def test_batched_face_locations(self):
img = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'obama.jpg'))
images = [img, img, img]
batched_detected_faces = api.batch_face_locations(images, number_of_times_to_upsample=0)
for detected_faces in batched_detected_faces:
self.assertEqual(len(detected_faces), 1)
self.assertEqual(detected_faces[0], (154, 611, 390, 375))
def test_batched_face_locations(self):
img = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'obama.jpg'))
images = [img, img, img]
batched_detected_faces = api.batch_face_locations(images, number_of_times_to_upsample=0)
for detected_faces in batched_detected_faces:
self.assertEqual(len(detected_faces), 1)
self.assertEqual(detected_faces[0], (154, 611, 390, 375))
def save_keypoints_video(video_path, input_folder, output_folder, tolerance,
batch_size):
video = cv2.VideoCapture(str(video_path))
out_path = move_path(video_path, input_folder, output_folder)
out_path = out_path.with_suffix('')
known_faces = []
frame_count = 0
frames = []
while video.isOpened():
ret, frame = video.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = scale_frame(frame)
frames.append(frame)
if (len(frames) == batch_size) or not ret:
batch_of_face_locations = face_recognition.batch_face_locations(
frames,
number_of_times_to_upsample=upsample,
batch_size=batch_size)
# Now let's list all the faces we found in all batch_size frames
for i, face_locations in enumerate(batch_of_face_locations):
frame_count += 1
encodings, landmarks = face_recognition.face_encodings_and_landmarks(
frames[i], known_face_locations=locations)
num_faces = landmarks.shape[0]
if num_faces == 0:
continue
for face_idx, (encoding, landmark_array) in enumerate(
zip(encodings, landmarks)):
distances = face_recognition.face_distance(
known_faces, encoding)
if len(distances) == 0 or distances.min() > tolerance:
known_idx = len(known_faces)
known_faces.append(encoding)
else:
known_idx = int(np.argmin(distances))
known_faces[known_idx] = np.mean(
[known_faces[known_idx], encoding], axis=0)
file_path = Path(out_path, F"{known_idx}",
F"{frame_count}.npy")
file_path.parent.mkdir(parents=True, exist_ok=True)
np.save(file_path, landmark_array)
# Clear the frames array to start the next batch
frames = []
print(F"finished video {out_path}")