人像美妆是近几年来深受广大女孩儿群体喜欢的修图功能之一，目前市面中做的比较好的有美妆相机、玩美彩妆、天天P图等APP，当然还有一些PC专用的秀图软件，本文将给大家做个算法初识；
什么是人像美妆？通俗的看个样例图：
本人对AI美妆的一些看法如下：
1.妆容自然，逼真；
2.鲁棒性高，不受五官遮挡影响；
3.速度越快越好；
4.完全智能化，可以针对不同人像照片智能匹配最优妆容；
目前传统美妆的优缺点：
优点：
1.妆容种类丰富，可自由搭配，用户自主选择；
美妆相机和玩美彩妆两款App均提供了数十种不同的妆容效果，供用户自由选择；
2.上妆速度快，可以实时处理；
玩美彩妆、美妆相机、天天P图、无他相机、FaceU等APP均已支持实时上妆效果；
缺点：
1.妆容鲁棒性不高，被光线，五官遮挡影响较大；
2.逼真度不高；
3.无法完全智能化；
目前市面基于传统算法的美妆类APP均无法达到上述3点；
传统人像美妆算法流程：
1.妆容模版制作(Photoshop等编辑软件制作，由设计完成)
2.人脸检测，特征点识别；
这一步骤主要通过人脸检测+人脸对齐来获得N个特征点，目前开源的有Dlib，OpenCV等，商用版有商汤科技、旷世科技、虹软科技等，以及腾讯、美图等；
这里给出一个开源的人脸检测+对齐(68点位)的资源链接：https://github.com/XiuSdk/cnn-facial-landmark
3.基于人脸特征点，将模版变形，对齐到人脸五官区域；
变形算法有很多，仿射变换，IDW变换，MLS变换，RMLS变换等；
相关代码连接：
MLS(MLS代码在博客内容中)
如果大家懒得看博客，这里给出MLS变形代码：
static void setSrcPoints(const vector<PointD> &qsrc, vector<PointD> &newDotL, int* nPoint) {
*nPoint = qsrc.size();
newDotL.clear();
newDotL.reserve(*nPoint);
for (size_t i = 0; i < qsrc.size(); i++)
newDotL.push_back(qsrc[i]);
}
static void setDstPoints(const vector<PointD> &qdst,vector<PointD> &oldDotL, int* nPoint) {
*nPoint = qdst.size();
oldDotL.clear();
oldDotL.reserve(*nPoint);
for (size_t i = 0; i < qdst.size(); i++) oldDotL.push_back(qdst[i]);
}
static double bilinear_interp(double x, double y, double v11, double v12,
double v21, double v22) {
return (v11 * (1 - y) + v12 * y) * (1 - x) + (v21 * (1 - y) + v22 * y) * x;
}
static double calcArea(const vector<PointD> &V) {
PointD lt, rb;
lt.x = lt.y = 1e10;
rb.x = rb.y = -1e10;
for (vector<PointD >::const_iterator i = V.begin(); i != V.end();
i++) {
if (i->x < lt.x) lt.x = i->x;
if (i->x > rb.x) rb.x = i->x;
if (i->y < lt.y) lt.y = i->y;
if (i->y > rb.y) rb.y = i->y;
}
return (rb.x - lt.x) * (rb.y - lt.y);
}
static void calcDelta_rigid(int srcW, int srcH, int tarW, int tarH, double alpha, int gridSize, int nPoint, int preScale, double *rDx, double *rDy, vector<PointD> &oldDotL, vector<PointD> &newDotL)
{
int i, j, k;
PointD swq, qstar, newP, tmpP;
double sw;
double ratio;
if (preScale) {
ratio = sqrt(calcArea(newDotL) / calcArea(oldDotL));
for (i = 0; i < nPoint; i++) {
newDotL[i].x *= 1 / ratio;
newDotL[i].y *= 1 / ratio;
}
}
double *w = new double[nPoint];
if (nPoint < 2) {
//rDx.setTo(0);
//rDy.setTo(0);
return;
}
PointD swp, pstar, curV, curVJ, Pi, PiJ, Qi;
double miu_r;
for (i = 0;; i += gridSize) {
if (i >= tarW && i < tarW + gridSize - 1)
i = tarW - 1;
else if (i >= tarW)
break;
