!2330 Implemented 3 new BBox Image Aug Ops - RandomVerticalFlipWithBBox,...

!2330 Implemented 3 new BBox Image Aug Ops - RandomVerticalFlipWithBBox, RandomCropWithBBox, RandomCropAndResizeWithBbox - with supporting code 
Merge pull request !2330 from danishnxt/AugOpsD1

graphengine @ 45ca7863

-Subproject commit 1350673d51b3f8535bc217a7780e6a0b52ff9a41
+Subproject commit 45ca7863ac6410c8e2f83168481ddc6b43bcea33

mindspore/ccsrc/dataset/api/python_bindings.cc

@@ -56,13 +56,16 @@
 #include "dataset/kernels/image/pad_op.h"
 #include "dataset/kernels/image/random_color_adjust_op.h"
 #include "dataset/kernels/image/random_crop_and_resize_op.h"
+#include "dataset/kernels/image/random_crop_and_resize_with_bbox_op.h"
 #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_op.h"
 #include "dataset/kernels/image/random_resize_op.h"
 #include "dataset/kernels/image/random_rotation_op.h"
 #include "dataset/kernels/image/random_vertical_flip_op.h"
+#include "dataset/kernels/image/random_vertical_flip_with_bbox_op.h"
 #include "dataset/kernels/image/rescale_op.h"
 #include "dataset/kernels/image/resize_bilinear_op.h"
 #include "dataset/kernels/image/resize_op.h"
@@ -381,6 +384,12 @@ void bindTensorOps2(py::module *m) {
     *m, "RandomVerticalFlipOp", "Tensor operation to randomly flip an image vertically.")
     .def(py::init<float>(), py::arg("probability") = RandomVerticalFlipOp::kDefProbability);
 
+  (void)py::class_<RandomVerticalFlipWithBBoxOp, TensorOp, std::shared_ptr<RandomVerticalFlipWithBBoxOp>>(
+    *m, "RandomVerticalFlipWithBBoxOp",
+    "Tensor operation to randomly flip an image vertically"
+    " and adjust bounding boxes.")
+    .def(py::init<float>(), py::arg("probability") = RandomVerticalFlipWithBBoxOp::kDefProbability);
+
   (void)py::class_<RandomCropOp, TensorOp, std::shared_ptr<RandomCropOp>>(*m, "RandomCropOp",
                                                                           "Gives random crop of specified size "
                                                                           "Takes crop size")
@@ -392,6 +401,20 @@ void bindTensorOps2(py::module *m) {
          py::arg("fillG") = RandomCropOp::kDefFillG, py::arg("fillB") = RandomCropOp::kDefFillB);
   (void)py::class_<HwcToChwOp, TensorOp, std::shared_ptr<HwcToChwOp>>(*m, "ChannelSwapOp").def(py::init<>());
 
+  (void)py::class_<RandomCropWithBBoxOp, TensorOp, std::shared_ptr<RandomCropWithBBoxOp>>(*m, "RandomCropWithBBoxOp",
+                                                                                          "Gives random crop of given "
+                                                                                          "size + adjusts bboxes "
+                                                                                          "Takes crop size")
+    .def(py::init<int32_t, int32_t, int32_t, int32_t, int32_t, int32_t, BorderType, bool, uint8_t, uint8_t, uint8_t>(),
+         py::arg("cropHeight"), py::arg("cropWidth"), py::arg("padTop") = RandomCropWithBBoxOp::kDefPadTop,
+         py::arg("padBottom") = RandomCropWithBBoxOp::kDefPadBottom,
+         py::arg("padLeft") = RandomCropWithBBoxOp::kDefPadLeft,
+         py::arg("padRight") = RandomCropWithBBoxOp::kDefPadRight,
+         py::arg("borderType") = RandomCropWithBBoxOp::kDefBorderType,
+         py::arg("padIfNeeded") = RandomCropWithBBoxOp::kDefPadIfNeeded,
+         py::arg("fillR") = RandomCropWithBBoxOp::kDefFillR, py::arg("fillG") = RandomCropWithBBoxOp::kDefFillG,
+         py::arg("fillB") = RandomCropWithBBoxOp::kDefFillB);
+
   (void)py::class_<OneHotOp, TensorOp, std::shared_ptr<OneHotOp>>(
     *m, "OneHotOp", "Tensor operation to apply one hot encoding. Takes number of classes.")
     .def(py::init<int32_t>());
@@ -488,6 +511,20 @@ void bindTensorOps3(py::module *m) {
          py::arg("interpolation") = RandomCropAndResizeOp::kDefInterpolation,
          py::arg("maxIter") = RandomCropAndResizeOp::kDefMaxIter);
 
