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GitOrigin-RevId: d8caa66de45839696f5bd0786ad3bfbcb9cff632
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MediaPipe Team
2020-12-09 22:43:33 -05:00
committed by chuoling
parent f15da632de
commit 2b58cceec9
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mediapipe/python/solutions/holistic_test.py
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# 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 math
import os
from absl.testing import absltest
from absl.testing import parameterized
import cv2
import numpy as np
import numpy.testing as npt
# resources dependency
from mediapipe.python.solutions import holistic as mp_holistic
TEST_IMAGE_PATH = 'mediapipe/python/solutions/testdata'
POSE_DIFF_THRESHOLOD = 30 # pixels
HAND_DIFF_THRESHOLOD = 10 # pixels
EXPECTED_POSE_COORDINATES_PREDICTION = [[593, 645], [593, 626], [599, 621],
[605, 617], [575, 637], [569, 640],
[563, 643], [621, 616], [565, 652],
[617, 652], [595, 667], [714, 662],
[567, 749], [792, 559], [497, 844],
[844, 435], [407, 906], [866, 403],
[381, 921], [859, 392], [366, 922],
[850, 405], [381, 918], [707, 948],
[631, 940], [582, 1122], [599, 1097],
[495, 1277], [641, 1239], [485, 1300],
[658, 1257], [453, 1332], [626, 1308]]
EXPECTED_LEFT_HAND_COORDINATES_PREDICTION = [[843, 404], [862, 395], [876, 383],
[887, 369], [896, 359], [854, 367],
[868, 347], [879, 346], [885, 349],
[843, 362], [859, 341], [871, 340],
[878, 344], [837, 361], [849, 341],
[859, 338], [867, 339], [834, 361],
[841, 346], [848, 342], [854, 341]]
EXPECTED_RIGHT_HAND_COORDINATES_PREDICTION = [[391, 934], [371,
930], [354, 930],
[340, 934], [328,
939], [350, 938],
[339, 946], [347,
951], [355, 952],
[356, 946], [346,
955], [358, 956],
[366, 953], [361,
952], [354, 959],
[364, 958], [372,
954], [366, 957],
[359, 963], [364, 962],
[368, 960]]
class PoseTest(parameterized.TestCase):
def _verify_output_landmarks(self, landmark_list, image_shape, num_landmarks,
expected_results, diff_thresholds):
self.assertLen(landmark_list.landmark, num_landmarks)
image_rows, image_cols, _ = image_shape
pose_coordinates = [(math.floor(landmark.x * image_cols),
math.floor(landmark.y * image_rows))
for landmark in landmark_list.landmark]
prediction_error = np.abs(
np.asarray(pose_coordinates) -
np.asarray(expected_results[:num_landmarks]))
npt.assert_array_less(prediction_error, diff_thresholds)
def test_invalid_image_shape(self):
holistic = mp_holistic.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):
holistic = mp_holistic.Holistic()
image = np.zeros([100, 100, 3], dtype=np.uint8)
image.fill(255)
results = holistic.process(image)
self.assertIsNone(results.pose_landmarks)
holistic.close()
@parameterized.named_parameters(('static_image_mode', True, 3),
('video_mode', False, 3))
def test_upper_body_model(self, static_image_mode, num_frames):
image_path = os.path.join(os.path.dirname(__file__), 'testdata/pose.jpg')
holistic = mp_holistic.Holistic(
static_image_mode=static_image_mode, upper_body_only=True)
image = cv2.imread(image_path)
for _ in range(num_frames):
results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
self._verify_output_landmarks(results.pose_landmarks, image.shape, 25,
EXPECTED_POSE_COORDINATES_PREDICTION,
POSE_DIFF_THRESHOLOD)
self._verify_output_landmarks(results.left_hand_landmarks, image.shape,
21,
EXPECTED_LEFT_HAND_COORDINATES_PREDICTION,
HAND_DIFF_THRESHOLOD)
self._verify_output_landmarks(results.right_hand_landmarks, image.shape,
21,
EXPECTED_RIGHT_HAND_COORDINATES_PREDICTION,
HAND_DIFF_THRESHOLOD)
# TODO: Verify the correctness of the face landmarks.
self.assertLen(results.face_landmarks.landmark, 468)
holistic.close()
@parameterized.named_parameters(('static_image_mode', True, 3),
('video_mode', False, 3))
def test_full_body_model(self, static_image_mode, num_frames):
image_path = os.path.join(os.path.dirname(__file__), 'testdata/pose.jpg')
holistic = mp_holistic.Holistic(static_image_mode=static_image_mode)
image = cv2.imread(image_path)
for _ in range(num_frames):
results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
self._verify_output_landmarks(results.pose_landmarks, image.shape, 33,
EXPECTED_POSE_COORDINATES_PREDICTION,
POSE_DIFF_THRESHOLOD)
self._verify_output_landmarks(results.left_hand_landmarks, image.shape,
21,
EXPECTED_LEFT_HAND_COORDINATES_PREDICTION,
HAND_DIFF_THRESHOLOD)
self._verify_output_landmarks(results.right_hand_landmarks, image.shape,
21,
EXPECTED_RIGHT_HAND_COORDINATES_PREDICTION,
HAND_DIFF_THRESHOLOD)
# TODO: Verify the correctness of the face landmarks.
self.assertLen(results.face_landmarks.landmark, 468)
holistic.close()
if __name__ == '__main__':
absltest.main()