深度学习AI美颜系列---AI瘦身效果算法揭秘

https://blog.csdn.net/Trent1985/article/details/80667611

 

深度学习AI美颜系列---AI瘦身效果算法揭秘

2018年06月12日 17:31:26 Trent1985 阅读数 5681更多

分类专栏: 深度学习AI美颜系列 SF图像滤镜/美颜/美妆算法详解与实战

版权声明:本文为博主原创文章,遵循 CC 4.0 BY-SA 版权协议,转载请附上原文出处链接和本声明。

本文链接:https://blog.csdn.net/Trent1985/article/details/80667611

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:

 
  1. static void setSrcPoints(const vector &qsrc, vector &newDotL, int* nPoint) {

  2. *nPoint = qsrc.size();

  3. newDotL.clear();

  4. newDotL.reserve(*nPoint);

  5. for (size_t i = 0; i < qsrc.size(); i++)

  6. newDotL.push_back(qsrc[i]);

  7. }

  8.  
  9. static void setDstPoints(const vector &qdst,vector &oldDotL, int* nPoint) {

  10. *nPoint = qdst.size();

  11. oldDotL.clear();

  12. oldDotL.reserve(*nPoint);

  13.  
  14. for (size_t i = 0; i < qdst.size(); i++) oldDotL.push_back(qdst[i]);

  15. }

  16. static double bilinear_interp(double x, double y, double v11, double v12,

  17. double v21, double v22) {

  18. return (v11 * (1 - y) + v12 * y) * (1 - x) + (v21 * (1 - y) + v22 * y) * x;

  19. }

  20.  
  21. static double calcArea(const vector &V) {

  22. PointD lt, rb;

  23. lt.x = lt.y = 1e10;

  24. rb.x = rb.y = -1e10;

  25. for (vector::const_iterator i = V.begin(); i != V.end();

  26. i++) {

  27. if (i->x < lt.x) lt.x = i->x;

  28. if (i->x > rb.x) rb.x = i->x;

  29. if (i->y < lt.y) lt.y = i->y;

  30. if (i->y > rb.y) rb.y = i->y;

  31. }

  32. return (rb.x - lt.x) * (rb.y - lt.y);

  33. }

  34. 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 &oldDotL, vector &newDotL)

  35. {

  36. int i, j, k;

  37. PointD swq, qstar, newP, tmpP;

  38. double sw;

  39.  
  40. double ratio;

  41.  
  42. if (preScale) {

  43. ratio = sqrt(calcArea(newDotL) / calcArea(oldDotL));

  44. for (i = 0; i < nPoint; i++) {

  45. newDotL[i].x *= 1 / ratio;

  46. newDotL[i].y *= 1 / ratio;

  47. }

  48. }

  49. double *w = new double[nPoint];

  50.  
  51. if (nPoint < 2) {

  52. //rDx.setTo(0);

  53. //rDy.setTo(0);

  54. return;

  55. }

  56. PointD swp, pstar, curV, curVJ, Pi, PiJ, Qi;

  57. double miu_r;

  58.  
  59. for (i = 0;; i += gridSize) {

  60. if (i >= tarW && i < tarW + gridSize - 1)

  61. i = tarW - 1;

  62. else if (i >= tarW)

  63. break;

  64. for (j = 0;; j += gridSize) {

  65. if (j >= tarH && j < tarH + gridSize - 1)

  66. j = tarH - 1;

  67. else if (j >= tarH)

  68. break;

  69. sw = 0;

  70. swp.x = swp.y = 0;

  71. swq.x = swq.y = 0;

  72. newP.x = newP.y = 0;

  73. curV.x = i;

  74. curV.y = j;

  75. for (k = 0; k < nPoint; k++) {

  76. if ((i == oldDotL[k].x) && j == oldDotL[k].y) break;

  77. if (alpha == 1)

  78. w[k] = 1 / ((i - oldDotL[k].x) * (i - oldDotL[k].x) +

  79. (j - oldDotL[k].y) * (j - oldDotL[k].y));

  80. else

  81. w[k] = pow((i - oldDotL[k].x) * (i - oldDotL[k].x) +

  82. (j - oldDotL[k].y) * (j - oldDotL[k].y),

  83. -alpha);

  84. sw = sw + w[k];

  85. swp.x = swp.x + w[k] * oldDotL[k].x;

