Added AutoContrast Op

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

@@ -48,6 +48,7 @@
 #include "minddata/dataset/kernels/data/slice_op.h"
 #include "minddata/dataset/kernels/data/to_float16_op.h"
 #include "minddata/dataset/kernels/data/type_cast_op.h"
+#include "minddata/dataset/kernels/image/auto_contrast_op.h"
 #include "minddata/dataset/kernels/image/bounding_box_augment_op.h"
 #include "minddata/dataset/kernels/image/center_crop_op.h"
 #include "minddata/dataset/kernels/image/cut_out_op.h"
@@ -362,6 +363,11 @@ void bindTensorOps1(py::module *m) {
   (void)py::class_<TensorOp, std::shared_ptr<TensorOp>>(*m, "TensorOp")
     .def("__deepcopy__", [](py::object &t, py::dict memo) { return t; });
 
+  (void)py::class_<AutoContrastOp, TensorOp, std::shared_ptr<AutoContrastOp>>(
+    *m, "AutoContrastOp", "Tensor operation to apply autocontrast on an image.")
+    .def(py::init<float, std::vector<uint32_t>>(), py::arg("cutoff") = AutoContrastOp::kCutOff,
+         py::arg("ignore") = AutoContrastOp::kIgnore);
+
   (void)py::class_<NormalizeOp, TensorOp, std::shared_ptr<NormalizeOp>>(
     *m, "NormalizeOp", "Tensor operation to normalize an image. Takes mean and std.")
     .def(py::init<float, float, float, float, float, float>(), py::arg("meanR"), py::arg("meanG"), py::arg("meanB"),

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

@@ -1,6 +1,7 @@
 file(GLOB_RECURSE _CURRENT_SRC_FILES RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "*.cc")
 set_property(SOURCE ${_CURRENT_SRC_FILES} PROPERTY COMPILE_DEFINITIONS SUBMODULE_ID=mindspore::SubModuleId::SM_MD)
 add_library(kernels-image OBJECT
+    auto_contrast_op.cc
     center_crop_op.cc
     cut_out_op.cc
     decode_op.cc

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

@@ -0,0 +1,34 @@
+/**
+ * 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 <algorithm>
+#include <utility>
+#include <vector>
+
+#include "minddata/dataset/kernels/image/auto_contrast_op.h"
+#include "minddata/dataset/kernels/image/image_utils.h"
+
+namespace mindspore {
+namespace dataset {
+
+const float AutoContrastOp::kCutOff = 0.0;
+const std::vector<uint32_t> AutoContrastOp::kIgnore = {};
+
+Status AutoContrastOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
+  IO_CHECK(input, output);
+  return AutoContrast(input, output, cutoff_, ignore_);
+}
+}  // namespace dataset
+}  // namespace mindspore

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

@@ -0,0 +1,61 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef DATASET_KERNELS_IMAGE_AUTO_CONTRAST_OP_H_
+#define DATASET_KERNELS_IMAGE_AUTO_CONTRAST_OP_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#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"
+
+namespace mindspore {
+namespace dataset {
+class AutoContrastOp : public TensorOp {
+ public:
+  /// Default cutoff to be used
+  static const float kCutOff;
+  /// Default ignore to be used
+  static const std::vector<uint32_t> kIgnore;
+
+  AutoContrastOp(const float &cutoff, const std::vector<uint32_t> &ignore) : cutoff_(cutoff), ignore_(ignore) {}
+
+  ~AutoContrastOp() override = default;
+
+  /// Provide stream operator for displaying it
+  friend std::ostream &operator<<(std::ostream &out, const AutoContrastOp &so) {
+    so.Print(out);
+    return out;
+  }
+
+  void Print(std::ostream &out) const override { out << Name(); }
+
+  std::string Name() const override { return kAutoContrastOp; }
+
+  Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
+
+ private:
+  float cutoff_;
+  std::vector<uint32_t> ignore_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // DATASET_KERNELS_IMAGE_AUTO_CONTRAST_OP_H_

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

@@ -585,6 +585,109 @@ Status AdjustContrast(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tens
   return Status::OK();
 }
 
