yangakw yangakw

换脸(未测试)

in 记录read (225) 文章转载请注明来源!

#!/usr/bin/python

Copyright (c) 2015 Matthew Earl

Permission is hereby granted, free of charge, to any person obtaining a copy

of this software and associated documentation files (the "Software"), to deal

in the Software without restriction, including without limitation the rights

to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

copies of the Software, and to permit persons to whom the Software is

furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included

in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS

OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF

MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN

NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,

DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR

OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE

USE OR OTHER DEALINGS IN THE SOFTWARE.

"""
This is the code behind the Switching Eds blog post:
http://matthewearl.github.io/2015/07/28/switching-eds-with-python/
See the above for an explanation of the code below.
To run the script you'll need to install dlib (http://dlib.net) including its
Python bindings, and OpenCV. You'll also need to obtain the trained model from
sourceforge:
http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2
Unzip with bunzip2 and change PREDICTOR_PATH to refer to this file. The
script is run like so:
./faceswap.py
If successful, a file output.jpg will be produced with the facial features
from <head image> replaced with the facial features from <face image>.
"""

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 ||sRp1,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[

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[

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)).astype(im2_blur.dtype)

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)

jrotty WeChat Pay

微信打赏

jrotty Alipay

支付宝打赏

文章二维码

扫描二维码,在手机上阅读!

发表新评论
博客已运行
© 2017 yangakw
PREVIOUS NEXT
雷姆
拉姆