# Copyright 2020 The MediaPipe Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for mediapipe.python.solutions.pose.""" import os import tempfile # pylint: disable=unused-import from typing import NamedTuple from absl.testing import absltest from absl.testing import parameterized import cv2 import numpy as np import numpy.testing as npt # resources dependency # undeclared dependency from mediapipe.python.solutions import drawing_styles from mediapipe.python.solutions import drawing_utils as mp_drawing from mediapipe.python.solutions import holistic as mp_holistic TEST_IMAGE_PATH = 'mediapipe/python/solutions/testdata' POSE_DIFF_THRESHOLD = 30 # pixels HAND_DIFF_THRESHOLD = 30 # pixels EXPECTED_POSE_LANDMARKS = np.array([[782, 243], [791, 232], [796, 233], [801, 233], [773, 231], [766, 231], [759, 232], [802, 242], [751, 239], [791, 258], [766, 258], [830, 301], [708, 298], [910, 248], [635, 234], [954, 161], [593, 136], [961, 137], [583, 110], [952, 132], [592, 106], [950, 141], [596, 115], [793, 500], [724, 502], [874, 626], [640, 629], [965, 756], [542, 760], [962, 779], [533, 781], [1025, 797], [487, 803]]) EXPECTED_LEFT_HAND_LANDMARKS = np.array([[958, 167], [950, 161], [945, 151], [945, 141], [947, 134], [945, 136], [939, 122], [935, 113], [931, 106], [951, 134], [946, 118], [942, 108], [938, 100], [957, 135], [954, 120], [951, 111], [948, 103], [964, 138], [964, 128], [965, 122], [965, 117]]) EXPECTED_RIGHT_HAND_LANDMARKS = np.array([[590, 135], [602, 125], [609, 114], [613, 103], [617, 96], [596, 100], [595, 84], [594, 74], [593, 68], [588, 100], [586, 84], [585, 73], [584, 65], [581, 103], [579, 89], [579, 79], [579, 72], [575, 109], [571, 99], [570, 93], [569, 87]]) class PoseTest(parameterized.TestCase): def _landmarks_list_to_array(self, landmark_list, image_shape): rows, cols, _ = image_shape return np.asarray([(lmk.x * cols, lmk.y * rows) for lmk in landmark_list.landmark]) def _assert_diff_less(self, array1, array2, threshold): npt.assert_array_less(np.abs(array1 - array2), threshold) def _annotate(self, frame: np.ndarray, results: NamedTuple, idx: int): mp_drawing.draw_landmarks( frame, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION, landmark_drawing_spec=None, connection_drawing_spec=drawing_styles .get_default_face_mesh_tesselation_style()) mp_drawing.draw_landmarks( frame, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, landmark_drawing_spec=drawing_styles.get_default_pose_landmarks_style()) path = os.path.join(tempfile.gettempdir(), self.id().split('.')[-1] + '_frame_{}.png'.format(idx)) cv2.imwrite(path, frame) def test_invalid_image_shape(self): with mp_holistic.Holistic() as holistic: with self.assertRaisesRegex( ValueError, 'Input image must contain three channel rgb data.'): holistic.process(np.arange(36, dtype=np.uint8).reshape(3, 3, 4)) def test_blank_image(self): with mp_holistic.Holistic() as holistic: image = np.zeros([100, 100, 3], dtype=np.uint8) image.fill(255) results = holistic.process(image) self.assertIsNone(results.pose_landmarks) @parameterized.named_parameters(('static_lite', True, 0, False, 3), ('static_full', True, 1, False, 3), ('static_heavy', True, 2, False, 3), ('video_lite', False, 0, False, 3), ('video_full', False, 1, False, 3), ('video_heavy', False, 2, False, 3), ('static_full_refine_face', True, 1, True, 3), ('video_full_refine_face', False, 1, True, 3)) def test_on_image(self, static_image_mode, model_complexity, refine_face_landmarks, num_frames): image_path = os.path.join(os.path.dirname(__file__), 'testdata/holistic.jpg') image = cv2.imread(image_path) with mp_holistic.Holistic( static_image_mode=static_image_mode, model_complexity=model_complexity, refine_face_landmarks=refine_face_landmarks) as holistic: for idx in range(num_frames): results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) self._annotate(image.copy(), results, idx) self._assert_diff_less( self._landmarks_list_to_array(results.pose_landmarks, image.shape), EXPECTED_POSE_LANDMARKS, POSE_DIFF_THRESHOLD) self._assert_diff_less( self._landmarks_list_to_array(results.left_hand_landmarks, image.shape), EXPECTED_LEFT_HAND_LANDMARKS, HAND_DIFF_THRESHOLD) self._assert_diff_less( self._landmarks_list_to_array(results.right_hand_landmarks, image.shape), EXPECTED_RIGHT_HAND_LANDMARKS, HAND_DIFF_THRESHOLD) # TODO: Verify the correctness of the face landmarks. self.assertLen(results.face_landmarks.landmark, 478 if refine_face_landmarks else 468) if __name__ == '__main__': absltest.main()