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101 lines
3.2 KiB
101 lines
3.2 KiB
5 years ago
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# Copyright 2019 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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"""
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Testing the random vertical flip op in DE
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"""
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import matplotlib.pyplot as plt
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import mindspore.dataset.transforms.vision.c_transforms as vision
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import numpy as np
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import mindspore.dataset as ds
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from mindspore import log as logger
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DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
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SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
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def v_flip(image):
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"""
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Apply the random_vertical
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"""
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# with the seed provided in this test case, it will always flip.
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# that's why we flip here too
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image = image[::-1, :, :]
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return image
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def visualize(image_de_random_vertical, image_pil_random_vertical, mse, image_original):
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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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plt.subplot(141)
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plt.imshow(image_original)
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plt.title("Original image")
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plt.subplot(142)
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plt.imshow(image_de_random_vertical)
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plt.title("DE random_vertical image")
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plt.subplot(143)
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plt.imshow(image_pil_random_vertical)
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plt.title("vertically flipped image")
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plt.subplot(144)
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plt.imshow(image_de_random_vertical - image_pil_random_vertical)
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plt.title("Difference image, mse : {}".format(mse))
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plt.show()
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def test_random_vertical_op():
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"""
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Test random_vertical
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"""
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logger.info("Test random_vertical")
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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decode_op = vision.Decode()
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random_vertical_op = vision.RandomVerticalFlip()
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data1 = data1.map(input_columns=["image"], operations=decode_op)
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data1 = data1.map(input_columns=["image"], operations=random_vertical_op)
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data2 = data2.map(input_columns=["image"], operations=decode_op)
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num_iter = 0
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for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
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# with the seed value, we can only guarantee the first number generated
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if num_iter > 0:
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break
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image_v_flipped = item1["image"]
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image = item2["image"]
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image_v_flipped_2 = v_flip(image)
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diff = image_v_flipped - image_v_flipped_2
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mse = np.sum(np.power(diff, 2))
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logger.info("image_{}, mse: {}".format(num_iter + 1, mse))
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# Uncomment below line if you want to visualize images
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# visualize(image_v_flipped, image_v_flipped_2, mse, image)
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num_iter += 1
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if __name__ == "__main__":
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test_random_vertical_op()
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