def test_face(self, static_image_mode: bool, refine_landmarks: bool,
num_frames: int):
image_path = os.path.join(os.path.dirname(__file__),
'testdata/portrait.jpg')
image = cv2.imread(image_path)
rows, cols, _ = image.shape
with mp_faces.FaceMesh(static_image_mode=static_image_mode,
refine_landmarks=refine_landmarks,
min_detection_confidence=0.5) as faces:
for idx in range(num_frames):
results = faces.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
self._annotate(image.copy(), results, idx, refine_landmarks)
multi_face_landmarks = []
for landmarks in results.multi_face_landmarks:
self.assertLen(
landmarks.landmark,
mp_faces.FACEMESH_NUM_LANDMARKS_WITH_IRISES if
refine_landmarks else mp_faces.FACEMESH_NUM_LANDMARKS)
x = [landmark.x * cols for landmark in landmarks.landmark]
y = [landmark.y * rows for landmark in landmarks.landmark]
face_landmarks = np.column_stack((x, y))
multi_face_landmarks.append(face_landmarks)
self.assertLen(multi_face_landmarks, 1)
# Verify the eye landmarks are correct as sanity check.
for eye_idx, gt_lds in EYE_INDICES_TO_LANDMARKS.items():
prediction_error = np.abs(
np.asarray(multi_face_landmarks[0][eye_idx]) -
np.asarray(gt_lds))
npt.assert_array_less(prediction_error, DIFF_THRESHOLD)
if refine_landmarks:
for iris_idx, gt_lds in IRIS_INDICES_TO_LANDMARKS.items():
prediction_error = np.abs(
np.asarray(multi_face_landmarks[0][iris_idx]) -
np.asarray(gt_lds))
npt.assert_array_less(prediction_error, DIFF_THRESHOLD)
def test_face(self, static_image_mode: bool, num_frames: int):
image_path = os.path.join(os.path.dirname(__file__), 'testdata/face.jpg')
faces = mp_faces.FaceMesh(
static_image_mode=static_image_mode, min_detection_confidence=0.5)
image = cv2.flip(cv2.imread(image_path), 1)
def process_one_frame():
results = faces.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
multi_face_landmarks = []
for landmarks in results.multi_face_landmarks:
self.assertLen(landmarks.landmark, 468)
x = [landmark.x for landmark in landmarks.landmark]
y = [landmark.y for landmark in landmarks.landmark]
face_landmarks = np.transpose(np.stack((y, x))) * image.shape[0:2]
multi_face_landmarks.append(face_landmarks)
self.assertLen(multi_face_landmarks, 1)
# Verify the eye landmarks are correct as sanity check.
for idx, gt_lds in EYE_INDICES_TO_LANDMARKS.items():
prediction_error = np.abs(
np.asarray(multi_face_landmarks[0][idx]) - np.asarray(gt_lds))
npt.assert_array_less(prediction_error, DIFF_THRESHOLOD)
for _ in range(num_frames):
process_one_frame()
faces.close()
def test_blank_image(self): faces = mp_faces.FaceMesh() image = np.zeros([100, 100, 3], dtype=np.uint8) image.fill(255) results = faces.process(image) self.assertIsNone(results.multi_face_landmarks) faces.close()
def test_invalid_image_shape(self):
with mp_faces.FaceMesh() 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))