Added Posterize Op

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

@@ -42,6 +42,7 @@
 #include "minddata/dataset/kernels/image/random_crop_with_bbox_op.h"
 #include "minddata/dataset/kernels/image/random_horizontal_flip_op.h"
 #include "minddata/dataset/kernels/image/random_horizontal_flip_with_bbox_op.h"
+#include "minddata/dataset/kernels/image/random_posterize_op.h"
 #include "minddata/dataset/kernels/image/random_resize_op.h"
 #include "minddata/dataset/kernels/image/random_resize_with_bbox_op.h"
 #include "minddata/dataset/kernels/image/random_rotation_op.h"
@@ -142,6 +143,13 @@ PYBIND_REGISTER(RandomAffineOp, 1, ([](const py::module *m) {
                          py::arg("fill_value") = RandomAffineOp::kFillValue);
                 }));
 
+PYBIND_REGISTER(RandomPosterizeOp, 1, ([](const py::module *m) {
+                  (void)py::class_<RandomPosterizeOp, TensorOp, std::shared_ptr<RandomPosterizeOp>>(
+                    *m, "RandomPosterizeOp", "Tensor operation to apply random posterize operation on an image.")
+                    .def(py::init<uint8_t, uint8_t>(), py::arg("min_bit") = RandomPosterizeOp::kMinBit,
+                         py::arg("max_bit") = RandomPosterizeOp::kMaxBit);
+                }));
+
 PYBIND_REGISTER(
   RandomResizeWithBBoxOp, 1, ([](const py::module *m) {
     (void)py::class_<RandomResizeWithBBoxOp, TensorOp, std::shared_ptr<RandomResizeWithBBoxOp>>(

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

@@ -32,6 +32,7 @@
 #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"
+#include "minddata/dataset/kernels/image/random_posterize_op.h"
 #include "minddata/dataset/kernels/image/random_rotation_op.h"
 #include "minddata/dataset/kernels/image/random_sharpness_op.h"
 #include "minddata/dataset/kernels/image/random_solarize_op.h"
@@ -217,6 +218,16 @@ std::shared_ptr<RandomHorizontalFlipOperation> RandomHorizontalFlip(float prob)
   return op;
 }
 
+// Function to create RandomPosterizeOperation.
+std::shared_ptr<RandomPosterizeOperation> RandomPosterize(uint8_t min_bit, uint8_t max_bit) {
+  auto op = std::make_shared<RandomPosterizeOperation>(min_bit, max_bit);
+  // Input validation
+  if (!op->ValidateParams()) {
+    return nullptr;
+  }
+  return op;
+}
+
 // Function to create RandomRotationOperation.
 std::shared_ptr<RandomRotationOperation> RandomRotation(std::vector<float> degrees, InterpolationMode resample,
                                                         bool expand, std::vector<float> center,
@@ -725,6 +736,31 @@ std::shared_ptr<TensorOp> RandomHorizontalFlipOperation::Build() {
   return tensor_op;
 }
 
+// RandomPosterizeOperation
+RandomPosterizeOperation::RandomPosterizeOperation(uint8_t min_bit, uint8_t max_bit)
+    : min_bit_(min_bit), max_bit_(max_bit) {}
+
+bool RandomPosterizeOperation::ValidateParams() {
+  if (min_bit_ < 1 || min_bit_ > 8) {
+    MS_LOG(ERROR) << "RandomPosterize: min_bit value is out of range [1-8]: " << min_bit_;
+    return false;
+  }
+  if (max_bit_ < 1 || max_bit_ > 8) {
+    MS_LOG(ERROR) << "RandomPosterize: max_bit value is out of range [1-8]: " << max_bit_;
+    return false;
+  }
+  if (max_bit_ < min_bit_) {
+    MS_LOG(ERROR) << "RandomPosterize: max_bit value is less than min_bit: max =" << max_bit_ << ", min = " << min_bit_;
+    return false;
+  }
+  return true;
+}
+
+std::shared_ptr<TensorOp> RandomPosterizeOperation::Build() {
+  std::shared_ptr<RandomPosterizeOp> tensor_op = std::make_shared<RandomPosterizeOp>(min_bit_, max_bit_);
+  return tensor_op;
+}
+
 // Function to create RandomRotationOperation.
 RandomRotationOperation::RandomRotationOperation(std::vector<float> degrees, InterpolationMode interpolation_mode,
                                                  bool expand, std::vector<float> center,