for (j = 0;; j += gridSize) {
if (j >= tarH && j < tarH + gridSize - 1)
j = tarH - 1;
else if (j >= tarH)
break;
sw = 0;
swp.x = swp.y = 0;
swq.x = swq.y = 0;
newP.x = newP.y = 0;
curV.x = i;
curV.y = j;
for (k = 0; k < nPoint; k++) {
if ((i == oldDotL[k].x) && j == oldDotL[k].y) break;
if (alpha == 1)
w[k] = 1 / ((i - oldDotL[k].x) * (i - oldDotL[k].x) +
(j - oldDotL[k].y) * (j - oldDotL[k].y));
else
w[k] = pow((i - oldDotL[k].x) * (i - oldDotL[k].x) +
(j - oldDotL[k].y) * (j - oldDotL[k].y),
-alpha);
sw = sw + w[k];
swp.x = swp.x + w[k] * oldDotL[k].x;
swp.y = swp.y + w[k] * oldDotL[k].y;
swq.x = swq.x + w[k] * newDotL[k].x;
swq.y = swq.y + w[k] * newDotL[k].y;
}
if (k == nPoint) {
pstar.x = (1 / sw) * swp.x;
pstar.y = (1 / sw) * swp.y;
qstar.x = 1 / sw * swq.x;
qstar.y = 1 / sw * swq.y;
// Calc miu_r
double s1 = 0, s2 = 0;
for (k = 0; k < nPoint; k++) {
if (i == oldDotL[k].x && j == oldDotL[k].y) continue;
Pi.x = oldDotL[k].x - pstar.x;
Pi.y = oldDotL[k].y - pstar.y;
PiJ.x = -Pi.y, PiJ.y = Pi.x;
Qi.x = newDotL[k].x - qstar.x;
Qi.y = newDotL[k].y - qstar.y;
s1 += w[k] * (Qi.x*Pi.x+Qi.y*Pi.y);
s2 += w[k] * (Qi.x*PiJ.x+Qi.y*PiJ.y);
}
miu_r = sqrt(s1 * s1 + s2 * s2);
curV.x -= pstar.x;
curV.y -= pstar.y;
curVJ.x = -curV.y, curVJ.y = curV.x;
for (k = 0; k < nPoint; k++) {
if (i == oldDotL[k].x && j == oldDotL[k].y) continue;
Pi.x = oldDotL[k].x - pstar.x;
Pi.y = oldDotL[k].y - pstar.y;
PiJ.x = -Pi.y, PiJ.y = Pi.x;
tmpP.x = (Pi.x*curV.x+Pi.y*curV.y)* newDotL[k].x -
(PiJ.x*curV.x+PiJ.y*curV.y)* newDotL[k].y;
tmpP.y = -(Pi.x*curVJ.x+Pi.y*curVJ.y) * newDotL[k].x +
(PiJ.x*curVJ.x+PiJ.y*curVJ.y) * newDotL[k].y;
tmpP.x *= w[k] / miu_r;
tmpP.y *= w[k] / miu_r;
newP.x += tmpP.x;
newP.y += tmpP.y;
}
newP.x += qstar.x;
newP.y += qstar.y;
} else {
newP = newDotL[k];
}
if (preScale) {
rDx[j * tarW + i] = newP.x * ratio - i;
rDy[j * tarW + i] = newP.y * ratio - j;
} else {
rDx[j * tarW + i] = newP.x - i;
rDy[j * tarW + i] = newP.y - j;
}
}
}
delete[] w;
if (preScale!=0) {
for (i = 0; i < nPoint; i++){
newDotL[i].x *= ratio;
newDotL[i].y *= ratio;
}
}
}
static void calcDelta_Similarity(int srcW, int srcH, int tarW, int tarH, double alpha, int gridSize, int nPoint, int preScale, double *rDx, double *rDy, vector<PointD> &oldDotL, vector<PointD> &newDotL)
{
int i, j, k;
PointD swq, qstar, newP, tmpP;
double sw;
double ratio;
if (preScale) {