+  (void)py::class_<RandomCropAndResizeWithBBoxOp, TensorOp, std::shared_ptr<RandomCropAndResizeWithBBoxOp>>(
+    *m, "RandomCropAndResizeWithBBoxOp",
+    "Tensor operation to randomly crop an image (with BBoxes) and resize to a given size."
+    "Takes output height and width and"
+    "optional parameters for lower and upper bound for aspect ratio (h/w) and scale,"
+    "interpolation mode, and max attempts to crop")
+    .def(py::init<int32_t, int32_t, float, float, float, float, InterpolationMode, int32_t>(), py::arg("targetHeight"),
+         py::arg("targetWidth"), py::arg("scaleLb") = RandomCropAndResizeWithBBoxOp::kDefScaleLb,
+         py::arg("scaleUb") = RandomCropAndResizeWithBBoxOp::kDefScaleUb,
+         py::arg("aspectLb") = RandomCropAndResizeWithBBoxOp::kDefAspectLb,
+         py::arg("aspectUb") = RandomCropAndResizeWithBBoxOp::kDefAspectUb,
+         py::arg("interpolation") = RandomCropAndResizeWithBBoxOp::kDefInterpolation,
+         py::arg("maxIter") = RandomCropAndResizeWithBBoxOp::kDefMaxIter);
+
   (void)py::class_<RandomColorAdjustOp, TensorOp, std::shared_ptr<RandomColorAdjustOp>>(
     *m, "RandomColorAdjustOp",
     "Tensor operation to adjust an image's color randomly."

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

@@ -10,14 +10,17 @@ add_library(kernels-image OBJECT
     pad_op.cc
     random_color_adjust_op.cc
     random_crop_decode_resize_op.cc
+    random_crop_and_resize_with_bbox_op.cc
     random_crop_and_resize_op.cc
     random_crop_op.cc
+    random_crop_with_bbox_op.cc
     random_horizontal_flip_op.cc
         random_horizontal_flip_bbox_op.cc
         bounding_box_augment_op.cc
     random_resize_op.cc
     random_rotation_op.cc
     random_vertical_flip_op.cc
+    random_vertical_flip_with_bbox_op.cc
     rescale_op.cc
     resize_bilinear_op.cc
     resize_op.cc

