125 lines
4.2 KiB
Python
125 lines
4.2 KiB
Python
# Copyright 2020 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.face_mesh."""
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import os
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import tempfile # pylint: disable=unused-import
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from typing import NamedTuple
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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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import numpy.testing as npt
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# resources dependency
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# undeclared dependency
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from mediapipe.python.solutions import drawing_utils as mp_drawing
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from mediapipe.python.solutions import face_mesh as mp_faces
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TEST_IMAGE_PATH = 'mediapipe/python/solutions/testdata'
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DIFF_THRESHOLD = 5 # pixels
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EYE_INDICES_TO_LANDMARKS = {
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33: [178, 345],
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7: [179, 348],
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163: [178, 352],
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144: [179, 357],
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145: [179, 365],
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153: [179, 371],
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154: [178, 378],
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155: [177, 381],
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133: [177, 383],
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246: [175, 347],
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161: [174, 350],
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160: [172, 355],
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159: [170, 362],
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158: [171, 368],
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157: [172, 375],
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173: [175, 380],
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263: [176, 467],
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249: [177, 464],
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390: [177, 460],
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373: [178, 455],
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374: [179, 448],
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380: [179, 441],
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381: [178, 435],
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382: [177, 432],
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362: [177, 430],
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466: [175, 465],
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388: [173, 462],
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387: [171, 457],
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386: [170, 450],
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385: [171, 444],
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384: [172, 437],
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398: [175, 432]
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}
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class FaceMeshTest(parameterized.TestCase):
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def _annotate(self, frame: np.ndarray, results: NamedTuple, idx: int):
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drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
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for face_landmarks in results.multi_face_landmarks:
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mp_drawing.draw_landmarks(
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image=frame,
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landmark_list=face_landmarks,
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landmark_drawing_spec=drawing_spec)
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path = os.path.join(tempfile.gettempdir(), self.id().split('.')[-1] +
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'_frame_{}.png'.format(idx))
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cv2.imwrite(path, frame)
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def test_invalid_image_shape(self):
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with mp_faces.FaceMesh() as faces:
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with self.assertRaisesRegex(
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ValueError, 'Input image must contain three channel rgb data.'):
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faces.process(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_faces.FaceMesh() as faces:
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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 = faces.process(image)
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self.assertIsNone(results.multi_face_landmarks)
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@parameterized.named_parameters(('static_image_mode', True, 1),
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('video_mode', False, 5))
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def test_face(self, static_image_mode: bool, num_frames: int):
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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_faces.FaceMesh(
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static_image_mode=static_image_mode,
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min_detection_confidence=0.5) as faces:
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for idx in range(num_frames):
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results = faces.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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self._annotate(image.copy(), results, idx)
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multi_face_landmarks = []
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for landmarks in results.multi_face_landmarks:
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self.assertLen(landmarks.landmark, 468)
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x = [landmark.x for landmark in landmarks.landmark]
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y = [landmark.y for landmark in landmarks.landmark]
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face_landmarks = np.transpose(np.stack((y, x))) * image.shape[0:2]
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multi_face_landmarks.append(face_landmarks)
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self.assertLen(multi_face_landmarks, 1)
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# Verify the eye landmarks are correct as sanity check.
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for eye_idx, gt_lds in EYE_INDICES_TO_LANDMARKS.items():
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prediction_error = np.abs(
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np.asarray(multi_face_landmarks[0][eye_idx]) - np.asarray(gt_lds))
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npt.assert_array_less(prediction_error, DIFF_THRESHOLD)
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if __name__ == '__main__':
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absltest.main()
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