[feat] [assidtant] [I40GZN] add new data ops RandomEqualize

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

@@ -43,6 +43,7 @@
 #include "minddata/dataset/kernels/ir/vision/random_crop_decode_resize_ir.h"
 #include "minddata/dataset/kernels/ir/vision/random_crop_ir.h"
 #include "minddata/dataset/kernels/ir/vision/random_crop_with_bbox_ir.h"
+#include "minddata/dataset/kernels/ir/vision/random_equalize_ir.h"
 #include "minddata/dataset/kernels/ir/vision/random_horizontal_flip_ir.h"
 #include "minddata/dataset/kernels/ir/vision/random_horizontal_flip_with_bbox_ir.h"
 #include "minddata/dataset/kernels/ir/vision/random_invert_ir.h"
@@ -364,6 +365,16 @@ PYBIND_REGISTER(RandomCropWithBBoxOperation, 1, ([](const py::module *m) {
                       }));
                 }));
 
+PYBIND_REGISTER(RandomEqualizeOperation, 1, ([](const py::module *m) {
+                  (void)py::class_<vision::RandomEqualizeOperation, TensorOperation,
+                                   std::shared_ptr<vision::RandomEqualizeOperation>>(*m, "RandomEqualizeOperation")
+                    .def(py::init([](float prob) {
+                      auto random_equalize = std::make_shared<vision::RandomEqualizeOperation>(prob);
+                      THROW_IF_ERROR(random_equalize->ValidateParams());
+                      return random_equalize;
+                    }));
+                }));
+
 PYBIND_REGISTER(RandomHorizontalFlipOperation, 1, ([](const py::module *m) {
                   (void)py::class_<vision::RandomHorizontalFlipOperation, TensorOperation,
                                    std::shared_ptr<vision::RandomHorizontalFlipOperation>>(

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

@@ -47,6 +47,7 @@
 #include "minddata/dataset/kernels/ir/vision/random_crop_decode_resize_ir.h"
 #include "minddata/dataset/kernels/ir/vision/random_crop_ir.h"
 #include "minddata/dataset/kernels/ir/vision/random_crop_with_bbox_ir.h"
+#include "minddata/dataset/kernels/ir/vision/random_equalize_ir.h"
 #include "minddata/dataset/kernels/ir/vision/random_horizontal_flip_ir.h"
 #include "minddata/dataset/kernels/ir/vision/random_horizontal_flip_with_bbox_ir.h"
 #include "minddata/dataset/kernels/ir/vision/random_invert_ir.h"
@@ -602,6 +603,18 @@ std::shared_ptr<TensorOperation> RandomCropWithBBox::Parse() {
                                                        data_->fill_value_, data_->padding_mode_);
 }
 
+// RandomEqualize Transform Operation.
+struct RandomEqualize::Data {
+  explicit Data(float prob) : probability_(prob) {}
+  float probability_;
+};
+
+RandomEqualize::RandomEqualize(float prob) : data_(std::make_shared<Data>(prob)) {}
+
+std::shared_ptr<TensorOperation> RandomEqualize::Parse() {
+  return std::make_shared<RandomEqualizeOperation>(data_->probability_);
+}
+
 // RandomHorizontalFlip.
 struct RandomHorizontalFlip::Data {
   explicit Data(float prob) : probability_(prob) {}

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

@@ -520,6 +520,27 @@ class RandomCropWithBBox final : public TensorTransform {
   std::shared_ptr<Data> data_;
 };
 
+/// \brief Randomly apply histogram equalization on the input image with a given probability.
+class RandomEqualize final : public TensorTransform {
+ public:
+  /// \brief Constructor.
+  /// \param[in] prob A float representing the probability of equalization, which
+  ///     must be in range of [0, 1] (default=0.5).
+  explicit RandomEqualize(float prob = 0.5);
+
+  /// \brief Destructor.
+  ~RandomEqualize() = default;
+
+ protected:
+  /// \brief The function to convert a TensorTransform object into a TensorOperation object.
+  /// \return Shared pointer to TensorOperation object.
+  std::shared_ptr<TensorOperation> Parse() override;
+
+ private:
+  struct Data;
+  std::shared_ptr<Data> data_;
+};
+
 /// \brief Randomly flip the input image horizontally with a given probability.
 class RandomHorizontalFlip final : public TensorTransform {
  public:

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

@@ -36,6 +36,7 @@ add_library(kernels-image OBJECT
     random_crop_and_resize_op.cc
     random_crop_op.cc
     random_crop_with_bbox_op.cc
+    random_equalize_op.cc
     random_horizontal_flip_op.cc
     random_horizontal_flip_with_bbox_op.cc
     random_invert_op.cc

