RandomColor

mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/kernels/image/bindings.cc

@@ -32,6 +32,7 @@
 #include "minddata/dataset/kernels/image/normalize_op.h"
 #include "minddata/dataset/kernels/image/pad_op.h"
 #include "minddata/dataset/kernels/image/random_affine_op.h"
+#include "minddata/dataset/kernels/image/random_color_op.h"
 #include "minddata/dataset/kernels/image/random_color_adjust_op.h"
 #include "minddata/dataset/kernels/image/random_crop_and_resize_op.h"
 #include "minddata/dataset/kernels/image/random_crop_and_resize_with_bbox_op.h"
@@ -273,6 +274,14 @@ PYBIND_REGISTER(
            py::arg("targetWidth") = RandomResizeOp::kDefTargetWidth);
   }));
 
+PYBIND_REGISTER(RandomColorOp, 1, ([](const py::module *m) {
+                  (void)py::class_<RandomColorOp, TensorOp, std::shared_ptr<RandomColorOp>>(
+                    *m, "RandomColorOp",
+                    "Tensor operation to blend an image with its grayscale version with random weights"
+                    "Takes min and max for the range of random weights")
+                    .def(py::init<float, float>(), py::arg("min"), py::arg("max"));
+                }));
+
 PYBIND_REGISTER(RandomColorAdjustOp, 1, ([](const py::module *m) {
                   (void)py::class_<RandomColorAdjustOp, TensorOp, std::shared_ptr<RandomColorAdjustOp>>(
                     *m, "RandomColorAdjustOp",

mindspore/ccsrc/minddata/dataset/api/transforms.cc

@@ -27,6 +27,7 @@
 #include "minddata/dataset/kernels/data/one_hot_op.h"
 #include "minddata/dataset/kernels/image/pad_op.h"
 #include "minddata/dataset/kernels/image/random_affine_op.h"
+#include "minddata/dataset/kernels/image/random_color_op.h"
 #include "minddata/dataset/kernels/image/random_color_adjust_op.h"
 #include "minddata/dataset/kernels/image/random_crop_op.h"
 #include "minddata/dataset/kernels/image/random_horizontal_flip_op.h"
@@ -140,6 +141,21 @@ std::shared_ptr<PadOperation> Pad(std::vector<int32_t> padding, std::vector<uint
   return op;
 }
 
+// Function to create RandomColorOperation.
+std::shared_ptr<RandomColorOperation> RandomColor(float t_lb, float t_ub) {
+  auto op = std::make_shared<RandomColorOperation>(t_lb, t_ub);
+  // Input validation
+  if (!op->ValidateParams()) {
+    return nullptr;
+  }
+  return op;
+}
+
+std::shared_ptr<TensorOp> RandomColorOperation::Build() {
+  std::shared_ptr<RandomColorOp> tensor_op = std::make_shared<RandomColorOp>(t_lb_, t_ub_);
+  return tensor_op;
+}
+
 // Function to create RandomColorAdjustOperation.
 std::shared_ptr<RandomColorAdjustOperation> RandomColorAdjust(std::vector<float> brightness,
                                                               std::vector<float> contrast,
@@ -475,6 +491,18 @@ std::shared_ptr<TensorOp> PadOperation::Build() {
   return tensor_op;
 }
 
+// RandomColorOperation.
+RandomColorOperation::RandomColorOperation(float t_lb, float t_ub) : t_lb_(t_lb), t_ub_(t_ub) {}
+
+bool RandomColorOperation::ValidateParams() {
+  // Do some input validation.
+  if (t_lb_ > t_ub_) {
+    MS_LOG(ERROR) << "RandomColor: lower bound must be less or equal to upper bound";
+    return false;
+  }
+  return true;
+}
+
 // RandomColorAdjustOperation.
 RandomColorAdjustOperation::RandomColorAdjustOperation(std::vector<float> brightness, std::vector<float> contrast,
                                                        std::vector<float> saturation, std::vector<float> hue)

mindspore/ccsrc/minddata/dataset/core/cv_tensor.h

@@ -70,7 +70,7 @@ class CVTensor : public Tensor {
 
   /// Get a reference to the CV::Mat
   /// \return a reference to the internal CV::Mat
-  cv::Mat mat() const { return mat_; }
+  cv::Mat &mat() { return mat_; }
 