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

@@ -62,6 +62,7 @@ class RandomColorOperation;
 class RandomColorAdjustOperation;
 class RandomCropOperation;
 class RandomHorizontalFlipOperation;
+class RandomPosterizeOperation;
 class RandomRotationOperation;
 class RandomSharpnessOperation;
 class RandomSolarizeOperation;
@@ -220,6 +221,13 @@ std::shared_ptr<RandomCropOperation> RandomCrop(std::vector<int32_t> size, std::
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<RandomHorizontalFlipOperation> RandomHorizontalFlip(float prob = 0.5);
 
+/// \brief Function to create a RandomPosterize TensorOperation.
+/// \notes Tensor operation to perform random posterize.
+/// \param[in] min_bit - uint8_t representing the minimum bit in range. (Default=8)
+/// \param[in] max_bit - uint8_t representing the maximum bit in range. (Default=8)
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<RandomPosterizeOperation> RandomPosterize(uint8_t min_bit = 8, uint8_t max_bit = 8);
+
 /// \brief Function to create a RandomRotation TensorOp
 /// \notes Rotates the image according to parameters
 /// \param[in] degrees A float vector size 2, representing the starting and ending degree
@@ -521,6 +529,21 @@ class RandomHorizontalFlipOperation : public TensorOperation {
   float probability_;
 };
 
+class RandomPosterizeOperation : public TensorOperation {
+ public:
+  explicit RandomPosterizeOperation(uint8_t min_bit = 8, uint8_t max_bit = 8);
+
+  ~RandomPosterizeOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  bool ValidateParams() override;
+
+ private:
+  uint8_t min_bit_;
+  uint8_t max_bit_;
+};
+
 class RandomRotationOperation : public TensorOperation {
  public:
   RandomRotationOperation(std::vector<float> degrees, InterpolationMode interpolation_mode, bool expand,

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

@@ -17,6 +17,7 @@ add_library(kernels-image OBJECT
     mixup_batch_op.cc
     normalize_op.cc
     pad_op.cc
+    posterize_op.cc
     random_affine_op.cc
     random_color_adjust_op.cc
     random_crop_decode_resize_op.cc
@@ -27,6 +28,7 @@ add_library(kernels-image OBJECT
     random_horizontal_flip_op.cc
     random_horizontal_flip_with_bbox_op.cc
     bounding_box_augment_op.cc
+    random_posterize_op.cc
     random_resize_op.cc
     random_rotation_op.cc
     random_select_subpolicy_op.cc

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

@@ -0,0 +1,50 @@
+/**
+ * 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/posterize_op.h"
+
+#include <opencv2/imgcodecs.hpp>
+
+namespace mindspore {
+namespace dataset {
+
+const uint8_t PosterizeOp::kBit = 8;
+
+PosterizeOp::PosterizeOp(uint8_t bit) : bit_(bit) {}
+
+Status PosterizeOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
+  uint8_t mask_value = ~((uint8_t)(1 << (8 - bit_)) - 1);
+  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("Input Tensor is not in shape of <H,W,C> or <H,W>");
+  }
+  std::vector<uint8_t> lut_vector;
+  for (std::size_t i = 0; i < 256; i++) {
+    lut_vector.push_back(i & mask_value);
+  }
+  cv::Mat in_image = input_cv->mat();
+  cv::Mat output_img;
+  cv::LUT(in_image, lut_vector, output_img);
+  std::shared_ptr<CVTensor> result_tensor;
+  RETURN_IF_NOT_OK(CVTensor::CreateFromMat(output_img, &result_tensor));
+  *output = std::static_pointer_cast<Tensor>(result_tensor);
+  return Status::OK();
+}
+}  // namespace dataset
+}  // namespace mindspore

