如何在window下运行Discriminatively Trained Deformable Part Models代码 (转)

http://blog.csdn.net/dreamd1987/article/details/7396620

Discriminatively Trained Deformable Part Models的官网：http://www.cs.brown.edu/~pff/latent/

目前做目标检测最好的一个算法。

移植到windows下。

参考了pozen同学的博客：http://blog.csdn.net/pozen/article/details/7023742

启发很大，下面说说自己的调试过程：

1、把用到的文件dt.cc resize.cc fconv.cc features.cc的后缀都修改为cpp

改不改都可以

2、dt.cpp中加：#define int32_t int

3、features.cpp、resize.cpp、fconv.cpp中加入

#define bzero(a, b) memset(a, 0, b)
int round(float a) { float tmp = a - (int)a; if( tmp >= 0.5 ) return (int)a + 1; else return (int)a; }

4、resize.cpp中的alphainfo ofs[len] 换成：alphainfo *ofs = new alphainfo[len] 当然要记得释放这个内存，但要注意放得位置。

之后就可以在matlab中运行compile进行编译了，这时会出现一些重定义的错误，应该是vc6.0对c++的一些不兼容，修改下就可以了。

5、输入demo()。查看结果。

贴出resize.cpp源代码（调试中因为内存错误折腾了很久）

#include <math.h>
#include <assert.h>
#include <string.h>
#include "mex.h"

#define bzero(a, b) memset(a, 0, b)
int round(float a) { float tmp = a - (int)a; if( tmp >= 0.5 ) return (int)a + 1; else return (int)a; }

// struct used for caching interpolation values
struct alphainfo {
int si, di;
double alpha;
};

// copy src into dst using interpolation values
void alphacopy(double *src, double *dst, struct alphainfo *ofs, int n) {
struct alphainfo *end = ofs + n;
while (ofs != end) {
dst[ofs->di] += ofs->alpha * src[ofs->si];
ofs++;
}
}

// resize along each column
// result is transposed, so we can apply it twice for a complete resize
void resize1dtran(double *src, int sheight, double *dst, int dheight,
int width, int chan) {
double scale = (double)dheight/(double)sheight;
double invscale = (double)sheight/(double)dheight;

// we cache the interpolation values since they can be
// shared among different columns
int len = (int)ceil(dheight*invscale) + 2*dheight;
//alphainfo ofs[len];
alphainfo *ofs = new alphainfo[len];
int k = 0;
for (int dy = 0; dy < dheight; dy++) {
double fsy1 = dy * invscale;
double fsy2 = fsy1 + invscale;
int sy1 = (int)ceil(fsy1);
int sy2 = (int)floor(fsy2);

if (sy1 - fsy1 > 1e-3) {
assert(k < len);
assert(sy1-1 >= 0);
ofs[k].di = dy*width;
ofs[k].si = sy1-1;
ofs[k++].alpha = (sy1 - fsy1) * scale;
}

for (int sy = sy1; sy < sy2; sy++) {
assert(k < len);
assert(sy < sheight);
ofs[k].di = dy*width;
ofs[k].si = sy;
ofs[k++].alpha = scale;
}

if (fsy2 - sy2 > 1e-3) {
assert(k < len);
assert(sy2 < sheight);
ofs[k].di = dy*width;
ofs[k].si = sy2;
ofs[k++].alpha = (fsy2 - sy2) * scale;
}
}

// delete [] ofs;
// resize each column of each color channel
bzero(dst, chan*width*dheight*sizeof(double));
for (int c = 0; c < chan; c++) {
for (int x = 0; x < width; x++) {
double *s = src + c*width*sheight + x*sheight;
double *d = dst + c*width*dheight + x;
alphacopy(s, d, ofs, k);
}
}
delete [] ofs;
}

// main function
// takes a double color image and a scaling factor
// returns resized image
mxArray *resize(const mxArray *mxsrc, const mxArray *mxscale) {
double *src = (double *)mxGetPr(mxsrc);
const int *sdims = (int*)mxGetDimensions(mxsrc);
if (mxGetNumberOfDimensions(mxsrc) != 3 ||
mxGetClassID(mxsrc) != mxDOUBLE_CLASS)
mexErrMsgTxt("Invalid input");

double scale = mxGetScalar(mxscale);
if (scale > 1)
mexErrMsgTxt("Invalid scaling factor");

int ddims[3];
ddims[0] = (int)round(sdims[0]*scale);
ddims[1] = (int)round(sdims[1]*scale);
ddims[2] = sdims[2];
mxArray *mxdst = mxCreateNumericArray(3, (mwSize*)ddims, mxDOUBLE_CLASS, mxREAL);
double *dst = (double *)mxGetPr(mxdst);

double *tmp = (double *)mxCalloc(ddims[0]*sdims[1]*sdims[2], sizeof(double));
resize1dtran(src, sdims[0], tmp, ddims[0], sdims[1], sdims[2]);
resize1dtran(tmp, sdims[1], dst, ddims[1], ddims[0], sdims[2]);
mxFree(tmp);

return mxdst;
}

// matlab entry point
// dst = resize(src, scale)
// image should be color with double values
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) {
if (nrhs != 2)
mexErrMsgTxt("Wrong number of inputs");
if (nlhs != 1)
mexErrMsgTxt("Wrong number of outputs");
plhs[0] = resize(prhs[0], prhs[1]);
}