+Status AutoContrast(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, const float &cutoff,
+                    const std::vector<uint32_t> &ignore) {
+  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>");
+    }
+    // Reshape to extend dimension if rank is 2 for algorithm to work. then reshape output to be of rank 2 like input
+    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);
+    cv::Mat b_hist, g_hist, r_hist;
+    // Establish the number of bins and set variables for histogram
+    int32_t hist_size = 256;
+    int32_t channels = 0;
+    float range[] = {0, 256};
+    const float *hist_range[] = {range};
+    bool uniform = true, accumulate = false;
+    // Set up lookup table for LUT(Look up table algorithm)
+    std::vector<int32_t> table;
+    std::vector<cv::Mat> image_result;
+    for (std::size_t layer = 0; layer < planes.size(); layer++) {
+      // Reset lookup table
+      table = std::vector<int32_t>{};
+      // Calculate Histogram for channel
+      cv::Mat hist;
+      cv::calcHist(&planes[layer], 1, &channels, cv::Mat(), hist, 1, &hist_size, hist_range, uniform, accumulate);
+      hist.convertTo(hist, CV_32SC1);
+      std::vector<int32_t> hist_vec;
+      hist.col(0).copyTo(hist_vec);
+      // Ignore values in ignore
+      for (const auto &item : ignore) hist_vec[item] = 0;
+      int32_t n = std::accumulate(hist_vec.begin(), hist_vec.end(), 0);
+      // Find pixel values that are in the low cutoff and high cutoff.
+      int32_t cut = static_cast<int32_t>((cutoff / 100.0) * n);
+      if (cut != 0) {
+        for (int32_t lo = 0; lo < 256 && cut > 0; lo++) {
+          if (cut > hist_vec[lo]) {
+            cut -= hist_vec[lo];
+            hist_vec[lo] = 0;
+          } else {
+            hist_vec[lo] -= cut;
+            cut = 0;
+          }
+        }
+        cut = static_cast<int32_t>((cutoff / 100.0) * n);
+        for (int32_t hi = 255; hi >= 0 && cut > 0; hi--) {
+          if (cut > hist_vec[hi]) {
+            cut -= hist_vec[hi];
+            hist_vec[hi] = 0;
+          } else {
+            hist_vec[hi] -= cut;
+            cut = 0;
+          }
+        }
+      }
+      int32_t lo = 0;
+      int32_t hi = 255;
+      for (; lo < 256 && !hist_vec[lo]; lo++) {
+      }
+      for (; hi >= 0 && !hist_vec[hi]; hi--) {
+      }
+      if (hi <= lo) {
+        for (int32_t i = 0; i < 256; i++) {
+          table.push_back(i);
+        }
+      } else {
+        float scale = 255.0 / (hi - lo);
+        float offset = -1 * lo * scale;
+        for (int32_t i = 0; i < 256; i++) {
+          int32_t ix = static_cast<int32_t>(i * scale + offset);
+          ix = std::max(ix, 0);
+          ix = std::min(ix, 255);
+          table.push_back(ix);
+        }
+      }
+      cv::Mat result_layer;
+      cv::LUT(planes[layer], table, result_layer);
+      image_result.push_back(result_layer);
+    }
+    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 auto contrast");
+  }
+  return Status::OK();
+}
+
 Status AdjustSaturation(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, const float &alpha) {
   try {
     std::shared_ptr<CVTensor> input_cv = CVTensor::AsCVTensor(input);

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

@@ -175,6 +175,14 @@ Status AdjustBrightness(const std::shared_ptr<Tensor> &input, std::shared_ptr<Te
 // @param output: Adjusted image of same shape and type.
 Status AdjustContrast(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, const float &alpha);
 
+// Returns image with contrast maximized.
+// @param input: Tensor of shape <H,W,3>/<H,W,1>/<H,W> in RGB/Grayscale and any OpenCv compatible type, see CVTensor.
+// @param cutoff: Cutoff percentage of how many pixels are to be removed (high pixels change to 255 and low change to 0)
+//                from the high and low ends of the histogram.
+// @param ignore: Pixel values to be ignored in the algorithm.
+Status AutoContrast(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, const float &cutoff,
+                    const std::vector<uint32_t> &ignore);
+
 // Returns image with adjusted saturation.
 // @param input: Tensor of shape <H,W,3> in RGB order and any OpenCv compatible type, see CVTensor.
 // @param alpha: Alpha value to adjust saturation by. Should be a positive number.

