一個換臉的小程序

import cv2
import dlib
import numpy
import sys
PREDICTOR_PATH = "/home/matt/dlib-18.16/shape_predictor_68_face_landmarks.dat"
SCALE_FACTOR = 1
FEATHER_AMOUNT = 11
FACE_POINTS = list(range(17, 68))
MOUTH_POINTS = list(range(48, 61))
RIGHT_BROW_POINTS = list(range(17, 22))
LEFT_BROW_POINTS = list(range(22, 27))
RIGHT_EYE_POINTS = list(range(36, 42))
LEFT_EYE_POINTS = list(range(42, 48))
NOSE_POINTS = list(range(27, 35))
JAW_POINTS = list(range(0, 17))



# Points used to line up the images.
ALIGN_POINTS = (LEFT_BROW_POINTS + RIGHT_EYE_POINTS + LEFT_EYE_POINTS +
                              RIGHT_BROW_POINTS + NOSE_POINTS + MOUTH_POINTS)
# Points from the second image to overlay on the first. The convex hull of each
# element will be overlaid.
OVERLAY_POINTS = [
   LEFT_EYE_POINTS + RIGHT_EYE_POINTS + LEFT_BROW_POINTS + RIGHT_BROW_POINTS,
   NOSE_POINTS + MOUTH_POINTS,
]
# Amount of blur to use during colour correction, as a fraction of the
# pupillary distance.
COLOUR_CORRECT_BLUR_FRAC = 0.6
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(PREDICTOR_PATH)
class TooManyFaces(Exception):
   pass
class NoFaces(Exception):
   pass



def get_landmarks(im):
   rects = detector(im, 1)
   if len(rects) > 1:
       raise TooManyFaces
   if len(rects) == 0:
       raise NoFaces
   return numpy.matrix([[p.x, p.y] for p in predictor(im, rects[0]).parts()])



def annotate_landmarks(im, landmarks):
   im = im.copy()
   for idx, point in enumerate(landmarks):
       pos = (point[0, 0], point[0, 1])
       cv2.putText(im, str(idx), pos,
                   fontFace=cv2.FONT_HERSHEY_SCRIPT_SIMPLEX,
                   fontScale=0.4,
                   color=(0, 0, 255))
       cv2.circle(im, pos, 3, color=(0, 255, 255))
   return im



def draw_convex_hull(im, points, color):
   points = cv2.convexHull(points)
   cv2.fillConvexPoly(im, points, color=color)



def get_face_mask(im, landmarks):
   im = numpy.zeros(im.shape[:2], dtype=numpy.float64)
   for group in OVERLAY_POINTS:
       draw_convex_hull(im,
                        landmarks[group],
                        color=1)
   im = numpy.array([im, im, im]).transpose((1, 2, 0))
   im = (cv2.GaussianBlur(im, (FEATHER_AMOUNT, FEATHER_AMOUNT), 0) > 0) * 1.0
   im = cv2.GaussianBlur(im, (FEATHER_AMOUNT, FEATHER_AMOUNT), 0)
   return im
def transformation_from_points(points1, points2):
   """
   Return an affine transformation [s * R | T] such that:
       sum ||s*R*p1,i + T - p2,i||^2
   is minimized.
   """
   # Solve the procrustes problem by subtracting centroids, scaling by the
   # standard deviation, and then using the SVD to calculate the rotation. See
   # the following for more details:
   #   https://en.wikipedia.org/wiki/Orthogonal_Procrustes_problem
   points1 = points1.astype(numpy.float64)
   points2 = points2.astype(numpy.float64)
   c1 = numpy.mean(points1, axis=0)
   c2 = numpy.mean(points2, axis=0)
   points1 -= c1
   points2 -= c2
   s1 = numpy.std(points1)
   s2 = numpy.std(points2)
   points1 /= s1
   points2 /= s2
   U, S, Vt = numpy.linalg.svd(points1.T * points2)
   # The R we seek is in fact the transpose of the one given by U * Vt. This
   # is because the above formulation assumes the matrix goes on the right
   # (with row vectors) where as our solution requires the matrix to be on the
   # left (with column vectors).
   R = (U * Vt).T
   return numpy.vstack([numpy.hstack(((s2 / s1) * R,
                                      c2.T - (s2 / s1) * R * c1.T)),
                        numpy.matrix([0., 0., 1.])])
def read_im_and_landmarks(fname):
   im = cv2.imread(fname, cv2.IMREAD_COLOR)
   im = cv2.resize(im, (im.shape[1] * SCALE_FACTOR,
                        im.shape[0] * SCALE_FACTOR))
   s = get_landmarks(im)
   return im, s
def warp_im(im, M, dshape):
   output_im = numpy.zeros(dshape, dtype=im.dtype)
   cv2.warpAffine(im,
                  M[:2],
                  (dshape[1], dshape[0]),
                  dst=output_im,
                  borderMode=cv2.BORDER_TRANSPARENT,
                  flags=cv2.WARP_INVERSE_MAP)
   return output_im
def correct_colours(im1, im2, landmarks1):
   blur_amount = COLOUR_CORRECT_BLUR_FRAC * numpy.linalg.norm(
                             numpy.mean(landmarks1[LEFT_EYE_POINTS], axis=0) -
                             numpy.mean(landmarks1[RIGHT_EYE_POINTS], axis=0))
   blur_amount = int(blur_amount)
   if blur_amount % 2 == 0:
       blur_amount += 1
   im1_blur = cv2.GaussianBlur(im1, (blur_amount, blur_amount), 0)
   im2_blur = cv2.GaussianBlur(im2, (blur_amount, blur_amount), 0)
   # Avoid divide-by-zero errors.
   im2_blur += 128 * (im2_blur <= 1.0)
   return (im2.astype(numpy.float64) * im1_blur.astype(numpy.float64) /
                                               im2_blur.astype(numpy.float64))
im1, landmarks1 = read_im_and_landmarks(sys.argv[1])
im2, landmarks2 = read_im_and_landmarks(sys.argv[2])
M = transformation_from_points(landmarks1[ALIGN_POINTS],
                              landmarks2[ALIGN_POINTS])
mask = get_face_mask(im2, landmarks2)
warped_mask = warp_im(mask, M, im1.shape)
combined_mask = numpy.max([get_face_mask(im1, landmarks1), warped_mask],
                         axis=0)
warped_im2 = warp_im(im2, M, im1.shape)
warped_corrected_im2 = correct_colours(im1, warped_im2, landmarks1)
output_im = im1 * (1.0 - combined_mask) + warped_corrected_im2 * combined_mask
cv2.imwrite('output.jpg', output_im)