   /// Get a copy of the CV::Mat
   /// \return a copy of internal CV::Mat

mindspore/ccsrc/minddata/dataset/include/transforms.h

@@ -57,6 +57,7 @@ class NormalizeOperation;
 class OneHotOperation;
 class PadOperation;
 class RandomAffineOperation;
+class RandomColorOperation;
 class RandomColorAdjustOperation;
 class RandomCropOperation;
 class RandomHorizontalFlipOperation;
@@ -162,6 +163,14 @@ std::shared_ptr<RandomAffineOperation> RandomAffine(
   InterpolationMode interpolation = InterpolationMode::kNearestNeighbour,
   const std::vector<uint8_t> &fill_value = {0, 0, 0});
 
+/// \brief Blends an image with its grayscale version with random weights
+///        t and 1 - t generated from a given range. If the range is trivial
+///        then the weights are determinate and t equals the bound of the interval
+/// \param[in] t_lb lower bound on the range of random weights
+/// \param[in] t_lb upper bound on the range of random weights
+/// \return Shared pointer to the current TensorOp
+std::shared_ptr<RandomColorOperation> RandomColor(float t_lb, float t_ub);
+
 /// \brief Randomly adjust the brightness, contrast, saturation, and hue of the input image
 /// \param[in] brightness Brightness adjustment factor. Must be a vector of one or two values
 ///     if it's a vector of two values it needs to be in the form of [min, max]. Default value is {1, 1}
@@ -417,6 +426,21 @@ class RandomAffineOperation : public TensorOperation {
   std::vector<uint8_t> fill_value_;
 };
 
+class RandomColorOperation : public TensorOperation {
+ public:
+  RandomColorOperation(float t_lb, float t_ub);
+
+  ~RandomColorOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  bool ValidateParams() override;
+
+ private:
+  float t_lb_;
+  float t_ub_;
+};
+
 class RandomColorAdjustOperation : public TensorOperation {
  public:
   RandomColorAdjustOperation(std::vector<float> brightness = {1.0, 1.0}, std::vector<float> contrast = {1.0, 1.0},

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

@@ -44,5 +44,6 @@ add_library(kernels-image OBJECT
     uniform_aug_op.cc
     resize_with_bbox_op.cc
     random_resize_with_bbox_op.cc
+    random_color_op.cc
     )
 add_dependencies(kernels-image kernels-soft-dvpp-image)

mindspore/ccsrc/minddata/dataset/kernels/image/random_color_op.cc

@@ -0,0 +1,60 @@
+/**
+ * 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 "minddata/dataset/kernels/image/random_color_op.h"
+#include "minddata/dataset/core/cv_tensor.h"
+namespace mindspore {
+namespace dataset {
+
+RandomColorOp::RandomColorOp(float t_lb, float t_ub) : rnd_(GetSeed()), dist_(t_lb, t_ub), t_lb_(t_lb), t_ub_(t_ub) {}
+
+Status RandomColorOp::Compute(const std::shared_ptr<Tensor> &in, std::shared_ptr<Tensor> *out) {
+  IO_CHECK(in, out);
+  if (in->Rank() != 3) {
+    RETURN_STATUS_UNEXPECTED("image must have 3 channels");
+  }
+  // 0.5 pixel precision assuming an 8 bit image
+  const auto eps = 0.00195;
+  const auto t = dist_(rnd_);
+  if (abs(t - 1.0) < eps) {
+    // Just return input? Can we do it given that input would otherwise get consumed in CVTensor constructor anyway?
+    *out = in;
+    return Status::OK();
+  }
+  auto cvt_in = CVTensor::AsCVTensor(in);
+  auto m1 = cvt_in->mat();
+  cv::Mat gray;
+  // gray is allocated without using the allocator
+  cv::cvtColor(m1, gray, cv::COLOR_RGB2GRAY);
+  // luminosity is not preserved, consider using weights.
+  cv::Mat temp[3] = {gray, gray, gray};
+  cv::Mat cv_out;
+  cv::merge(temp, 3, cv_out);
+  std::shared_ptr<CVTensor> cvt_out;
+  CVTensor::CreateFromMat(cv_out, &cvt_out);
+  if (abs(t - 0.0) < eps) {
+    // return grayscale
+    *out = std::static_pointer_cast<Tensor>(cvt_out);
+    return Status::OK();
+  }
+  // return blended image. addWeighted takes care of overflow for uint8_t
+  cv::addWeighted(m1, t, cvt_out->mat(), 1 - t, 0, cvt_out->mat());
+  *out = std::static_pointer_cast<Tensor>(cvt_out);
+  return Status::OK();
+}
+
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/image/random_color_op.h