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

@@ -87,6 +87,7 @@ namespace mindspore {
 namespace dataset {
 
 // image
+constexpr char kAutoContrastOp[] = "AutoContrastOp";
 constexpr char kBoundingBoxAugmentOp[] = "BoundingBoxAugmentOp";
 constexpr char kDecodeOp[] = "DecodeOp";
 constexpr char kCenterCropOp[] = "CenterCropOp";

mindspore/dataset/transforms/vision/c_transforms.py

@@ -47,7 +47,7 @@ from .utils import Inter, Border
 from .validators import check_prob, check_crop, check_resize_interpolation, check_random_resize_crop, \
     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_bounding_box_augment_cpp, \
-    check_random_select_subpolicy_op, FLOAT_MAX_INTEGER
+    check_random_select_subpolicy_op, check_auto_contrast, FLOAT_MAX_INTEGER
 
 DE_C_INTER_MODE = {Inter.NEAREST: cde.InterpolationMode.DE_INTER_NEAREST_NEIGHBOUR,
                    Inter.LINEAR: cde.InterpolationMode.DE_INTER_LINEAR,
@@ -71,6 +71,24 @@ def parse_padding(padding):
     return padding
 
 
+class AutoContrast(cde.AutoContrastOp):
+    """
+    Apply auto contrast on input image.
+
+    Args:
+        cutoff (float, optional): Percent of pixels to cut off from the histogram (default=0.0).
+        ignore (int or sequence, optional): Pixel values to ignore (default=None).
+    """
+
+    @check_auto_contrast
+    def __init__(self, cutoff=0.0, ignore=None):
+        if ignore is None:
+            ignore = []
+        if isinstance(ignore, int):
+            ignore = [ignore]
+        super().__init__(cutoff, ignore)
+
+
 class Invert(cde.InvertOp):
     """
     Apply invert on input image in RGB mode.

mindspore/dataset/transforms/vision/validators.py

@@ -530,6 +530,27 @@ def check_bounding_box_augment_cpp(method):
     return new_method
 
 
+def check_auto_contrast(method):
+    """Wrapper method to check the parameters of AutoContrast ops (python and cpp)."""
+
+    @wraps(method)
+    def new_method(self, *args, **kwargs):
+        [cutoff, ignore], _ = parse_user_args(method, *args, **kwargs)
+        type_check(cutoff, (int, float), "cutoff")
+        check_value(cutoff, [0, 100], "cutoff")
+        if ignore is not None:
+            type_check(ignore, (list, tuple, int), "ignore")
+        if isinstance(ignore, int):
+            check_value(ignore, [0, 255], "ignore")
+        if isinstance(ignore, (list, tuple)):
+            for item in ignore:
+                type_check(item, (int,), "item")
+                check_value(item, [0, 255], "ignore")
+        return method(self, *args, **kwargs)
+
+    return new_method
+
+
 def check_uniform_augment_py(method):
     """Wrapper method to check the parameters of python UniformAugment op."""
 

tests/ut/python/dataset/test_autocontrast.py

@@ -16,20 +16,22 @@
 Testing AutoContrast op in DE
 """
 import numpy as np
-
 import mindspore.dataset.engine as de
 import mindspore.dataset.transforms.vision.py_transforms as F
+import mindspore.dataset.transforms.vision.c_transforms as C
 from mindspore import log as logger
-from util import visualize_list, diff_mse
+from util import visualize_list, diff_mse, save_and_check_md5
 
 DATA_DIR = "../data/dataset/testImageNetData/train/"
 
+GENERATE_GOLDEN = False
+
 
-def test_auto_contrast(plot=False):
+def test_auto_contrast_py(plot=False):
     """
     Test AutoContrast
     """
-    logger.info("Test AutoContrast")
+    logger.info("Test AutoContrast Python Op")
 
     # Original Images
     ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
@@ -78,9 +80,156 @@ def test_auto_contrast(plot=False):
         mse[i] = diff_mse(images_auto_contrast[i], images_original[i])
     logger.info("MSE= {}".format(str(np.mean(mse))))
 
+    # Compare with expected md5 from images
+    filename = "autcontrast_01_result_py.npz"
+    save_and_check_md5(ds_auto_contrast, filename, generate_golden=GENERATE_GOLDEN)
+
     if plot:
         visualize_list(images_original, images_auto_contrast)
 
