圆形矢量场field driven strength效果
2024-04-10 14:13:13
/************中心环形矢量场*马鞍矢量场*****卷积白噪声纹理***********/#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <malloc.h>
#include <cv.h>
#include <ml.h>
#include <highgui.h>
#include <iostream>
using namespace std;//#define SQUARE_FLOW_FIELD_SZ 400
#define DISCRETE_FILTER_SIZE 2048 //离散的滤波尺寸,
#define LOWPASS_FILTR_LENGTH 10.00000f //低通滤波长度，滤波核kernel
#define LINE_SQUARE_CLIP_MAX 100000.0f //线性平方夹
#define VECTOR_COMPONENT_MIN 0.050000f //矢量分量最小值void CenterFiled(int n_xres, int n_yres, float* pVectr);
void NormalizVectrs(int n_xres, int n_yres, float* pVectr,float* vecSize,float*normMag);
void GenBoxFiltrLUT(int LUTsiz, float* p_LUT0, float* p_LUT1);
void MakeWhiteNoise(int n_xres, int n_yres, float* pNoise);
void nNoise(int n_xres, int n_yres, float* pNoise,float*pVectr,float*newNoise,float* normMag);
void FlowImagingLIC(int n_xres, int n_yres, float* pVectr, float* newNoise, float* pImage, float* p_LUT0, float* p_LUT1, float krnlen);
void WriteImage2PPM(int n_xres, int n_yres, float* pImage, char* f_name);
void gray(int n_xres,int n_yres,float * pImage);
void color(int n_xres, int n_yres,float *pImage,float* vecSize);
double maxvecmag;void main()
{
// int n_xres = SQUARE_FLOW_FIELD_SZ;
// int n_yres = SQUARE_FLOW_FIELD_SZ;
// float* pVectr = (float* ) malloc( sizeof(float ) * n_xres * n_yres * 2 );
// float* p_LUT0 = (float* ) malloc( sizeof(float ) * DISCRETE_FILTER_SIZE);
// float* p_LUT1 = (float* ) malloc( sizeof(float ) * DISCRETE_FILTER_SIZE);
// float* pNoise = (float* ) malloc( sizeof(float) * n_xres * n_yres );
// float* pImage = (float* ) malloc( sizeof(float) * n_xres * n_yres );//NormalizVectrs(n_xres, n_yres, pVectr);// int info[2];
// FILE* fp = fopen("fieldfile/Flow_16.vec", "rb"); //打开矢量场数据文件
// fread(info,sizeof(int),2,fp);int n_yres=400;int n_xres =400;float* pVectr = (float* ) malloc( sizeof(float ) *n_xres*n_yres* 2 );float* p_LUT0 = (float* ) malloc( sizeof(float ) * DISCRETE_FILTER_SIZE);//设置滤波器参数float* p_LUT1 = (float* ) malloc( sizeof(float ) * DISCRETE_FILTER_SIZE);float* pNoise = (float* ) malloc( sizeof(float) *n_xres*n_yres );float* pImage = (float* ) malloc( sizeof(float) * n_xres*n_yres );float* vecSize = (float* ) malloc( sizeof(float) * n_xres*n_yres );float* newNoise = (float* ) malloc( sizeof(float) *n_xres*n_yres );float* normMag = (float* ) malloc( sizeof(float) *n_xres*n_yres );// cout<<"row="<<n_xres<<";"<<"col="<<n_yres<<endl;// //float * pVectr = new float[row*col*2];//fread(pVectr,sizeof(float),n_xres*n_yres*2,fp);//读入矢量场数据，是未归一化的矢量场CenterFiled(n_xres, n_yres, pVectr);NormalizVectrs(n_xres, n_yres, pVectr,vecSize,normMag);//利用刘占平的数据文件必须有归一化MakeWhiteNoise(n_xres, n_yres, pNoise);nNoise(n_xres,n_yres,pNoise,pVectr,newNoise,normMag);GenBoxFiltrLUT(DISCRETE_FILTER_SIZE, p_LUT0, p_LUT1);FlowImagingLIC(n_xres, n_yres, pVectr, newNoise, pImage, p_LUT0, p_LUT1, LOWPASS_FILTR_LENGTH);gray(n_xres,n_yres,pImage);color(n_xres, n_yres,pImage,vecSize);//WriteImage2PPM(n_xres, n_yres, pImage, "LIC.ppm");//system("pause");