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

@@ -0,0 +1,56 @@
+/**
+ * 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_POSTERIZE_OP_H_
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_POSTERIZE_OP_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "minddata/dataset/core/cv_tensor.h"
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+class PosterizeOp : public TensorOp {
+ public:
+  /// Default values
+  static const uint8_t kBit;
+
+  /// \brief Constructor
+  /// \param[in] bit: bits to use
+  explicit PosterizeOp(uint8_t bit = kBit);
+
+  ~PosterizeOp() override = default;
+
+  std::string Name() const override { return kPosterizeOp; }
+
+  Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
+
+  /// Member variables
+ private:
+  std::string kPosterizeOp = "PosterizeOp";
+
+ protected:
+  uint8_t bit_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_POSTERIZE_OP_H_

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

@@ -0,0 +1,40 @@
+/**
+ * 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_posterize_op.h"
+
+#include <random>
+#include <opencv2/imgcodecs.hpp>
+
+#include "minddata/dataset/util/random.h"
+
+namespace mindspore {
+namespace dataset {
+
+const uint8_t RandomPosterizeOp::kMinBit = 8;
+const uint8_t RandomPosterizeOp::kMaxBit = 8;
+
+RandomPosterizeOp::RandomPosterizeOp(uint8_t min_bit, uint8_t max_bit)
+    : PosterizeOp(min_bit), min_bit_(min_bit), max_bit_(max_bit) {
+  rnd_.seed(GetSeed());
+}
+
+Status RandomPosterizeOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
+  bit_ = (min_bit_ == max_bit_) ? min_bit_ : std::uniform_int_distribution<uint8_t>(min_bit_, max_bit_)(rnd_);
+  return PosterizeOp::Compute(input, output);
+}
+}  // namespace dataset
+}  // namespace mindspore

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

@@ -0,0 +1,55 @@
+/**
+ * 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_RANDOM_POSTERIZE_OP_H_
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_RANDOM_POSTERIZE_OP_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "minddata/dataset/kernels/image/posterize_op.h"
+
+namespace mindspore {
+namespace dataset {
+class RandomPosterizeOp : public PosterizeOp {
+ public:
+  /// Default values
+  static const uint8_t kMinBit;
+  static const uint8_t kMaxBit;
+
+  /// \brief Constructor
+  /// \param[in] min_bit: Minimum bit in range
+  /// \param[in] max_bit: Maximum bit in range
+  explicit RandomPosterizeOp(uint8_t min_bit = kMinBit, uint8_t max_bit = kMaxBit);
+
+  ~RandomPosterizeOp() override = default;
+
+  std::string Name() const override { return kRandomPosterizeOp; }
+
+  Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
+
+  /// Member variables
+ private:
+  std::string kRandomPosterizeOp = "RandomPosterizeOp";
+  uint8_t min_bit_;
+  uint8_t max_bit_;
+  std::mt19937 rnd_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_RANDOM_POSTERIZE_OP_H_

mindspore/dataset/transforms/vision/c_transforms.py

@@ -50,7 +50,7 @@ from .validators import check_prob, check_crop, check_resize_interpolation, chec
     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, \
-    check_cut_mix_batch_c
+    check_cut_mix_batch_c, check_posterize
 
 DE_C_INTER_MODE = {Inter.NEAREST: cde.InterpolationMode.DE_INTER_NEAREST_NEIGHBOUR,
                    Inter.LINEAR: cde.InterpolationMode.DE_INTER_LINEAR,
@@ -459,6 +459,26 @@ class RandomHorizontalFlipWithBBox(cde.RandomHorizontalFlipWithBBoxOp):
         super().__init__(prob)
 
 
+class RandomPosterize(cde.RandomPosterizeOp):
+    """
+    Reduce the number of bits for each color channel.
+
+    Args:
+        bits (sequence or int): Range of random posterize to compress image.
+            bits values should always be in range of [1,8], and include at
+            least one integer values in the given range. It should be in
+            (min, max) or integer format. If min=max, then it is a single fixed
+            magnitude operation (default=8).
+    """
+
+    @check_posterize
+    def __init__(self, bits=(8, 8)):
+        self.bits = bits
+        if isinstance(bits, int):
+            bits = (bits, bits)
+        super().__init__(bits[0], bits[1])
+
+
 class RandomVerticalFlip(cde.RandomVerticalFlipOp):
     """
     Flip the input image vertically, randomly with a given probability.
@@ -676,6 +696,7 @@ class RandomColor(cde.RandomColorOp):
     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

