Added cpp Equalize op

adding missed files

Fixed Errors

fixed bugs

fixed bugs

fixed bugs

fixed bugs

fixed bugs

Updated Python UT for Equalize op

Added cpp Equalize op

adding missed files

Fixed Errors

fixed bugs

fixed bugs

fixed bugs

fixed bugs

fixed bugs

Updated Python UT for Equalize op

Fixed comment style

update files

deleted files

added files

added image_utils.h

Fixed PyLint and CPPLint Errors

updated tensor_op.h

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

@@ -53,6 +53,7 @@
 #include "minddata/dataset/kernels/image/center_crop_op.h"
 #include "minddata/dataset/kernels/image/cut_out_op.h"
 #include "minddata/dataset/kernels/image/decode_op.h"
+#include "minddata/dataset/kernels/image/equalize_op.h"
 #include "minddata/dataset/kernels/image/hwc_to_chw_op.h"
 #include "minddata/dataset/kernels/image/image_utils.h"
 #include "minddata/dataset/kernels/image/invert_op.h"
@@ -374,6 +375,10 @@ void bindTensorOps1(py::module *m) {
     .def(py::init<float, float, float, float, float, float>(), py::arg("meanR"), py::arg("meanG"), py::arg("meanB"),
          py::arg("stdR"), py::arg("stdG"), py::arg("stdB"));
 
+  (void)py::class_<EqualizeOp, TensorOp, std::shared_ptr<EqualizeOp>>(
+    *m, "EqualizeOp", "Tensor operation to apply histogram equalization on images.")
+    .def(py::init<>());
+
   (void)py::class_<InvertOp, TensorOp, std::shared_ptr<InvertOp>>(*m, "InvertOp",
                                                                   "Tensor operation to apply invert on RGB images.")
     .def(py::init<>());

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

@@ -5,6 +5,7 @@ add_library(kernels-image OBJECT
     center_crop_op.cc
     cut_out_op.cc
     decode_op.cc
+    equalize_op.cc
     hwc_to_chw_op.cc
     image_utils.cc
     invert_op.cc

mindspore/ccsrc/minddata/dataset/kernels/image/equalize_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 "minddata/dataset/kernels/image/equalize_op.h"
+#include "minddata/dataset/kernels/image/image_utils.h"
+
+namespace mindspore {
+namespace dataset {
+
+// only supports RGB images
+
+Status EqualizeOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
+  IO_CHECK(input, output);
+  return Equalize(input, output);
+}
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/image/equalize_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 MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_EQUALIZE_OP_H_
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_EQUALIZE_OP_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+class EqualizeOp : public TensorOp {
+ public:
+  EqualizeOp() {}
+  ~EqualizeOp() = default;
+
+  // Description: A function that prints info about the node
+  void Print(std::ostream &out) const override { out << Name(); }
+
+  Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
+
+  std::string Name() const override { return kEqualizeOp; }
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_EQUALIZE_OP_H_

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

@@ -749,6 +749,46 @@ Status AdjustHue(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *
   return Status::OK();
 }
 
+Status Equalize(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
+  try {
+    std::shared_ptr<CVTensor> input_cv = CVTensor::AsCVTensor(input);
+    if (!input_cv->mat().data) {
+      RETURN_STATUS_UNEXPECTED("Could not convert to CV Tensor");
+    }
+    if (input_cv->Rank() != 3 && input_cv->Rank() != 2) {
+      RETURN_STATUS_UNEXPECTED("Shape not <H,W,C> or <H,W>");
+    }
+    // For greyscale images, extend dimension if rank is 2 and reshape output to be of rank 2.
+    if (input_cv->Rank() == 2) {
+      RETURN_IF_NOT_OK(input_cv->ExpandDim(2));
+    }
+    // Get number of channels and image matrix
+    std::size_t num_of_channels = input_cv->shape()[2];
+    if (num_of_channels != 1 && num_of_channels != 3) {
+      RETURN_STATUS_UNEXPECTED("Number of channels is not 1 or 3.");
+    }
+    cv::Mat image = input_cv->mat();
+    // Separate the image to channels
+    std::vector<cv::Mat> planes(num_of_channels);
+    cv::split(image, planes);
+    // Equalize each channel separately
+    std::vector<cv::Mat> image_result;
+    for (std::size_t layer = 0; layer < planes.size(); layer++) {
+      cv::Mat channel_result;
+      cv::equalizeHist(planes[layer], channel_result);
+      image_result.push_back(channel_result);
+    }
+    cv::Mat result;
+    cv::merge(image_result, result);
+    std::shared_ptr<CVTensor> output_cv = std::make_shared<CVTensor>(result);
+    if (input_cv->Rank() == 2) output_cv->Squeeze();
+    (*output) = std::static_pointer_cast<Tensor>(output_cv);
+  } catch (const cv::Exception &e) {
+    RETURN_STATUS_UNEXPECTED("Error in equalize.");
+  }
+  return Status::OK();
+}
+
 Status Erase(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, int32_t box_height,
              int32_t box_width, int32_t num_patches, bool bounded, bool random_color, std::mt19937 *rnd, uint8_t fill_r,
              uint8_t fill_g, uint8_t fill_b) {

