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GitOrigin-RevId: d073f8e21be2fcc0e503cb97c6695078b6b75310
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MediaPipe Team
@@ -13,7 +13,6 @@
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# limitations under the License.
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"""Tests for mediapipe.python.solutions.pose."""
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import math
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import os
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from absl.testing import absltest
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@@ -26,117 +25,118 @@ import numpy.testing as npt
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from mediapipe.python.solutions import holistic as mp_holistic
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TEST_IMAGE_PATH = 'mediapipe/python/solutions/testdata'
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POSE_DIFF_THRESHOLOD = 30 # pixels
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HAND_DIFF_THRESHOLOD = 10 # pixels
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EXPECTED_POSE_COORDINATES_PREDICTION = [[593, 645], [593, 626], [599, 621],
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[605, 617], [575, 637], [569, 640],
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[563, 643], [621, 616], [565, 652],
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[617, 652], [595, 667], [714, 662],
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[567, 749], [792, 559], [497, 844],
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[844, 435], [407, 906], [866, 403],
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[381, 921], [859, 392], [366, 922],
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[850, 405], [381, 918], [707, 948],
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[631, 940], [582, 1122], [599, 1097],
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[495, 1277], [641, 1239], [485, 1300],
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[658, 1257], [453, 1332], [626, 1308]]
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EXPECTED_LEFT_HAND_COORDINATES_PREDICTION = [[843, 404], [862, 395], [876, 383],
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[887, 369], [896, 359], [854, 367],
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[868, 347], [879, 346], [885, 349],
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[843, 362], [859, 341], [871, 340],
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[878, 344], [837, 361], [849, 341],
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[859, 338], [867, 339], [834, 361],
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[841, 346], [848, 342], [854, 341]]
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EXPECTED_RIGHT_HAND_COORDINATES_PREDICTION = [[391, 934], [371,
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930], [354, 930],
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[340, 934], [328,
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939], [350, 938],
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[339, 946], [347,
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951], [355, 952],
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[356, 946], [346,
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955], [358, 956],
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[366, 953], [361,
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952], [354, 959],
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[364, 958], [372,
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954], [366, 957],
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[359, 963], [364, 962],
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[368, 960]]
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POSE_DIFF_THRESHOLD = 30 # pixels
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HAND_DIFF_THRESHOLD = 30 # pixels
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EXPECTED_UPPER_BODY_LANDMARKS = np.array([[457, 289], [465, 278], [467, 278],
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[470, 277], [461, 279], [461, 279],
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[461, 279], [485, 277], [474, 278],
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[468, 296], [463, 297], [542, 324],
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[449, 327], [614, 321], [376, 318],
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[680, 322], [312, 310], [697, 320],
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[293, 305], [699, 314], [289, 302],
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[693, 316], [296, 305], [515, 451],
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[467, 453]])
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EXPECTED_FULL_BODY_LANDMARKS = np.array([[460, 287], [469, 277], [472, 276],
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[475, 276], [464, 277], [463, 277],
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[463, 276], [492, 277], [472, 277],
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[471, 295], [465, 295], [542, 323],
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[448, 318], [619, 319], [372, 313],
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[695, 316], [296, 308], [717, 313],
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[273, 304], [718, 304], [280, 298],
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[709, 307], [289, 303], [521, 470],
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[459, 466], [626, 533], [364, 500],
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[704, 616], [347, 614], [710, 631],
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[357, 633], [737, 625], [306, 639]])
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EXPECTED_LEFT_HAND_LANDMARKS = np.array([[698, 314], [712, 314], [721, 314],
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[727, 314], [732, 313], [728, 309],
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[738, 309], [745, 308], [751, 307],
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[724, 310], [735, 309], [742, 309],
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[747, 307], [719, 312], [727, 313],
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[729, 312], [731, 311], [713, 315],
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[717, 315], [719, 314], [719, 313]])
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EXPECTED_RIGHT_HAND_LANDMARKS = np.array([[293, 307], [284, 306], [277, 304],
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[271, 303], [266, 303], [271, 302],
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[261, 302], [254, 301], [247, 299],
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[272, 303], [261, 303], [253, 301],
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[245, 299], [275, 304], [266, 303],
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[258, 302], [252, 300], [279, 305],
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[273, 305], [268, 304], [263, 303]])
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class PoseTest(parameterized.TestCase):
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def _verify_output_landmarks(self, landmark_list, image_shape, num_landmarks,
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expected_results, diff_thresholds):
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self.assertLen(landmark_list.landmark, num_landmarks)
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image_rows, image_cols, _ = image_shape
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pose_coordinates = [(math.floor(landmark.x * image_cols),
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math.floor(landmark.y * image_rows))
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for landmark in landmark_list.landmark]
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prediction_error = np.abs(
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np.asarray(pose_coordinates) -
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np.asarray(expected_results[:num_landmarks]))
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npt.assert_array_less(prediction_error, diff_thresholds)
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def _landmarks_list_to_array(self, landmark_list, image_shape):
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rows, cols, _ = image_shape
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return np.asarray([(lmk.x * cols, lmk.y * rows)
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for lmk in landmark_list.landmark])
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def _assert_diff_less(self, array1, array2, threshold):
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npt.assert_array_less(np.abs(array1 - array2), threshold)
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def test_invalid_image_shape(self):
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holistic = mp_holistic.Holistic()
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with self.assertRaisesRegex(
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ValueError, 'Input image must contain three channel rgb data.'):
