mirror of
https://github.com/opencv/opencv.git
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242 lines
5.8 KiB
C++
242 lines
5.8 KiB
C++
#include "precomp.hpp"
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#include "_lsvm_resizeimg.h"
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#include <stdio.h>
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#include <assert.h>
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#include <math.h>
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IplImage* resize_opencv(IplImage* img, float scale)
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{
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IplImage* imgTmp;
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int W, H, tW, tH;
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W = img->width;
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H = img->height;
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tW = (int)(((float)W) * scale + 0.5);
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tH = (int)(((float)H) * scale + 0.5);
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imgTmp = cvCreateImage(cvSize(tW , tH), img->depth, img->nChannels);
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cvResize(img, imgTmp, CV_INTER_AREA);
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return imgTmp;
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}
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//
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///*
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// * Fast image subsampling.
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// * This is used to construct the feature pyramid.
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// */
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//
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//// struct used for caching interpolation values
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//typedef struct {
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// int si, di;
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// float alpha;
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//}alphainfo;
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//
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//// copy src into dst using pre-computed interpolation values
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//void alphacopy(float *src, float *dst, alphainfo *ofs, int n) {
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// int i;
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// for(i = 0; i < n; i++){
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// dst[ofs[i].di] += ofs[i].alpha * src[ofs[i].si];
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// }
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//}
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//
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//int round(float val){
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// return (int)(val + 0.5);
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//}
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//void bzero(float * arr, int cnt){
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// int i;
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// for(i = 0; i < cnt; i++){
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// arr[i] = 0.0f;
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// }
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//}
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//// resize along each column
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//// result is transposed, so we can apply it twice for a complete resize
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//void resize1dtran(float *src, int sheight, float *dst, int dheight,
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// int width, int chan) {
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// alphainfo *ofs;
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// float scale = (float)dheight/(float)sheight;
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// float invscale = (float)sheight/(float)dheight;
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//
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// // we cache the interpolation values since they can be
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// // shared among different columns
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// int len = (int)ceilf(dheight*invscale) + 2*dheight;
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// int k = 0;
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// int dy;
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// float fsy1;
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// float fsy2;
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// int sy1;
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// int sy2;
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// int sy;
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// int c, x;
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// float *s, *d;
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//
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// ofs = (alphainfo *) malloc (sizeof(alphainfo) * len);
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// for (dy = 0; dy < dheight; dy++) {
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// fsy1 = dy * invscale;
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// fsy2 = fsy1 + invscale;
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// sy1 = (int)ceilf(fsy1);
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// sy2 = (int)floorf(fsy2);
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//
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// if (sy1 - fsy1 > 1e-3) {
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// assert(k < len);
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// assert(sy1 - 1 >= 0);
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// ofs[k].di = dy*width;
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// ofs[k].si = sy1-1;
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// ofs[k++].alpha = (sy1 - fsy1) * scale;
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// }
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//
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// for (sy = sy1; sy < sy2; sy++) {
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// assert(k < len);
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// assert(sy < sheight);
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// ofs[k].di = dy*width;
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// ofs[k].si = sy;
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// ofs[k++].alpha = scale;
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// }
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//
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// if (fsy2 - sy2 > 1e-3) {
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// assert(k < len);
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// assert(sy2 < sheight);
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// ofs[k].di = dy*width;
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// ofs[k].si = sy2;
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// ofs[k++].alpha = (fsy2 - sy2) * scale;
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// }
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// }
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//
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// // resize each column of each color channel
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// bzero(dst, chan*width*dheight);
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// for (c = 0; c < chan; c++) {
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// for (x = 0; x < width; x++) {
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// s = src + c*width*sheight + x*sheight;
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// d = dst + c*width*dheight + x;
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// alphacopy(s, d, ofs, k);
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// }
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// }
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// free(ofs);
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//}
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//
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//IplImage * resize_article_dp(IplImage * img, float scale, const int k){
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// IplImage * imgTmp;
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// float W, H;
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// unsigned char *dataSrc;
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// float * dataf;