@@ -0,0 +1,62 @@
+/**
+ * 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 MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_RANDOM_COLOR_OP_H
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_RANDOM_COLOR_OP_H
+
+#include <memory>
+#include <random>
+#include <vector>
+#include <string>
+#include <opencv2/imgproc/imgproc.hpp>
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/core/cv_tensor.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/status.h"
+#include "minddata/dataset/util/random.h"
+
+namespace mindspore {
+namespace dataset {
+
+/// \class RandomColorOp random_color_op.h
+/// \brief Blends an image with its grayscale version with random weights
+///        t and 1 - t generated from a given range.
+///        If the range is trivial then the weights are determinate and
+///        t equals the bound of the interval
+class RandomColorOp : public TensorOp {
+ public:
+  RandomColorOp() = default;
+  /// \brief Constructor
+  /// \param[in] t_lb lower bound for the random weights
+  /// \param[in] t_ub upper bound for the random weights
+  RandomColorOp(float t_lb, float t_ub);
+  /// \brief the main function performing computations
+  /// \param[in] in 2- or 3- dimensional tensor representing an image
+  /// \param[out] out 2- or 3- dimensional tensor representing an image
+  /// with the same dimensions as in
+  Status Compute(const std::shared_ptr<Tensor> &in, std::shared_ptr<Tensor> *out) override;
+  /// \brief returns the name of the op
+  std::string Name() const override { return kRandomColorOp; }
+
+ private:
+  std::mt19937 rnd_;
+  std::uniform_real_distribution<float> dist_;
+  float t_lb_;
+  float t_ub_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_RANDOM_COLOR_OP_H

mindspore/ccsrc/minddata/dataset/kernels/tensor_op.h

@@ -129,6 +129,7 @@ constexpr char kSwapRedBlueOp[] = "SwapRedBlueOp";
 constexpr char kUniformAugOp[] = "UniformAugOp";
 constexpr char kSoftDvppDecodeRandomCropResizeJpegOp[] = "SoftDvppDecodeRandomCropResizeJpegOp";
 constexpr char kSoftDvppDecodeReiszeJpegOp[] = "SoftDvppDecodeReiszeJpegOp";
+constexpr char kRandomColorOp[] = "RandomColorOp";
 
 // text
 constexpr char kBasicTokenizerOp[] = "BasicTokenizerOp";

mindspore/dataset/transforms/vision/c_transforms.py

@@ -46,7 +46,8 @@ import mindspore._c_dataengine as cde
 from .utils import Inter, Border
 from .validators import check_prob, check_crop, check_resize_interpolation, check_random_resize_crop, \
     check_mix_up_batch_c, check_normalize_c, check_random_crop, check_random_color_adjust, check_random_rotation, \
-    check_range, check_resize, check_rescale, check_pad, check_cutout, check_uniform_augment_cpp, \
+    check_range, check_resize, check_rescale, check_pad, check_cutout, \
+    check_uniform_augment_cpp, \
     check_bounding_box_augment_cpp, check_random_select_subpolicy_op, check_auto_contrast, check_random_affine, \
     check_random_solarize, check_soft_dvpp_decode_random_crop_resize_jpeg, check_positive_degrees, FLOAT_MAX_INTEGER
 
@@ -628,6 +629,21 @@ class CenterCrop(cde.CenterCropOp):
         super().__init__(*size)
 
 
+class RandomColor(cde.RandomColorOp):
+    """
+    Adjust the color of the input image by a fixed or random degree.
+
+    Args:
+         degrees (sequence): Range of random color adjustment degrees.
+            It should be in (min, max) format. If min=max, then it is a
+            single fixed magnitude operation (default=(0.1,1.9)).
+            Works with 3-channel color images.
+    """
+
+    @check_positive_degrees
+    def __init__(self, degrees=(0.1, 1.9)):
+        super().__init__(*degrees)
+
 class RandomColorAdjust(cde.RandomColorAdjustOp):
     """
     Randomly adjust the brightness, contrast, saturation, and hue of the input image.