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

@@ -0,0 +1,34 @@
+/**
+ * Copyright 2021 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_equalize_op.h"
+
+#include "minddata/dataset/kernels/image/image_utils.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+const float RandomEqualizeOp::kDefProbability = 0.5;
+
+Status RandomEqualizeOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
+  IO_CHECK(input, output);
+  if (distribution_(rnd_)) {
+    return Equalize(input, output);
+  }
+  *output = input;
+  return Status::OK();
+}
+}  // namespace dataset
+}  // namespace mindspore

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

@@ -0,0 +1,59 @@
+/**
+ * Copyright 2021 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_IMAGE_RANDOM_EQUALIZE_OP_H_
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_RANDOM_EQUALIZE_OP_H_
+
+#include <memory>
+#include <random>
+#include <string>
+
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/random.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+class RandomEqualizeOp : public TensorOp {
+ public:
+  // Default values, also used by python_bindings.cc
+  static const float kDefProbability;
+
+  explicit RandomEqualizeOp(float prob = kDefProbability) : distribution_(prob) {
+    is_deterministic_ = false;
+    rnd_.seed(GetSeed());
+  }
+
+  ~RandomEqualizeOp() override = default;
+
+  // Provide stream operator for displaying it
+  friend std::ostream &operator<<(std::ostream &out, const RandomEqualizeOp &so) {
+    so.Print(out);
+    return out;
+  }
+
+  Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
+
+  std::string Name() const override { return kRandomEqualizeOp; }
+
+ private:
+  std::mt19937 rnd_;
+  std::bernoulli_distribution distribution_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_RANDOM_EQUALIZE_OP_H_

mindspore/ccsrc/minddata/dataset/kernels/ir/vision/CMakeLists.txt

@@ -28,6 +28,7 @@ set(DATASET_KERNELS_IR_VISION_SRC_FILES
         random_crop_decode_resize_ir.cc
         random_crop_ir.cc
         random_crop_with_bbox_ir.cc
+        random_equalize_ir.cc
         random_horizontal_flip_ir.cc
         random_horizontal_flip_with_bbox_ir.cc
         random_invert_ir.cc

mindspore/ccsrc/minddata/dataset/kernels/ir/vision/random_equalize_ir.cc

@@ -0,0 +1,61 @@
+/**
+ * Copyright 2021 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/ir/vision/random_equalize_ir.h"
+
+#ifndef ENABLE_ANDROID
+#include "minddata/dataset/kernels/image/random_equalize_op.h"
+#endif
+
+#include "minddata/dataset/kernels/ir/validators.h"
+
+namespace mindspore {
+namespace dataset {
+namespace vision {
+#ifndef ENABLE_ANDROID
+// RandomEqualizeOperation
+RandomEqualizeOperation::RandomEqualizeOperation(float prob) : TensorOperation(true), probability_(prob) {}
+
+RandomEqualizeOperation::~RandomEqualizeOperation() = default;
+
+std::string RandomEqualizeOperation::Name() const { return kRandomEqualizeOperation; }
+
+Status RandomEqualizeOperation::ValidateParams() {
+  RETURN_IF_NOT_OK(ValidateProbability("RandomEqualize", probability_));
+  return Status::OK();
+}
+
+std::shared_ptr<TensorOp> RandomEqualizeOperation::Build() {
+  std::shared_ptr<RandomEqualizeOp> tensor_op = std::make_shared<RandomEqualizeOp>(probability_);
+  return tensor_op;
+}
+
+Status RandomEqualizeOperation::to_json(nlohmann::json *out_json) {
+  (*out_json)["prob"] = probability_;
+  return Status::OK();
+}
+
+Status RandomEqualizeOperation::from_json(nlohmann::json op_params, std::shared_ptr<TensorOperation> *operation) {
+  CHECK_FAIL_RETURN_UNEXPECTED(op_params.find("prob") != op_params.end(), "Failed to find prob");
+  float prob = op_params["prob"];
+  *operation = std::make_shared<vision::RandomEqualizeOperation>(prob);
+  return Status::OK();
+}
+
+#endif
+}  // namespace vision
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/ir/vision/random_equalize_ir.h

