Update RandomHorizontalFlipWithBBox and BoundingBouxAugment C++ Ops to use floats

mindspore/ccsrc/dataset/api/python_bindings.cc

@@ -60,7 +60,7 @@
 #include "dataset/kernels/image/random_crop_decode_resize_op.h"
 #include "dataset/kernels/image/random_crop_op.h"
 #include "dataset/kernels/image/random_crop_with_bbox_op.h"
-#include "dataset/kernels/image/random_horizontal_flip_bbox_op.h"
+#include "dataset/kernels/image/random_horizontal_flip_with_bbox_op.h"
 #include "dataset/kernels/image/random_horizontal_flip_op.h"
 #include "dataset/kernels/image/random_resize_op.h"
 #include "dataset/kernels/image/random_resize_with_bbox_op.h"

mindspore/ccsrc/dataset/kernels/image/CMakeLists.txt

@@ -15,7 +15,7 @@ add_library(kernels-image OBJECT
     random_crop_op.cc
     random_crop_with_bbox_op.cc
     random_horizontal_flip_op.cc
-    random_horizontal_flip_bbox_op.cc
+    random_horizontal_flip_with_bbox_op.cc
     bounding_box_augment_op.cc
     random_resize_op.cc
     random_rotation_op.cc

mindspore/ccsrc/dataset/kernels/image/bounding_box_augment_op.cc

@@ -43,28 +43,29 @@ Status BoundingBoxAugmentOp::Compute(const TensorRow &input, TensorRow *output)
   std::shared_ptr<Tensor> crop_out;
   std::shared_ptr<Tensor> res_out;
   std::shared_ptr<CVTensor> input_restore = CVTensor::AsCVTensor(input[0]);
-
   for (uint32_t i = 0; i < num_to_aug; i++) {
-    uint32_t min_x = 0;
-    uint32_t min_y = 0;
-    uint32_t b_w = 0;
-    uint32_t b_h = 0;
+    float min_x = 0;
+    float min_y = 0;
+    float b_w = 0;
+    float b_h = 0;
     // get the required items
-    input[1]->GetItemAt<uint32_t>(&min_x, {selected_boxes[i], 0});
-    input[1]->GetItemAt<uint32_t>(&min_y, {selected_boxes[i], 1});
-    input[1]->GetItemAt<uint32_t>(&b_w, {selected_boxes[i], 2});
-    input[1]->GetItemAt<uint32_t>(&b_h, {selected_boxes[i], 3});
-    Crop(input_restore, &crop_out, min_x, min_y, b_w, b_h);
+    RETURN_IF_NOT_OK(input[1]->GetItemAt<float>(&min_x, {selected_boxes[i], 0}));
+    RETURN_IF_NOT_OK(input[1]->GetItemAt<float>(&min_y, {selected_boxes[i], 1}));
+    RETURN_IF_NOT_OK(input[1]->GetItemAt<float>(&b_w, {selected_boxes[i], 2}));
+    RETURN_IF_NOT_OK(input[1]->GetItemAt<float>(&b_h, {selected_boxes[i], 3}));
+    RETURN_IF_NOT_OK(Crop(input_restore, &crop_out, static_cast<int>(min_x), static_cast<int>(min_y),
+                          static_cast<int>(b_w), static_cast<int>(b_h)));
     // transform the cropped bbox region
-    transform_->Compute(crop_out, &res_out);
+    RETURN_IF_NOT_OK(transform_->Compute(crop_out, &res_out));
     // place the transformed region back in the restored input
     std::shared_ptr<CVTensor> res_img = CVTensor::AsCVTensor(res_out);
     // check if transformed crop is out of bounds of the box
     if (res_img->mat().cols > b_w || res_img->mat().rows > b_h || res_img->mat().cols < b_w ||
         res_img->mat().rows < b_h) {
       // if so, resize to fit in the box
-      std::shared_ptr<TensorOp> resize_op = std::make_shared<ResizeOp>(b_h, b_w);
-      resize_op->Compute(std::static_pointer_cast<Tensor>(res_img), &res_out);
+      std::shared_ptr<TensorOp> resize_op =
+        std::make_shared<ResizeOp>(static_cast<int32_t>(b_h), static_cast<int32_t>(b_w));
+      RETURN_IF_NOT_OK(resize_op->Compute(std::static_pointer_cast<Tensor>(res_img), &res_out));
       res_img = CVTensor::AsCVTensor(res_out);
     }
     res_img->mat().copyTo(input_restore->mat()(cv::Rect(min_x, min_y, res_img->mat().cols, res_img->mat().rows)));

mindspore/ccsrc/dataset/kernels/image/random_horizontal_flip_bbox_op.cc → mindspore/ccsrc/dataset/kernels/image/random_horizontal_flip_with_bbox_op.cc