  86. swp.y = swp.y + w[k] * oldDotL[k].y;

  87. swq.x = swq.x + w[k] * newDotL[k].x;

  88. swq.y = swq.y + w[k] * newDotL[k].y;

  89. }

  90. if (k == nPoint) {

  91. pstar.x = (1 / sw) * swp.x;

  92. pstar.y = (1 / sw) * swp.y;

  93. qstar.x = 1 / sw * swq.x;

  94. qstar.y = 1 / sw * swq.y;

  95. // Calc miu_r

  96. double s1 = 0, s2 = 0;

  97. for (k = 0; k < nPoint; k++) {

  98. if (i == oldDotL[k].x && j == oldDotL[k].y) continue;

  99. Pi.x = oldDotL[k].x - pstar.x;

  100. Pi.y = oldDotL[k].y - pstar.y;

  101. PiJ.x = -Pi.y, PiJ.y = Pi.x;

  102. Qi.x = newDotL[k].x - qstar.x;

  103. Qi.y = newDotL[k].y - qstar.y;

  104. s1 += w[k] * (Qi.x*Pi.x+Qi.y*Pi.y);

  105. s2 += w[k] * (Qi.x*PiJ.x+Qi.y*PiJ.y);

  106. }

  107. miu_r = sqrt(s1 * s1 + s2 * s2);

  108. curV.x -= pstar.x;

  109. curV.y -= pstar.y;

  110.  
  111. curVJ.x = -curV.y, curVJ.y = curV.x;

  112.  
  113. for (k = 0; k < nPoint; k++) {

  114. if (i == oldDotL[k].x && j == oldDotL[k].y) continue;

  115. Pi.x = oldDotL[k].x - pstar.x;

  116. Pi.y = oldDotL[k].y - pstar.y;

  117. PiJ.x = -Pi.y, PiJ.y = Pi.x;

  118. tmpP.x = (Pi.x*curV.x+Pi.y*curV.y)* newDotL[k].x -

  119. (PiJ.x*curV.x+PiJ.y*curV.y)* newDotL[k].y;

  120. tmpP.y = -(Pi.x*curVJ.x+Pi.y*curVJ.y) * newDotL[k].x +

  121. (PiJ.x*curVJ.x+PiJ.y*curVJ.y) * newDotL[k].y;

  122. tmpP.x *= w[k] / miu_r;

  123. tmpP.y *= w[k] / miu_r;

  124. newP.x += tmpP.x;

  125. newP.y += tmpP.y;

  126. }

  127. newP.x += qstar.x;

  128. newP.y += qstar.y;

  129. } else {

  130. newP = newDotL[k];

  131. }

  132.  
  133. if (preScale) {

  134. rDx[j * tarW + i] = newP.x * ratio - i;

  135. rDy[j * tarW + i] = newP.y * ratio - j;

  136. } else {

  137. rDx[j * tarW + i] = newP.x - i;

  138. rDy[j * tarW + i] = newP.y - j;

  139. }

  140. }

  141. }

  142. delete[] w;

  143.  
  144. if (preScale!=0) {

  145. for (i = 0; i < nPoint; i++){

  146. newDotL[i].x *= ratio;

  147. newDotL[i].y *= ratio;

  148. }

  149. }

  150. }

  151. 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 &oldDotL, vector &newDotL)

  152. {

  153. int i, j, k;

  154.  
  155. PointD swq, qstar, newP, tmpP;

  156. double sw;

  157.  
  158. double ratio;

  159.  
  160. if (preScale) {

  161. ratio = sqrt(calcArea(newDotL) / calcArea(oldDotL));

  162. for (i = 0; i < nPoint; i++) {

  163. newDotL[i].x *= 1 / ratio;

  164. newDotL[i].y *= 1 / ratio;

  165. }

  166. }

  167. double *w = new double[nPoint];

  168.  
  169. if (nPoint < 2) {

  170. return;

  171. }

  172.  
  173. PointD swp, pstar, curV, curVJ, Pi, PiJ;

  174. double miu_s;

  175.  
  176. for (i = 0;; i += gridSize) {

  177. if (i >= tarW && i < tarW + gridSize - 1)

  178. i = tarW - 1;

  179. else if (i >= tarW)

  180. break;

  181. for (j = 0;; j += gridSize) {

  182. if (j >= tarH && j < tarH + gridSize - 1)

  183. j = tarH - 1;