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

@@ -16,6 +16,7 @@
 #include "dataset/kernels/image/image_utils.h"
 #include <opencv2/imgproc/types_c.h>
 #include <algorithm>
+#include <vector>
 #include <stdexcept>
 #include <utility>
 #include <opencv2/imgcodecs.hpp>
@@ -724,5 +725,101 @@ Status Pad(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output
     RETURN_STATUS_UNEXPECTED("Unexpected error in pad");
   }
 }
+// -------- BBOX OPERATIONS -------- //
+void UpdateBBoxesForCrop(std::shared_ptr<Tensor> *bboxList, size_t *bboxCount, int *CB_Xmin, int *CB_Ymin, int *CB_Xmax,
+                         int *CB_Ymax) {
+  // PASS LIST, COUNT OF BOUNDING BOXES
+  // Also PAss X/Y Min/Max of image cropped region - normally obtained from 'GetCropBox' functions
+  uint32_t bb_Xmin_t, bb_Ymin_t, bb_Xmax_t, bb_Ymax_t;
+
+  std::vector<int> correctInd;
+  std::vector<uint32_t> copyVals;
+  dsize_t bboxDim = (*bboxList)->shape()[1];
+  bool retFlag = false;  // true unless overlap found
+  for (int i = 0; i < *bboxCount; i++) {
+    int bb_Xmin, bb_Xmax, bb_Ymin, bb_Ymax;
+    (*bboxList)->GetUnsignedIntAt(&bb_Xmin_t, {i, 0});
+    (*bboxList)->GetUnsignedIntAt(&bb_Ymin_t, {i, 1});
+    (*bboxList)->GetUnsignedIntAt(&bb_Xmax_t, {i, 2});
+    (*bboxList)->GetUnsignedIntAt(&bb_Ymax_t, {i, 3});
+    bb_Xmin = bb_Xmin_t;
+    bb_Ymin = bb_Ymin_t;
+    bb_Xmax = bb_Xmax_t;
+    bb_Ymax = bb_Ymax_t;
+    bb_Xmax = bb_Xmin + bb_Xmax;
+    bb_Ymax = bb_Ymin + bb_Ymax;
+    // check for image / BB overlap
+    if (((bb_Xmin > *CB_Xmax) || (bb_Ymin > *CB_Ymax)) || ((bb_Xmax < *CB_Xmin) || (bb_Ymax < *CB_Ymin))) {
+      retFlag = true;  // no overlap found
+    }
+    if (retFlag) {  // invalid bbox no longer within image region - reset to zero
+      continue;
+    }
+    // Update this bbox and select it to move to the final output tensor
+    correctInd.push_back(i);
+    // adjust BBox corners by bringing into new CropBox if beyond
+    // Also reseting/adjusting for boxes to lie within CropBox instead of Image - subtract CropBox Xmin/YMin
+    bb_Xmin = bb_Xmin - (std::min(0, (bb_Xmin - *CB_Xmin)) + *CB_Xmin);
+    bb_Xmax = bb_Xmax - (std::max(0, (bb_Xmax - *CB_Xmax)) + *CB_Xmin);
+    bb_Ymin = bb_Ymin - (std::min(0, (bb_Ymin - *CB_Ymin)) + *CB_Ymin);
+    bb_Ymax = bb_Ymax - (std::max(0, (bb_Ymax - *CB_Ymax)) + *CB_Ymin);
+    // reset min values and calculate width/height from Box corners
+    (*bboxList)->SetItemAt({i, 0}, (uint32_t)(bb_Xmin));
+    (*bboxList)->SetItemAt({i, 1}, (uint32_t)(bb_Ymin));
+    (*bboxList)->SetItemAt({i, 2}, (uint32_t)(bb_Xmax - bb_Xmin));
+    (*bboxList)->SetItemAt({i, 3}, (uint32_t)(bb_Ymax - bb_Ymin));
+  }
+  // create new tensor and copy over bboxes still valid to the image
+  // bboxes outside of new cropped region are ignored - empty tensor returned in case of none
+  *bboxCount = correctInd.size();
+  uint32_t temp;
+  for (auto slice : correctInd) {  // for every index in the loop
+    for (int ix = 0; ix < bboxDim; ix++) {
+      (*bboxList)->GetUnsignedIntAt(&temp, {slice, ix});
+      copyVals.push_back(temp);
+    }
+  }
+  std::shared_ptr<Tensor> retV;
+  Tensor::CreateTensor(&retV, copyVals, TensorShape({(dsize_t)bboxCount, bboxDim}));
+  (*bboxList) = retV;  // reset pointer
+}
+
+void PadBBoxes(std::shared_ptr<Tensor> *bboxList, size_t *bboxCount, int32_t *pad_top, int32_t *pad_left) {
+  uint32_t xMin = 0;
+  uint32_t yMin = 0;
+  for (int i = 0; i < *bboxCount; i++) {
+    (*bboxList)->GetUnsignedIntAt(&xMin, {i, 0});
+    (*bboxList)->GetUnsignedIntAt(&yMin, {i, 1});
+    xMin = xMin + (uint32_t)(*pad_left);  // should not be negative
+    yMin = yMin + (uint32_t)(*pad_top);
+    (*bboxList)->SetItemAt({i, 0}, xMin);
+    (*bboxList)->SetItemAt({i, 1}, yMin);
+  }
+}
+
+void UpdateBBoxesForResize(std::shared_ptr<Tensor> *bboxList, size_t *bboxCount, int32_t *target_width_,
+                           int32_t *target_height_, int *orig_width, int *orig_height) {
+  uint32_t bb_Xmin, bb_Ymin, bb_Xwidth, bb_Ywidth;
+  // cast to float to preseve fractional
+  double W_aspRatio = (*target_width_ * 1.0) / (*orig_width * 1.0);
+  double H_aspRatio = (*target_height_ * 1.0) / (*orig_height * 1.0);
+  for (int i = 0; i < *bboxCount; i++) {
+    // for each bounding box
+    (*bboxList)->GetUnsignedIntAt(&bb_Xmin, {i, 0});
+    (*bboxList)->GetUnsignedIntAt(&bb_Ymin, {i, 1});
+    (*bboxList)->GetUnsignedIntAt(&bb_Xwidth, {i, 2});
+    (*bboxList)->GetUnsignedIntAt(&bb_Ywidth, {i, 3});
+    // update positions and widths
+    bb_Xmin = bb_Xmin * W_aspRatio;
+    bb_Ymin = bb_Ymin * H_aspRatio;
+    bb_Xwidth = bb_Xwidth * W_aspRatio;
+    bb_Ywidth = bb_Ywidth * H_aspRatio;
+    // reset bounding box values
+    (*bboxList)->SetItemAt({i, 0}, (uint32_t)bb_Xmin);
+    (*bboxList)->SetItemAt({i, 1}, (uint32_t)bb_Ymin);
+    (*bboxList)->SetItemAt({i, 2}, (uint32_t)bb_Xwidth);
+    (*bboxList)->SetItemAt({i, 3}, (uint32_t)bb_Ywidth);
+  }
+}
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/dataset/kernels/image/image_utils.h