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

@@ -200,6 +200,12 @@ Status AdjustSaturation(const std::shared_ptr<Tensor> &input, std::shared_ptr<Te
 // @param output: Adjusted image of same shape and type.
 Status AdjustHue(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, const float &hue);
 
+/// \brief Returns image with equalized histogram.
+/// \param[in] input: Tensor of shape <H,W,3>/<H,W,1>/<H,W> in RGB/Grayscale and
+///                   any OpenCv compatible type, see CVTensor.
+/// \param[out] output: Equalized image of same shape and type.
+Status Equalize(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output);
+
 // Masks out a random section from the image with set dimension
 // @param input: input Tensor
 // @param output: cutOut Tensor

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

@@ -92,6 +92,7 @@ constexpr char kBoundingBoxAugmentOp[] = "BoundingBoxAugmentOp";
 constexpr char kDecodeOp[] = "DecodeOp";
 constexpr char kCenterCropOp[] = "CenterCropOp";
 constexpr char kCutOutOp[] = "CutOutOp";
+constexpr char kEqualizeOp[] = "EqualizeOp";
 constexpr char kHwcToChwOp[] = "HwcToChwOp";
 constexpr char kInvertOp[] = "InvertOp";
 constexpr char kNormalizeOp[] = "NormalizeOp";

mindspore/dataset/transforms/vision/c_transforms.py

@@ -89,6 +89,13 @@ class AutoContrast(cde.AutoContrastOp):
         super().__init__(cutoff, ignore)
 
 
+class Equalize(cde.EqualizeOp):
+    """
+    Apply histogram equalization on input image.
+    does not have input arguments.
+    """
+
+
 class Invert(cde.InvertOp):
     """
     Apply invert on input image in RGB mode.

tests/ut/python/dataset/test_equalize.py

@@ -18,6 +18,7 @@ Testing Equalize op in DE
 import numpy as np
 
 import mindspore.dataset.engine as de
+import mindspore.dataset.transforms.vision.c_transforms as C
 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
@@ -26,9 +27,9 @@ DATA_DIR = "../data/dataset/testImageNetData/train/"
 
 GENERATE_GOLDEN = False
 
-def test_equalize(plot=False):
+def test_equalize_py(plot=False):
     """
-    Test Equalize
+    Test Equalize py op
     """
     logger.info("Test Equalize")
 
@@ -83,9 +84,141 @@ def test_equalize(plot=False):
         visualize_list(images_original, images_equalize)
 