mindspore/dataset/transforms/vision/validators.py

@@ -609,21 +609,23 @@ def check_uniform_augment_py(method):
 
 
 def check_positive_degrees(method):
-    """A wrapper method to check degrees parameter in RandSharpness and RandColor"""
+    """A wrapper method to check degrees parameter in RandomSharpness and RandomColor ops (python and cpp)"""
 
     @wraps(method)
     def new_method(self, *args, **kwargs):
         [degrees], _ = parse_user_args(method, *args, **kwargs)
-        if isinstance(degrees, (list, tuple)):
+
+        if degrees is not None:
+            if not isinstance(degrees, (list, tuple)):
+                raise TypeError("degrees must be either a tuple or a list.")
+            type_check_list(degrees, (int, float), "degrees")
             if len(degrees) != 2:
-                raise ValueError("Degrees must be a sequence with length 2.")
-            for value in degrees:
-                check_value(value, (0., FLOAT_MAX_INTEGER))
-            check_positive(degrees[0], "degrees[0]")
+                raise ValueError("degrees must be a sequence with length 2.")
+            for degree in degrees:
+                check_value(degree, (0, FLOAT_MAX_INTEGER))
             if degrees[0] > degrees[1]:
-                raise ValueError("Degrees should be in (min,max) format. Got (max,min).")
-        else:
-            raise TypeError("Degrees should be a tuple or list.")
+                raise ValueError("degrees should be in (min,max) format. Got (max,min).")
+
         return method(self, *args, **kwargs)
 
     return new_method
@@ -698,4 +700,5 @@ def check_random_solarize(method):
             raise ValueError("threshold must be in min max format numbers")
 
         return method(self, *args, **kwargs)
+
     return new_method

tests/ut/cpp/dataset/CMakeLists.txt

@@ -39,6 +39,7 @@ SET(DE_UT_SRCS
         project_op_test.cc
         queue_test.cc
         random_affine_op_test.cc
+        random_color_op_test.cc
         random_crop_op_test.cc
         random_crop_with_bbox_op_test.cc
         random_crop_decode_resize_op_test.cc

tests/ut/cpp/dataset/c_api_transforms_test.cc

@@ -63,10 +63,10 @@ TEST_F(MindDataTestPipeline, TestCutOut) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-  i++;
-  auto image = row["image"];
-  MS_LOG(INFO) << "Tensor image shape: " << image->shape();
-  iter->GetNextRow(&row);
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    iter->GetNextRow(&row);
   }
 
   EXPECT_EQ(i, 20);
@@ -160,8 +160,9 @@ TEST_F(MindDataTestPipeline, TestHwcToChw) {
     auto image = row["image"];
     MS_LOG(INFO) << "Tensor image shape: " << image->shape();
     // check if the image is in NCHW
-    EXPECT_EQ(batch_size == image->shape()[0] && 3 == image->shape()[1] 
-              && 2268 == image->shape()[2] && 4032 == image->shape()[3], true);
+    EXPECT_EQ(batch_size == image->shape()[0] && 3 == image->shape()[1] && 2268 == image->shape()[2] &&
+                4032 == image->shape()[3],
+              true);
     iter->GetNextRow(&row);
   }
   EXPECT_EQ(i, 20);
@@ -186,7 +187,7 @@ TEST_F(MindDataTestPipeline, TestMixUpBatchFail1) {
   EXPECT_NE(one_hot_op, nullptr);
 
   // Create a Map operation on ds
-  ds = ds->Map({one_hot_op},{"label"});
+  ds = ds->Map({one_hot_op}, {"label"});
   EXPECT_NE(ds, nullptr);
 
   std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch(-1);
@@ -209,7 +210,7 @@ TEST_F(MindDataTestPipeline, TestMixUpBatchSuccess1) {
   EXPECT_NE(one_hot_op, nullptr);
 
   // Create a Map operation on ds
-  ds = ds->Map({one_hot_op},{"label"});
+  ds = ds->Map({one_hot_op}, {"label"});
   EXPECT_NE(ds, nullptr);
 
   std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch(0.5);
@@ -258,7 +259,7 @@ TEST_F(MindDataTestPipeline, TestMixUpBatchSuccess2) {
   EXPECT_NE(one_hot_op, nullptr);
 
