简介
基于dlib进行人脸互换操作。
实践
1. 环境准备
# pip install boost
# pip install cmake
# pip install dlib
如果仍然安装失败，请参考 https://blog.csdn.net/qq_45070929/article/details/118612849，进行c++相关组件安装教程。
2. 代码如下
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 4 15:10:26 2017
@author: Quantum Liu
reference:https://github.com/matthewearl/faceswap
"""
# pip install boost
# pip install cmake
# pip install dlib
import sys, os, traceback
import cv2
import dlib
import numpy as np
class TooManyFaces(Exception):
'''
定位到太多脸
'''
pass
class NoFace(Exception):
'''
没脸
'''
pass
class Faceswapper():
'''
人脸交换器类
实例化时载入多个头照片资源
'''
def __init__(self, heads_list=[], predictor_path="H:/FaceSwapper/shape_predictor_68_face_landmarks.dat"):
'''
head_list:./data/shape_predictor_68_face_landmarks.dat
头（背景和发型）来源图片的路径的字符串列表，根据此列表在实例化时载入多个头像资源，
并获得面部识别点坐标，以字典形式存储，键名为文件名
predictor_path:
dlib资源的路径
'''
# 五官等标记点
self.PREDICTOR_PATH = predictor_path
self.FACE_POINTS = list(range(17, 68))
self.MOUTH_POINTS = list(range(48, 61))
self.RIGHT_BROW_POINTS = list(range(17, 22))
self.LEFT_BROW_POINTS = list(range(22, 27))
self.RIGHT_EYE_POINTS = list(range(36, 42))
self.LEFT_EYE_POINTS = list(range(42, 48))
self.NOSE_POINTS = list(range(27, 35))
self.JAW_POINTS = list(range(0, 17))
# 人脸的完整标记点
self.ALIGN_POINTS = (self.LEFT_BROW_POINTS + self.RIGHT_EYE_POINTS + self.LEFT_EYE_POINTS +
self.RIGHT_BROW_POINTS + self.NOSE_POINTS + self.MOUTH_POINTS)
# 来自第二张图（脸）的标记点，眼、眉、鼻子、嘴，这一部分标记点将覆盖第一张图的对应标记点
self.OVERLAY_POINTS = [
self.LEFT_EYE_POINTS + self.RIGHT_EYE_POINTS + self.LEFT_BROW_POINTS + self.RIGHT_BROW_POINTS,
self.NOSE_POINTS + self.MOUTH_POINTS]
# 颜色校正参数
self.COLOUR_CORRECT_BLUR_FRAC = 0.6
# 人脸定位、特征提取器，来自dlib
self.detector = dlib.get_frontal_face_detector()
self.predictor = dlib.shape_predictor(self.PREDICTOR_PATH)
# 头像资源
self.heads = {}
if heads_list:
self.load_heads(heads_list)
def load_heads(self, heads_list):
'''
根据head_list添加更多头像资源
'''
self.heads.update({os.path.split(name)[-1]: (self.read_and_mark(name)) for name in heads_list})
def get_landmarks(self, im, fname, n=1):
'''
人脸定位和特征提取，定位到两张及以上脸或者没有人脸将抛出异常
im:
照片的numpy数组
fname:
照片名字的字符串
返回值:
人脸特征(x,y)坐标的矩阵
'''
rects = self.detector(im, 1)
if len(rects) > n:
raise TooManyFaces('No face in ' + fname)
if len(rects) < 0:
raise NoFace('Too many faces in ' + fname)
return np.matrix([[p.x, p.y] for p in self.predictor(im, rects[0]).parts()])
def read_im(self, fname, scale=1):
'''
读取图片
'''
# =============================================================================
# im = cv2.imread(fname, cv2.IMREAD_COLOR)
# =============================================================================
im = cv2.imdecode(np.fromfile(fname, dtype=np.uint8), -1)
if type(im) == type(None):
print(fname)
raise ValueError('Opencv read image {} error, got None'.format(fname))
return im
def read_and_mark(self, fname):
im = self.read_im(fname)
return im, self.get_landmarks(im, fname)
def resize(self, im_head, landmarks_head, im_face, landmarks_face):
'''
根据头照片和脸照片的大小（分辨率）调整图片大小，增强融合效果
'''
scale = np.sqrt((im_head.shape[0] * im_head.shape[1]) / (im_face.shape[0] * im_face.shape[1]))
if scale > 1:
im_head = cv2.resize(im_head, (int(im_head.shape[1] / scale), int(im_head.shape[0] / scale)))
landmarks_head = (landmarks_head / scale).astype(landmarks_head.dtype)
else:
im_face = cv2.resize(im_face, (int(im_face.shape[1] * scale), int(im_face.shape[0] * scale)))
landmarks_face = (landmarks_face * scale).astype(landmarks_face.dtype)
return im_head, landmarks_head, im_face, landmarks_face
def draw_convex_hull(self, im, points, color):
'''
勾画多凸边形
'''
points = cv2.convexHull(points)
cv2.fillConvexPoly(im, points, color=color)
def get_face_mask(self, im, landmarks, ksize=(11, 11)):
'''
获得面部遮罩
'''
mask = np.zeros(im.shape[:2], dtype=np.float64)
for group in self.OVERLAY_POINTS:
self.draw_convex_hull(mask,
landmarks[group],
color=1)
mask = np.array([mask, mask, mask]).transpose((1, 2, 0))
mask = (cv2.GaussianBlur(mask, ksize, 0) > 0) * 1.0
mask = cv2.GaussianBlur(mask, ksize, 0)
return mask
def transformation_from_points(self, points1, points2):
"""
Return an affine transformation [s * R | T] such that:
sum ||s*R*p1,i + T - p2,i||^2
is minimized.