@@ -162,6 +162,28 @@ def check_crop(method):
     return new_method
 
 
+def check_posterize(method):
+    """"A wrapper that wraps a parameter checker to the original function(posterize operation)."""
+
+    @wraps(method)
+    def new_method(self, *args, **kwargs):
+        [bits], _ = parse_user_args(method, *args, **kwargs)
+        if bits is not None:
+            type_check(bits, (list, tuple, int), "bits")
+        if isinstance(bits, int):
+            check_value(bits, [1, 8])
+        if isinstance(bits, (list, tuple)):
+            if len(bits) != 2:
+                raise TypeError("Size of bits should be a single integer or a list/tuple (min, max) of length 2.")
+            for item in bits:
+                check_uint8(item, "bits")
+            # also checks if min <= max
+            check_range(bits, [1, 8])
+        return method(self, *args, **kwargs)
+
+    return new_method
+
+
 def check_resize_interpolation(method):
     """A wrapper that wraps a parameter checker to the original function(resize interpolation operation)."""
 

tests/ut/cpp/dataset/c_api_transforms_test.cc

@@ -789,6 +789,120 @@ TEST_F(MindDataTestPipeline, TestRandomColorAdjust) {
   iter->Stop();
 }
 
+TEST_F(MindDataTestPipeline, TestRandomPosterizeFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomPosterize with invalid params.";
+
+  // Create objects for the tensor ops
+  // Invalid max > 8
+  std::shared_ptr<TensorOperation> posterize = vision::RandomPosterize(1, 9);
+  EXPECT_EQ(posterize, nullptr);
+  // Invalid min < 1
+  posterize = vision::RandomPosterize(0, 8);
+  EXPECT_EQ(posterize, nullptr);
+  // min > max
+  posterize = vision::RandomPosterize(8, 1);
+  EXPECT_EQ(posterize, nullptr);
+}
+
+TEST_F(MindDataTestPipeline, TestRandomPosterizeSuccess1) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomPosterizeSuccess1 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> posterize =
+    vision::RandomPosterize(1, 4);
+  EXPECT_NE(posterize, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({posterize});
+  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, TestRandomPosterizeSuccess2) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomPosterizeSuccess2 with 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> posterize = vision::RandomPosterize();
+  EXPECT_NE(posterize, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({posterize});
+  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, TestRandomSharpness) {
   MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomSharpness.";
 

tests/ut/cpp/dataset/common/cvop_common.cc

@@ -154,6 +154,10 @@ void CVOpCommon::CheckImageShapeAndData(const std::shared_ptr<Tensor> &output_te
       expect_image_path = dir_path + "imagefolder/apple_expect_random_sharpness.jpg";
       actual_image_path = dir_path + "imagefolder/apple_actual_random_sharpness.jpg";
       break;
+    case kRandomPosterize:
+      expect_image_path = dir_path + "imagefolder/apple_expect_random_posterize.jpg";
+      actual_image_path = dir_path + "imagefolder/apple_actual_random_posterize.jpg";
+      break;
     default:
       MS_LOG(INFO) << "Not pass verification! Operation type does not exists.";
       EXPECT_EQ(0, 1);

tests/ut/cpp/dataset/common/cvop_common.h

@@ -42,6 +42,7 @@ class CVOpCommon : public Common {
     kRandomSharpness,
     kInvert,
     kRandomAffine,
+    kRandomPosterize,
     kAutoContrast,
     kEqualize
   };

tests/ut/cpp/dataset/random_posterize_op_test.cc

@@ -0,0 +1,41 @@
+/**
+ * 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_posterize_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 MindDataTestRandomPosterizeOp : public UT::CVOP::CVOpCommon {
+ public:
+  MindDataTestRandomPosterizeOp() : CVOpCommon() {}
+};
+
+TEST_F(MindDataTestRandomPosterizeOp, TestOp1) {
+  MS_LOG(INFO) << "Doing testRandomPosterize.";
+
+  std::shared_ptr<Tensor> output_tensor;
+  std::unique_ptr<RandomPosterizeOp> op(new RandomPosterizeOp(1, 1));
+  EXPECT_TRUE(op->OneToOne());
+  Status s = op->Compute(input_tensor_, &output_tensor);
+  EXPECT_TRUE(s.IsOk());
+  CheckImageShapeAndData(output_tensor, kRandomPosterize);
+}

