最近一段时间，抖音、微视、美图纷纷推出了视频实时瘦身的特效，可以说是火了一把！本文将给大家做个技术揭秘！
商汤基于深度学习研发了整套瘦身SDK，包括了瘦腿，瘦腰，瘦胳膊，瘦头型等等功能，并给出了酷炫的实时瘦身视频，惊艳到了众人！本文将以瘦腰和瘦腿为例，给大家详细讲解一下。
瘦身从算法角度来讲，包含两个模块：①人体轮廓特征点检测模块；②人体变形模块
[人体轮廓特征点检测模块]
人体轮廓特征点检测模块好比人脸特征点检测，需要检测到代表人体轮廓的一些点位信息，但是技术上比人脸特征点更加复杂，因为人体是非刚体，比人脸的变化更多，更复杂；
目前人体轮廓特征点检测主要方法就是人体姿态估计；从2015年开始，陆续出现了很多人体姿态估计的论文，这里举例如：
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
这篇论文中所提供的深度学习模型与效果图如下：
这里我们不详细介绍人体姿态估计的算法，主要以讲瘦身的流程为主；
上面的姿态点与我们想要的轮廓点还有差别，在掌握了上面的人体姿态估计算法之后，我们可以使用人体轮廓点的样本进行训练，这样就可以得到如下图所示的人体轮廓特征点：
图片来自网络，为了避免侵权，做了人脸遮挡；
[人体变形模块]
人体变形模块主要是依据人体特征点，将人体变形，也就是瘦身美型；
变形的算法有很多，比如MLS移动最小二乘法变形算法，IWD反距离加权变形算法，MLSR变形算法等等，相关连接如下：
MLSR变形算法实现：点击打开链接
IWD反距离加权变形算法实现：点击打开链接
MLS移动最小二乘变形算法实现：点击打开链接，代码链接点击打开链接
本人MLS代码如下：
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;
};
本人这里使用MLS变形来实现瘦身效果：
1，根据人体姿态估计检测得到原图人体的特征点；
2，计算得到瘦腿之后的人体特征点；
3，计算得到瘦腰之后的人体特征点；
1-3如下图所示：
4，使用MLS，将原图特征点分别变换到瘦腿特征点和瘦腰特征点，进而得到两个相应的效果图如下(这里给出四个效果)：
至此，瘦身效果的算法流程讲完，至于实时处理，那是代码优化和算法优化的事情，只要速度够快，一切都不是问题！
上面只是以瘦腿和瘦腰为例，实际上还可以瘦胳膊，丰胸等等，来真正的达到美颜瘦身！
本人的DEMO界面如下：
本人QQ1358009172
最后给出本人的DEMO：点击打开链接