// fclose(fp);
// fp=NULL;free(pVectr); pVectr = NULL;free(p_LUT0); p_LUT0 = NULL;free(p_LUT1); p_LUT1 = NULL;free(pNoise); pNoise = NULL;free(pImage); pImage = NULL;}/// 中心环形矢量场图形 synthesize a saddle-shaped vector field ///
void CenterFiled(int n_xres, int n_yres, float* pVectr)
{ float vec_x = 0.0f;float vec_y = 0.0f;float vcMag = 0.0f;float scale = 0.0f;for(int i = 0; i < n_xres; i ++){for(int j = 0; j < n_yres; j ++){ int index = i*n_yres+j;vec_x = -(float)i/n_xres+0.5f;vec_y = (float)j/n_yres-0.5f;// vec_x = -i/n_xres+0.5f;//如果不进行float形式转化，生成的矢量场不正确// vec_y = j/n_yres-0.5f;// vcMag = sqrt(vec_x*vec_x+vec_y*vec_y);
// scale = (vcMag<0.001f)?0.0f:1.0f/vcMag;
// vec_x*=scale;
// vec_y*=scale;pVectr[2*index]=vec_x;pVectr[2*index+1]=vec_y;// int index = ( (n_yres - 1 - j) * n_xres + i ) << 1;//向左移动一位// pVectr[index ] = - ( j / (n_yres - 1.0f) - 0.5f );// cout<<"pVectr[index ]="<<pVectr[index ];// pVectr[index + 1] = i / (n_xres - 1.0f) - 0.5f; // cout<<"pVectr[index ]="<<pVectr[index +1 ];}}
}/// normalize the vector field ///
void NormalizVectrs(int n_xres, int n_yres, float* pVectr,float* vecSize,float*normMag)
{
// float* vecSize = (float* ) malloc( sizeof(float) * n_xres*n_yres );double magind;double scale;double mag;double x=10;;矢量归一化**与**矢量大小归一化不一样//矢量归一化如下：for(int j = 0; j < n_yres; j ++)for(int i = 0; i < n_xres; i ++)//图像时竖着绘制的，一列一列的画的{ int index = (j * n_xres + i) << 1;//0,2,4,6,8...向左移一位，index值加2，因为有vec_x,vec_y代表一个像素点的矢量值float vcMag = float( sqrt( double(pVectr[index] * pVectr[index] + pVectr[index + 1] * pVectr[index + 1]) ) );vecSize[j * n_xres + i]=vcMag;//存储矢量原始大小//cout<<"vcmag="<<vcMag<<endl;if (vcMag>maxvecmag){maxvecmag=vcMag;}//cout<<vcMag<<endl;float scale = (vcMag == 0.0f) ? 0.0f : 1.0f / vcMag;//矢量大小归一化后的矢量值//cout<<"scale"<<scale<<endl;//normMag[j*n_yres+i] = scale;pVectr[index ]=pVectr[index ]*scale;pVectr[index + 1] *= scale;}for(int j = 0; j < n_yres; j ++)for(int i = 0; i < n_xres; i ++)//图像时竖着绘制的，一列一列的画的{ //归一化矢量大小normMag[j * n_xres + i] = (float)vecSize[j * n_xres + i]/maxvecmag;//cout<< normMag[j * n_xres + i]<<endl;}
}/// make white noise as the LIC input texture ///
void MakeWhiteNoise(int n_xres, int n_yres, float* pNoise)
{ IplImage * NoiseImg=cvCreateImage(cvSize(n_yres,n_xres),IPL_DEPTH_8U,1);CvScalar s;for(int j = 0; j < n_yres; j ++){for(int i = 0; i < n_xres; i ++)// for(int j = 0; j < n_yres; j=j +10)//产生稀疏白噪声// {// for(int i = 0; i < n_xres; i=i+10){ int r = rand();r = ( (r & 0xff) + ( (r & 0xff00) >> 8 ) ) & 0xff;pNoise[j * n_xres + i] = (float) r;s = cvGet2D(NoiseImg,i,j);s.val[0]=r;s.val[1]=r;s.val[2]=r;cvSet2D(NoiseImg,i,j,s);}}}void nNoise(int n_xres, int n_yres, float*pNoise,float* pVectr,float*newNoise,float* normMag)