   // Create a Map operation on ds
-  ds = ds->Map({one_hot_op},{"label"});
+  ds = ds->Map({one_hot_op}, {"label"});
   EXPECT_NE(ds, nullptr);
 
   std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch();
@@ -379,10 +380,10 @@ TEST_F(MindDataTestPipeline, TestPad) {
 
   uint64_t i = 0;
   while (row.size() != 0) {
-  i++;
-  auto image = row["image"];
-  MS_LOG(INFO) << "Tensor image shape: " << image->shape();
-  iter->GetNextRow(&row);
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    iter->GetNextRow(&row);
   }
 
   EXPECT_EQ(i, 20);
@@ -504,6 +505,61 @@ TEST_F(MindDataTestPipeline, TestRandomAffineSuccess2) {
   iter->Stop();
 }
 
+TEST_F(MindDataTestPipeline, TestRandomColor) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomColor with non-default params.";
+
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Repeat operation on ds
+  int32_t repeat_num = 2;
+  ds = ds->Repeat(repeat_num);
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> random_color_op_1 = vision::RandomColor(0.0, 0.0);
+  EXPECT_NE(random_color_op_1, nullptr);
+
+  std::shared_ptr<TensorOperation> random_color_op_2 = vision::RandomColor(1.0, 0.1);
+  EXPECT_EQ(random_color_op_2, nullptr);
+
+  std::shared_ptr<TensorOperation> random_color_op_3 = vision::RandomColor(0.0, 1.1);
+  EXPECT_NE(random_color_op_3, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({random_color_op_1, random_color_op_3});
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Batch operation on ds
+  int32_t batch_size = 1;
+  ds = ds->Batch(batch_size);
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 20);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
 TEST_F(MindDataTestPipeline, TestRandomColorAdjust) {
   // Create an ImageFolder Dataset
   std::string folder_path = datasets_root_path_ + "/testPK/data/";
@@ -780,7 +836,8 @@ TEST_F(MindDataTestPipeline, TestRandomSolarize) {
   EXPECT_NE(ds, nullptr);
 
   // Create objects for the tensor ops
-  std::shared_ptr<TensorOperation> random_solarize = mindspore::dataset::api::vision::RandomSolarize(23, 23); //vision::RandomSolarize();
+  std::shared_ptr<TensorOperation> random_solarize =
+    mindspore::dataset::api::vision::RandomSolarize(23, 23);  // vision::RandomSolarize();
   EXPECT_NE(random_solarize, nullptr);
 
   // Create a Map operation on ds

tests/ut/cpp/dataset/random_color_op_test.cc

@@ -0,0 +1,99 @@
+/**
+ * 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 "common/common.h"
+#include "common/cvop_common.h"
+#include "minddata/dataset/kernels/image/random_color_op.h"
+#include "minddata/dataset/core/cv_tensor.h"
+#include "utils/log_adapter.h"
+
+using namespace mindspore::dataset;
+using mindspore::LogStream;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::MsLogLevel::INFO;
+
+class MindDataTestRandomColorOp : public UT::CVOP::CVOpCommon {
+ public:
+  MindDataTestRandomColorOp() : CVOpCommon(), shape({3, 3, 3}) {
+    std::shared_ptr<Tensor> in;
+    std::shared_ptr<Tensor> gray;
+
+    (void)Tensor::CreateEmpty(shape, DataType(DataType::DE_UINT8), &in);
+    (void)Tensor::CreateEmpty(shape, DataType(DataType::DE_UINT8), &input_tensor);
+    Status s = in->Fill<uint8_t>(42);
+    s = input_tensor->Fill<uint8_t>(42);
+    cvt_in = CVTensor::AsCVTensor(in);
+    cv::Mat m2;
+    auto m1 = cvt_in->mat();
+    cv::cvtColor(m1, m2, cv::COLOR_RGB2GRAY);
+    cv::Mat temp[3] = {m2 , m2 , m2 };
+    cv::Mat cv_out;
+    cv::merge(temp, 3, cv_out);
+    std::shared_ptr<CVTensor> cvt_out;
+    CVTensor::CreateFromMat(cv_out, &cvt_out);
+    gray_tensor = std::static_pointer_cast<Tensor>(cvt_out);
+  }
+  TensorShape shape;
+  std::shared_ptr<Tensor> input_tensor;
+  std::shared_ptr<CVTensor> cvt_in;
+  std::shared_ptr<Tensor> gray_tensor;
+};
+
+int64_t Compare(std::shared_ptr<Tensor> t1, std::shared_ptr<Tensor> t2) {
+  auto shape = t1->shape();
+  int64_t sum = 0;
+  for (auto i = 0; i < shape[0]; i++) {
+    for (auto j = 0; j < shape[1]; j++) {
+      for (auto k = 0; k < shape[2]; k++) {
+        uint8_t value1;
+        uint8_t value2;
+        (void)t1->GetItemAt<uint8_t>(&value1, {i, j, k});
+        (void)t2->GetItemAt<uint8_t>(&value2, {i, j, k});
+        sum += abs(static_cast<int>(value1) - static_cast<int>(value2));
+      }
+    }
+  }
+  return sum;
+}
+
+// these tests are tautological, write better tests when the requirements for the output are determined
+// e. g. how do we want to convert to gray and what does it mean to blend with a gray image (pre- post- gamma corrected,
+// what weights).
+TEST_F(MindDataTestRandomColorOp, TestOp1) {
+  std::shared_ptr<Tensor> output_tensor;
+  auto op = RandomColorOp(1, 1);
+  auto s = op.Compute(input_tensor, &output_tensor);
+  auto res = Compare(input_tensor, output_tensor);
+  EXPECT_EQ(0, res);
+}
+
+TEST_F(MindDataTestRandomColorOp, TestOp2) {
+  std::shared_ptr<Tensor> output_tensor;
+  auto op = RandomColorOp(0, 0);
+  auto s = op.Compute(input_tensor, &output_tensor);
+  EXPECT_TRUE(s.IsOk());
+  auto res = Compare(output_tensor, gray_tensor);
+  EXPECT_EQ(res, 0);
+}
+
+TEST_F(MindDataTestRandomColorOp, TestOp3) {
+  std::shared_ptr<Tensor> output_tensor;
+  auto op = RandomColorOp(0.0, 1.0);
+  for (auto i = 0; i < 1; i++) {
+    auto s = op.Compute(input_tensor, &output_tensor);
+    EXPECT_TRUE(s.IsOk());
+  }
+}

