|
|
@ -497,10 +497,68 @@ void features_VOC(int part, int total) |
|
|
|
} |
|
|
|
} |
|
|
|
} |
|
|
|
} |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
void features_VOC_image(char *image_file, char *image_dir, char *out_dir) |
|
|
|
|
|
|
|
{ |
|
|
|
|
|
|
|
int i,j; |
|
|
|
|
|
|
|
network net = parse_network_cfg("cfg/voc_features.cfg"); |
|
|
|
|
|
|
|
char image_path[1024]; |
|
|
|
|
|
|
|
sprintf(image_path, "%s%s",image_dir, image_file); |
|
|
|
|
|
|
|
char out_path[1024]; |
|
|
|
|
|
|
|
sprintf(out_path, "%s%s.txt",out_dir, image_file); |
|
|
|
|
|
|
|
printf("%s\n", image_file); |
|
|
|
|
|
|
|
FILE *fp = fopen(out_path, "w"); |
|
|
|
|
|
|
|
if(fp == 0) file_error(out_path); |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
IplImage* src = 0; |
|
|
|
|
|
|
|
if( (src = cvLoadImage(image_path,-1)) == 0 ) file_error(image_path); |
|
|
|
|
|
|
|
int w = src->width; |
|
|
|
|
|
|
|
int h = src->height; |
|
|
|
|
|
|
|
int sbin = 8; |
|
|
|
|
|
|
|
int interval = 10; |
|
|
|
|
|
|
|
double scale = pow(2., 1./interval); |
|
|
|
|
|
|
|
int m = (w<h)?w:h; |
|
|
|
|
|
|
|
int max_scale = 1+floor((double)log((double)m/(5.*sbin))/log(scale)); |
|
|
|
|
|
|
|
image *ims = calloc(max_scale+interval, sizeof(image)); |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
for(i = 0; i < interval; ++i){ |
|
|
|
|
|
|
|
double factor = 1./pow(scale, i); |
|
|
|
|
|
|
|
double ih = round(h*factor); |
|
|
|
|
|
|
|
double iw = round(w*factor); |
|
|
|
|
|
|
|
int ex_h = round(ih/4.) - 2; |
|
|
|
|
|
|
|
int ex_w = round(iw/4.) - 2; |
|
|
|
|
|
|
|
ims[i] = features_output_size(net, src, ex_h, ex_w); |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
ih = round(h*factor); |
|
|
|
|
|
|
|
iw = round(w*factor); |
|
|
|
|
|
|
|
ex_h = round(ih/8.) - 2; |
|
|
|
|
|
|
|
ex_w = round(iw/8.) - 2; |
|
|
|
|
|
|
|
ims[i+interval] = features_output_size(net, src, ex_h, ex_w); |
|
|
|
|
|
|
|
for(j = i+interval; j < max_scale; j += interval){ |
|
|
|
|
|
|
|
factor /= 2.; |
|
|
|
|
|
|
|
ih = round(h*factor); |
|
|
|
|
|
|
|
iw = round(w*factor); |
|
|
|
|
|
|
|
ex_h = round(ih/8.) - 2; |
|
|
|
|
|
|
|
ex_w = round(iw/8.) - 2; |
|
|
|
|
|
|
|
ims[j+interval] = features_output_size(net, src, ex_h, ex_w); |
|
|
|
|
|
|
|
} |
|
|
|
|
|
|
|
} |
|
|
|
|
|
|
|
for(i = 0; i < max_scale+interval; ++i){ |
|
|
|
|
|
|
|
image out = ims[i]; |
|
|
|
|
|
|
|
fprintf(fp, "%d, %d, %d\n",out.c, out.h, out.w); |
|
|
|
|
|
|
|
for(j = 0; j < out.c*out.h*out.w; ++j){ |
|
|
|
|
|
|
|
if(j != 0)fprintf(fp, ","); |
|
|
|
|
|
|
|
fprintf(fp, "%g", out.data[j]); |
|
|
|
|
|
|
|
} |
|
|
|
|
|
|
|
fprintf(fp, "\n"); |
|
|
|
|
|
|
|
free_image(out); |
|
|
|
|
|
|
|
} |
|
|
|
|
|
|
|
free(ims); |
|
|
|
|
|
|
|
fclose(fp); |
|
|
|
|
|
|
|
cvReleaseImage(&src); |
|
|
|
|
|
|
|
} |
|
|
|
|
|
|
|
|
|
|
|
int main(int argc, char *argv[]) |
|
|
|
int main(int argc, char *argv[]) |
|
|
|
{ |
|
|
|
{ |
|
|
|
int part = atoi(argv[1]); |
|
|
|
|
|
|
|
int total = atoi(argv[2]); |
|
|
|
|
|
|
|
//feenableexcept(FE_DIVBYZERO | FE_INVALID | FE_OVERFLOW);
|
|
|
|
//feenableexcept(FE_DIVBYZERO | FE_INVALID | FE_OVERFLOW);
|
|
|
|
|
|
|
|
|
|
|
|
//test_blas();
|
|
|
|
//test_blas();
|
|
|
@ -511,7 +569,8 @@ int main(int argc, char *argv[]) |
|
|
|
//test_nist();
|
|
|
|
//test_nist();
|
|
|
|
//test_full();
|
|
|
|
//test_full();
|
|
|
|
//train_VOC();
|
|
|
|
//train_VOC();
|
|
|
|
features_VOC(part, total); |
|
|
|
features_VOC_image(argv[1], argv[2], argv[3]); |
|
|
|
|
|
|
|
printf("Success!\n"); |
|
|
|
//test_random_preprocess();
|
|
|
|
//test_random_preprocess();
|
|
|
|
//test_random_classify();
|
|
|
|
//test_random_classify();
|
|
|
|
//test_parser();
|
|
|
|
//test_parser();
|
|
|
|