{for(int j = 0; j < n_yres; j ++){for(int i = 0; i < n_xres; i ++){ newNoise[j*n_yres+i]=255*normMag[j*n_yres+i]+pNoise[j*n_yres+i]-128;}}
}
/// generate box filter LUTs ///
void GenBoxFiltrLUT(int LUTsiz, float* p_LUT0, float* p_LUT1)
{ for(int i = 0; i < LUTsiz; i ++) p_LUT0[i] = p_LUT1[i] = i;
}/// flow imaging (visualization) through Line Integral Convolution ///
void FlowImagingLIC(int n_xres, int n_yres, float* pVectr, float* newNoise, float* pImage, float* p_LUT0, float* p_LUT1, float krnlen)
{ int vec_id; ///ID in the VECtor buffer (for the input flow field)int advDir; ///ADVection DIRection (0: positive; 1: negative)int advcts; ///number of ADVeCTion stepS per direction (a step counter)int ADVCTS = int(krnlen * 3); ///MAXIMUM number of advection steps per direction to break dead loops //跳出死循环的条件float vctr_x; ///x-component of the Vector at the forefront pointfloat vctr_y; ///y-component of the Vector at the forefront pointfloat clp0_x; ///x-coordinate of CLiP point 0 (current)float clp0_y; ///y-coordinate of CLiP point 0 (current)float clp1_x; ///x-coordinate of CLiP point 1 (next )float clp1_y; ///y-coordinate of CLiP point 1 (next )float samp_x; ///x-coordinate of the Sample in the current pixelfloat samp_y; ///y-coordinate of the Sample in the current pixelfloat tmpLen; ///Temporary Length of a trial clipped-segment实验剪辑片段的临时长度float segLen; ///Segment Lengthfloat curLen; ///Current Length of the streamlinefloat prvLen; ///Previous Length of the streamline float W_ACUM; ///Accumulated Weight from the seed to the current streamline forefrontfloat texVal; ///Texture Valuefloat smpWgt; ///Weight of the current Samplefloat t_acum[2]; ///two Accumulated composite Textures for the two directions, perspectively 两个方向的纹理卷积累加和float w_acum[2]; ///two Accumulated Weighting values for the two directions, perspectively 两个方向的权重和float* wgtLUT = NULL; ///WeiGhT Look Up Table pointing to the target filter LUT权重查找表float len2ID = (DISCRETE_FILTER_SIZE - 1) / krnlen; ///map a curve Length To an ID in the LUTdouble scale;//颜色映射表比例double maxmag;double magind;double mag;double x = 0.1;//x为非线性映射因子，且x！=1IplImage * licImage = cvCreateImage(cvSize(n_yres,n_xres),IPL_DEPTH_8U,3);CvScalar s;///for each pixel in the 2D output LIC image///对输出图像的每一个像素for(int j = 0; j < n_yres; j ++){for(int i = 0; i < n_xres; i ++){ ///init the composite texture accumulators and the weight accumulators///每一个像素点为起始点，初始化一次权重和卷积和t_acum[0] = t_acum[1] = w_acum[0] = w_acum[1] = 0.0f;//初始化正反方向卷积和及权重和///for either advection direction///分别计算正反方向的卷积和及权重和for(advDir = 0; advDir < 2; advDir ++){ ///init the step counter, curve-length measurer, and streamline seed/////初始化当前方向上前进的步数和当前流线的总长advcts = 0;//前进的步数curLen = 0.0f;clp0_x = i + 0.5f;//当前点的坐标clp0_y = j + 0.5f;///access the target filter LUT///LUT显示查找表wgtLUT = (advDir == 0) ? p_LUT0 : p_LUT1;//当前噪声点所对应的权重系数///until the streamline is advected long enough or a tightly spiralling center / focus is encountered///while( curLen < krnlen && advcts < ADVCTS ) //??????