|
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).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() #-*- 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).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) function(){ //MT4教程 http://www.kaifx.cn/mt4.html cv2.waitKey(0) if __name__ == '__main__': main()
| 
收藏
淘帖
踩