@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 #include <utility>
-#include "dataset/kernels/image/random_horizontal_flip_bbox_op.h"
+#include "dataset/kernels/image/random_horizontal_flip_with_bbox_op.h"
 #include "dataset/kernels/image/image_utils.h"
 #include "dataset/util/status.h"
 #include "dataset/core/cv_tensor.h"
@@ -31,21 +31,19 @@ Status RandomHorizontalFlipWithBBoxOp::Compute(const TensorRow &input, TensorRow
     // To test bounding boxes algorithm, create random bboxes from image dims
     size_t num_of_boxes = input[1]->shape()[0];      // set to give number of bboxes
     float img_center = (input[0]->shape()[1] / 2.);  // get the center of the image
-
     for (int i = 0; i < num_of_boxes; i++) {
-      uint32_t b_w = 0;  // bounding box width
-      uint32_t min_x = 0;
+      float b_w = 0;  // bounding box width
+      float min_x = 0;
       // get the required items
-      input[1]->GetItemAt<uint32_t>(&min_x, {i, 0});
-      input[1]->GetItemAt<uint32_t>(&b_w, {i, 2});
+      RETURN_IF_NOT_OK(input[1]->GetItemAt<float>(&min_x, {i, 0}));
+      RETURN_IF_NOT_OK(input[1]->GetItemAt<float>(&b_w, {i, 2}));
       // do the flip
       float diff = img_center - min_x;       // get distance from min_x to center
-      uint32_t refl_min_x = diff + img_center;  // get reflection of min_x
-      uint32_t new_min_x = refl_min_x - b_w;    // subtract from the reflected min_x to get the new one
-      input[1]->SetItemAt<uint32_t>({i, 0}, new_min_x);
+      float refl_min_x = diff + img_center;  // get reflection of min_x
+      float new_min_x = refl_min_x - b_w;    // subtract from the reflected min_x to get the new one
+      RETURN_IF_NOT_OK(input[1]->SetItemAt<float>({i, 0}, new_min_x));
     }
-    (*output).push_back(nullptr);
-    (*output).push_back(nullptr);
+    (*output).resize(2);
     // move input to output pointer of bounding boxes
     (*output)[1] = std::move(input[1]);
     // perform HorizontalFlip on the image
@@ -55,6 +53,5 @@ Status RandomHorizontalFlipWithBBoxOp::Compute(const TensorRow &input, TensorRow
   *output = input;
   return Status::OK();
 }
-
 }  // namespace dataset
 }  // namespace mindspore

tests/ut/python/dataset/test_bounding_box_augment.py

+# 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 bounding box augment op in DE
+"""
+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
+
+GENERATE_GOLDEN = False
+
+# updated VOC dataset with correct annotations
+DATA_DIR = "../data/dataset/testVOC2012_2"
+DATA_DIR_2 = ["../data/dataset/testCOCO/train/",
+              "../data/dataset/testCOCO/annotations/train.json"]  # DATA_DIR, ANNOTATION_DIR
+
+
+def test_bounding_box_augment_with_rotation_op(plot_vis=False):
+    """
+    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")
+
+    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)
+
+    # map to apply ops
+    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)
+
+    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 (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")
+
+    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.RandomCrop(50), 0.5)
+
+    # map to apply ops
+    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 = [], []
+
+    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 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")
+
+    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)
+
+    # 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_op_coco_c(plot_vis=False):
+    """
+    Prints images and bboxes side by side with and without BoundingBoxAugment Op applied,
+    Testing with COCO dataset
+    """
+    logger.info("test_bounding_box_augment_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.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1), 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_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)
+
+    # 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.float32)))
+    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.float32)))
+    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 BoundingBoxAugment op with invalid input ratio
+    """
+    logger.info("test_bounding_box_augment_invalid_ratio_c")
+
+    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)
+        # 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_bounding_box_augment_invalid_bounds_c():
+    """
+    Test BoundingBoxAugment op with invalid bboxes.
+    """
+    logger.info("test_bounding_box_augment_invalid_bounds_c")
+
+    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(plot_vis=False)
+    test_bounding_box_augment_with_crop_op(plot_vis=False)
+    test_bounding_box_augment_op_coco_c(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_with_bbox.py

+# 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
+
+# updated VOC dataset with correct annotations
+DATA_DIR = "../data/dataset/testVOC2012_2"
+DATA_DIR_2 = ["../data/dataset/testCOCO/train/",
+              "../data/dataset/testCOCO/annotations/train.json"]  # DATA_DIR, ANNOTATION_DIR
+
+
+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", "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_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_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)
+
+    # 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)
+
+    # 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.float32)))
+    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.float32)))
+    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)
+        # 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_flip_with_bbox_op_coco_c(plot_vis=False)
+    test_random_horizontal_flip_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()