瘦脸大眼算法
大眼算法实现：
效果图展示
import dlib
import cv2
import numpy as np
import math
predictor_path='shape_predictor_68_face_landmarks.dat'
#使用dlib自带的frontal_face_detector作为我们的特征提取器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)
def landmark_dec_dlib_fun(img_src):
img_gray = cv2.cvtColor(img_src,cv2.COLOR_BGR2GRAY)
land_marks = []
rects = detector(img_gray,0)
for i in range(len(rects)):
land_marks_node = np.matrix([[p.x,p.y] for p in predictor(img_gray,rects[i]).parts()])
# for idx,point in enumerate(land_marks_node):
# # 68点坐标
# pos = (point[0,0],point[0,1])
# print(idx,pos)
# # 利用cv2.circle给每个特征点画一个圈，共68个
# cv2.circle(img_src, pos, 5, color=(0, 255, 0))
# # 利用cv2.putText输出1-68
# font = cv2.FONT_HERSHEY_SIMPLEX
# cv2.putText(img_src, str(idx + 1), pos, font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
land_marks.append(land_marks_node)
return land_marks
'''
方法： Interactive Image Warping 局部平移算法
'''
def localTranslationWarp(srcImg,startX,startY,endX,endY,radius):
ddradius = float(radius * radius)
copyImg = np.zeros(srcImg.shape, np.uint8)
copyImg = srcImg.copy()
# 计算公式中的|m-c|^2
ddmc = (endX - startX) * (endX - startX) + (endY - startY) * (endY - startY)
H, W, C = srcImg.shape
for i in range(W):
for j in range(H):
#计算该点是否在形变圆的范围之内
#优化，第一步，直接判断是会在（startX,startY)的矩阵框中
if math.fabs(i-startX)>radius and math.fabs(j-startY)>radius:
continue
distance = ( i - startX ) * ( i - startX) + ( j - startY ) * ( j - startY )
if(distance < ddradius):
#计算出（i,j）坐标的原坐标
#计算公式中右边平方号里的部分
rnorm=math.sqrt(distance)/radius
ratio=1-(rnorm-1)*(rnorm-1)*0.5
#映射原位置
UX = startX +ratio * ( i - startX )
UY = startY +ratio * ( j - startY )
#根据双线性插值法得到UX，UY的值
value = BilinearInsert(srcImg,UX,UY)
#改变当前 i ，j的值
copyImg[j,i] =value
return copyImg
#双线性插值法
def BilinearInsert(src,ux,uy):
w,h,c = src.shape
if c == 3:
x1=int(ux)
x2=x1+1
y1=int(uy)
y2=y1+1
part1=src[y1,x1].astype(np.float)*(float(x2)-ux)*(float(y2)-uy)
part2=src[y1,x2].astype(np.float)*(ux-float(x1))*(float(y2)-uy)
part3=src[y2,x1].astype(np.float) * (float(x2) - ux)*(uy-float(y1))
part4 = src[y2,x2].astype(np.float) * (ux-float(x1)) * (uy - float(y1))
insertValue=part1+part2+part3+part4
return insertValue.astype(np.int8)
def face_thin_auto(src):
landmarks = landmark_dec_dlib_fun(src)
#如果未检测到人脸关键点，就不进行瘦脸
if len(landmarks) == 0:
return
for landmarks_node in landmarks:
left_landmark= landmarks_node[38]
left_landmark_down=landmarks_node[27]
right_landmark = landmarks_node[43]
right_landmark_down = landmarks_node[27]
endPt = landmarks_node[30]
#计算第4个点到第6个点的距离作为瘦脸距离
r_left=math.sqrt((left_landmark[0,0]-left_landmark_down[0,0])*(left_landmark[0,0]-left_landmark_down[0,0])+
(left_landmark[0,1] - left_landmark_down[0,1]) * (left_landmark[0,1] - left_landmark_down[0, 1]))
# 计算第14个点到第16个点的距离作为瘦脸距离
r_right=math.sqrt((right_landmark[0,0]-right_landmark_down[0,0])*(right_landmark[0,0]-right_landmark_down[0,0])+
(right_landmark[0,1] -right_landmark_down[0,1]) * (right_landmark[0,1] -right_landmark_down[0, 1]))
#瘦左边脸
thin_image = localTranslationWarp(src,left_landmark[0,0],left_landmark[0,1],endPt[0,0],endPt[0,1],r_left)
#瘦右边脸
thin_image = localTranslationWarp(thin_image, right_landmark[0,0], right_landmark[0,1], endPt[0,0],endPt[0,1], r_right)
#显示
cv2.imshow('thin',thin_image)
cv2.imwrite('thin.jpg',thin_image)
def main():
src = cv2.imread('2.jpg')
cv2.imshow('src', src)
face_thin_auto(src)
cv2.waitKey(0)
if __name__ == '__main__':
main()
瘦脸算法：公式
效果图展示