ratio = sqrt(calcArea(newDotL) / calcArea(oldDotL));
for (i = 0; i < nPoint; i++) {
newDotL[i].x *= 1 / ratio;
newDotL[i].y *= 1 / ratio;
}
}
double *w = new double[nPoint];
if (nPoint < 2) {
return;
}
PointD swp, pstar, curV, curVJ, Pi, PiJ;
double miu_s;
for (i = 0;; i += gridSize) {
if (i >= tarW && i < tarW + gridSize - 1)
i = tarW - 1;
else if (i >= tarW)
break;
for (j = 0;; j += gridSize) {
if (j >= tarH && j < tarH + gridSize - 1)
j = tarH - 1;
else if (j >= tarH)
break;
sw = 0;
swp.x = swp.y = 0;
swq.x = swq.y = 0;
newP.x = newP.y = 0;
curV.x = i;
curV.y = j;
for (k = 0; k < nPoint; k++) {
if ((i == oldDotL[k].x) && j == oldDotL[k].y) break;
w[k] = 1 / ((i - oldDotL[k].x) * (i - oldDotL[k].x) +
(j - oldDotL[k].y) * (j - oldDotL[k].y));
sw = sw + w[k];
swp.x = swp.x + w[k] * oldDotL[k].x;
swp.y = swp.y + w[k] * oldDotL[k].y;
swq.x = swq.x + w[k] * newDotL[k].x;
swq.y = swq.y + w[k] * newDotL[k].y;
}
if (k == nPoint) {
pstar.x = (1 / sw) * swp.x;
pstar.y = (1 / sw) * swp.y;
qstar.x = 1 / sw * swq.x;
qstar.y = 1 / sw * swq.y;
// Calc miu_s
miu_s = 0;
for (k = 0; k < nPoint; k++) {
if (i == oldDotL[k].x && j == oldDotL[k].y) continue;
Pi.x = oldDotL[k].x - pstar.x;
Pi.y = oldDotL[k].y - pstar.y;
miu_s += w[k] * (Pi.x*Pi.x+Pi.y*Pi.y);
}
curV.x -= pstar.x;
curV.y -= pstar.y;
curVJ.x = -curV.y, curVJ.y = curV.x;
for (k = 0; k < nPoint; k++) {
if (i == oldDotL[k].x && j == oldDotL[k].y) continue;
Pi.x = oldDotL[k].x - pstar.x;
Pi.y = oldDotL[k].y - pstar.y;
PiJ.x = -Pi.y, PiJ.y = Pi.x;
tmpP.x = (Pi.x*curV.x+Pi.y*curV.y) * newDotL[k].x -
(PiJ.x*curV.x+PiJ.y*curV.y) * newDotL[k].y;
tmpP.y = -(Pi.x*curVJ.x+Pi.y*curVJ.y) * newDotL[k].x +
(PiJ.x*curVJ.x+PiJ.y*curVJ.y) * newDotL[k].y;
tmpP.x *= w[k] / miu_s;
tmpP.y *= w[k] / miu_s;
newP.x += tmpP.x;
newP.y += tmpP.y;
}
newP.x += qstar.x;
newP.y += qstar.y;
} else {
newP = newDotL[k];
}
rDx[j * tarW + i] = newP.x - i;
rDy[j * tarW + i] = newP.y - j;
}
}
delete[] w;
if (preScale!=0) {
for (i = 0; i < nPoint; i++){
newDotL[i].x *= ratio;
newDotL[i].y *= ratio;
}
}
}
static int GetNewImg(unsigned char* oriImg, int width, int height, int stride, unsigned char* tarImg, int tarW, int tarH, int tarStride, int gridSize, double* rDx, double* rDy, double transRatio)
{
int i, j;
double di, dj;
double nx, ny;
int nxi, nyi, nxi1, nyi1;
double deltaX, deltaY;
double w, h;
int ni, nj;
int pos, posa, posb, posc, posd;
for (i = 0; i < tarH; i += gridSize)