tests/ut/python/dataset/test_random_color.py

@@ -16,9 +16,11 @@
 Testing RandomColor op in DE
 """
 import numpy as np
+import pytest
 
 import mindspore.dataset as ds
 import mindspore.dataset.engine as de
+import mindspore.dataset.transforms.vision.c_transforms as vision
 import mindspore.dataset.transforms.vision.py_transforms as F
 from mindspore import log as logger
 from util import visualize_list, diff_mse, save_and_check_md5, \
@@ -26,11 +28,17 @@ from util import visualize_list, diff_mse, save_and_check_md5, \
 
 DATA_DIR = "../data/dataset/testImageNetData/train/"
 
+C_DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
+C_SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
+
+MNIST_DATA_DIR = "../data/dataset/testMnistData"
+
 GENERATE_GOLDEN = False
 
-def test_random_color(degrees=(0.1, 1.9), plot=False):
+
+def test_random_color_py(degrees=(0.1, 1.9), plot=False):
     """
-    Test RandomColor
+    Test Python RandomColor
     """
     logger.info("Test RandomColor")
 
@@ -85,9 +93,53 @@ def test_random_color(degrees=(0.1, 1.9), plot=False):
         visualize_list(images_original, images_random_color)
 
 
-def test_random_color_md5():
+def test_random_color_c(degrees=(0.1, 1.9), plot=False, run_golden=True):
     """
-    Test RandomColor with md5 check
+    Test Cpp RandomColor
+    """
+    logger.info("test_random_color_op")
+
+    original_seed = config_get_set_seed(10)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+
+    # Decode with rgb format set to True
+    data1 = ds.TFRecordDataset(C_DATA_DIR, C_SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data2 = ds.TFRecordDataset(C_DATA_DIR, C_SCHEMA_DIR, columns_list=["image"], shuffle=False)
+
+    # Serialize and Load dataset requires using vision.Decode instead of vision.Decode().
+    if degrees is None:
+        c_op = vision.RandomColor()
+    else:
+        c_op = vision.RandomColor(degrees)
+
+    data1 = data1.map(input_columns=["image"], operations=[vision.Decode()])
+    data2 = data2.map(input_columns=["image"], operations=[vision.Decode(), c_op])
+
+    image_random_color_op = []
+    image = []
+
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        actual = item1["image"]
+        expected = item2["image"]
+        image.append(actual)
+        image_random_color_op.append(expected)
+
+    if run_golden:
+        # Compare with expected md5 from images
+        filename = "random_color_op_02_result.npz"
+        save_and_check_md5(data2, filename, generate_golden=GENERATE_GOLDEN)
+
+    if plot:
+        visualize_list(image, image_random_color_op)
+
+    # Restore configuration
+    ds.config.set_seed(original_seed)
+    ds.config.set_num_parallel_workers((original_num_parallel_workers))
+
+
+def test_random_color_py_md5():
+    """
+    Test Python RandomColor with md5 check
     """
     logger.info("Test RandomColor with md5 check")
     original_seed = config_get_set_seed(10)
@@ -110,8 +162,94 @@ def test_random_color_md5():
     ds.config.set_num_parallel_workers((original_num_parallel_workers))
 
