text: # Copyright 2019 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 RandomCropAndResize op in DE
  """
  import matplotlib.pyplot as plt
  import mindspore.dataset.transforms.vision.c_transforms as vision
  
  from mindspore import log as logger
  import mindspore.dataset as ds
  
  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 visualize(a, mse, original):
      """
      visualizes the image using DE op and Numpy op
      """
      plt.subplot(141)
      plt.imshow(original)
      plt.title("Original image")
  
      plt.subplot(142)
      plt.imshow(a)
      plt.title("DE random_crop image")
  
      plt.subplot(143)
      plt.imshow(a - original)
      plt.title("Difference image, mse : {}".format(mse))
      plt.show()
  
  
  def test_random_crop_op():
      """
      Test RandomCrop Op
      """
      logger.info("test_random_crop_op")
  
      # First dataset
      data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
      random_crop_op = vision.RandomCrop([512, 512], [200, 200, 200, 200])
      decode_op = vision.Decode()
      data1 = data1.map(input_columns=["image"], operations=decode_op)
      data1 = data1.map(input_columns=["image"], operations=random_crop_op)
  
      # Second dataset
      data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
      data2 = data2.map(input_columns=["image"], operations=decode_op)
  
      for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
          image1 = item1["image"]
          image2 = item2["image"]
  
  
  if __name__ == "__main__":
      test_random_crop_op()
  
  