!2587 Implementing Python UTs for BoundingBoxAugment and RandomHorizontalFlipWithBBox

Merge pull request !2587 from imaaamin/object_ops_ut

tests/ut/python/dataset/test_bounding_box_augment.py

@@ -15,92 +15,18 @@
 """
 Testing the bounding box augment op in DE
 """
-from enum import Enum
+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
+import numpy as np
 import mindspore.log as logger
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.vision.c_transforms as c_vision
-import matplotlib.pyplot as plt
-import matplotlib.patches as patches
-import numpy as np
 
 GENERATE_GOLDEN = False
 
 DATA_DIR = "../data/dataset/testVOC2012_2"
 
 
-class BoxType(Enum):
-    """
-    Defines box types for test cases
-    """
-    WidthOverflow = 1
-    HeightOverflow = 2
-    NegativeXY = 3
-    OnEdge = 4
-    WrongShape = 5
-
-
-def add_bad_annotation(img, bboxes, box_type):
-    """
-    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
-    :param box_type: type of bad box
-    :return: bboxes with bad examples added
-    """
-    height = img.shape[0]
-    width = img.shape[1]
-    if box_type == BoxType.WidthOverflow:
-        # use box that overflows on width
-        return img, np.array([[0, 0, width + 1, height, 0, 0, 0]]).astype(np.uint32)
-
-    if box_type == BoxType.HeightOverflow:
-        # use box that overflows on height
-        return img, np.array([[0, 0, width, height + 1, 0, 0, 0]]).astype(np.uint32)
-
-    if box_type == BoxType.NegativeXY:
-        # use box with negative xy
-        return img, np.array([[-10, -10, width, height, 0, 0, 0]]).astype(np.uint32)
-
-    if box_type == BoxType.OnEdge:
-        # use box that covers the whole image
-        return img, np.array([[0, 0, width, height, 0, 0, 0]]).astype(np.uint32)
-
-    if 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):
-    """
-    :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
-    """
-    try:
-        test_op = c_vision.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1),
-                                              1)  # 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=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 fix_annotate(bboxes):
     """
     Fix annotations to format followed by mindspore.
@@ -117,153 +43,217 @@ def fix_annotate(bboxes):
     return bboxes
 
 
-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):
+def test_bounding_box_augment_with_rotation_op(plot_vis=False):
     """
-    :param unaugmented_data: original data
-    :param augment_data: data after augmentations
-    :return: None
-    """
-    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_bounding_box_augment_with_rotation_op(plot=False):
-    """
-    Test BoundingBoxAugment op
+    Test BoundingBoxAugment op (passing rotation op as transform)
     Prints images side by side with and without Aug applied + bboxes to compare and test
     """
     logger.info("test_bounding_box_augment_with_rotation_op")
 
-    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)
+    original_seed = config_get_set_seed(0)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+
+    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)
 
+    # Ratio is set to 1 to apply rotation on all bounding boxes.
     test_op = c_vision.BoundingBoxAugment(c_vision.RandomRotation(90), 1)
-    # DEFINE TEST OP HERE -- (PROB 1 IN CASE OF RANDOM)
 
     # 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)
+    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_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)
+    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+                            output_columns=["image", "annotation"],
+                            columns_order=["image", "annotation"],
+                            operations=[test_op])
 
+    filename = "bounding_box_augment_rotation_c_result.npz"
+    save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
 
-def test_bounding_box_augment_with_crop_op(plot=False):
+    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_bounding_box_augment_with_crop_op(plot_vis=False):
     """
-    Test BoundingBoxAugment op
+    Test BoundingBoxAugment op (passing crop op as transform)
     Prints images side by side with and without Aug applied + bboxes to compare and test
     """
     logger.info("test_bounding_box_augment_with_crop_op")
 
-    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)
+    original_seed = config_get_set_seed(1)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+
+    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)
 
+    # Ratio is set to 1 to apply rotation on all bounding boxes.
     test_op = c_vision.BoundingBoxAugment(c_vision.RandomCrop(90), 1)
 
     # 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)
+    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_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)
+    dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
+                            output_columns=["image", "annotation"],
+                            columns_order=["image", "annotation"],
+                            operations=[test_op])
+
+    filename = "bounding_box_augment_crop_c_result.npz"
+    save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
 
+    unaugSamp, augSamp = [], []
 
-def test_bounding_box_augment_valid_ratio_c(plot=False):
+    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_bounding_box_augment_valid_ratio_c(plot_vis=False):
     """
-    Test RandomHorizontalFlipWithBBox op
+    Test BoundingBoxAugment op (testing with valid ratio, less than 1.
     Prints images side by side with and without Aug applied + bboxes to compare and test
     """
     logger.info("test_bounding_box_augment_valid_ratio_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)
+    original_seed = config_get_set_seed(1)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+
+    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.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1), 0.9)
-    # DEFINE TEST OP HERE -- (PROB 1 IN CASE OF RANDOM)
 
     # 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)
+    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])  # Add column for "annotation"
+    filename = "bounding_box_augment_valid_ratio_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_bounding_box_augment_valid_edge_c(plot_vis=False):
+    """
+    Test BoundingBoxAugment 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_bounding_box_augment_valid_edge_c")
+
+    original_seed = config_get_set_seed(1)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+
+    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.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1), 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
-    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)
+    # 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])
+    filename = "bounding_box_augment_valid_edge_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_bounding_box_augment_invalid_ratio_c():
     """
-    Test RandomHorizontalFlipWithBBox op with invalid input probability
+    Test BoundingBoxAugment op with invalid input ratio
     """
     logger.info("test_bounding_box_augment_invalid_ratio_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)
+    dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
 
     try:
         # ratio range is from 0 - 1
         test_op = c_vision.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1), 1.5)
         # 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)
+        dataVoc2 = dataVoc2.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"
+        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 is not" in str(error)
@@ -275,20 +265,24 @@ def test_bounding_box_augment_invalid_bounds_c():
     """
     logger.info("test_bounding_box_augment_invalid_bounds_c")
 
