69 lines
2.6 KiB
Python
69 lines
2.6 KiB
Python
# Copyright 2021 The MediaPipe Authors.
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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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"""Tests for mediapipe.python.solutions.selfie_segmentation."""
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import os
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from absl.testing import absltest
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from absl.testing import parameterized
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import cv2
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import numpy as np
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# resources dependency
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# undeclared dependency
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from mediapipe.python.solutions import selfie_segmentation as mp_selfie_segmentation
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TEST_IMAGE_PATH = 'mediapipe/python/solutions/testdata'
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class SelfieSegmentationTest(parameterized.TestCase):
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def _draw(self, frame: np.ndarray, mask: np.ndarray):
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frame = np.minimum(frame, np.stack((mask,) * 3, axis=-1))
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path = os.path.join(tempfile.gettempdir(), self.id().split('.')[-1] + '.png')
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cv2.imwrite(path, frame)
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def test_invalid_image_shape(self):
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with mp_selfie_segmentation.SelfieSegmentation() as selfie_segmentation:
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with self.assertRaisesRegex(
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ValueError, 'Input image must contain three channel rgb data.'):
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selfie_segmentation.process(
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np.arange(36, dtype=np.uint8).reshape(3, 3, 4))
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def test_blank_image(self):
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with mp_selfie_segmentation.SelfieSegmentation() as selfie_segmentation:
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image = np.zeros([100, 100, 3], dtype=np.uint8)
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image.fill(255)
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results = selfie_segmentation.process(image)
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normalized_segmentation_mask = (results.segmentation_mask *
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255).astype(int)
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self.assertLess(np.amax(normalized_segmentation_mask), 1)
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@parameterized.named_parameters(('general', 0), ('landscape', 1))
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def test_segmentation(self, model_selection):
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image_path = os.path.join(os.path.dirname(__file__),
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'testdata/portrait.jpg')
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image = cv2.imread(image_path)
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with mp_selfie_segmentation.SelfieSegmentation(
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model_selection=model_selection) as selfie_segmentation:
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results = selfie_segmentation.process(
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cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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normalized_segmentation_mask = (results.segmentation_mask *
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255).astype(int)
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self._draw(image.copy(), normalized_segmentation_mask)
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if __name__ == '__main__':
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absltest.main()
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