@@ -225,7 +225,39 @@ Status Erase(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *outp
 Status Pad(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, const int32_t &pad_top,
            const int32_t &pad_bottom, const int32_t &pad_left, const int32_t &pad_right, const BorderType &border_types,
            uint8_t fill_r = 0, uint8_t fill_g = 0, uint8_t fill_b = 0);
+
+// -------- BBOX OPERATIONS -------- //
+// Updates and checks bounding boxes for new cropped region of image
+// @param bboxList: A tensor contaning bounding box tensors
+// @param bboxCount: total Number of bounding boxes - required within caller function to run update loop
+// @param CB_Xmin: Images's CropBox Xmin coordinate
+// @param CB_Xmin: Images's CropBox Ymin coordinate
+// @param CB_Xmax: Images's CropBox Xmax coordinate - (Xmin + width)
+// @param CB_Xmax: Images's CropBox Ymax coordinate - (Ymin + height)
+void UpdateBBoxesForCrop(std::shared_ptr<Tensor> *bboxList, size_t *bboxCount, int *CB_Xmin, int *CB_Ymin, int *CB_Xmax,
+                         int *CB_Ymax);
+
+// Updates bounding boxes with required Top and Left padding
+// Top and Left padding amounts required to adjust bboxs min X,Y values according to padding 'push'
+// Top/Left since images 0,0 coordinate is taken from top left
+// @param bboxList: A tensor contaning bounding box tensors
+// @param bboxCount: total Number of bounding boxes - required within caller function to run update loop
+// @param pad_top: Total amount of padding applied to image top
+// @param pad_left: Total amount of padding applied to image left side
+void PadBBoxes(std::shared_ptr<Tensor> *bboxList, size_t *bboxCount, int32_t *pad_top, int32_t *pad_left);
+
+// Updates bounding boxes for an Image Resize Operation - Takes in set of valid BBoxes
+// For e.g those that remain after a crop
+// @param bboxList: A tensor contaning bounding box tensors
+// @param bboxCount: total Number of bounding boxes - required within caller function to run update loop
+// @param bboxList: A tensor contaning bounding box tensors
+// @param target_width_: required width of image post resize
+// @param target_width_: required height of image post resize
+// @param orig_width: current width of image pre resize
+// @param orig_height: current height of image pre resize
+void UpdateBBoxesForResize(std::shared_ptr<Tensor> *bboxList, size_t *bboxCount, int32_t *target_width_,
+                           int32_t *target_height_, int *orig_width, int *orig_height);
+
 }  // namespace dataset
 }  // namespace mindspore
-
 #endif  // DATASET_KERNELS_IMAGE_IMAGE_UTILS_H_

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

+/**
+ * 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.
+ */
+
+#include <random>
+#include <utility>
+
+#include "dataset/util/random.h"
+#include "dataset/util/status.h"
+#include "dataset/kernels/image/image_utils.h"
+#include "dataset/kernels/image/random_crop_and_resize_with_bbox_op.h"
+
+namespace mindspore {
+namespace dataset {
+
+Status RandomCropAndResizeWithBBoxOp::Compute(const TensorRow &input, TensorRow *output) {
+  IO_CHECK_VECTOR(input, output);
+  BOUNDING_BOX_CHECK(input);
+  CHECK_FAIL_RETURN_UNEXPECTED(input[0]->shape().Size() >= 2, "The shape of input is abnormal");
+
+  (*output).push_back(nullptr);  // init memory for return vector
+  (*output).push_back(nullptr);
+  (*output)[1] = std::move(input[1]);  // move boxes over to output
+
+  size_t bboxCount = input[1]->shape()[0];  // number of rows in bbox tensor
+  int h_in = input[0]->shape()[0];
+  int w_in = input[0]->shape()[1];
+  int x = 0;
+  int y = 0;
+  int crop_height = 0;
+  int crop_width = 0;
+
+  (void)RandomCropAndResizeOp::GetCropBox(h_in, w_in, &x, &y, &crop_height, &crop_width);
+
+  int maxX = x + crop_width;  // max dims of selected CropBox on image
+  int maxY = y + crop_height;
+
+  UpdateBBoxesForCrop(&(*output)[1], &bboxCount, &x, &y, &maxX, &maxY);  // IMAGE_UTIL
+  RETURN_IF_NOT_OK(CropAndResize(input[0], &(*output)[0], x, y, crop_height, crop_width, target_height_, target_width_,
+                                 interpolation_));
+
+  UpdateBBoxesForResize(&(*output)[1], &bboxCount, &target_width_, &target_height_, &crop_width, &crop_height);
+  return Status::OK();
+}
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/dataset/kernels/image/random_crop_and_resize_with_bbox_op.h

+/**
+ * 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.
+ */
+#ifndef DATASET_KERNELS_IMAGE_RANDOM_CROP_AND_RESIZE_WITH_BBOX_OP_H_
+#define DATASET_KERNELS_IMAGE_RANDOM_CROP_AND_RESIZE_WITH_BBOX_OP_H_
+
+#include "dataset/kernels/image/random_crop_and_resize_op.h"
+
+namespace mindspore {
+namespace dataset {
+
+class RandomCropAndResizeWithBBoxOp : public RandomCropAndResizeOp {
+ public:
+  //  Constructor for RandomCropAndResizeWithBBoxOp, with default value and passing to base class constructor
+  RandomCropAndResizeWithBBoxOp(int32_t target_height, int32_t target_width, float scale_lb = kDefScaleLb,
+                                float scale_ub = kDefScaleUb, float aspect_lb = kDefAspectLb,
+                                float aspect_ub = kDefAspectUb, InterpolationMode interpolation = kDefInterpolation,
+                                int32_t max_iter = kDefMaxIter)
+      : RandomCropAndResizeOp(target_height, target_width, scale_lb, scale_ub, aspect_lb, aspect_ub, interpolation,
+                              max_iter) {}
+
+  ~RandomCropAndResizeWithBBoxOp() override = default;
+
+  void Print(std::ostream &out) const override {
+    out << "RandomCropAndResizeWithBBox: " << RandomCropAndResizeOp::target_height_ << " "
+        << RandomCropAndResizeOp::target_width_;
+  }
+
+  Status Compute(const TensorRow &input, TensorRow *output) override;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // DATASET_KERNELS_IMAGE_RANDOM_CROP_AND_RESIZE_WITH_BBOX_OP_H_