计算仿射矩阵
参考：https://github.com/matthewearl/faceswap/blob/master/faceswap.py
"""
# 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(np.float64)
points2 = points2.astype(np.float64)
c1 = np.mean(points1, axis=0)
c2 = np.mean(points2, axis=0)
points1 -= c1
points2 -= c2
s1 = np.std(points1)
s2 = np.std(points2)
points1 /= s1
points2 /= s2
U, S, Vt = np.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 np.vstack([np.hstack(((s2 / s1) * R,
c2.T - (s2 / s1) * R * c1.T)),
np.matrix([0., 0., 1.])])
def warp_im(self, im, M, dshape):
'''
人脸位置仿射变换
'''
output_im = np.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(self, im1, im2, landmarks_head):
'''
颜色校正
'''
blur_amount = int(self.COLOUR_CORRECT_BLUR_FRAC * np.linalg.norm(
np.mean(landmarks_head[self.LEFT_EYE_POINTS], axis=0) -
np.mean(landmarks_head[self.RIGHT_EYE_POINTS], axis=0)))
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)
im2_blur += (128 * (im2_blur <= 1.0)).astype(im2_blur.dtype)
return im2.astype(np.float64) * im1_blur.astype(np.float64) / im2_blur.astype(np.float64)
def swap(self, head_name, face_path):
'''
主函数 人脸交换
head_name：
头资源的键名字符串
face_path:
脸来源的图像路径名
'''
im_head, landmarks_head, im_face, landmarks_face = self.resize(*self.heads[head_name],
*self.read_and_mark(face_path))
M = self.transformation_from_points(landmarks_head[self.ALIGN_POINTS],
landmarks_face[self.ALIGN_POINTS])
face_mask = self.get_face_mask(im_face, landmarks_face)
warped_mask = self.warp_im(face_mask, M, im_head.shape)
combined_mask = np.max([self.get_face_mask(im_head, landmarks_head), warped_mask],
axis=0)
warped_face = self.warp_im(im_face, M, im_head.shape)
warped_corrected_im2 = self.correct_colours(im_head, warped_face, landmarks_head)
out = im_head * (1.0 - combined_mask) + warped_corrected_im2 * combined_mask
return out
def save(self, output_path, output_im):
'''
保存图片
'''
cv2.imencode('.jpg', output_im)[1].tofile(output_path)
# =============================================================================
# cv2.imwrite(os.path.abspath(output_path.encode('utf-8').decode('gbk')), output_im)
# =============================================================================
if __name__ == '__main__':
'''
命令行运行：
python faceswapper.py <头路径> <脸路径> <输出图片路径>(可选，默认./output.jpg)
'''
head, face_path, out = sys.argv[1], sys.argv[2], (sys.argv[3] if len(sys.argv) >= 4 else 'output.jpg')
swapper = Faceswapper([head])
output_im = swapper.swap(os.path.split(head)[-1], face_path) # 返回的numpy数组
swapper.save(out, output_im)
output_im[output_im > 254.9] = 254.9
# cv2.imshow('',output_im.astype('uint8'))
# cv2.waitKey()
小结
参考： https://github.com/matthewearl/faceswap https://gitee.com/wllsxz/mask-changing/blob/master/faceswapper.py
安装教程：https://blog.csdn.net/qq_45070929/article/details/118612849