@@ -0,0 +1,61 @@
+/**
+ * Copyright 2021 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_IR_VISION_RANDOM_EQUALIZE_IR_H_
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IR_VISION_RANDOM_EQUALIZE_IR_H_
+
+#include <map>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "include/api/status.h"
+#include "minddata/dataset/include/dataset/constants.h"
+#include "minddata/dataset/include/dataset/transforms.h"
+#include "minddata/dataset/kernels/ir/tensor_operation.h"
+
+namespace mindspore {
+namespace dataset {
+
+namespace vision {
+
+constexpr char kRandomEqualizeOperation[] = "RandomEqualize";
+
+class RandomEqualizeOperation : public TensorOperation {
+ public:
+  explicit RandomEqualizeOperation(float prob);
+
+  ~RandomEqualizeOperation();
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+  std::string Name() const override;
+
+  Status to_json(nlohmann::json *out_json) override;
+
+  static Status from_json(nlohmann::json op_params, std::shared_ptr<TensorOperation> *operation);
+
+ private:
+  float probability_;
+};
+
+}  // namespace vision
+}  // namespace dataset
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IR_VISION_RANDOM_EQUALIZE_IR_H_

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

@@ -88,6 +88,7 @@ constexpr char kRandomCropAndResizeWithBBoxOp[] = "RandomCropAndResizeWithBBoxOp
 constexpr char kRandomCropDecodeResizeOp[] = "RandomCropDecodeResizeOp";
 constexpr char kRandomCropOp[] = "RandomCropOp";
 constexpr char kRandomCropWithBBoxOp[] = "RandomCropWithBBoxOp";
+constexpr char kRandomEqualizeOp[] = "RandomEqualizeOp";
 constexpr char kRandomHorizontalFlipWithBBoxOp[] = "RandomHorizontalFlipWithBBoxOp";
 constexpr char kRandomHorizontalFlipOp[] = "RandomHorizontalFlipOp";
 constexpr char kRandomInvertOp[] = "RandomInvertOp";

mindspore/dataset/vision/c_transforms.py

@@ -1082,6 +1082,28 @@ class RandomCropWithBBox(ImageTensorOperation):
                                                border_type)
 
 
+class RandomEqualize(ImageTensorOperation):
+    """
+    Apply histogram equalization on the input image with a given probability.
+
+    Args:
+        prob (float, optional): Probability of the image being equalized, which
+            must be in range of [0, 1] (default=0.5).
+
+    Examples:
+        >>> transforms_list = [c_vision.Decode(), c_vision.RandomEqualize(0.5)]
+        >>> image_folder_dataset = image_folder_dataset.map(operations=transforms_list,
+        ...                                                 input_columns=["image"])
+    """
+
+    @check_prob
+    def __init__(self, prob=0.5):
+        self.prob = prob
+
+    def parse(self):
+        return cde.RandomEqualizeOperation(self.prob)
+
+
 class RandomHorizontalFlip(ImageTensorOperation):
     """
     Randomly flip the input image horizontally with a given probability.

tests/ut/cpp/dataset/c_api_vision_r_to_z_test.cc

@@ -350,3 +350,49 @@ TEST_F(MindDataTestPipeline, TestRGB2BGR) {
   iter1->Stop();
   iter2->Stop();
 }
+
+TEST_F(MindDataTestPipeline, TestRandomEqualize) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomEqualize.";
+
+  std::string MindDataPath = "data/dataset";
+  std::string folder_path = MindDataPath + "/testImageNetData/train/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
+  EXPECT_NE(ds, nullptr);
+
+  auto random_equalize_op = vision::RandomEqualize(0.5);
+
+  ds = ds->Map({random_equalize_op});
+  EXPECT_NE(ds, nullptr);
+
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+  std::unordered_map<std::string, mindspore::MSTensor> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    iter->GetNextRow(&row);
+  }
+  EXPECT_EQ(i, 2);
+
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestRandomEqualizeInvalidProb) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomEqualizeInvalidProb.";
+
+  std::string MindDataPath = "data/dataset";
+  std::string folder_path = MindDataPath + "/testImageNetData/train/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, std::make_shared<RandomSampler>(false, 2));
+  EXPECT_NE(ds, nullptr);
+
+  auto random_equalize_op = vision::RandomEqualize(1.5);
+
+  ds = ds->Map({random_equalize_op});
+  EXPECT_NE(ds, nullptr);
+
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_EQ(iter, nullptr);
+}

tests/ut/cpp/dataset/execute_test.cc

@@ -1126,3 +1126,17 @@ TEST_F(MindDataTestExecute, TestRandomAutoContrastEager) {
   Status rc = transform(image, &image);
   EXPECT_EQ(rc, Status::OK());
 }
+
+TEST_F(MindDataTestExecute, TestRandomEqualizeEager) {
+  MS_LOG(INFO) << "Doing MindDataTestExecute-TestRandomEqualizeEager.";
+  // Read images
+  auto image = ReadFileToTensor("data/dataset/apple.jpg");
+
+  // Transform params
+  auto decode = vision::Decode();
+  auto random_equalize_op = vision::RandomEqualize(0.6);
+
+  auto transform = Execute({decode, random_equalize_op});
+  Status rc = transform(image, &image);
+  EXPECT_EQ(rc, Status::OK());
+}