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

@@ -48,44 +48,81 @@ RandomCropOp::RandomCropOp(int32_t crop_height, int32_t crop_width, int32_t pad_
   rnd_.seed(GetSeed());
 }
 
-Status RandomCropOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
-  IO_CHECK(input, output);
-
-  // Apply padding first then crop
-  std::shared_ptr<Tensor> pad_image;
+Status RandomCropOp::ImagePadding(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *pad_image,
+                                  int32_t *t_pad_top, int32_t *t_pad_bottom, int32_t *t_pad_left, int32_t *t_pad_right,
+                                  int32_t *padded_image_w, int32_t *padded_image_h, bool *crop_further) {
+  *t_pad_top = pad_top_;
+  *t_pad_bottom = pad_bottom_;
+  *t_pad_left = pad_left_;
+  *t_pad_right = pad_right_;
 
   RETURN_IF_NOT_OK(
-    Pad(input, &pad_image, pad_top_, pad_bottom_, pad_left_, pad_right_, border_type_, fill_r_, fill_g_, fill_b_));
-  CHECK_FAIL_RETURN_UNEXPECTED(pad_image->shape().Size() >= 2, "Abnormal shape");
-  int32_t padded_image_h = pad_image->shape()[0];
-  int32_t padded_image_w = pad_image->shape()[1];
-  // no need to crop if same  size
-  if (padded_image_h == crop_height_ && padded_image_w == crop_width_) {
-    *output = pad_image;
+    Pad(input, pad_image, pad_top_, pad_bottom_, pad_left_, pad_right_, border_type_, fill_r_, fill_g_, fill_b_));
+  CHECK_FAIL_RETURN_UNEXPECTED((*pad_image)->shape().Size() >= 2, "Abnormal shape");
+
+  *padded_image_h = (*pad_image)->shape()[0];
+  *padded_image_w = (*pad_image)->shape()[1];
+
+  if (*padded_image_h == crop_height_ && *padded_image_w == crop_width_) {
+    *crop_further = false;  //  no need for further crop
     return Status::OK();
-  }
-  if (pad_if_needed_) {
+  } else if (pad_if_needed_) {
     // check the dimensions of the image for padding, if we do need padding, then we change the pad values
-    if (padded_image_h < crop_height_) {
-      RETURN_IF_NOT_OK(Pad(pad_image, &pad_image, crop_height_ - padded_image_h, crop_height_ - padded_image_h, 0, 0,
+    if (*padded_image_h < crop_height_) {
+      RETURN_IF_NOT_OK(Pad(*pad_image, pad_image, crop_height_ - *padded_image_h, crop_height_ - *padded_image_h, 0, 0,
                            border_type_, fill_r_, fill_g_, fill_b_));
+
+      // update pad total above/below
+      t_pad_top += (crop_height_ - *padded_image_h);
+      t_pad_bottom += (crop_height_ - *padded_image_h);
     }
-    if (padded_image_w < crop_width_) {
-      RETURN_IF_NOT_OK(Pad(pad_image, &pad_image, 0, 0, crop_width_ - padded_image_w, crop_width_ - padded_image_w,
+    if (*padded_image_w < crop_width_) {
+      RETURN_IF_NOT_OK(Pad(*pad_image, pad_image, 0, 0, crop_width_ - *padded_image_w, crop_width_ - *padded_image_w,
                            border_type_, fill_r_, fill_g_, fill_b_));
+      // update pad total left/right
+      t_pad_left += (crop_width_ - *padded_image_w);
+      t_pad_right += (crop_width_ - *padded_image_w);
     }
-    padded_image_h = pad_image->shape()[0];
-    padded_image_w = pad_image->shape()[1];
+    *padded_image_h = (*pad_image)->shape()[0];
+    *padded_image_w = (*pad_image)->shape()[1];
   }
-  if (padded_image_h < crop_height_ || padded_image_w < crop_width_ || crop_height_ == 0 || crop_width_ == 0) {
+
+  if (*padded_image_h < crop_height_ || *padded_image_w < crop_width_ || crop_height_ == 0 || crop_width_ == 0) {
     return Status(StatusCode::kShapeMisMatch, __LINE__, __FILE__,
                   "Crop size is greater than the image dimensions or is zero.");
   }
-  // random top corner
-  int x = std::uniform_int_distribution<int>(0, padded_image_w - crop_width_)(rnd_);
-  int y = std::uniform_int_distribution<int>(0, padded_image_h - crop_height_)(rnd_);
+  return Status::OK();
+}
+
+void RandomCropOp::GenRandomXY(int *x, int *y, int32_t *padded_image_w, int32_t *padded_image_h) {
+  // GenCropPoints for cropping
+  *x = std::uniform_int_distribution<int>(0, *padded_image_w - crop_width_)(rnd_);
+  *y = std::uniform_int_distribution<int>(0, *padded_image_h - crop_height_)(rnd_);
+}
+
+Status RandomCropOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
+  IO_CHECK(input, output);
+
+  // Apply padding first then crop
+  std::shared_ptr<Tensor> pad_image;
+  int32_t t_pad_top, t_pad_bottom, t_pad_left, t_pad_right;
+  int32_t padded_image_w;
+  int32_t padded_image_h;
+  bool crop_further = true;  // whether image needs further cropping based on new size & requirements
+
+  RETURN_IF_NOT_OK(  // error code sent back directly
+    ImagePadding(input, &pad_image, &t_pad_top, &t_pad_bottom, &t_pad_left, &t_pad_right, &padded_image_w,
+                 &padded_image_h, &crop_further));
+  if (!crop_further) {
+    *output = pad_image;
+    return Status::OK();
+  }
+
+  int x, y;
+  GenRandomXY(&x, &y, &padded_image_w, &padded_image_h);
   return Crop(pad_image, output, x, y, crop_width_, crop_height_);
 }
+
 Status RandomCropOp::OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) {
   RETURN_IF_NOT_OK(TensorOp::OutputShape(inputs, outputs));
   outputs.clear();