{///access the vector at the sample///vec_id = ( int(clp0_y) * n_xres + int(clp0_x) ) << 1;//vec_id相当于indexvctr_x = pVectr[vec_id ];//clp0_y相当于当前像素列坐标，clp0_x相当于当前像素的横坐标vctr_y = pVectr[vec_id + 1];///in case of a critical point///遇到零向量，结束循环if( vctr_x == 0.0f && vctr_y == 0.0f ){ t_acum[advDir] = (advcts == 0) ? 0.0f : t_acum[advDir]; ///this line is indeed unnecessaryw_acum[advDir] = (advcts == 0) ? 1.0f : w_acum[advDir];break;}///negate the vector for the backward-advection case///相反的方向取相反的方向vctr_x = (advDir == 0) ? vctr_x : -vctr_x;//因为矢量是用x,y方向的值合成的，所以反向的值就是负的vctr_y = (advDir == 0) ? vctr_y : -vctr_y;//这儿可以就计算矢量大小、归一化运算？？？？？？？？？？？？前面已经归一化了//。。。。。。//
// mag= sqrt(vctr_x*vctr_x+vctr_y*vctr_y);
// if (mag>maxmag)
// {
// maxmag=mag;
// }///clip the segment against the pixel boundaries --- find the shorter from the two clipped segments//////replace all if-statements whenever possible as they might affect the computational speed///影响算法计算速度segLen = LINE_SQUARE_CLIP_MAX;segLen = (vctr_x < -VECTOR_COMPONENT_MIN) ? ( int( clp0_x ) - clp0_x ) / vctr_x : segLen;//int（0.5）=0segLen = (vctr_x > VECTOR_COMPONENT_MIN) ? ( int( int(clp0_x) + 1.5f ) - clp0_x ) / vctr_x : segLen;segLen = (vctr_y < -VECTOR_COMPONENT_MIN) ? ( ( ( tmpLen = ( int( clp0_y) - clp0_y ) / vctr_y ) < segLen ) ? tmpLen : segLen ) : segLen;segLen = (vctr_y > VECTOR_COMPONENT_MIN) ?( ( ( tmpLen = ( int( int(clp0_y) + 1.5f ) - clp0_y ) / vctr_y ) < segLen ) ? tmpLen : segLen ) : segLen;///update the curve-length measurers///prvLen = curLen;curLen+= segLen;//segLen是单步步长segLen+= 0.0004f;//步长？？？// segLen+= 0.0001f;//步长？？？///check if the filter has reached either end///segLen = (curLen > krnlen) ? ( (curLen = krnlen) - prvLen ) : segLen;///obtain the next clip point///clp1_x = clp0_x + vctr_x * segLen;clp1_y = clp0_y + vctr_y * segLen;///obtain the middle point of the segment as the texture-contributing sample///samp_x = (clp0_x + clp1_x) * 0.5f;samp_y = (clp0_y + clp1_y) * 0.5f;///obtain the texture value of the sample///texVal = newNoise[ int(samp_y) * n_xres + int(samp_x) ];///update the accumulated weight and the accumulated composite texture (texture x weight)///W_ACUM = wgtLUT[ int(curLen * len2ID) ];smpWgt = W_ACUM - w_acum[advDir]; w_acum[advDir] = W_ACUM; t_acum[advDir] += texVal * smpWgt;//当前噪声点的权重系数///update the step counter and the "current" clip point///advcts ++;clp0_x = clp1_x;clp0_y = clp1_y;///check if the streamline has gone beyond the flow field///if( clp0_x < 0.0f || clp0_x >= n_xres || clp0_y < 0.0f || clp0_y >= n_yres) break;} }///normalize the accumulated composite texture///texVal = (t_acum[0] + t_acum[1]) / (w_acum[0] + w_acum[1]);///clamp the texture value against the displayable intensity range [0, 255]texVal = (texVal < 0.0f) ? 0.0f : texVal/255;//cout<<"texval="<<texVal<<endl;texVal = (texVal > 255.0f) ? 255.0f : texVal; //cout<<texVal<<endl;pImage[j * n_xres + i] = (float) texVal;} }}
