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104 lines
3.6 KiB
104 lines
3.6 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import matplotlib.pyplot as plt
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import numpy as np
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import mindspore.dataset.engine as de
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import mindspore.dataset.transforms.vision.py_transforms as F
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from mindspore import log as logger
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DATA_DIR = "../data/dataset/testImageNetData/train/"
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def visualize(image_original, image_random_sharpness):
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"""
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visualizes the image using DE op and Numpy op
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"""
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num = len(image_random_sharpness)
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for i in range(num):
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plt.subplot(2, num, i + 1)
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plt.imshow(image_original[i])
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plt.title("Original image")
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plt.subplot(2, num, i + num + 1)
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plt.imshow(image_random_sharpness[i])
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plt.title("DE Random Sharpness image")
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plt.show()
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def test_random_sharpness(degrees=(0.1, 1.9), plot=False):
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"""
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Test RandomSharpness
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"""
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logger.info("Test RandomSharpness")
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# Original Images
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ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
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transforms_original = F.ComposeOp([F.Decode(),
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F.Resize((224, 224)),
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F.ToTensor()])
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ds_original = ds.map(input_columns="image",
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operations=transforms_original())
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ds_original = ds_original.batch(512)
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for idx, (image, label) in enumerate(ds_original):
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if idx == 0:
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images_original = np.transpose(image, (0, 2, 3, 1))
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else:
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images_original = np.append(images_original,
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np.transpose(image, (0, 2, 3, 1)),
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axis=0)
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# Random Sharpness Adjusted Images
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ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
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transforms_random_sharpness = F.ComposeOp([F.Decode(),
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F.Resize((224, 224)),
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F.RandomSharpness(degrees=degrees),
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F.ToTensor()])
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ds_random_sharpness = ds.map(input_columns="image",
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operations=transforms_random_sharpness())
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ds_random_sharpness = ds_random_sharpness.batch(512)
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for idx, (image, label) in enumerate(ds_random_sharpness):
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if idx == 0:
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images_random_sharpness = np.transpose(image, (0, 2, 3, 1))
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else:
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images_random_sharpness = np.append(images_random_sharpness,
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np.transpose(image, (0, 2, 3, 1)),
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axis=0)
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num_samples = images_original.shape[0]
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mse = np.zeros(num_samples)
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for i in range(num_samples):
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mse[i] = np.mean((images_random_sharpness[i] - images_original[i]) ** 2)
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logger.info("MSE= {}".format(str(np.mean(mse))))
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if plot:
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visualize(images_original, images_random_sharpness)
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if __name__ == "__main__":
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test_random_sharpness()
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test_random_sharpness(plot=True)
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test_random_sharpness(degrees=(0.5, 1.5), plot=True)
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