114 lines
4.1 KiB
Python
114 lines
4.1 KiB
Python
# Copyright 2021-2022 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 GaussianBlur Python API
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"""
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import cv2
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import mindspore.dataset as ds
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import mindspore.dataset.vision as vision
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from mindspore import log as logger
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from util import visualize_image, diff_mse
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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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IMAGE_FILE = "../data/dataset/apple.jpg"
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def test_gaussian_blur_pipeline(plot=False):
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"""
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Feature: GaussianBlur
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Description: Test GaussianBlur of Cpp implementation
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Expectation: Output is the same as expected output
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"""
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logger.info("test_gaussian_blur_pipeline")
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# First dataset
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dataset1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
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decode_op = vision.Decode()
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gaussian_blur_op = vision.GaussianBlur(3, 3)
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dataset1 = dataset1.map(operations=decode_op, input_columns=["image"])
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dataset1 = dataset1.map(operations=gaussian_blur_op, input_columns=["image"])
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# Second dataset
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dataset2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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dataset2 = dataset2.map(operations=decode_op, input_columns=["image"])
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num_iter = 0
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for data1, data2 in zip(dataset1.create_dict_iterator(num_epochs=1, output_numpy=True),
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dataset2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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if num_iter > 0:
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break
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gaussian_blur_ms = data1["image"]
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original = data2["image"]
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gaussian_blur_cv = cv2.GaussianBlur(original, (3, 3), 3)
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mse = diff_mse(gaussian_blur_ms, gaussian_blur_cv)
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logger.info("gaussian_blur_{}, mse: {}".format(num_iter + 1, mse))
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assert mse == 0
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num_iter += 1
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if plot:
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visualize_image(original, gaussian_blur_ms, mse, gaussian_blur_cv)
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def test_gaussian_blur_eager():
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"""
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Feature: GaussianBlur
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Description: Test GaussianBlur in eager mode
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Expectation: Output is the same as expected output
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"""
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logger.info("test_gaussian_blur_eager")
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img = cv2.imread(IMAGE_FILE)
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img_ms = vision.GaussianBlur((3, 5), (3.5, 3.5))(img)
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img_cv = cv2.GaussianBlur(img, (3, 5), 3.5, 3.5)
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mse = diff_mse(img_ms, img_cv)
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assert mse == 0
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def test_gaussian_blur_exception():
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"""
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Feature: GaussianBlur
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Description: Test GaussianBlur with invalid parameters
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Expectation: Error is raised as expected
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"""
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logger.info("test_gaussian_blur_exception")
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try:
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_ = vision.GaussianBlur([2, 2])
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except ValueError as e:
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logger.info("Got an exception in GaussianBlur: {}".format(str(e)))
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assert "not an odd value" in str(e)
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try:
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_ = vision.GaussianBlur(3.0, [3, 3])
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except TypeError as e:
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logger.info("Got an exception in GaussianBlur: {}".format(str(e)))
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assert "not of type [<class 'int'>, <class 'list'>, <class 'tuple'>]" in str(e)
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try:
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_ = vision.GaussianBlur(3, -3)
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except ValueError as e:
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logger.info("Got an exception in GaussianBlur: {}".format(str(e)))
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assert "not within the required interval" in str(e)
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try:
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_ = vision.GaussianBlur(3, [3, 3, 3])
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except TypeError as e:
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logger.info("Got an exception in GaussianBlur: {}".format(str(e)))
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assert "should be a single number or a list/tuple of length 2" in str(e)
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if __name__ == "__main__":
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test_gaussian_blur_pipeline(plot=False)
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test_gaussian_blur_eager()
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test_gaussian_blur_exception()
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