# 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 AutoContrast op in DE
"""
import numpy as np

import mindspore.dataset.engine as de
import mindspore.dataset.transforms.vision.py_transforms as F
from mindspore import log as logger
from util import visualize_list, diff_mse

DATA_DIR = "../data/dataset/testImageNetData/train/"


def test_auto_contrast(plot=False):
    """
    Test AutoContrast
    """
    logger.info("Test AutoContrast")

    # Original Images
    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)

    transforms_original = F.ComposeOp([F.Decode(),
                                       F.Resize((224, 224)),
                                       F.ToTensor()])

    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 = np.transpose(image, (0, 2, 3, 1))
        else:
            images_original = np.append(images_original,
                                        np.transpose(image, (0, 2, 3, 1)),
                                        axis=0)

            # AutoContrast Images
    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)

    transforms_auto_contrast = F.ComposeOp([F.Decode(),
                                            F.Resize((224, 224)),
                                            F.AutoContrast(),
                                            F.ToTensor()])

    ds_auto_contrast = ds.map(input_columns="image",
                              operations=transforms_auto_contrast())

    ds_auto_contrast = ds_auto_contrast.batch(512)

    for idx, (image, _) in enumerate(ds_auto_contrast):
        if idx == 0:
            images_auto_contrast = np.transpose(image, (0, 2, 3, 1))
        else:
            images_auto_contrast = np.append(images_auto_contrast,
                                             np.transpose(image, (0, 2, 3, 1)),
                                             axis=0)

    num_samples = images_original.shape[0]
    mse = np.zeros(num_samples)
    for i in range(num_samples):
        mse[i] = diff_mse(images_auto_contrast[i], images_original[i])
    logger.info("MSE= {}".format(str(np.mean(mse))))

    if plot:
        visualize_list(images_original, images_auto_contrast)


if __name__ == "__main__":
    test_auto_contrast(plot=True)