1、介绍：
输入一张图片，控制脸部的轮廓，达到脸部变瘦的特效
2、下载模型
文章百度链接提供
3、配置环境
conda create -n facethin python=3.8
conda activate facethin
pip install opencv-python
pip install dlib-19.19.0-cp38-cp38-win_amd64.whl4、核心代码
import cv2
import dlib
import numpy as np
# 获取图像中的人脸关键点
# 输入
# img ： 图像
# det_face ： 人脸检测器
# det_landmarks ： 人脸关键点检测器
def get_landmarks_points(img,det_face,det_landmarks):
# 转换为灰度
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 检测人脸区域
face_rects = det_face(gray, 0)
# 获取68个关键点
landmarks = det_landmarks(gray, face_rects[0])
# 获取关键点的坐标
landmarks_points = []
parts = landmarks.parts()
for part in parts:
landmarks_points.append((part.x,part.y))
return landmarks_points
# 双线性差值
def BilinearInsert(src,pt_U):
ux = pt_U[0]
uy = pt_U[1]
x1=np.float32(int(ux))
x2=x1+1
y1=np.float32(int(uy))
y2=y1+1
v1 = np.float32(src[int(y1),int(x1)])
v2 = np.float32(src[int(y1),int(x2)])
v3 = np.float32(src[int(y2),int(x1)])
v4 = np.float32(src[int(y2),int(x2)])
part1 = v1 * (x2 - ux) * (y2 - uy)
part2 = v2 * (ux - x1) * (y2 - uy)
part3 = v3 * (x2 - ux) * (uy - y1)
part4 = v4 * (ux - x1) * (uy - y1)
insertValue=part1+part2+part3+part4
return insertValue.astype(np.uint8)
def localTranslationWap(img,pt_C,pt_M,r,a):
h,w,c = img.shape
# 文件拷贝
copy_img = np.zeros_like(img)
copy_img = img.copy()
# 创建蒙板
mask = np.zeros((h,w),dtype = np.uint8)
cv2.circle(mask,pt_C,np.int32(r),255,cv2.FILLED)
# 计算 CM 之间的距离
pt_C = np.float32(pt_C)
pt_M = np.float32(pt_M)
dis_M_C = np.dot((pt_C-pt_M),(pt_C-pt_M))
# 只对蒙板内大于0的数进行处理
for i in range(w):
for j in range(h):
# 只计算半径内的像素
if mask[j,i] ==0:
continue
# 计算 XC之间的距离
pt_X = np.array([i,j],dtype = np.float32)
dis_X_C = np.dot((pt_X-pt_C),(pt_X-pt_C))
# 计算缩放比例
radio = (r*r-dis_X_C)/(r*r-dis_X_C+a*dis_M_C)
radio = radio*radio
# 计算 目标图像（i，j）处由源图像U点替换
pt_U = pt_X-radio*(pt_M-pt_C)
# 利用双线性差值法，计算U点处的像素值
# value = BilinearInsert(img,pt_U)
# copy_img[j,i] = value
# 直接获取U点的值
pt_u = np.int32(pt_U)
copy_img[j,i] = img[pt_u[1],pt_u[0]]
return copy_img
# 滑块的响应函数
def empty(a):
pass
if __name__ == "__main__":
# 创建滑块
# cv2.namedWindow("TrackBars")
# cv2.resizeWindow("TrackBars",640,30)
# cv2.createTrackbar("a","TrackBars",60,200,empty)
# 创建人脸检测器
det_face = dlib.get_frontal_face_detector()
# 加载标志点检测器
############################################加载模型###############################################################
det_landmarks = dlib.shape_predictor("shape_predictor_68_face_landmarks_GTX.dat") # 68点
###########################################################################################################
# 打开图片
####################################读入图片的地址#####################################################
img = cv2.imread('./3/1.jpg')
###########################################################################################################
# 获取源图像的68个关键点的坐标
landmarks = get_landmarks_points(img,det_face,det_landmarks)
landmarks = np.array(landmarks)
# 瘦脸程度调节
a = 0.8
# 右脸参数
pt_C_right = landmarks[3]
pt_M = landmarks[30]