 
-def test_equalize_md5():
+def test_equalize_c(plot=False):
     """
-    Test Equalize with md5 check
+    Test Equalize Cpp op
+    """
+    logger.info("Test Equalize cpp op")
+
+    # Original Images
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
+
+    transforms_original = [C.Decode(), C.Resize(size=[224, 224])]
+
+    ds_original = ds.map(input_columns="image",
+                         operations=transforms_original)
+
+    ds_original = ds_original.batch(512)
+
+    for idx, (image, _) in enumerate(ds_original):
+        if idx == 0:
+            images_original = image
+        else:
+            images_original = np.append(images_original,
+                                        image,
+                                        axis=0)
+
+    # Equalize Images
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
+
+    transform_equalize = [C.Decode(), C.Resize(size=[224, 224]),
+                          C.Equalize()]
+
+    ds_equalize = ds.map(input_columns="image",
+                         operations=transform_equalize)
+
+    ds_equalize = ds_equalize.batch(512)
+
+    for idx, (image, _) in enumerate(ds_equalize):
+        if idx == 0:
+            images_equalize = image
+        else:
+            images_equalize = np.append(images_equalize,
+                                        image,
+                                        axis=0)
+    if plot:
+        visualize_list(images_original, images_equalize)
+
+    num_samples = images_original.shape[0]
+    mse = np.zeros(num_samples)
+    for i in range(num_samples):
+        mse[i] = diff_mse(images_equalize[i], images_original[i])
+    logger.info("MSE= {}".format(str(np.mean(mse))))
+
+
+def test_equalize_py_c(plot=False):
+    """
+    Test Equalize Cpp op and python op
+    """
+    logger.info("Test Equalize cpp and python op")
+
+    # equalize Images in cpp
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
+    ds = ds.map(input_columns=["image"],
+                operations=[C.Decode(), C.Resize((224, 224))])
+
+    ds_c_equalize = ds.map(input_columns="image",
+                           operations=C.Equalize())
+
+    ds_c_equalize = ds_c_equalize.batch(512)
+
+    for idx, (image, _) in enumerate(ds_c_equalize):
+        if idx == 0:
+            images_c_equalize = image
+        else:
+            images_c_equalize = np.append(images_c_equalize,
+                                          image,
+                                          axis=0)
+
+    # Equalize images in python
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
+    ds = ds.map(input_columns=["image"],
+                operations=[C.Decode(), C.Resize((224, 224))])
+
+    transforms_p_equalize = F.ComposeOp([lambda img: img.astype(np.uint8),
+                                         F.ToPIL(),
+                                         F.Equalize(),
+                                         np.array])
+
+    ds_p_equalize = ds.map(input_columns="image",
+                           operations=transforms_p_equalize())
+
+    ds_p_equalize = ds_p_equalize.batch(512)
+
+    for idx, (image, _) in enumerate(ds_p_equalize):
+        if idx == 0:
+            images_p_equalize = image
+        else:
+            images_p_equalize = np.append(images_p_equalize,
+                                          image,
+                                          axis=0)
+
+    num_samples = images_c_equalize.shape[0]
+    mse = np.zeros(num_samples)
+    for i in range(num_samples):
+        mse[i] = diff_mse(images_p_equalize[i], images_c_equalize[i])
+    logger.info("MSE= {}".format(str(np.mean(mse))))
+
+    if plot:
+        visualize_list(images_c_equalize, images_p_equalize, visualize_mode=2)
+
+
+def test_equalize_one_channel():
+    """
+     Test Equalize cpp op with one channel image
+     """
+    logger.info("Test Equalize C Op With One Channel Images")
+
+    c_op = C.Equalize()
+
+    try:
+        ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
+        ds = ds.map(input_columns=["image"],
+                    operations=[C.Decode(),
+                                C.Resize((224, 224)),
+                                lambda img: np.array(img[:, :, 0])])
+
+        ds.map(input_columns="image",
+               operations=c_op)
+
+    except RuntimeError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert "The shape" in str(e)
+
+
+def test_equalize_md5_py():
+    """
+    Test Equalize py op with md5 check
     """
     logger.info("Test Equalize")
 
@@ -101,6 +234,31 @@ def test_equalize_md5():
     save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
 
 
+def test_equalize_md5_c():
+    """
+    Test Equalize cpp op with md5 check
+    """
+    logger.info("Test Equalize cpp op with md5 check")
+
+    # Generate dataset
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
+
+    transforms_equalize = [C.Decode(),
+                           C.Resize(size=[224, 224]),
+                           C.Equalize(),
+                           F.ToTensor()]
+
+    data = ds.map(input_columns="image", operations=transforms_equalize)
+    # Compare with expected md5 from images
+    filename = "equalize_01_result_c.npz"
+    save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
+
+
 if __name__ == "__main__":
-    test_equalize(plot=True)
-    test_equalize_md5()
+    test_equalize_py(plot=False)
+    test_equalize_c(plot=False)
+    test_equalize_py_c(plot=False)
+    test_equalize_one_channel()
+    test_equalize_md5_py()
+    test_equalize_md5_c()
+    
\ No newline at end of file