void gray(int n_xres,int n_yres,float *pImage)
{CvScalar s;IplImage * GrayImage = cvCreateImage(cvSize(n_yres,n_xres),IPL_DEPTH_8U,1);for (int i = 0;i<GrayImage->width;i++){for (int j = 0;j<GrayImage->height;j++){s.val[0] = pImage[i*n_xres+j]*255;s.val[1] = pImage[i*n_xres+j]*255;s.val[2] = pImage[i*n_xres+j]*255;//cout<<s.val[0]<<endl;cvSet2D(GrayImage,i,j,s);}}cvSaveImage("gray.jpg",GrayImage,0);cvWaitKey(0);IplImage *gray = cvLoadImage("gray.jpg",1);//直方图尺寸int r_bins =256, b_bins = 256; CvHistogram* hist; { int hist_size[] = { r_bins, b_bins }; float r_ranges[] = { 0, 255 }; // hue is [0,180] float b_ranges[] = { 0, 255 }; float* ranges[] = { r_ranges,b_ranges }; hist = cvCreateHist( 2, hist_size, CV_HIST_ARRAY, ranges,1); } cvReleaseImage(&GrayImage);}
// void color(int n_xres,int n_yres,float *pImage,float* vecSize)
// {
// IplImage * licImage = cvCreateImage(cvSize(n_yres,n_xres),IPL_DEPTH_8U,3);
// IplImage* img = cvLoadImage("11.jpg",1);
// //IplImage* destImg = cvCreateImage(cvSize(n_yres,n_xres),IPL_DEPTH_8U,3);
// //int length=img->width;
// int k = 0;
//
// double magind;
// double mag;
// double newMag;
// double x = 0.01;//x为非线性映射因子，且x！=1
//
// CvScalar colorTable[500];
// CvScalar s,s1;
// //cout<<"h="<<img->height<<";"<<"w"<<img->width<<endl;
// //system("pause");
// for (int i = 0;i < img->width;i++)
// {
// s = cvGet2D(img,1,i);
// colorTable[i] =s;
// //cout<<"colorTable[i]="<<colorTable[i].val[0]<<endl;
// }
// for (int i = 0;i<n_xres;i++)
// {
// for (int j= 0; j <n_yres;j++)
// {
//
// if (k>=img->width)
// {
// k=0;
// }
// double scale= pImage[j * n_xres + i];
//
//
// mag = vecSize[j * n_xres + i];
// //cout<<"mag="<<mag<<endl;
// //********矢量大小归一化******
// magind = mag/maxvecmag;
// //cout<<"maxvecmag="<<maxvecmag<<endl;
// //cout<<"magind="<<magind<<endl;
// //非线性颜色增强LIC
// newMag = (pow(x,magind)-1)/(x-1);
// //cout<<"newMag="<<newMag<<endl;
// // cout<<"scale="<<scale<<endl;
//
// //最终的输出颜色值计算如下
// /*color = table[newMag];*/
//
// s = cvGet2D(licImage,i,j);
//
// //渐变颜色映射表
// int k = int(newMag*446);
// s1.val[0]=colorTable[k].val[0]*(k+1-newMag*446)+colorTable[k+1].val[0]*(newMag*446-k);
// s1.val[1]=colorTable[k].val[1]*(k+1-newMag*446)+colorTable[k+1].val[1]*(newMag*446-k);
// s1.val[2]=colorTable[k].val[2]*(k+1-newMag*446)+colorTable[k+1].val[2]*(newMag*446-k);
// s1.val[0]*=scale;
// s1.val[1]*=scale;
// s1.val[2]*=scale;
//
//
// //离散型颜色映射表
// // int destcolorIndext=int(446*mag*100);
// // s1.val[0]=colorTable[destcolorIndext].val[0]*scale;
// // s1.val[1]=colorTable[destcolorIndext].val[1]*scale;
// // s1.val[2]=colorTable[destcolorIndext].val[2]*scale;
//
// cvSet2D(licImage,i,j,s1);
