基于Ohta颜色空间的火焰颜色识别

float HistogramRange1[2] = {0,255};
float *HistogramRange[1] = {&HistogramRange1[0]};
typedef enum {back,object} entropy_state;
// 计算当前位置的能量熵
double caculateCurrentEntropy(CvHistogram * Histogram1, int cur_threshold, entropy_state state)
CvPoint *Point;
for (int i = 0;i<contour->total;i++)
{
Point = (CvPoint*)cvGetSeqElem(contour,i);
point[i].x =Point->x;
point[i].y = Point->y;
IplImage *OtsuImg1 = cvCreateImage( cvGetSize(img), IPL_DEP = clock();
OtsuImg = process_rgb(img);
OtsuImg1=MaxEntropy(OtsuImg,OtsuImg1);
// 循环测试每个分割点，寻找到最大的阈值分割点
for(int i = 0; i < HistogramBins; i++)
{
double cur_entropy = caculateCurrentEntropy(hist, i, object) + caculateCurrentEntropy(hist, i, back);
IplImage * tempdst1=cvCreateImage(cvGetSize(img),IPL_DEPTH_8U,1);
clock_t start, finish;
double duration;
cvNamedWindow("img",1);
cvNamedWindow("OstuImg",1);
cvShowImage("OstuImg",OtsuImg);
cvShowImage("OstuImg1",OtsuImg1);
cvShowImage("tian",tempdst1);
cvShowImage("dst",dst);
// cvSaveImage("deng2src.bmp",img);
#include "cv.h"
#include "highgui.h"
#include <stdio.h>
#include <iostream.h>
#include <stdlib.h>
#include <time.h>
//OSTU阈值分割算法
int HistogramBins = 256;
//颜色分割算法
IplImage* process_rgb(IplImage*img ){
cvSplit( img, b, g, r, NULL );
cvAddWeighted( b, 1.0/3.0, g, 1.0/3.0, 0.0, g);
cvAddWeighted( r, 1.0/3.0, g, 1.0, 0.0, g);
cvZero(tempdst1);
// cvZero(temp_iamge);
for( ; contour != 0; contour = contour->h_next)
{ //轮廓的方向影响面积的符号。因此函数也许会返回负的结果。应用函数 fabs() 得到面积的绝对值。
double area = cvContourArea( contour,CV_WHOLE_SEQ );
cvReleaseImage( &g );
cvReleaseImage( &b );
cvReleaseImage(&img);
cvReleaseImage(&dst);
cvReleaseImage( &temp1);
cvReleaseImage(&temp2);
cvReleaseImage(&OtsuImg);
IplImage *temp1 = cvCreateImage( cvGetSize(img), IPL_DEPTH_8U, 1 );
IplImage *temp2 = cvCreateImage( cvGetSize(img), IPL_DEPTH_8U, 1 );
IplImage *r = cvCreateImage( cvGetSize(img), IPL_DEPTH_8U, 1 );
CvHistogram * hist = cvCreateHist(1, &HistogramBins, CV_HIST_ARRAY, HistogramRange);
cvCalcHist(&src, hist);
double maxentropy = -1.0;
int max_index = -1;
}
int pts[1] = {contour->total};
cvFillPoly(tempdst1,&point,pts,1,CV_RGB(255,255,255));//填充多边形内部
}
cvCopy(img,dst,tempdst1);
cvShowImage("img",img);
// cvInRangeS( r, cvScalar( 18.0, 0.0, 0.0 ), cvScalar( 120.0, 0.0, 0.0 ), temp2);
// cvAnd(temp1, temp2, dst1);
// cvCopy(img, dst, dst1 );
cvAnd(r,temp1,r);
cvNamedWindow("OstuImg1",1);
cvNamedWindow("tian",1);
cvNamedWindow("dst",1);
// cvNamedWindow("gray",1);
IplImage *OtsuImg = cvCreateImage( cvGetSize(img), IPL_DEPTH_8U, 1 );
continue;
double percentage = cvQueryHistValue_1D(Histogram1, i) / total;
cur_entropy += - percentage * logf(percentage); // 能量的定义公式
}
return cur_entropy;
{
total += (int)cvQueryHistValue_1D(Histogram1, i);
}
double cur_entropy = 0.0;
for(i = start; i < end; i++)
{
if((int)cvQueryHistValue_1D(Histogram1,i) == 0)
finish = clock();
duration = (double)(finish - start) / CLOCKS_PER_SEC;
printf( "%f seconds\n", duration );
cvFindContours(OtsuImg1,storage,&contour,sizeof(CvContour),CV_RETR_CCOMP ,CV_CHAIN_APPROX_SIMPLE);
cvReleaseHist(&hist);
return dst;
}
/**********************************************************************************/
/**********************************************************************************/
cvInRangeS( g, cvScalar(100.0, 0.0, 0.0 ),cvScalar( 255.0, 0.0, 0.0 ), temp1);
cvSub(r, b, g);
cvAddWeighted( g, 1.0/2.0, r, 0.0, 0.0, r); // r=(r-b)/2
cvReleaseImage(&OtsuImg1);
cvReleaseImage(&tempdst1);
cvReleaseMemStorage(&storage);
return 0;
}
// 寻找最大熵阈值并分割
IplImage* MaxEntropy(IplImage *src, IplImage *dst)
{
assert(src != NULL);
assert(src->depth == 8 && dst->depth == 8);
assert(src->nChannels == 1);