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holistic.process(np.arange(36, dtype=np.uint8).reshape(3, 3, 4))
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with mp_holistic.Holistic() as holistic:
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with self.assertRaisesRegex(
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ValueError, 'Input image must contain three channel rgb data.'):
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holistic.process(np.arange(36, dtype=np.uint8).reshape(3, 3, 4))
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def test_blank_image(self):
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holistic = mp_holistic.Holistic()
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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 = holistic.process(image)
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self.assertIsNone(results.pose_landmarks)
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holistic.close()
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with mp_holistic.Holistic() as holistic:
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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 = holistic.process(image)
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self.assertIsNone(results.pose_landmarks)
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@parameterized.named_parameters(('static_image_mode', True, 3),
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('video_mode', False, 3))
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def test_upper_body_model(self, static_image_mode, num_frames):
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image_path = os.path.join(os.path.dirname(__file__), 'testdata/pose.jpg')
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holistic = mp_holistic.Holistic(
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static_image_mode=static_image_mode, upper_body_only=True)
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image = cv2.imread(image_path)
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for _ in range(num_frames):
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results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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self._verify_output_landmarks(results.pose_landmarks, image.shape, 25,
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EXPECTED_POSE_COORDINATES_PREDICTION,
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POSE_DIFF_THRESHOLOD)
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self._verify_output_landmarks(results.left_hand_landmarks, image.shape,
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21,
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EXPECTED_LEFT_HAND_COORDINATES_PREDICTION,
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HAND_DIFF_THRESHOLOD)
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self._verify_output_landmarks(results.right_hand_landmarks, image.shape,
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21,
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EXPECTED_RIGHT_HAND_COORDINATES_PREDICTION,
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HAND_DIFF_THRESHOLOD)
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# TODO: Verify the correctness of the face landmarks.
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self.assertLen(results.face_landmarks.landmark, 468)
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holistic.close()
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with mp_holistic.Holistic(
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static_image_mode=static_image_mode, upper_body_only=True) as holistic:
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image = cv2.imread(image_path)
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for _ in range(num_frames):
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results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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self._assert_diff_less(
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self._landmarks_list_to_array(results.pose_landmarks, image.shape),
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EXPECTED_UPPER_BODY_LANDMARKS,
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POSE_DIFF_THRESHOLD)
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self._assert_diff_less(
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self._landmarks_list_to_array(results.left_hand_landmarks,
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image.shape),
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EXPECTED_LEFT_HAND_LANDMARKS,
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HAND_DIFF_THRESHOLD)
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self._assert_diff_less(
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self._landmarks_list_to_array(results.right_hand_landmarks,
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image.shape),
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EXPECTED_RIGHT_HAND_LANDMARKS,
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HAND_DIFF_THRESHOLD)
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# TODO: Verify the correctness of the face landmarks.
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self.assertLen(results.face_landmarks.landmark, 468)
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@parameterized.named_parameters(('static_image_mode', True, 3),
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('video_mode', False, 3))
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def test_full_body_model(self, static_image_mode, num_frames):
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image_path = os.path.join(os.path.dirname(__file__), 'testdata/pose.jpg')
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holistic = mp_holistic.Holistic(static_image_mode=static_image_mode)
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image = cv2.imread(image_path)
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for _ in range(num_frames):
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results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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self._verify_output_landmarks(results.pose_landmarks, image.shape, 33,
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EXPECTED_POSE_COORDINATES_PREDICTION,
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POSE_DIFF_THRESHOLOD)
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self._verify_output_landmarks(results.left_hand_landmarks, image.shape,
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21,
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EXPECTED_LEFT_HAND_COORDINATES_PREDICTION,
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HAND_DIFF_THRESHOLOD)
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self._verify_output_landmarks(results.right_hand_landmarks, image.shape,
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21,
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EXPECTED_RIGHT_HAND_COORDINATES_PREDICTION,
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HAND_DIFF_THRESHOLOD)
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# TODO: Verify the correctness of the face landmarks.
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self.assertLen(results.face_landmarks.landmark, 468)
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holistic.close()
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with mp_holistic.Holistic(static_image_mode=static_image_mode) as holistic:
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for _ in range(num_frames):
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results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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self._assert_diff_less(
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self._landmarks_list_to_array(results.pose_landmarks, image.shape),
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EXPECTED_FULL_BODY_LANDMARKS,
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POSE_DIFF_THRESHOLD)
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self._assert_diff_less(
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self._landmarks_list_to_array(results.left_hand_landmarks,
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image.shape),
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EXPECTED_LEFT_HAND_LANDMARKS,
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HAND_DIFF_THRESHOLD)
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self._assert_diff_less(
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self._landmarks_list_to_array(results.right_hand_landmarks,
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image.shape),
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EXPECTED_RIGHT_HAND_LANDMARKS,
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HAND_DIFF_THRESHOLD)
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# TODO: Verify the correctness of the face landmarks.
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self.assertLen(results.face_landmarks.landmark, 468)
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if __name__ == '__main__':
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