for (j = 0; j < tarW; j += gridSize) {
ni = i + gridSize, nj = j + gridSize;
w = h = gridSize;
if (ni >= tarH) ni = tarH - 1, h = ni - i + 1;
if (nj >= tarW) nj = tarW - 1, w = nj - j + 1;
for (di = 0; di < h; di++)
for (dj = 0; dj < w; dj++) {
deltaX =
bilinear_interp(di / h, dj / w, rDx[i * tarW + j], rDx[i * tarW + nj],
rDx[ni * tarW + j], rDx[ni * tarW + nj]);
deltaY =
bilinear_interp(di / h, dj / w, rDy[i * tarW + j], rDy[i * tarW + nj],
rDy[ni * tarW + j], rDy[ni * tarW + nj]);
nx = j + dj + deltaX * transRatio;
ny = i + di + deltaY * transRatio;
if (nx > width - 1) nx = width - 1;
if (ny > height - 1) ny = height - 1;
if (nx < 0) nx = 0;
if (ny < 0) ny = 0;
nxi = int(nx);
nyi = int(ny);
nxi1 = ceil(nx);
nyi1 = ceil(ny);
pos = (int)(i + di) * tarStride + ((int)(j + dj) << 2);
posa = nyi * stride + (nxi << 2);
posb = nyi * stride + (nxi1 << 2);
posc = nyi1 * stride + (nxi << 2);
posd = nyi1 * stride + (nxi1 << 2);
tarImg[pos] = (unsigned char)bilinear_interp(ny - nyi, nx - nxi, oriImg[posa], oriImg[posb], oriImg[posc], oriImg[posd]);
tarImg[pos + 1] = (unsigned char)bilinear_interp(ny - nyi, nx - nxi, oriImg[posa + 1],oriImg[posb + 1], oriImg[posc + 1], oriImg[posd + 1]);
tarImg[pos + 2] = (unsigned char)bilinear_interp(ny - nyi, nx - nxi, oriImg[posa + 2],oriImg[posb + 2], oriImg[posc + 2], oriImg[posd + 2]);
tarImg[pos + 3] = (unsigned char)bilinear_interp(ny - nyi, nx - nxi, oriImg[posa + 3],oriImg[posb + 3], oriImg[posc + 3], oriImg[posd + 3]);
}
}
return 0;
};
static void MLSImageWrapping(unsigned char* oriImg,int width, int height, int stride,const vector<PointD > &qsrc, const vector<PointD > &qdst, unsigned char* tarImg, int outW, int outH, int outStride, double transRatio, int preScale, int gridSize, int method)
{
int srcW = width;
int srcH = height;
int tarW = outW;
int tarH = outH;
double alpha = 1;
int nPoint;
int len = tarH * tarW;
vector<PointD> oldDotL, newDotL;
double *rDx = NULL,*rDy = NULL;
setSrcPoints(qsrc,newDotL,&nPoint);
setDstPoints(qdst,oldDotL,&nPoint);
rDx = (double*)malloc(sizeof(double) * len);
rDy = (double*)malloc(sizeof(double) * len);
memset(rDx, 0, sizeof(double) * len);
memset(rDy, 0, sizeof(double) * len);
if(method!=0)
calcDelta_Similarity(srcW, srcH, tarW, tarH, alpha, gridSize, nPoint, preScale, rDx, rDy, oldDotL, newDotL);
else
calcDelta_rigid(srcW, srcH, tarW, tarH, alpha, gridSize, nPoint, preScale, rDx, rDy, oldDotL, newDotL);