  184. else if (j >= tarH)

  185. break;

  186. sw = 0;

  187. swp.x = swp.y = 0;

  188. swq.x = swq.y = 0;

  189. newP.x = newP.y = 0;

  190. curV.x = i;

  191. curV.y = j;

  192. for (k = 0; k < nPoint; k++) {

  193. if ((i == oldDotL[k].x) && j == oldDotL[k].y) break;

  194. w[k] = 1 / ((i - oldDotL[k].x) * (i - oldDotL[k].x) +

  195. (j - oldDotL[k].y) * (j - oldDotL[k].y));

  196. sw = sw + w[k];

  197. swp.x = swp.x + w[k] * oldDotL[k].x;

  198. swp.y = swp.y + w[k] * oldDotL[k].y;

  199. swq.x = swq.x + w[k] * newDotL[k].x;

  200. swq.y = swq.y + w[k] * newDotL[k].y;

  201. }

  202. if (k == nPoint) {

  203. pstar.x = (1 / sw) * swp.x;

  204. pstar.y = (1 / sw) * swp.y;

  205. qstar.x = 1 / sw * swq.x;

  206. qstar.y = 1 / sw * swq.y;

  207. // Calc miu_s

  208. miu_s = 0;

  209. for (k = 0; k < nPoint; k++) {

  210. if (i == oldDotL[k].x && j == oldDotL[k].y) continue;

  211.  
  212. Pi.x = oldDotL[k].x - pstar.x;

  213. Pi.y = oldDotL[k].y - pstar.y;

  214. miu_s += w[k] * (Pi.x*Pi.x+Pi.y*Pi.y);

  215. }

  216.  
  217. curV.x -= pstar.x;

  218. curV.y -= pstar.y;

  219. curVJ.x = -curV.y, curVJ.y = curV.x;

  220.  
  221. for (k = 0; k < nPoint; k++) {

  222. if (i == oldDotL[k].x && j == oldDotL[k].y) continue;

  223.  
  224. Pi.x = oldDotL[k].x - pstar.x;

  225. Pi.y = oldDotL[k].y - pstar.y;

  226. PiJ.x = -Pi.y, PiJ.y = Pi.x;

  227.  
  228. tmpP.x = (Pi.x*curV.x+Pi.y*curV.y) * newDotL[k].x -

  229. (PiJ.x*curV.x+PiJ.y*curV.y) * newDotL[k].y;

  230. tmpP.y = -(Pi.x*curVJ.x+Pi.y*curVJ.y) * newDotL[k].x +

  231. (PiJ.x*curVJ.x+PiJ.y*curVJ.y) * newDotL[k].y;

  232. tmpP.x *= w[k] / miu_s;

  233. tmpP.y *= w[k] / miu_s;

  234. newP.x += tmpP.x;

  235. newP.y += tmpP.y;

  236. }

  237. newP.x += qstar.x;

  238. newP.y += qstar.y;

  239. } else {

  240. newP = newDotL[k];

  241. }

  242.  
  243. rDx[j * tarW + i] = newP.x - i;

  244. rDy[j * tarW + i] = newP.y - j;

  245. }

  246. }

  247.  
  248. delete[] w;

  249. if (preScale!=0) {

  250. for (i = 0; i < nPoint; i++){

  251. newDotL[i].x *= ratio;

  252. newDotL[i].y *= ratio;

  253. }

  254. }

  255. }

  256. 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)

  257. {

  258. int i, j;

  259. double di, dj;

  260. double nx, ny;

  261. int nxi, nyi, nxi1, nyi1;

  262. double deltaX, deltaY;

  263. double w, h;

  264. int ni, nj;

  265. int pos, posa, posb, posc, posd;

  266. for (i = 0; i < tarH; i += gridSize)

  267. for (j = 0; j < tarW; j += gridSize) {

  268. ni = i + gridSize, nj = j + gridSize;

  269. w = h = gridSize;

  270. if (ni >= tarH) ni = tarH - 1, h = ni - i + 1;

  271. if (nj >= tarW) nj = tarW - 1, w = nj - j + 1;

  272. for (di = 0; di < h; di++)

  273. for (dj = 0; dj < w; dj++) {

  274. deltaX =

  275. bilinear_interp(di / h, dj / w, rDx[i * tarW + j], rDx[i * tarW + nj],

  276. rDx[ni * tarW + j], rDx[ni * tarW + nj]);

