def test_face(self):
image_path = os.path.join(os.path.dirname(__file__),
'testdata/face.jpg')
image = cv2.imread(image_path)
with mp_faces.FaceDetection(min_detection_confidence=0.5) as faces:
for idx in range(5):
results = faces.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
self._annotate(image.copy(), results, idx)
location_data = results.detections[0].location_data
x = [
keypoint.x for keypoint in location_data.relative_keypoints
]
y = [
keypoint.y for keypoint in location_data.relative_keypoints
]
face_keypoints = np.transpose(np.stack(
(y, x))) * image.shape[0:2]
prediction_error = np.abs(
np.asarray(face_keypoints) -
np.asarray(EXPECTED_FACE_KEY_POINTS))
self.assertLen(results.detections, 1)
self.assertLen(location_data.relative_keypoints, 6)
npt.assert_array_less(prediction_error, DIFF_THRESHOLD)
def test_blank_image(self):
image = np.zeros([100, 100, 3], dtype=np.uint8)
image.fill(255)
with mp_faces.FaceDetection(min_detection_confidence=0.5) as faces:
results = faces.process(image)
self.assertIsNone(results.detections)
def test_invalid_image_shape(self):
with mp_faces.FaceDetection() as faces:
with self.assertRaisesRegex(
ValueError,
'Input image must contain three channel rgb data.'):
faces.process(np.arange(36, dtype=np.uint8).reshape(3, 3, 4))