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faceFatModule.py
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import paddlehub as hub
import cv2
import numpy as np
import math
import CVTools
face_landmark = hub.Module(name="face_landmark_localization")
def landmark_dec_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.append(land_marks_node)
results = face_landmark.keypoint_detection(images=[img_src],
paths=None,
batch_size=1,
use_gpu=False,
output_dir='face_landmark_output',
visualization=False)
# print('emoi baidu landmark',len(results),len(results[0]))
for result in results: # one result for one pic
# print(len(result['data']))
land_marks.append(result['data'])
return land_marks[0]#one pic for one element
'''
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
def translationFaceWarp(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 find_biggest_face(landmarks):
areaList=[]
for landmark in landmarks:
lan=np.array(landmark)
areaList.append(np.max(lan)*np.min(lan))
print('areaList',areaList)
index=areaList.index(max(areaList))
index=0
return landmarks[index]
def swap_face(src,landmarks_node,ratio=0.8):
left_landmark = landmarks_node[3]
left_landmark_down = landmarks_node[31]
right_landmark = landmarks_node[13]
right_landmark_down = landmarks_node[31]
endPtleft = landmarks_node[33]
endPtright = landmarks_node[33]
# 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] - left_landmark_down[0]) * (left_landmark[0] - left_landmark_down[0]) +
(left_landmark[1] - left_landmark_down[1]) * (left_landmark[1] - left_landmark_down[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] - right_landmark_down[0]) * (right_landmark[0] - right_landmark_down[0]) +
(right_landmark[1] - right_landmark_down[1]) * (right_landmark[1] - right_landmark_down[1]))
r_right *=( max(0.8,ratio)+0)
r_left *=( max(0.8,ratio)+0)
print(r_right, r_left)
# 瘦左边脸
thin_image = translationFaceWarp(src, left_landmark[0], left_landmark[1], endPtleft[0],
endPtleft[1], r_left)
# thin_image = localWiderWarp(src, left_landmark[ 0], left_landmark[ 1],
# (endPtleft[ 0]+left_landmark[ 0])/2, (left_landmark[ 1]+endPtleft[ 1])/2,
# r_left)
# 瘦右边脸
thin_image = translationFaceWarp(thin_image, right_landmark[0], right_landmark[1], endPtright[0],
endPtright[1], r_right)
# thin_image = localWiderWarp(thin_image, right_landmark[ 0], right_landmark[ 1],
# (endPtright[ 0]+right_landmark[ 0])/2, (right_landmark[ 1]+endPtright[ 1])/2,
# r_left)
return thin_image
class face_morph():
def __init__(self,landmarker,cartoon_maker):
self.landmarker=landmarker
self.cartoon_maker=cartoon_maker
self.perspect_size=256
def run(self,user_bot=None,src_img=[],morph_img=[]):
landmark=[]
# user_bot.roiImg
if len(src_img)!=0:
landmarks = self.landmarker.run(src_img)
else:
img=cv2.imread(user_bot.imgPath)
landmarks = self.landmarker.run(img)
# 如果未检测到人脸关键点，就不进行瘦脸
if len(landmarks) == 0:
return user_bot
landmarks_node=find_biggest_face(landmarks)
ratio=user_bot.emoitionRatio
# for landmarks_node in landmarks:
if user_bot is not None:
morph_image = swap_face(img, landmarks_node, ratio)
# landmarks_node=np.array(landmarks_node)
inputImg = CVTools.roiChoice([landmarks_node], morph_image, self.perspect_size)
morph_image=self.cartoon_maker.process(inputImg)
else:
if len(morph_img)!=0:
inputImg=morph_img
else:
inputImg=src_img
morph_image = swap_face(inputImg, landmarks_node, ratio)
# print(cv2.imwrite('roi.jpg',user_bot.roiImg))
# print(cv2.imwrite('inputImg.jpg',inputImg))
##
if user_bot is not None:
user_bot.specialImg=morph_image
return user_bot
else:
return morph_image
# 显示
# cv2.imshow('thin', thin_image)
# cv2.imwrite('thin.jpg', thin_image)
# cv2.imwrite('mask.jpg',mask)
if __name__=='__main__':
from landmarkModule import landmarker
from cartonModule import cartoon_face
la=landmarker(False)
cm = cartoon_face(la)
cf=face_morph(la,cm)
src_img=cv2.imread('roi.jpg')
from botClass import bot
user_bot=bot()
user_bot.imgPath='pic/25033812051166452013.jpg'
# user_bot.roiCartoon=cv2.imread('inputImg.jpg')
out=cf.run(user_bot)
# print('in',image.shape,'out',out.shape)
# cv2.imwrite('../facefatter.jpg', out.specialImg)
# print('time', time.time() - t1)
#
# src = cv2.imread('a10.png')[:,:,:3]
# src = cv2.imread('wuyifan.jpg')[:,:,:3]
#
# face_thin_auto(src)