[1] | |||||
| if self.box_type == BoxType.WidthOverflow: | |||||
| # use box that overflows on width | |||||
| return img, np.array([[0, 0, width + 1, height - 1, 0, 0, 0]]).astype(np.uint32) | |||||
| if self.box_type == BoxType.HeightOverflow: | |||||
| # use box that overflows on height | |||||
| return img, np.array([[0, 0, width - 1, height + 1, 0, 0, 0]]).astype(np.uint32) | |||||
| if self.box_type == BoxType.NegativeXY: | |||||
| # use box with negative xy | |||||
| return img, np.array([[-10, -10, width - 1, height - 1, 0, 0, 0]]).astype(np.uint32) | |||||
| if self.box_type == BoxType.OnEdge: | |||||
| # use box that covers the whole image | |||||
| return img, np.array([[0, 0, width - 1, height - 1, 0, 0, 0]]).astype(np.uint32) | |||||
| if self.box_type == BoxType.WrongShape: | |||||
| # use box that covers the whole image | |||||
| return img, np.array([[0, 0, width - 1]]).astype(np.uint32) | |||||
| return img, bboxes | |||||
| def check_bad_box(data, box_type, expected_error): | |||||
| try: | |||||
| test_op = c_vision.ResizeWithBBox(100) | |||||
| 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=AddBadAnnotation(box_type)) # Add column for "annotation" | |||||
| # 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 e: | |||||
| logger.info("Got an exception in DE: {}".format(str(e))) | |||||
| assert expected_error in str(e) | |||||
| def add_bounding_boxes(axis, bboxes): | |||||
| """ | |||||
| :param axis: axis to modify | |||||
| :param bboxes: bounding boxes to draw on the axis | |||||
| :return: None | |||||
| """ | |||||
| for bbox in bboxes: | |||||
| rect = patches.Rectangle((bbox[0], bbox[1]), | |||||
| bbox[2], bbox[3], | |||||
| linewidth=1, edgecolor='r', facecolor='none') | |||||
| # Add the patch to the Axes | |||||
| axis.add_patch(rect) | |||||
| def visualize(unaugmented_data, augment_data): | |||||
| for idx, (un_aug_item, aug_item) in enumerate( | |||||
| zip(unaugmented_data.create_dict_iterator(), augment_data.create_dict_iterator())): | |||||
| axis = plt.subplot(141) | |||||
| plt.imshow(un_aug_item["image"]) | |||||
| add_bounding_boxes(axis, un_aug_item["annotation"]) # add Orig BBoxes | |||||
| plt.title("Original" + str(idx + 1)) | |||||
| logger.info("Original ", str(idx + 1), " :", un_aug_item["annotation"]) | |||||
| axis = plt.subplot(142) | |||||
| plt.imshow(aug_item["image"]) | |||||
| add_bounding_boxes(axis, aug_item["annotation"]) # add AugBBoxes | |||||
| plt.title("Augmented" + str(idx + 1)) | |||||
| logger.info("Augmented ", str(idx + 1), " ", aug_item["annotation"], "\n") | |||||
| plt.show() | |||||
| def test_resize_with_bbox_op(plot=False): | |||||
| """ | |||||
| Test resize_with_bbox_op | |||||
| """ | |||||
| logger.info("Test resize with bbox") | |||||
| # original images | |||||
| data_original = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| # augmented images | |||||
| data_augmented = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| data_original = data_original.map(input_columns=["annotation"], | |||||
| output_columns=["annotation"], | |||||
| operations=fix_annotate) | |||||
| test_op = c_vision.ResizeWithBBox(200) | |||||
| data_augmented = data_augmented.map(input_columns=["annotation"], | |||||
| output_columns=["annotation"], | |||||
| operations=fix_annotate) | |||||
| 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]) | |||||
| # define map operations | |||||
| test_op = c_vision.ResizeWithBBox(100) # input value being the target size of resizeOp | |||||
| filename = "resize_with_bbox_op_01_c_result.npz" | |||||
| save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN) | |||||
| data_augmented = data_augmented.map(input_columns=["image", "annotation"], | |||||
| output_columns=["image", "annotation"], | |||||
| columns_order=["image", "annotation"], operations=[test_op]) | |||||
| if plot: | |||||
| visualize(data_original, data_augmented) | |||||
| unaugSamp, augSamp = [], [] | |||||
| for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()): | |||||
| unaugSamp.append(unAug) | |||||
| augSamp.append(Aug) | |||||
| def test_resize_with_bbox_invalid_bounds(): | |||||
| data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| check_bad_box(data_voc2, BoxType.WidthOverflow, "bounding boxes is out of bounds of the image") | |||||
| data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| check_bad_box(data_voc2, BoxType.HeightOverflow, "bounding boxes is out of bounds of the image") | |||||
| data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| check_bad_box(data_voc2, BoxType.NegativeXY, "min_x") | |||||
| data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| check_bad_box(data_voc2, BoxType.WrongShape, "4 features") | |||||
| if plot_vis: | |||||
| visualize_with_bounding_boxes(unaugSamp, augSamp) | |||||
| def test_resize_with_bbox_invalid_size(): | |||||
| def test_resize_with_bbox_op_edge_c(plot_vis=False): | |||||
| """ | """ | ||||
| Test resize_with_bbox_op | |||||
| """ | |||||
| logger.info("Test resize with bbox with invalid target size") | |||||
| Prints images and bboxes side by side with and without ResizeWithBBox Op applied, | |||||
| applied on dynamically generated edge case, expected to pass. edge case is when bounding | |||||
| box has dimensions as the image itself. | |||||
| """ | |||||
| logger.info("test_resize_with_bbox_op_edge_c") | |||||
| dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", | |||||
| decode=True, shuffle=False) | |||||
| # original images | |||||
| data = 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) | |||||
| data = data.map(input_columns=["annotation"], | |||||
| output_columns=["annotation"], | |||||
| operations=fix_annotate) | |||||
| test_op = c_vision.ResizeWithBBox(500) | |||||
| # negative target size as input | |||||
| try: | |||||
| test_op = c_vision.ResizeWithBBox(-10) | |||||
| 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 | |||||
| data = data.map(input_columns=["image", "annotation"], | |||||
| output_columns=["image", "annotation"], | |||||
| columns_order=["image", "annotation"], | |||||
| operations=[test_op]) # Add column for "annotation" | |||||
| # maps to convert data into valid edge case data | |||||
| dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"], | |||||
| output_columns=["image", "annotation"], | |||||
| columns_order=["image", "annotation"], | |||||
| operations=[lambda img, bboxes: ( | |||||
| img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))]) | |||||
| for _, _ in enumerate(data.create_dict_iterator()): | |||||
| break | |||||
| # Test Op added to list of Operations here | |||||
| dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"], | |||||
| output_columns=["image", "annotation"], | |||||
| columns_order=["image", "annotation"], | |||||
| operations=[lambda img, bboxes: ( | |||||
| img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op]) | |||||
| except ValueError as e: | |||||
| logger.info("Got an exception in DE: {}".format(str(e))) | |||||
| assert "Input is not" in str(e) | |||||
| unaugSamp, augSamp = [], [] | |||||
| # zero target size as input | |||||
| try: | |||||
| test_op = c_vision.ResizeWithBBox(0) | |||||
| for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()): | |||||
| unaugSamp.append(unAug) | |||||
| augSamp.append(Aug) | |||||
| # 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" | |||||
| if plot_vis: | |||||
| visualize_with_bounding_boxes(unaugSamp, augSamp) | |||||
| for _, _ in enumerate(data.create_dict_iterator()): | |||||
| break | |||||
| except ValueError as e: | |||||
| logger.info("Got an exception in DE: {}".format(str(e))) | |||||
| assert "Input is not" in str(e) | |||||
| def test_resize_with_bbox_op_invalid_c(): | |||||
| """ | |||||
| Test ResizeWithBBox Op on invalid constructor parameters, expected to raise ValueError | |||||
| """ | |||||
| logger.info("test_resize_with_bbox_op_invalid_c") | |||||
| # invalid input shape | |||||
| try: | try: | ||||
| test_op = c_vision.ResizeWithBBox((10, 10, 10)) | |||||
| # invalid interpolation value | |||||
| c_vision.ResizeWithBBox(400, interpolation="invalid") | |||||
| # 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" | |||||
| except ValueError as err: | |||||
| logger.info("Got an exception in DE: {}".format(str(err))) | |||||
| assert "interpolation" in str(err) | |||||
| for _, _ in enumerate(data.create_dict_iterator()): | |||||
| break | |||||
| except TypeError as e: | |||||
| logger.info("Got an exception in DE: {}".format(str(e))) | |||||
| assert "Size should be" in str(e) | |||||
| def test_resize_with_bbox_invalid_interpolation(): | |||||
| def test_resize_with_bbox_op_bad_c(): | |||||
| """ | """ | ||||
| Test resize_with_bbox_op | |||||
| """ | |||||
| logger.info("Test resize with bbox with invalid interpolation size") | |||||
| # original images | |||||
| data = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| data = data.map(input_columns=["annotation"], | |||||
| output_columns=["annotation"], | |||||
| operations=fix_annotate) | |||||
| # invalid interpolation | |||||
| try: | |||||
| test_op = c_vision.ResizeWithBBox(100, interpolation="invalid") | |||||
| # 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" | |||||
| Tests ResizeWithBBox Op with invalid bounding boxes, expected to catch multiple errors | |||||
| """ | |||||
| logger.info("test_resize_with_bbox_op_bad_c") | |||||
| test_op = c_vision.ResizeWithBBox((200, 300)) | |||||
| for _, _ in enumerate(data.create_dict_iterator()): | |||||
| break | |||||
| data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WidthOverflow, "bounding boxes is out of bounds of the image") | |||||
| data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| check_bad_bbox(data_voc2, test_op, InvalidBBoxType.HeightOverflow, "bounding boxes is out of bounds of the image") | |||||
| data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| check_bad_bbox(data_voc2, test_op, InvalidBBoxType.NegativeXY, "min_x") | |||||
| data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False) | |||||
| check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WrongShape, "4 features") | |||||
| except ValueError as e: | |||||
| logger.info("Got an exception in DE: {}".format(str(e))) | |||||
| assert "interpolation" in str(e) | |||||
| if __name__ == "__main__": | if __name__ == "__main__": | ||||
| test_resize_with_bbox_op(plot=False) | |||||
| test_resize_with_bbox_invalid_bounds() | |||||
| test_resize_with_bbox_invalid_size() | |||||
| test_resize_with_bbox_invalid_interpolation() | |||||
| test_resize_with_bbox_op_c(plot_vis=False) | |||||
| test_resize_with_bbox_op_edge_c(plot_vis=False) | |||||
| test_resize_with_bbox_op_invalid_c() | |||||
| test_resize_with_bbox_op_bad_c() | |||||