def geometry_msgs_points_to_face_landmarks(geometry_msgs_points):
points = dlib.points()
[
points.append(dlib.point(int(p.x), int(p.y)))
for p in geometry_msgs_points
]
return points
def substitute_points(dlib_points, mtcnn_points):
#face left, face right, nose, mouth left, mouth right
SUBSTITUTE = [0, 16, 30, 48, 54]
new_points = dlib.points()
len_mtcnn = len(mtcnn_points) // 2
for i in range(len_mtcnn):
dlib_points[SUBSTITUTE[i]] = dlib.point(
round(float(mtcnn_points[i])),
round(float(mtcnn_points[i + len_mtcnn])))
return dlib_points
def test_points():
ps = points()
ps.resize(5)
assert len(ps) == 5
for i in range(5):
assert ps[i].x == 0
assert ps[i].y == 0
ps.clear()
assert len(ps) == 0
ps.extend([point(1, 2), point(3, 4)])
assert len(ps) == 2
ser = pickle.dumps(ps, 2)
deser = pickle.loads(ser)
assert deser[0].x == 1
assert deser[0].y == 2
assert deser[1].x == 3
assert deser[1].y == 4
image, cv2.COLOR_BGR2RGB), minsize, pnet, rnet, onet,
threshold, factor)
print(str(path_to_image))
print(detected_faces)
left, top, right, bottom = get_points(detected_faces, 0)
face_box = dlib.rectangle(left, top, right, bottom)
#d = dlib.rectangle(1,2,3,4)
#print(str(path_to_image) + "----" + str(detected_faces[0]) + "----" + str(d))
# Get pose/landmarks of those faces
# Will be used as an input to the function that computes face encodings
# This allows the neural network to be able to produce similar numbers
#for faces of the same people, regardless of camera angle and/or face positioning in the image
shapes_faces = [shape_predictor(image, face_box)]
face_traits = dlib.points()
len_points = len(points) // 2
for i in range(len_points):
face_traits.append(dlib.point( round(float(points[i])) ,
round(float(points[i + len_points]))))
shapes_points = [dlib.full_object_detection(face_box, face_traits)]
#if ( str(path_to_image) == "pics/rakoruja.jpg"):
#print("\n\n--------------------------------------------")
#img = cv2.imread(path_to_image)
draw_facial_points(image, shapes_faces[0].parts(), 3)
cv2.imwrite(os.path.join("./", "OUTPUT-COLOURFUL" +
".jpg"), image)
cv2.waitKey(30)
for i in range(len(shapes_faces[0].parts())):