r_right = np.sqrt(np.dot(landmarks[3]-landmarks[5],landmarks[3]-landmarks[5]))
# 左脸参数
pt_C_left = landmarks[13]
r_left = np.sqrt(np.dot(landmarks[13]-landmarks[11],landmarks[13]-landmarks[11]))
# 减右脸
img_thin = localTranslationWap(img,pt_C_right,pt_M,r_right,a)
# 减左脸
img_thin = localTranslationWap(img_thin,pt_C_left,pt_M,r_left,a)
# 显示
cv2.imshow('input',img)
cv2.imshow('output',img_thin)
while True:
# a_new = cv2.getTrackbarPos("a","TrackBars")
########################################调整瘦脸的参数###################################################################
a_new = 70
###########################################################################################################
a_new = a_new/100
if a != a_new:
a = a_new
print("processing a= %.2f"%(a))
# 减右脸
img_thin = localTranslationWap(img,pt_C_right,pt_M,r_right,a)
# 减左脸
img_thin = localTranslationWap(img_thin,pt_C_left,pt_M,r_left,a)
##########################################输出图片的路径#############################################################
cv2.imwrite("6.jpg",img_thin)
###########################################################################################################
cv2.imshow('output',img_thin)
print("done")
key=cv2.waitKey(5000) & 0xFF
if key == ord('q'):
break
# # 右脸缩放
# pt_C_right = landmarks[3]
# pt_M = landmarks[30]
# r_right = np.sqrt(np.dot(landmarks[3]-landmarks[5],landmarks[3]-landmarks[5]))
# img_thin = localTranslationWap(img,pt_C_right,pt_M,r_right,a)
# # 左脸缩放
# pt_C_left = landmarks[13]
# pt_M = landmarks[30]
# r_left = np.sqrt(np.dot(landmarks[13]-landmarks[11],landmarks[13]-landmarks[11]))
# img_thin = localTranslationWap(img_thin,pt_C_left,pt_M,r_left,a)
# # 结果显示
# cv2.imshow('input',img)
# cv2.imshow('output',img_thin)
# # 显示原图
# cv2.circle(img,landmarks[3],2,(255,0,0),cv2.FILLED)
# cv2.circle(img,landmarks[5],2,(255,0,0),cv2.FILLED)
# cv2.circle(img,landmarks[30],2,(255,0,0),cv2.FILLED)
# cv2.circle(img,landmarks[3],np.int32(r_right),(255,0,0))
# cv2.line(img,landmarks[3],landmarks[30],(0,255,0),1)
# cv2.circle(img,landmarks[13],2,(255,0,0),cv2.FILLED)
# cv2.circle(img,landmarks[11],2,(255,0,0),cv2.FILLED)
# cv2.circle(img,landmarks[30],2,(255,0,0),cv2.FILLED)
# cv2.circle(img,landmarks[13],np.int32(r_left),(255,0,0))
# cv2.line(img,landmarks[13],landmarks[30],(0,255,0),1)
# cv2.imshow('input',img)
# cv2.circle(img_thin,landmarks[3],2,(255,0,0),cv2.FILLED)
# cv2.circle(img_thin,landmarks[5],2,(255,0,0),cv2.FILLED)
# cv2.circle(img_thin,landmarks[30],2,(255,0,0),cv2.FILLED)
# cv2.circle(img_thin,landmarks[3],np.int32(r_right),(255,0,0))
# cv2.line(img_thin,landmarks[3],landmarks[30],(0,255,0),1)
# cv2.circle(img_thin,landmarks[13],2,(255,0,0),cv2.FILLED)
# cv2.circle(img_thin,landmarks[11],2,(255,0,0),cv2.FILLED)
# cv2.circle(img_thin,landmarks[30],2,(255,0,0),cv2.FILLED)
# cv2.circle(img_thin,landmarks[13],np.int32(r_left),(255,0,0))
# cv2.line(img_thin,landmarks[13],landmarks[30],(0,255,0),1)
# cv2.imshow('output',img_thin)
# cv2.waitKey(0)
5、运行
python face_thin.py6、效果展示