mindspore/ccsrc/dataset/kernels/image/random_crop_op.h

@@ -50,11 +50,20 @@ class RandomCropOp : public TensorOp {
   void Print(std::ostream &out) const override { out << "RandomCropOp: " << crop_height_ << " " << crop_width_; }
 
   Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
+
+  Status ImagePadding(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *pad_image, int32_t *t_pad_top,
+                      int32_t *t_pad_bottom, int32_t *t_pad_left, int32_t *t_pad_right, int32_t *padded_image_w,
+                      int32_t *padded_image_h, bool *crop_further);
+
+  void GenRandomXY(int *x, int *y, int32_t *padded_image_w, int32_t *padded_image_h);
+
   Status OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) override;
 
- private:
+ protected:
   int32_t crop_height_ = 0;
   int32_t crop_width_ = 0;
+
+ private:
   int32_t pad_top_ = 0;
   int32_t pad_bottom_ = 0;
   int32_t pad_left_ = 0;

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

+/**
+ * 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.
+ */
+
+#include <random>
+#include <algorithm>
+#include <utility>
+
+#include "dataset/kernels/image/random_crop_with_bbox_op.h"
+#include "dataset/kernels/image/image_utils.h"
+#include "dataset/util/random.h"
+#include "dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+Status RandomCropWithBBoxOp::Compute(const TensorRow &input, TensorRow *output) {
+  IO_CHECK_VECTOR(input, output);
+  BOUNDING_BOX_CHECK(input);
+
+  std::shared_ptr<Tensor> pad_image;
+  int32_t t_pad_top, t_pad_bottom, t_pad_left, t_pad_right;
+  size_t boxCount = input[1]->shape()[0];  // number of rows
+
+  int32_t padded_image_h;
+  int32_t padded_image_w;
+
+  (*output).push_back(nullptr);
+  (*output).push_back(nullptr);
+  (*output)[1] = std::move(input[1]);  // since some boxes may be removed
+
+  bool crop_further = true;  // Whether further cropping will be required or not, true unless required size matches
+  RETURN_IF_NOT_OK(          // Error passed back to caller
+    RandomCropOp::ImagePadding(input[0], &pad_image, &t_pad_top, &t_pad_bottom, &t_pad_left, &t_pad_right,
+                               &padded_image_w, &padded_image_h, &crop_further));
+
+  // update bounding boxes with new values based on relevant image padding
+  if (t_pad_left || t_pad_bottom) {
+    PadBBoxes(&(*output)[1], &boxCount, &t_pad_left, &t_pad_top);
+  }
+  if (!crop_further) {
+    // no further cropping required
+    (*output)[0] = pad_image;
+    (*output)[1] = std::move(input[1]);
+    return Status::OK();
+  }
+
+  int x, y;
+  RandomCropOp::GenRandomXY(&x, &y, &padded_image_w, &padded_image_h);
+  int maxX = x + RandomCropOp::crop_width_;  // max dims of selected CropBox on image
+  int maxY = y + RandomCropOp::crop_height_;
+  UpdateBBoxesForCrop(&(*output)[1], &boxCount, &x, &y, &maxX, &maxY);
+  return Crop(pad_image, &(*output)[0], x, y, RandomCropOp::crop_width_, RandomCropOp::crop_height_);
+}
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/dataset/kernels/image/random_crop_with_bbox_op.h

