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import cv2
import dlib
import numpy as np
FACIAL_LANDMARKS_TEMPLATE = np.float32([
(0.0792396913815, 0.339223741112), (0.0829219487236, 0.456955367943),
(0.0967927109165, 0.575648016728), (0.122141515615, 0.691921601066),
(0.168687863544, 0.800341263616), (0.239789390707, 0.895732504778),
(0.325662452515, 0.977068762493), (0.422318282013, 1.04329000149),
(0.531777802068, 1.06080371126), (0.641296298053, 1.03981924107),
(0.738105872266, 0.972268833998), (0.824444363295, 0.889624082279),
(0.894792677532, 0.792494155836), (0.939395486253, 0.681546643421),
(0.96111933829, 0.562238253072), (0.970579841181, 0.441758925744),
(0.971193274221, 0.322118743967), (0.163846223133, 0.249151738053),
(0.21780354657, 0.204255863861), (0.291299351124, 0.192367318323),
(0.367460241458, 0.203582210627), (0.4392945113, 0.233135599851),
(0.586445962425, 0.228141644834), (0.660152671635, 0.195923841854),
(0.737466449096, 0.182360984545), (0.813236546239, 0.192828009114),
(0.8707571886, 0.235293377042), (0.51534533827, 0.31863546193),
(0.516221448289, 0.396200446263), (0.517118861835, 0.473797687758),
(0.51816430343, 0.553157797772), (0.433701156035, 0.604054457668),
(0.475501237769, 0.62076344024), (0.520712933176, 0.634268222208),
(0.565874114041, 0.618796581487), (0.607054002672, 0.60157671656),
(0.252418718401, 0.331052263829), (0.298663015648, 0.302646354002),
(0.355749724218, 0.303020650651), (0.403718978315, 0.33867711083),
(0.352507175597, 0.349987615384), (0.296791759886, 0.350478978225),
(0.631326076346, 0.334136672344), (0.679073381078, 0.29645404267),
(0.73597236153, 0.294721285802), (0.782865376271, 0.321305281656),
(0.740312274764, 0.341849376713), (0.68499850091, 0.343734332172),
(0.353167761422, 0.746189164237), (0.414587777921, 0.719053835073),
(0.477677654595, 0.706835892494), (0.522732900812, 0.717092275768),
(0.569832064287, 0.705414478982), (0.635195811927, 0.71565572516),
(0.69951672331, 0.739419187253), (0.639447159575, 0.805236879972),
(0.576410514055, 0.835436670169), (0.525398405766, 0.841706377792),
(0.47641545769, 0.837505914975), (0.41379548902, 0.810045601727),
(0.380084785646, 0.749979603086), (0.477955996282, 0.74513234612),
(0.523389793327, 0.748924302636), (0.571057789237, 0.74332894691),
(0.672409137852, 0.744177032192), (0.572539621444, 0.776609286626),
(0.5240106503, 0.783370783245), (0.477561227414, 0.778476346951)])
FL_MIN = np.min(FACIAL_LANDMARKS_TEMPLATE, axis=0)
FL_MAX = np.max(FACIAL_LANDMARKS_TEMPLATE, axis=0)
SCALED_LANDMARKS = (FACIAL_LANDMARKS_TEMPLATE - FL_MIN) / (FL_MAX - FL_MIN)
#: Landmark indices.
INNER_EYES_AND_BOTTOM_LIP = [39, 42, 57]
OUTER_EYES_AND_NOSE = [36, 45, 33]
def maybe_face_bounding_box(detector, img):
"""
Returns a bounding box if it finds
ONE face. Any other case, it returns
none
Args:
detector: dlib.get_front_face_detector()
img: image
"""
dets = detector(img, 1)
if len(dets) >= 1:
return dets
return None
def get_68_facial_landmarks(predictor, img, bb):
"""
Returns a list of 68 facial landmarks
Args:
predictor: dlibs.shape_predict(_FILE_)
img: input image
bb: bounding box containing the face
"""
points = predictor(img, bb)
return list(map(lambda p: (p.x, p.y), points.parts()))
def align_face_to_template(img, facial_landmarks, output_dim, landmarkIndices=INNER_EYES_AND_BOTTOM_LIP):
"""
Aligns image by warping it to fit the landmarks on
the image (src) to the landmarks on the template (dst)
Args:
img: src image to be aligned
facial_landmarks: list of 68 landmarks (obtained from dlib)
output_dim: image output dimension
"""
np_landmarks = np.float32(facial_landmarks)
np_landmarks_idx = np.array(landmarkIndices)
H = cv2.getAffineTransform(np_landmarks[np_landmarks_idx],
output_dim * SCALED_LANDMARKS[np_landmarks_idx])
warped = cv2.warpAffine(img, H, (output_dim, output_dim))
return warped