//如果说，做CLAHE的目的只是为了增强对比度，利于提取对象的话，用Retinex也是个不错的选择~
//代码引子哪里忘记了。。。
#include "stdafx.h"
# include
# include
# include
# include
# define MAX_RETINEX_SCALES 2 /* Retinex最多可采用的尺度的数目 */
# define MIN_GAUSSIAN_SCALE 16 /* 最小Gaussian尺度 */
# define MAX_GAUSSIAN_SCALE 250 /* 最大Gaussian尺度 */
typedef struct
{
int scale; /* 最大Retinex尺度 */
int nscales; /* 尺度个数 */
int scales_mode; /* Retinex尺度计算模式，有3种：UNIFORM, LOW, HIGH */
float cvar; /* 用于调整色彩动态范围的方差的倍乘系数 */
} RetinexParams;
/* 3种Retinex尺度计算模式，均匀，低和高，它们决定RetinexScales中的尺度数据 */
# define RETINEX_UNIFORM 0
# define RETINEX_LOW 1
# define RETINEX_HIGH 2
/* 多尺度Retinex中需要的各个Retinex尺度保存在下面数组中 */
static float RetinexScales[MAX_RETINEX_SCALES];
typedef struct
{
int N;
float sigma;
double B;
double b[4];
} gauss3_coefs;
/*
* Private variables.
*/
static RetinexParams rvals =
{
240, /* Scale */
3, /* Scales */
RETINEX_UNIFORM, /* Retinex processing mode */
1.2f /* A variant */
};
# define clip( val, minv, maxv ) (( val = (val < minv ? minv : val ) ) > maxv ? maxv : val )
/*
* calculate scale values for desired distribution.
*/
void retinex_scales_distribution( float* scales, int nscales, int mode, int s)
{
if ( nscales == 1 )
{ /* For one filter we choose the median scale */
scales[0] = (float)s / 2;
}
else if (nscales == 2)
{ /* For two filters we choose the median and maximum scale */
scales[0] = (float) s / 2;
scales[1] = (float) s;
}
else
{
float size_step = (float) s / (float) nscales;
int i;
switch( mode )
{
case RETINEX_UNIFORM:
for ( i = 0; i < nscales; ++i )
scales[i] = 2.0f + (float)i * size_step;
break;
case RETINEX_LOW:
size_step = (float)log(s - 2.0f) / (float) nscales;
for ( i = 0; i < nscales; ++i )
scales[i] = 2.0f + (float)pow (10, (i * size_step) / log (10.0));
break;
case RETINEX_HIGH:
size_step = (float) log(s - 2.0) / (float) nscales;
for ( i = 0; i < nscales; ++i )
scales[i] = s - (float)pow (10, (i * size_step) / log (10.0));
break;
default:
break;
}
}
}
/*
* Calculate the average and variance in one go.
*/
void compute_mean_var( float *src, float *mean, float *var, int size, int bytes )
{
float vsquared;
int i, j;
float *psrc;
vsquared = 0.0f;
*mean = 0.0f;
for ( i = 0; i < size; i+= bytes )
{
psrc = src+i;
for ( j = 0 ; j < 3 ; j++ )
{
*mean += psrc[j];
vsquared += psrc[j] * psrc[j];
}
}
*mean /= (float) size; /* mean */
vsquared /= (float) size; /* mean (x^2) */
*var = ( vsquared - (*mean * *mean) );
*var = (float)sqrt(*var); /* var */
}
/*
* Calculate the coefficients for the recursive filter algorithm
* Fast Computation of gaussian blurring.
*/
void compute_coefs3( gauss3_coefs * c, float sigma )
{
/*
* Papers: "Recursive Implementation of the gaussian filter.",
* Ian T. Young , Lucas J. Van Vliet, Signal Processing 44, Elsevier 1995.
* formula: 11b computation of q
* 8c computation of b0..b1
* 10 alpha is normalization constant B
*/
float q, q2, q3;
q = 0;
if ( sigma >= 2.5f )
{
q = 0.98711f * sigma - 0.96330f;
}
else if ( (sigma >= 0.5f) && (sigma < 2.5f) )
{
q = 3.97156f - 4.14554f * (float) sqrt ((double) 1 - 0.26891 * sigma);
}
else
{
q = 0.1147705018520355224609375f;
}
q2 = q * q;
q3 = q * q2;
c->b[0] = (1.57825f+(2.44413f*q)+(1.4281f *q2)+(0.422205f*q3));
c->b[1] = ( (2.44413f*q)+(2.85619f*q2)+(1.26661f *q3));
c->b[2] = ( -((1.4281f*q2)+(1.26661f *q3)));
c->b[3] = ( (0.422205f*q3));
c->B = 1.0f-((c->b[1]+c->b[2]+c->b[3])/c->b[0]);
c->sigma = sigma;
c->N = 3;
}
void gausssmooth( float *in, float *out, int size, int rowstride, gauss3_coefs *c )
{
/*
* Papers: "Recursive Implementation of the gaussian filter.",
* Ian T. Young , Lucas J. Van Vliet, Signal Processing 44, Elsevier 1995.