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// float *src, *dst, *tmp;
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// int i, j, kk, channels;
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// int index;
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// int widthStep;
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// int tW, tH;
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//
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// W = (float)img->width;
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// H = (float)img->height;
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// channels = img->nChannels;
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// widthStep = img->widthStep;
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//
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// tW = (int)(((float)W) * scale + 0.5f);
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// tH = (int)(((float)H) * scale + 0.5f);
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//
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// src = (float *)malloc(sizeof(float) * (int)(W * H * 3));
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//
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// dataSrc = (unsigned char*)(img->imageData);
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// index = 0;
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// for (kk = 0; kk < channels; kk++)
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// {
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// for (i = 0; i < W; i++)
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// {
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// for (j = 0; j < H; j++)
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// {
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// src[index++] = (float)dataSrc[j * widthStep + i * channels + kk];
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// }
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// }
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// }
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//
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// imgTmp = cvCreateImage(cvSize(tW , tH), IPL_DEPTH_32F, channels);
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//
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// dst = (float *)malloc(sizeof(float) * (int)(tH * tW) * channels);
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// tmp = (float *)malloc(sizeof(float) * (int)(tH * W) * channels);
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//
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// resize1dtran(src, (int)H, tmp, (int)tH, (int)W , 3);
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//
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// resize1dtran(tmp, (int)W, dst, (int)tW, (int)tH, 3);
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//
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// index = 0;
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// //dataf = (float*)imgTmp->imageData;
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// for (kk = 0; kk < channels; kk++)
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// {
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// for (i = 0; i < tW; i++)
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// {
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// for (j = 0; j < tH; j++)
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// {
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// dataf = (float*)(imgTmp->imageData + j * imgTmp->widthStep);
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// dataf[ i * channels + kk] = dst[index++];
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// }
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// }
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// }
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//
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// free(src);
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// free(dst);
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// free(tmp);
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// return imgTmp;
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//}
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//
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//IplImage * resize_article_dp1(IplImage * img, float scale, const int k){
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// IplImage * imgTmp;
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// float W, H;
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// float * dataf;
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// float *src, *dst, *tmp;
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// int i, j, kk, channels;
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// int index;
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// int widthStep;
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// int tW, tH;
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//
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// W = (float)img->width;
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// H = (float)img->height;
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// channels = img->nChannels;
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// widthStep = img->widthStep;
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//
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// tW = (int)(((float)W) * scale + 0.5f);
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// tH = (int)(((float)H) * scale + 0.5f);
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//
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// src = (float *)malloc(sizeof(float) * (int)(W * H) * 3);
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//
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// index = 0;
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// for (kk = 0; kk < channels; kk++)
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// {
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// for (i = 0; i < W; i++)
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// {
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// for (j = 0; j < H; j++)
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// {
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// src[index++] = (float)(*( (float *)(img->imageData + j * widthStep) + i * channels + kk));
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// }
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// }
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// }
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//
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// imgTmp = cvCreateImage(cvSize(tW , tH), IPL_DEPTH_32F, channels);
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//
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// dst = (float *)malloc(sizeof(float) * (int)(tH * tW) * channels);
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// tmp = (float *)malloc(sizeof(float) * (int)(tH * W) * channels);
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//
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// resize1dtran(src, (int)H, tmp, (int)tH, (int)W , 3);
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//
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// resize1dtran(tmp, (int)W, dst, (int)tW, (int)tH, 3);
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//
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// index = 0;
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// for (kk = 0; kk < channels; kk++)
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// {
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// for (i = 0; i < tW; i++)
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// {
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// for (j = 0; j < tH; j++)
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// {
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// dataf = (float *)(imgTmp->imageData + j * imgTmp->widthStep);
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// dataf[ i * channels + kk] = dst[index++];
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// }
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// }
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// }
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//
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// free(src);
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// free(dst);
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// free(tmp);
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// return imgTmp;
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//}
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//
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