+/**
+ * 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.
+ */
+#ifndef DATASET_KERNELS_IMAGE_RANDOM_CROP_WITH_BBOX_OP_H_
+#define DATASET_KERNELS_IMAGE_RANDOM_CROP_WITH_BBOX_OP_H_
+
+#include <memory>
+#include <vector>
+
+#include "dataset/kernels/image/random_crop_op.h"
+
+namespace mindspore {
+namespace dataset {
+class RandomCropWithBBoxOp : public RandomCropOp {
+ public:
+  //  Constructor for RandomCropWithBBoxOp, with default value and passing to base class constructor
+  RandomCropWithBBoxOp(int32_t crop_height, int32_t crop_width, int32_t pad_top = kDefPadTop,
+                       int32_t pad_bottom = kDefPadBottom, int32_t pad_left = kDefPadLeft,
+                       int32_t pad_right = kDefPadRight, BorderType border_types = kDefBorderType,
+                       bool pad_if_needed = kDefPadIfNeeded, uint8_t fill_r = kDefFillR, uint8_t fill_g = kDefFillG,
+                       uint8_t fill_b = kDefFillB)
+      : RandomCropOp(crop_height, crop_width, pad_top, pad_bottom, pad_left, pad_right, border_types, pad_if_needed,
+                     fill_r, fill_g, fill_b) {}
+
+  ~RandomCropWithBBoxOp() override = default;
+
+  void Print(std::ostream &out) const override {
+    out << "RandomCropWithBBoxOp: " << RandomCropOp::crop_height_ << " " << RandomCropOp::crop_width_;
+  }
+
+  Status Compute(const TensorRow &input, TensorRow *output) override;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // DATASET_KERNELS_IMAGE_RANDOM_CROP_WITH_BBOX_OP_H_

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

+/**
+ * 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.
+ */
+
+#include <utility>
+
+#include "dataset/util/status.h"
+#include "dataset/kernels/image/image_utils.h"
+#include "dataset/kernels/image/random_vertical_flip_with_bbox_op.h"
+
+namespace mindspore {
+namespace dataset {
+const float RandomVerticalFlipWithBBoxOp::kDefProbability = 0.5;
+Status RandomVerticalFlipWithBBoxOp::Compute(const TensorRow &input, TensorRow *output) {
+  IO_CHECK_VECTOR(input, output);
+  BOUNDING_BOX_CHECK(input);
+
+  if (distribution_(rnd_)) {
+    dsize_t imHeight = input[0]->shape()[0];
+    size_t boxCount = input[1]->shape()[0];  // number of rows in tensor
+
+    // one time allocation -> updated in the loop
+    // type defined based on VOC test dataset
+    for (int i = 0; i < boxCount; i++) {
+      uint32_t boxCorner_y = 0;
+      uint32_t boxHeight = 0;
+      uint32_t newBoxCorner_y = 0;
+      input[1]->GetUnsignedIntAt(&boxCorner_y, {i, 1});  // get min y of bbox
+      input[1]->GetUnsignedIntAt(&boxHeight, {i, 3});    // get height of bbox
+
+      // subtract (curCorner + height) from (max) for new Corner position
+      newBoxCorner_y = (imHeight - 1) - (boxCorner_y + boxHeight);
+      input[1]->SetItemAt({i, 1}, newBoxCorner_y);
+    }
+
+    (*output).push_back(nullptr);
+    (*output).push_back(nullptr);
+    (*output)[1] = std::move(input[1]);
+
+    return VerticalFlip(input[0], &(*output)[0]);
+  }
+  *output = input;
+  return Status::OK();
+}
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/dataset/kernels/image/random_vertical_flip_with_bbox_op.h

+/**
+ * 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.
+ */
+#ifndef DATASET_KERNELS_IMAGE_RANDOM_VERTICAL_FLIP_WITH_BBOX_OP_H_
+#define DATASET_KERNELS_IMAGE_RANDOM_VERTICAL_FLIP_WITH_BBOX_OP_H_
+
+#include <memory>
+#include <random>
+
+#include "dataset/core/tensor.h"
+#include "dataset/kernels/tensor_op.h"
+#include "dataset/util/status.h"
+#include "dataset/util/random.h"
+
+namespace mindspore {
+namespace dataset {
+class RandomVerticalFlipWithBBoxOp : public TensorOp {
+ public:
+  // Default values, also used by python_bindings.cc
+  static const float kDefProbability;
+  // Constructor for RandomVerticalFlipWithBBoxOp
+  // @param probability: Probablity of Image flipping, 0.5 by default
+  explicit RandomVerticalFlipWithBBoxOp(float probability = kDefProbability) : distribution_(probability) {
+    rnd_.seed(GetSeed());
+  }
+
+  ~RandomVerticalFlipWithBBoxOp() override = default;
+
+  void Print(std::ostream &out) const override { out << "RandomVerticalFlipWithBBoxOp"; }
+
+  Status Compute(const TensorRow &input, TensorRow *output) override;
+
+ private:
+  std::mt19937 rnd_;
+  std::bernoulli_distribution distribution_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // DATASET_KERNELS_IMAGE_RANDOM_VERTICAL_FLIP_WITH_BBOX_OP_H_

mindspore/dataset/transforms/vision/c_transforms.py

@@ -149,6 +149,54 @@ class RandomCrop(cde.RandomCropOp):
         super().__init__(*size, *padding, border_type, pad_if_needed, *fill_value)
 