 
+def test_auto_contrast_c(plot=False):
+    """
+    Test AutoContrast C Op
+    """
+    logger.info("Test AutoContrast C Op")
+
+    # AutoContrast Images
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
+    ds = ds.map(input_columns=["image"],
+                operations=[C.Decode(),
+                            C.Resize((224, 224))])
+    python_op = F.AutoContrast()
+    c_op = C.AutoContrast()
+    transforms_op = F.ComposeOp([lambda img: F.ToPIL()(img.astype(np.uint8)),
+                                 python_op,
+                                 np.array])()
+
+    ds_auto_contrast_py = ds.map(input_columns="image",
+                                 operations=transforms_op)
+
+    ds_auto_contrast_py = ds_auto_contrast_py.batch(512)
+
+    for idx, (image, _) in enumerate(ds_auto_contrast_py):
+        if idx == 0:
+            images_auto_contrast_py = image
+        else:
+            images_auto_contrast_py = np.append(images_auto_contrast_py,
+                                                image,
+                                                axis=0)
+
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
+    ds = ds.map(input_columns=["image"],
+                operations=[C.Decode(),
+                            C.Resize((224, 224))])
+
+    ds_auto_contrast_c = ds.map(input_columns="image",
+                                operations=c_op)
+
+    ds_auto_contrast_c = ds_auto_contrast_c.batch(512)
+
+    for idx, (image, _) in enumerate(ds_auto_contrast_c):
+        if idx == 0:
+            images_auto_contrast_c = image
+        else:
+            images_auto_contrast_c = np.append(images_auto_contrast_c,
+                                               image,
+                                               axis=0)
+
+    num_samples = images_auto_contrast_c.shape[0]
+    mse = np.zeros(num_samples)
+    for i in range(num_samples):
+        mse[i] = diff_mse(images_auto_contrast_c[i], images_auto_contrast_py[i])
+    logger.info("MSE= {}".format(str(np.mean(mse))))
+    np.testing.assert_equal(np.mean(mse), 0.0)
+
+    if plot:
+        visualize_list(images_auto_contrast_c, images_auto_contrast_py, visualize_mode=2)
+
+
+def test_auto_contrast_one_channel_c(plot=False):
+    """
+    Test AutoContrast C op with one channel
+    """
+    logger.info("Test AutoContrast C Op With One Channel Images")
+
+    # AutoContrast Images
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
+    ds = ds.map(input_columns=["image"],
+                operations=[C.Decode(),
+                            C.Resize((224, 224))])
+    python_op = F.AutoContrast()
+    c_op = C.AutoContrast()
+    # not using F.ToTensor() since it converts to floats
+    transforms_op = F.ComposeOp([lambda img: (np.array(img)[:, :, 0]).astype(np.uint8),
+                                 F.ToPIL(),
+                                 python_op,
+                                 np.array])()
+
+    ds_auto_contrast_py = ds.map(input_columns="image",
+                                 operations=transforms_op)
+
+    ds_auto_contrast_py = ds_auto_contrast_py.batch(512)
+
+    for idx, (image, _) in enumerate(ds_auto_contrast_py):
+        if idx == 0:
+            images_auto_contrast_py = image
+        else:
+            images_auto_contrast_py = np.append(images_auto_contrast_py,
+                                                image,
+                                                axis=0)
+
+    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_auto_contrast_c = ds.map(input_columns="image",
+                                operations=c_op)
+
+    ds_auto_contrast_c = ds_auto_contrast_c.batch(512)
+
+    for idx, (image, _) in enumerate(ds_auto_contrast_c):
+        if idx == 0:
+            images_auto_contrast_c = image
+        else:
+            images_auto_contrast_c = np.append(images_auto_contrast_c,
+                                               image,
+                                               axis=0)
+
+    num_samples = images_auto_contrast_c.shape[0]
+    mse = np.zeros(num_samples)
+    for i in range(num_samples):
+        mse[i] = diff_mse(images_auto_contrast_c[i], images_auto_contrast_py[i])
+    logger.info("MSE= {}".format(str(np.mean(mse))))
+    np.testing.assert_equal(np.mean(mse), 0.0)
+
+    if plot:
+        visualize_list(images_auto_contrast_c, images_auto_contrast_py, visualize_mode=2)
+
+
+def test_auto_contrast_invalid_input_c():
+    """
+    Test AutoContrast C Op with invalid params
+    """
+    logger.info("Test AutoContrast C Op with invalid params")
+    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])])
+        # invalid ignore
+        ds = ds.map(input_columns="image",
+                    operations=C.AutoContrast(ignore=255.5))
+    except TypeError as error:
+        logger.info("Got an exception in DE: {}".format(str(error)))
+        assert "Argument ignore with value 255.5 is not of type" in str(error)
+
+
 if __name__ == "__main__":
-    test_auto_contrast(plot=True)
+    test_auto_contrast_py(plot=True)
+    test_auto_contrast_c(plot=True)
+    test_auto_contrast_one_channel_c(plot=True)
+    test_auto_contrast_invalid_input_c()