forked from mindspore-Ecosystem/mindspore
74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
# 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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import mindspore.dataset.transforms.vision.c_transforms as vision
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import numpy as np
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import matplotlib.pyplot as plt
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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 visualize(image_original, image_cropped):
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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_cropped)
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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_cropped[i])
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plt.title("DE center_crop image")
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plt.show()
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def test_center_crop_op(height=375, width=375, plot=False):
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"""
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Test random_vertical
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"""
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logger.info("Test CenterCrop")
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
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decode_op = vision.Decode()
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# 3 images [375, 500] [600, 500] [512, 512]
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center_crop_op = vision.CenterCrop(height, width)
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data1 = data1.map(input_columns=["image"], operations=decode_op)
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data1 = data1.map(input_columns=["image"], operations=center_crop_op)
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
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data2 = data2.map(input_columns=["image"], operations=decode_op)
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image_cropped = []
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image = []
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for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
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image_cropped.append(item1["image"].copy())
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image.append(item2["image"].copy())
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if plot:
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visualize(image, image_cropped)
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if __name__ == "__main__":
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test_center_crop_op()
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test_center_crop_op(600, 600)
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test_center_crop_op(300, 600)
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test_center_crop_op(600, 300)
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