def detect(input_image, output_path, use_json, annotate_faces,
annotate_landmarks, face_color, landmark_color, save_chip):
img = io.imread(input_image)
d = Detector(input_image)
d.detect()
if use_json:
json = []
else:
print '\nImage: {}'.format(input_image)
print 'Number of cat faces detected: {}'.format(d.result.face_count)
if annotate_faces or annotate_landmarks:
w = img.shape[1]
for i, face in enumerate(d.result.faces):
shape = d.predictor(img, face)
if save_chip:
cropped = Image.open(input_image)
cropped = cropped.crop((face.left(),
face.top(),
face.right(),
face.bottom()))
chip_path = get_output_file(output_path,
input_image,
'_face_{}'.format(i),
'jpg')
cropped.save(chip_path)
if annotate_landmarks:
draw_landmark_annotation(img, shape, landmark_color,
int(w * 0.0025))
if annotate_faces:
draw_face_annotation(img, face, face_color, int(w * 0.005))
if use_json:
json.append(get_face_json(face, shape))
else:
print_face_info(i, face, shape)
if d.result.face_count > 0:
if annotate_faces or annotate_landmarks:
filename = get_output_file(output_path,
input_image,
'_annotated',
'jpg')
cv2.imwrite(filename, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
if use_json:
print json
def get_landmarks(input_image, img, face):
d = Detector(input_image)
shape = d.predictor(img, face)
return shape
def get_face(input_image, img):
d = Detector(input_image)
face = d.FaceDetectionDLIB(img)
return face