tests/ut/python/dataset/test_random_posterize.py

@@ -0,0 +1,149 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+Testing RandomPosterize op in DE
+"""
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.c_transforms as c_vision
+from mindspore import log as logger
+from util import visualize_list, save_and_check_md5, \
+    config_get_set_seed, config_get_set_num_parallel_workers
+
+GENERATE_GOLDEN = False
+
+DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
+SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
+
+
+def test_random_posterize_op_c(plot=False, run_golden=True):
+    """
+    Test RandomPosterize in C transformations
+    """
+    logger.info("test_random_posterize_op_c")
+
+    original_seed = config_get_set_seed(55)
+    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
+
+    # define map operations
+    transforms1 = [
+        c_vision.Decode(),
+        c_vision.RandomPosterize((1, 8))
+    ]
+
+    #  First dataset
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data1 = data1.map(input_columns=["image"], operations=transforms1)
+    #  Second dataset
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data2 = data2.map(input_columns=["image"], operations=[c_vision.Decode()])
+
+    image_posterize = []
+    image_original = []
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        image1 = item1["image"]
+        image2 = item2["image"]
+        image_posterize.append(image1)
+        image_original.append(image2)
+
+    if run_golden:
+        # check results with md5 comparison
+        filename = "random_posterize_01_result_c.npz"
+        save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
+
+    if plot:
+        visualize_list(image_original, image_posterize)
+
+    # Restore configuration
+    ds.config.set_seed(original_seed)
+    ds.config.set_num_parallel_workers(original_num_parallel_workers)
+
+
+def test_random_posterize_op_fixed_point_c(plot=False, run_golden=True):
+    """
+    Test RandomPosterize in C transformations with fixed point
+    """
+    logger.info("test_random_posterize_op_c")
+
+    # define map operations
+    transforms1 = [
+        c_vision.Decode(),
+        c_vision.RandomPosterize(1)
+    ]
+
+    #  First dataset
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data1 = data1.map(input_columns=["image"], operations=transforms1)
+    #  Second dataset
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
+    data2 = data2.map(input_columns=["image"], operations=[c_vision.Decode()])
+
+    image_posterize = []
+    image_original = []
+    for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        image1 = item1["image"]
+        image2 = item2["image"]
+        image_posterize.append(image1)
+        image_original.append(image2)
+
+    if run_golden:
+        # check results with md5 comparison
+        filename = "random_posterize_fixed_point_01_result_c.npz"
+        save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
+
+    if plot:
+        visualize_list(image_original, image_posterize)
+
+
+def test_random_posterize_exception_bit():
+    """
+    Test RandomPosterize: out of range input bits and invalid type
+    """
+    logger.info("test_random_posterize_exception_bit")
+    # Test max > 8
+    try:
+        _ = c_vision.RandomPosterize((1, 9))
+    except ValueError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "Input is not within the required interval of (1 to 8)."
+    # Test min < 1
+    try:
+        _ = c_vision.RandomPosterize((0, 7))
+    except ValueError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "Input is not within the required interval of (1 to 8)."
+    # Test max < min
+    try:
+        _ = c_vision.RandomPosterize((8, 1))
+    except ValueError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "Input is not within the required interval of (1 to 8)."
+    # Test wrong type (not uint8)
+    try:
+        _ = c_vision.RandomPosterize(1.1)
+    except TypeError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "Argument bits with value 1.1 is not of type (<class 'list'>, <class 'tuple'>, <class 'int'>)."
+    # Test wrong number of bits
+    try:
+        _ = c_vision.RandomPosterize((1, 1, 1))
+    except TypeError as e:
+        logger.info("Got an exception in DE: {}".format(str(e)))
+        assert str(e) == "Size of bits should be a single integer or a list/tuple (min, max) of length 2."
+
+
+if __name__ == "__main__":
+    test_random_posterize_op_c(plot=True)
+    test_random_posterize_op_fixed_point_c(plot=True)
+    test_random_posterize_exception_bit()