-    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")
+    test_op = c_vision.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1),
+                                          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")
 
 
 if __name__ == "__main__":
     # set to false to not show plots
-    test_bounding_box_augment_with_rotation_op(False)
-    test_bounding_box_augment_with_crop_op(False)
-    test_bounding_box_augment_valid_ratio_c(False)
+    test_bounding_box_augment_with_rotation_op(plot_vis=False)
+    test_bounding_box_augment_with_crop_op(plot_vis=False)
+    test_bounding_box_augment_valid_ratio_c(plot_vis=False)
+    test_bounding_box_augment_valid_edge_c(plot_vis=False)
     test_bounding_box_augment_invalid_ratio_c()
     test_bounding_box_augment_invalid_bounds_c()

tests/ut/python/dataset/test_random_horizontal_flip_bbox.py

@@ -1,266 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""
-Testing the random horizontal flip 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 mindspore.log as logger
-import mindspore.dataset as ds
-import mindspore.dataset.transforms.vision.c_transforms as c_vision
-
-GENERATE_GOLDEN = False
-
-DATA_DIR = "../data/dataset/testVOC2012_2"
-
-
-class BoxType(Enum):
-    """
-    Defines box types for test cases
-    """
-    WidthOverflow = 1
-    HeightOverflow = 2
-    NegativeXY = 3
-    OnEdge = 4
-    WrongShape = 5
-
-def add_bad_annotation(img, bboxes, box_type):
-    """
-    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
-    :param box_type: type of bad box
-    :return: bboxes with bad examples added
-    """
-    height = img.shape[0]
-    width = img.shape[1]
-    if box_type == BoxType.WidthOverflow:
-        # use box that overflows on width
-        return img, np.array([[0, 0, width + 1, height, 0, 0, 0]]).astype(np.uint32)
-
-    if box_type == BoxType.HeightOverflow:
-        # use box that overflows on height
-        return img, np.array([[0, 0, width, height + 1, 0, 0, 0]]).astype(np.uint32)
-
-    if box_type == BoxType.NegativeXY:
-        # use box with negative xy
-        return img, np.array([[-10, -10, width, height, 0, 0, 0]]).astype(np.uint32)
-
-    if box_type == BoxType.OnEdge:
-        # use box that covers the whole image
-        return img, np.array([[0, 0, width, height, 0, 0, 0]]).astype(np.uint32)
-
-    if 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 h_flip(image):
-    """
-    Apply the random_horizontal
-    """
-    # that's why we flip here too
-    image = image[:, ::-1, :]
-    return image
-
-
-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 fix_annotate(bboxes):
-    """
-    Fix annotations to format followed by mindspore.
-    :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
-    """
-    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
-    return bboxes
-
-
-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):
-    """
-    :param unaugmented_data: original data
-    :param augment_data: data after augmentations
-    :return: None
-    """
-    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_horizontal_bbox_op(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_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(1)
-
-    # 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_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_bbox_invalid_prob_c():
-    """
-    Test RandomHorizontalFlipWithBBox op with invalid input probability
-    """
-    logger.info("test_random_horizontal_bbox_invalid_prob_c")
-
-    data_voc2 = 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)
-        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"
-    except ValueError as error:
-        logger.info("Got an exception in DE: {}".format(str(error)))
-        assert "Input is not" in str(error)
-
-
-def test_random_horizontal_bbox_invalid_bounds_c():
-    """
-    Test RandomHorizontalFlipWithBBox op with invalid bounding boxes
-    """
-    logger.info("test_random_horizontal_bbox_invalid_bounds_c")
-
-    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 __name__ == "__main__":
-    # set to false to not show plots
-    test_random_horizontal_bbox_op(False)
-    test_random_horizontal_bbox_valid_prob_c(False)
-    test_random_horizontal_bbox_invalid_prob_c()
-    test_random_horizontal_bbox_invalid_bounds_c()

tests/ut/python/dataset/test_random_horizontal_flip_with_bbox.py

@@ -0,0 +1,229 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+Testing the random horizontal flip with bounding boxes op in DE
+"""
+import numpy as np
+import mindspore.log as logger
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.c_transforms as c_vision
+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
+
+DATA_DIR = "../data/dataset/testVOC2012_2"
+
+
+def fix_annotate(bboxes):
+    """
+    Fix annotations to format followed by mindspore.
+    :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
+    """
+    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
+    return bboxes
+
+
+def test_random_horizontal_flip_with_bbox_op_c(plot_vis=False):
+    """
+    Prints images side by side with and without Aug applied + bboxes to
+    compare and test
+    """
+    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)
+
+    # 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])
+
+    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)
+
+
+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_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)
+        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])  # Add column for "annotation"
+    except ValueError as error:
+        logger.info("Got an exception in DE: {}".format(str(error)))
+        assert "Input is not" in str(error)
+
+
+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")
+
+
+if __name__ == "__main__":
+    # set to false to not show plots
+    test_random_horizontal_flip_with_bbox_op_c(plot_vis=False)
+    test_random_horizontal_bbox_with_bbox_valid_rand_c(plot_vis=False)
+    test_random_horizontal_flip_with_bbox_valid_edge_c(plot_vis=False)
+    test_random_horizontal_flip_with_bbox_invalid_prob_c()
+    test_random_horizontal_flip_with_bbox_invalid_bounds_c()