 
+def test_compare_random_color_op(degrees=None, plot=False):
+    """
+    Compare Random Color op in Python and Cpp
+    """
+
+    logger.info("test_random_color_op")
+
+    original_seed = config_get_set_seed(5)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+
+    # Decode with rgb format set to True
+    data1 = ds.TFRecordDataset(C_DATA_DIR, C_SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data2 = ds.TFRecordDataset(C_DATA_DIR, C_SCHEMA_DIR, columns_list=["image"], shuffle=False)
+
+    if degrees is None:
+        c_op = vision.RandomColor()
+        p_op = F.RandomColor()
+    else:
+        c_op = vision.RandomColor(degrees)
+        p_op = F.RandomColor(degrees)
+
+    transforms_random_color_py = F.ComposeOp([lambda img: img.astype(np.uint8), F.ToPIL(),
+                                              p_op, np.array])
+
+    data1 = data1.map(input_columns=["image"], operations=[vision.Decode(), c_op])
+    data2 = data2.map(input_columns=["image"], operations=[vision.Decode()])
+    data2 = data2.map(input_columns=["image"], operations=transforms_random_color_py())
+
+    image_random_color_op = []
+    image = []
+
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        actual = item1["image"]
+        expected = item2["image"]
+        image_random_color_op.append(actual)
+        image.append(expected)
+        assert actual.shape == expected.shape
+        mse = diff_mse(actual, expected)
+        logger.info("MSE= {}".format(str(np.mean(mse))))
+
+    # Restore configuration
+    ds.config.set_seed(original_seed)
+    ds.config.set_num_parallel_workers(original_num_parallel_workers)
+
+    if plot:
+        visualize_list(image, image_random_color_op)
+
+
+def test_random_color_c_errors():
+    """
+    Test that Cpp RandomColor errors with bad input
+    """
+    with pytest.raises(TypeError) as error_info:
+        vision.RandomColor((12))
+    assert "degrees must be either a tuple or a list." in str(error_info.value)
+
+    with pytest.raises(TypeError) as error_info:
+        vision.RandomColor(("col", 3))
+    assert "Argument degrees[0] with value col is not of type (<class 'int'>, <class 'float'>)." in str(
+        error_info.value)
+
+    with pytest.raises(ValueError) as error_info:
+        vision.RandomColor((0.9, 0.1))
+    assert "degrees should be in (min,max) format. Got (max,min)." in str(error_info.value)
+
+    with pytest.raises(ValueError) as error_info:
+        vision.RandomColor((0.9,))
+    assert "degrees must be a sequence with length 2." in str(error_info.value)
+
+    # RandomColor Cpp Op will fail with one channel input
+    mnist_ds = de.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False)
+    mnist_ds = mnist_ds.map(input_columns="image", operations=vision.RandomColor())
+
+    with pytest.raises(RuntimeError) as error_info:
+        for _ in enumerate(mnist_ds):
+            pass
+    assert "Invalid number of channels in input image" in str(error_info.value)
+
+
 if __name__ == "__main__":
-    test_random_color()
-    test_random_color(plot=True)
-    test_random_color(degrees=(0.5, 1.5), plot=True)
-    test_random_color_md5()
+    test_random_color_py()
+    test_random_color_py(plot=True)
+    test_random_color_py(degrees=(0.5, 1.5), plot=True)
+    test_random_color_py_md5()
+
+    test_random_color_c()
+    test_random_color_c(plot=True)
+    test_random_color_c(degrees=(0.5, 1.5), plot=True, run_golden=False)
+    test_random_color_c(degrees=(0.1, 0.1), plot=True, run_golden=False)
+    test_compare_random_color_op(plot=True)
+    test_random_color_c_errors()