#!/usr/bin/env python3

# -*- coding: utf-8 -*- 

import dlib

import cv2

import numpy as np

import math

predictor_path='data/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('img/test6.jpg')

    cv2.imshow('src', src)

    face_thin_auto(src)

    cv2.waitKey(0)

if __name__ == '__main__':

    main()