#-*- coding:gb18030 -*-
import dlib
import cv2
import numpy as np
import math
predictor_path='shape_predictor_68_face_landmarks.dat'
#使用dlib自带的frontal_face_detector作为我们的特征提取器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)
def landmark_dec_dlib_fun(img_src):
img_gray = cv2.cvtColor(img_src,cv2.COLOR_BGR2GRAY)
land_marks = []
rects = detector(img_gray,0)
for i in range(len(rects)):
land_marks_node = np.matrix([[p.x,p.y] for p in predictor(img_gray,rects[i]).parts()])
# for idx,point in enumerate(land_marks_node):
# # 68点坐标
# pos = (point[0,0],point[0,1])
# print(idx,pos)
# # 利用cv2.circle给每个特征点画一个圈，共68个
# cv2.circle(img_src, pos, 5, color=(0, 255, 0))
# # 利用cv2.putText输出1-68
# font = cv2.FONT_HERSHEY_SIMPLEX
# cv2.putText(img_src, str(idx + 1), pos, font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
land_marks.append(land_marks_node)
return land_marks
'''
方法： Interactive Image Warping 局部平移算法
'''
def localTranslationWarp(srcImg,startX,startY,endX,endY,radius):
ddradius = float(radius * radius)
copyImg = np.zeros(srcImg.shape, np.uint8)
copyImg = srcImg.copy()
# 计算公式中的|m-c|^2
ddmc = (endX - startX) * (endX - startX) + (endY - startY) * (endY - startY)
H, W, C = srcImg.shape
for i in range(W):
for j in range(H):
#计算该点是否在形变圆的范围之内
#优化，第一步，直接判断是会在（startX,startY)的矩阵框中
if math.fabs(i-startX)>radius and math.fabs(j-startY)>radius:
continue
distance = ( i - startX ) * ( i - startX) + ( j - startY ) * ( j - startY )
if(distance < ddradius):
#计算出（i,j）坐标的原坐标
#计算公式中右边平方号里的部分
ratio=( ddradius-distance ) / ( ddradius - distance + ddmc)
ratio = ratio * ratio
#映射原位置
UX = i - ratio * ( endX - startX )
UY = j - ratio * ( endY - startY )
#根据双线性插值法得到UX，UY的值
value = BilinearInsert(srcImg,UX,UY)
#改变当前 i ，j的值
copyImg[j,i] =value
return copyImg
#双线性插值法
def BilinearInsert(src,ux,uy):
w,h,c = src.shape
if c == 3:
x1=int(ux)
x2=x1+1
y1=int(uy)
y2=y1+1
part1=src[y1,x1].astype(np.float)*(float(x2)-ux)*(float(y2)-uy)
part2=src[y1,x2].astype(np.float)*(ux-float(x1))*(float(y2)-uy)
part3=src[y2,x1].astype(np.float) * (float(x2) - ux)*(uy-float(y1))
part4 = src[y2,x2].astype(np.float) * (ux-float(x1)) * (uy - float(y1))
insertValue=part1+part2+part3+part4
return insertValue.astype(np.int8)
def face_thin_auto(src):
landmarks = landmark_dec_dlib_fun(src)
#如果未检测到人脸关键点，就不进行瘦脸
if len(landmarks) == 0:
return
for landmarks_node in landmarks:
left_landmark= landmarks_node[3]
left_landmark_down=landmarks_node[5]
right_landmark = landmarks_node[13]
right_landmark_down = landmarks_node[15]
endPt = landmarks_node[30]
#计算第4个点到第6个点的距离作为瘦脸距离
r_left=math.sqrt((left_landmark[0,0]-left_landmark_down[0,0])*(left_landmark[0,0]-left_landmark_down[0,0])+
(left_landmark[0,1] - left_landmark_down[0,1]) * (left_landmark[0,1] - left_landmark_down[0, 1]))
# 计算第14个点到第16个点的距离作为瘦脸距离
r_right=math.sqrt((right_landmark[0,0]-right_landmark_down[0,0])*(right_landmark[0,0]-right_landmark_down[0,0])+
(right_landmark[0,1] -right_landmark_down[0,1]) * (right_landmark[0,1] -right_landmark_down[0, 1]))
#瘦左边脸
thin_image = localTranslationWarp(src,left_landmark[0,0],left_landmark[0,1],endPt[0,0],endPt[0,1],r_left)
#瘦右边脸
thin_image = localTranslationWarp(thin_image, right_landmark[0,0], right_landmark[0,1], endPt[0,0],endPt[0,1], r_right)
#显示
cv2.imshow('thin',thin_image)
cv2.imwrite('thin.jpg',thin_image)
def main():
src = cv2.imread('timg4.jpg')
cv2.imshow('src', src)
face_thin_auto(src)
cv2.waitKey(0)
if __name__ == '__main__':
main()
一个整体的美颜界面程序如下