tests/ut/python/dataset/test_random_equalize.py

@@ -0,0 +1,129 @@
+# Copyright 2021 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 RandomEqualize op in DE
+"""
+import numpy as np
+
+import mindspore.dataset as ds
+from mindspore.dataset.vision.c_transforms import Decode, Resize, RandomEqualize, Equalize
+from mindspore import log as logger
+from util import visualize_list, visualize_image, diff_mse
+
+image_file = "../data/dataset/testImageNetData/train/class1/1_1.jpg"
+data_dir = "../data/dataset/testImageNetData/train/"
+
+
+def test_random_equalize_pipeline(plot=False):
+    """
+    Test RandomEqualize pipeline
+    """
+    logger.info("Test RandomEqualize pipeline")
+
+    # Original Images
+    data_set = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
+    transforms_original = [Decode(), Resize(size=[224, 224])]
+    ds_original = data_set.map(operations=transforms_original, input_columns="image")
+    ds_original = ds_original.batch(512)
+
+    for idx, (image, _) in enumerate(ds_original):
+        if idx == 0:
+            images_original = image.asnumpy()
+        else:
+            images_original = np.append(images_original,
+                                        image.asnumpy(),
+                                        axis=0)
+
+    # Randomly Equalized Images
+    data_set1 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
+    transform_random_equalize = [Decode(), Resize(size=[224, 224]), RandomEqualize(0.6)]
+    ds_random_equalize = data_set1.map(operations=transform_random_equalize, input_columns="image")
+    ds_random_equalize = ds_random_equalize.batch(512)
+    for idx, (image, _) in enumerate(ds_random_equalize):
+        if idx == 0:
+            images_random_equalize = image.asnumpy()
+        else:
+            images_random_equalize = np.append(images_random_equalize,
+                                               image.asnumpy(),
+                                               axis=0)
+    if plot:
+        visualize_list(images_original, images_random_equalize)
+
+    num_samples = images_original.shape[0]
+    mse = np.zeros(num_samples)
+    for i in range(num_samples):
+        mse[i] = diff_mse(images_random_equalize[i], images_original[i])
+    logger.info("MSE= {}".format(str(np.mean(mse))))
+
+
+def test_random_equalize_eager():
+    """
+    Test RandomEqualize eager.
+    """
+    img = np.fromfile(image_file, dtype=np.uint8)
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
+
+    img = Decode()(img)
+    img_equalized = Equalize()(img)
+    img_random_equalized = RandomEqualize(1.0)(img)
+    logger.info("Image.type: {}, Image.shape: {}".format(type(img_random_equalized), img_random_equalized.shape))
+
+    assert img_random_equalized.all() == img_equalized.all()
+
+
+def test_random_equalize_comp(plot=False):
+    """
+    Test RandomEqualize op compared with Equalize op.
+    """
+    random_equalize_op = RandomEqualize(prob=1.0)
+    equalize_op = Equalize()
+
+    dataset1 = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
+    for item in dataset1.create_dict_iterator(num_epochs=1, output_numpy=True):
+        image = item['image']
+    dataset1.map(operations=random_equalize_op, input_columns=['image'])
+    dataset2 = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
+    dataset2.map(operations=equalize_op, input_columns=['image'])
+    for item1, item2 in zip(dataset1.create_dict_iterator(num_epochs=1, output_numpy=True),
+                            dataset2.create_dict_iterator(num_epochs=1, output_numpy=True)):
+        image_random_equalized = item1['image']
+        image_equalized = item2['image']
+
+    mse = diff_mse(image_equalized, image_random_equalized)
+    assert mse == 0
+    logger.info("mse: {}".format(mse))
+    if plot:
+        visualize_image(image, image_random_equalized, mse, image_equalized)
+
+
+def test_random_equalize_invalid_prob():
+    """
+    Test eager. prob out of range.
+    """
+    logger.info("test_random_equalize_invalid_prob")
+    dataset = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
+    try:
+        random_equalize_op = RandomEqualize(1.5)
+        dataset = dataset.map(operations=random_equalize_op, input_columns=['image'])
+    except ValueError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert "Input prob is not within the required interval of [0.0, 1.0]." in str(e)
+
+
+if __name__ == "__main__":
+    test_random_equalize_pipeline(plot=True)
+    test_random_equalize_eager()
+    test_random_equalize_comp(plot=True)
+    test_random_equalize_invalid_prob()