// //cout<<"s1.val[0]="<<s1.val[0]<<endl;
// }
// }
// cvSaveImage("color.jpg",licImage,0);
//
// cvNamedWindow("lic_three channles",0 );
// cvShowImage("lic_three channles",licImage);
// cvWaitKey(0);
// system("pause");
// cvDestroyWindow("lic_three channles");
//
// cvReleaseImage(&licImage);
// }
/// write the LIC image to a PPM file ///
void color(int n_xres,int n_yres,float *pImage,float* vecSize)
{IplImage * licImage = cvCreateImage(cvSize(n_yres,n_xres),IPL_DEPTH_8U,3);IplImage* img = cvLoadImage("11.jpg",1);//IplImage* destImg = cvCreateImage(cvSize(n_yres,n_xres),IPL_DEPTH_8U,3);//int length=img->width; int k = 0;double magind;double mag;double newMag;double x = 0.01;//x为非线性映射因子，且x！=1CvScalar colorTable[500];CvScalar s,s1;//cout<<"h="<<img->height<<";"<<"w"<<img->width<<endl;//system("pause");for (int i = 0;i < img->width;i++){s = cvGet2D(img,1,i);colorTable[i] =s;//cout<<"colorTable[i]="<<colorTable[i].val[0]<<endl;}for (int i = 0;i<n_xres;i++){for (int j= 0; j <n_yres;j++){if (k>=img->width){k=0;}double scale= pImage[j * n_xres + i];mag = vecSize[j * n_xres + i];//cout<<"mag="<<mag<<endl;//********矢量大小归一化******magind = mag/maxvecmag;//cout<<"maxvecmag="<<maxvecmag<<endl;//cout<<"magind="<<magind<<endl;//非线性颜色增强LICnewMag = (pow(x,magind)-1)/(x-1);//cout<<"newMag="<<newMag<<endl;
// cout<<"scale="<<scale<<endl;//最终的输出颜色值计算如下/*color = table[newMag];*/s = cvGet2D(licImage,i,j);//渐变颜色映射表int k = int(newMag*446);s1.val[0]=colorTable[k].val[0]*(k+1-newMag*446)+colorTable[k+1].val[0]*(newMag*446-k);s1.val[1]=colorTable[k].val[1]*(k+1-newMag*446)+colorTable[k+1].val[1]*(newMag*446-k);s1.val[2]=colorTable[k].val[2]*(k+1-newMag*446)+colorTable[k+1].val[2]*(newMag*446-k);s1.val[0]*=scale;s1.val[1]*=scale;s1.val[2]*=scale;//离散型颜色映射表
// int destcolorIndext=int(446*mag*100);
// s1.val[0]=colorTable[destcolorIndext].val[0]*scale;
// s1.val[1]=colorTable[destcolorIndext].val[1]*scale;
// s1.val[2]=colorTable[destcolorIndext].val[2]*scale;cvSet2D(licImage,i,j,s1);//cout<<"s1.val[0]="<<s1.val[0]<<endl;}}cvSaveImage("color.jpg",licImage,0);cvNamedWindow("lic_three channles",0 );cvShowImage("lic_three channles",licImage);cvWaitKey(0);system("pause");cvDestroyWindow("lic_three channles");cvReleaseImage(&licImage);
}
void WriteImage2PPM(int n_xres, int n_yres, float* pImage, char* f_name)
{ FILE* o_file;if( ( o_file = fopen(f_name, "w") ) == NULL ) { printf("Can't open output file\n"); return; }fprintf(o_file, "P6\n%d %d\n255\n", n_xres, n_yres);for(int j = 0; j < n_yres; j ++)for(int i = 0; i < n_xres; i ++){unsigned char unchar = pImage[j * n_xres + i];//某点像素的灰度纹理值CvScalar s;/*s.val[0]s.val[1]s.val[2]fprintf(o_file, "%c%c%c", s.val[0], s.val[1], s.val[2]);//*/fprintf(o_file, "%c%c%c", unchar, unchar, unchar);//}fclose (o_file); o_file = NULL;
}圆形矢量场field driven strength效果相关推荐
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