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90 lines
2.8 KiB
90 lines
2.8 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 RandomRotation op in DE
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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 as ds
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import mindspore.dataset.transforms.vision.py_transforms as vision
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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 visualize(image_1, image_2):
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"""
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visualizes the image using RandomErasing and Cutout
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"""
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plt.subplot(141)
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plt.imshow(image_1)
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plt.title("RandomErasing")
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plt.subplot(142)
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plt.imshow(image_2)
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plt.title("Cutout")
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plt.subplot(143)
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plt.imshow(image_1 - image_2)
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plt.title("Difference image")
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plt.show()
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def test_random_erasing_op():
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"""
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Test RandomErasing and Cutout
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"""
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logger.info("test_random_erasing")
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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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transforms_1 = [
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vision.Decode(),
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vision.ToTensor(),
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vision.RandomErasing(value='random')
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]
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transform_1 = vision.ComposeOp(transforms_1)
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data1 = data1.map(input_columns=["image"], operations=transform_1())
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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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transforms_2 = [
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vision.Decode(),
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vision.ToTensor(),
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vision.Cutout(80)
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]
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transform_2 = vision.ComposeOp(transforms_2)
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data2 = data2.map(input_columns=["image"], operations=transform_2())
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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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num_iter += 1
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image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image_2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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logger.info("shape of image_1: {}".format(image_1.shape))
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logger.info("shape of image_2: {}".format(image_2.shape))
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logger.info("dtype of image_1: {}".format(image_1.dtype))
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logger.info("dtype of image_2: {}".format(image_2.dtype))
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# visualize(image_1, image_2)
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if __name__ == "__main__":
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test_random_erasing_op()
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