 
+class RandomCropWithBBox(cde.RandomCropWithBBoxOp):
+    """
+    Crop the input image at a random location, and adjust bounding boxes
+
+    Args:
+        size (int or sequence): The output size of the cropped image.
+            If size is an int, a square crop of size (size, size) is returned.
+            If size is a sequence of length 2, it should be (height, width).
+        padding (int or sequence, optional): The number of pixels to pad the image (default=None).
+            If padding is not None, pad image firstly with padding values.
+            If a single number is provided, it pads all borders with this value.
+            If a tuple or list of 2 values are provided, it pads the (left and top)
+            with the first value and (right and bottom) with the second value.
+            If 4 values are provided as a list or tuple,it pads the left, top, right and bottom respectively.
+        pad_if_needed (bool, optional): Pad the image if either side is smaller than
+            the given output size (default=False).
+        fill_value (int or tuple, optional): The pixel intensity of the borders if
+            the padding_mode is Border.CONSTANT (default=0). If it is a 3-tuple, it is used to
+            fill R, G, B channels respectively.
+        padding_mode (Border mode, optional): The method of padding (default=Border.CONSTANT). Can be any of
+            [Border.CONSTANT, Border.EDGE, Border.REFLECT, Border.SYMMETRIC].
+
+            - Border.CONSTANT, means it fills the border with constant values.
+
+            - Border.EDGE, means it pads with the last value on the edge.
+
+            - Border.REFLECT, means it reflects the values on the edge omitting the last
+              value of edge.
+
+            - Border.SYMMETRIC, means it reflects the values on the edge repeating the last
+              value of edge.
+    """
+
+    @check_random_crop
+    def __init__(self, size, padding=None, pad_if_needed=False, fill_value=0, padding_mode=Border.CONSTANT):
+        self.size = size
+        self.padding = padding
+        self.pad_if_needed = pad_if_needed
+        self.fill_value = fill_value
+        self.padding_mode = padding_mode.value
+        if padding is None:
+            padding = (0, 0, 0, 0)
+        if isinstance(fill_value, int):  # temporary fix
+            fill_value = tuple([fill_value] * 3)
+        border_type = DE_C_BORDER_TYPE[padding_mode]
+        super().__init__(*size, *padding, border_type, pad_if_needed, *fill_value)
+
+
 class RandomHorizontalFlip(cde.RandomHorizontalFlipOp):
     """
     Flip the input image horizontally, randomly with a given probability.
@@ -192,6 +240,20 @@ class RandomVerticalFlip(cde.RandomVerticalFlipOp):
         super().__init__(prob)
 
 
+class RandomVerticalFlipWithBBox(cde.RandomVerticalFlipWithBBoxOp):
+    """
+    Flip the input image vertically and adjust bounding boxes, randomly with a given probability.
+
+    Args:
+        prob (float): Probability of the image being flipped (default=0.5).
+    """
+
+    @check_prob
+    def __init__(self, prob=0.5):
+        self.prob = prob
+        super().__init__(prob)
+
+
 class BoundingBoxAug(cde.BoundingBoxAugOp):
     """
     Flip the input image vertically, randomly with a given probability.
@@ -237,6 +299,42 @@ class Resize(cde.ResizeOp):
             super().__init__(*size, interpoltn)
 
 
+class RandomResizedCropWithBBox(cde.RandomCropAndResizeWithBBoxOp):
+    """
+    Crop the input image to a random size and aspect ratio and adjust the Bounding Boxes accordingly
+
+    Args:
+        size (int or sequence): The size of the output image.
+            If size is an int, a square crop of size (size, size) is returned.
+            If size is a sequence of length 2, it should be (height, width).
+        scale (tuple, optional): Range (min, max) of respective size of the original
+            size to be cropped (default=(0.08, 1.0)).
+        ratio (tuple, optional): Range (min, max) of aspect ratio to be cropped
+            (default=(3. / 4., 4. / 3.)).
+        interpolation (Inter mode, optional): Image interpolation mode (default=Inter.BILINEAR).
+            It can be any of [Inter.BILINEAR, Inter.NEAREST, Inter.BICUBIC].
+
+            - Inter.BILINEAR, means interpolation method is bilinear interpolation.
+
+            - Inter.NEAREST, means interpolation method is nearest-neighbor interpolation.
+
+            - Inter.BICUBIC, means interpolation method is bicubic interpolation.
+
+        max_attempts (int, optional): The maximum number of attempts to propose a valid
+            crop_area (default=10). If exceeded, fall back to use center_crop instead.
+    """
+    @check_random_resize_crop
+    def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.),
+                 interpolation=Inter.BILINEAR, max_attempts=10):
+        self.size = size
+        self.scale = scale
+        self.ratio = ratio
+        self.interpolation = interpolation
+        self.max_attempts = max_attempts
+        interpoltn = DE_C_INTER_MODE[interpolation]
+        super().__init__(*size, *scale, *ratio, interpoltn, max_attempts)
+
+
 class RandomResizedCrop(cde.RandomCropAndResizeOp):
     """
     Crop the input image to a random size and aspect ratio.