GetNewImg(oriImg, srcW, srcH, stride, tarImg, tarW, tarH, outStride, gridSize, rDx, rDy, transRatio);
if(rDx != NULL)
free(rDx);
if(rDy != NULL)
free(rDy);
};
int f_TMLSImagewarpping(unsigned char* srcData, int width ,int height, int stride, unsigned char* dstData, int outW, int outH, int outStride, int srcPoint[], int dragPoint[], int pointNum, double intensity, int preScale, int gridSize, int method)
{
int res = 0;
vector<PointD> qDst;
vector<PointD> qSrc;
PointD point = {0};
int len = 0;
for(int i = 0; i < pointNum; i++)
{
len = (i << 1);
point.x = srcPoint[len];
point.y = srcPoint[len + 1];
qSrc.push_back(point);
point.x = dragPoint[len];
point.y = dragPoint[len + 1];
qDst.push_back(point);
}
MLSImageWrapping(srcData, width, height, stride, qSrc, qDst, dstData, outW, outH, outStride, intensity, preScale,gridSize, method);
return res;
};4.将模版与人脸五官图像进行融合；
融合算法主要有alpha融合，Photoshop图层混合，泊松融合等；
alpha融合： S * alpha + D*(1-alpha)
图层混合公式如下：
泊松融合：算法详解
上述过程即传统算法流程，其中对美妆效果起决定性的是人脸特征点识别，如果没有准确的特征点，再好的妆容模版，上妆效果也出不来；
比如下面的例子：
图1中，由于眼睛特征点位置不准确，睫毛妆容已经偏离了眼睛区域；
图2中，由于拍照光线较暗，腮红明显，逼真度过低；
图3中，由于人眼和眉毛被部分遮挡，因此，传统算法的睫毛和眉毛效果悬浮在了头发之上；
目前传统算法相关的论文资料如下：
Rule-Based Facial Makeup Recommendation System.
Example-Based Cosmetic Transfer.
Region-based Face Makeup using example face images.
Simulating Makeup through Physics-based Manipulation of Intrinsic Image Layers.
A Visual Representation for editing face images.
Digital Face Makeup By Example.
在传统算法中，有一种妆容迁移算法，该算法可以直接将一张妆容效果图中的妆容特征，迁移到任意一张人像照片中去，实际上也是与人脸特征点密不可分，具体连接可参考：https://blog.csdn.net/trent1985/article/details/70226779
目前AI美妆相关的论文资料如下：
Makeup Like a Superstar Deep Localized Makeup Transfer Network.
Examples-Rules Guided Deep Neural Network for Makeup Recommendation.
上述两篇基于深度学习的美妆算法论文主要思想有两个：
1，对于第一篇论文主要是对人像进行五官分析，获取肤色，眉毛颜色，唇色等等信息，然后进行不同妆容的最佳匹配，最后上妆；
框架如下：
2，对五官进行分别提取分类成不同的style，依据样例数据的特征style，进行最优匹配并上妆；
框架如下：
上述两篇算法论文，依旧是建立在人脸特征点的基础上研究的。
本人针对传统美妆算法，结合深度学习，做了如下改进：
1.只需要人脸检测框，不依赖于人脸特征点；
2.不受五官遮挡和光线影响；
3.妆容效果逼真度提高；
本人算法框架：
1.人脸检测，得到正方形人脸框，包含五官区域；
2.基于全卷积网络，以人脸框图像作为输入，上妆之后的人脸五官效果图作为输出，进行学习训练；
妆容模版使用如下模版：
在Fig.4模板妆容中，分别进行了眉毛处理，眼影、睫毛和唇彩的上妆，整体肤色以及其他内容均无调整；
训练中迭代10次，训练集和验证集准确率均达到了94%-95%，本人训练样本选取了500张，数据比较少，这里仅仅探讨可行性与方法分析；
3.使用2中的训练模型，对测试图进行上妆；
效果图如下：
上述效果图中我们可以看到，基于深度学习的美妆效果，避免了五官遮挡的影响，同时上妆效果更加自然，对环境光的鲁棒性也较高，本文这里未给出具体的网络模型与参数，不过思路大家已经可以借鉴！目前算法处于研究测试阶段，后续本人将公布完整的DEMO！
本人QQ：1358009172