  277. deltaY =

  278. bilinear_interp(di / h, dj / w, rDy[i * tarW + j], rDy[i * tarW + nj],

  279. rDy[ni * tarW + j], rDy[ni * tarW + nj]);

  280. nx = j + dj + deltaX * transRatio;

  281. ny = i + di + deltaY * transRatio;

  282. if (nx > width - 1) nx = width - 1;

  283. if (ny > height - 1) ny = height - 1;

  284. if (nx < 0) nx = 0;

  285. if (ny < 0) ny = 0;

  286. nxi = int(nx);

  287. nyi = int(ny);

  288. nxi1 = ceil(nx);

  289. nyi1 = ceil(ny);

  290. pos = (int)(i + di) * tarStride + ((int)(j + dj) << 2);

  291. posa = nyi * stride + (nxi << 2);

  292. posb = nyi * stride + (nxi1 << 2);

  293. posc = nyi1 * stride + (nxi << 2);

  294. posd = nyi1 * stride + (nxi1 << 2);

  295. tarImg[pos] = (unsigned char)bilinear_interp(ny - nyi, nx - nxi, oriImg[posa], oriImg[posb], oriImg[posc], oriImg[posd]);

  296. tarImg[pos + 1] = (unsigned char)bilinear_interp(ny - nyi, nx - nxi, oriImg[posa + 1],oriImg[posb + 1], oriImg[posc + 1], oriImg[posd + 1]);

  297. tarImg[pos + 2] = (unsigned char)bilinear_interp(ny - nyi, nx - nxi, oriImg[posa + 2],oriImg[posb + 2], oriImg[posc + 2], oriImg[posd + 2]);

  298. tarImg[pos + 3] = (unsigned char)bilinear_interp(ny - nyi, nx - nxi, oriImg[posa + 3],oriImg[posb + 3], oriImg[posc + 3], oriImg[posd + 3]);

  299. }

  300. }

  301. return 0;

  302. };

  303.  
  304. static void MLSImageWrapping(unsigned char* oriImg,int width, int height, int stride,const vector &qsrc, const vector &qdst, unsigned char* tarImg, int outW, int outH, int outStride, double transRatio, int preScale, int gridSize, int method)

  305. {

  306. int srcW = width;

  307. int srcH = height;

  308. int tarW = outW;

  309. int tarH = outH;

  310. double alpha = 1;

  311. int nPoint;

  312. int len = tarH * tarW;

  313. vector oldDotL, newDotL;

  314. double *rDx = NULL,*rDy = NULL;

  315. setSrcPoints(qsrc,newDotL,&nPoint);

  316. setDstPoints(qdst,oldDotL,&nPoint);

  317. rDx = (double*)malloc(sizeof(double) * len);

  318. rDy = (double*)malloc(sizeof(double) * len);

  319. memset(rDx, 0, sizeof(double) * len);

  320. memset(rDy, 0, sizeof(double) * len);

  321. if(method!=0)

  322. calcDelta_Similarity(srcW, srcH, tarW, tarH, alpha, gridSize, nPoint, preScale, rDx, rDy, oldDotL, newDotL);

  323. else

  324. calcDelta_rigid(srcW, srcH, tarW, tarH, alpha, gridSize, nPoint, preScale, rDx, rDy, oldDotL, newDotL);

  325. GetNewImg(oriImg, srcW, srcH, stride, tarImg, tarW, tarH, outStride, gridSize, rDx, rDy, transRatio);

  326. if(rDx != NULL)

  327. free(rDx);

  328. if(rDy != NULL)

  329. free(rDy);

  330. };

  331. 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)

  332. {

  333. int res = 0;

  334. vector qDst;

  335. vector qSrc;

  336. PointD point = {0};

  337. int len = 0;

  338. for(int i = 0; i < pointNum; i++)

  339. {

  340. len = (i << 1);

  341. point.x = srcPoint[len];

  342. point.y = srcPoint[len + 1];

  343. qSrc.push_back(point);

  344. point.x = dragPoint[len];

  345. point.y = dragPoint[len + 1];

  346. qDst.push_back(point);

  347. }

  348. MLSImageWrapping(srcData, width, height, stride, qSrc, qDst, dstData, outW, outH, outStride, intensity, preScale,gridSize, method);

  349. return res;

  350. };

 

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

你可能感兴趣的:(video)