* formula: 9a forward filter
* 9b backward filter
* fig7 algorithm
*/
int i,n, bufsize;
float *w1,*w2;
/* forward pass */
bufsize = size+3;
size -= 1;
w1 = (float *)malloc (bufsize * sizeof (float));
w2 = (float *)malloc (bufsize * sizeof (float));
w1[0] = in[0];
w1[1] = in[0];
w1[2] = in[0];
for ( i = 0 , n=3; i <= size ; i++, n++)
{
w1[n] = (float)(c->B*in[i*rowstride] +
((c->b[1]*w1[n-1] +
c->b[2]*w1[n-2] +
c->b[3]*w1[n-3] ) / c->b[0]));
}
/* backward pass */
w2[size+1]= w1[size+3];
w2[size+2]= w1[size+3];
w2[size+3]= w1[size+3];
for ( i = size, n = i; i >= 0; i--, n-- )
{
w2[n]= out[i * rowstride] = (float)(c->B*w1[n] +
((c->b[1]*w2[n+1] +
c->b[2]*w2[n+2] +
c->b[3]*w2[n+3] ) / c->b[0]));
}
free (w1);
free (w2);
}
/*
* This function is the heart of the algo.
* (a) Filterings at several scales and sumarize the results.
* (b) Calculation of the final values.
*/
void MSRCR( unsigned char * src, int width, int height, int bytes )
{
int scale, row, col;
int i, j;
int size;
int pos;
int channel;
unsigned char *psrc = NULL; /* backup pointer for src buffer */
float *dst = NULL; /* float buffer for algorithm */
float *pdst = NULL; /* backup pointer for float buffer */
float *in, *out;
int channelsize; /* Float memory cache for one channel */
float weight;
gauss3_coefs coef;
float mean, var;
float mini, range, maxi;
float alpha;
float gain;
float offset;
/* Allocate all the memory needed for algorithm*/
size = width * height * bytes;
dst = (float *)malloc (size * sizeof (float));
if (dst == NULL)
{
printf( "Failed to allocate memory" );
return;
}
memset( dst, 0, size * sizeof (float) );
channelsize = ( width * height );
in = (float *)malloc (channelsize * sizeof (float));
if (in == NULL)
{
free (dst);
printf( "Failed to allocate memory" );
return; /* do some clever stuff */
}
out = (float *)malloc (channelsize * sizeof (float));
if (out == NULL)
{
free (in);
free (dst);
printf( "Failed to allocate memory" );
return; /* do some clever stuff */
}
/*
Calculate the scales of filtering according to the
number of filter and their distribution.
*/
retinex_scales_distribution( RetinexScales,
rvals.nscales, rvals.scales_mode, rvals.scale );
/*
Filtering according to the various scales.
Summerize the results of the various filters according to a
specific weight(here equivalent for all).
*/
weight = 1.0f / (float) rvals.nscales;
/*
The recursive filtering algorithm needs different coefficients according
to the selected scale (~ = standard deviation of Gaussian).
*/
pos = 0;
for ( channel = 0; channel < 3; channel++ )
{
for ( i = 0, pos = channel; i < channelsize ; i++, pos += bytes )
{
/* 0-255 => 1-256 */
in[i] = (float)(src[pos] + 1.0);
}
for ( scale = 0; scale < rvals.nscales; scale++ )
{
compute_coefs3( &coef, RetinexScales[scale] );
/*
* Filtering (smoothing) Gaussian recursive.
*
* Filter rows first
*/
for ( row = 0; row < height; row++ )
{
pos = row * width;
gausssmooth( in + pos, out + pos, width, 1, &coef );
}
memcpy( in, out, channelsize * sizeof(float) );
memset( out, 0 , channelsize * sizeof(float) );
/*
* Filtering (smoothing) Gaussian recursive.
*
* Second columns
*/
for ( col = 0; col < width; col++ )
{
pos = col;
gausssmooth( in + pos, out + pos, height, width, &coef );
}
/*
Summarize the filtered values.
In fact one calculates a ratio between the original values and the filtered values.
*/
for ( i = 0, pos = channel; i < channelsize; i++, pos += bytes )
{
dst[pos] += weight * (float)( log(src[pos] + 1.0f) - log(out[i]) );
}
}
}
free(in);
free(out);
/*
Final calculation with original value and cumulated filter values.
The parameters gain, alpha and offset are constants.
*/
/* Ci(x,y)=log[a Ii(x,y)]-log[ Ei=1-s Ii(x,y)] */
alpha = 128.0f;
gain = 1.0f;
offset = 0.0f;
for ( i = 0; i < size; i += bytes )
{
float logl;
psrc = src+i;
pdst = dst+i;
logl = (float)log( (float)psrc[0] + (float)psrc[1] + (float)psrc[2] + 3.0f );
pdst[0] = gain * ((float)(log(alpha * (psrc[0]+1.0f)) - logl) * pdst[0]) + offset;
pdst[1] = gain * ((float)(log(alpha * (psrc[1]+1.0f)) - logl) * pdst[1]) + offset;
pdst[2] = gain * ((float)(log(alpha * (psrc[2]+1.0f)) - logl) * pdst[2]) + offset;
}
/*
Adapt the dynamics of the colors according to the statistics of the first and second order.
The use of the variance makes it possible to control the degree of saturation of the colors.
*/
pdst = dst;
compute_mean_var( pdst, &mean, &var, size, bytes );
mini = mean - rvals.cvar*var;
maxi = mean + rvals.cvar*var;
range = maxi - mini;
/*
printf( "variance: \t\t%7.4f\n", var * rvals.cvar );
printf( "mean: \t\t%7.4f\n", mean );
printf( "min: \t\t%7.4f\n", mini );
printf( "max: \t\t%7.4f\n", maxi );
printf( "range: \t\t%7.4f\n", range );
*/
if ( !range ) range = 1.0;
for ( i = 0; i < size; i+= bytes )
{
psrc = src + i;
pdst = dst + i;
for (j = 0 ; j < 3 ; j++)
{
float c = 255 * ( pdst[j] - mini ) / range;
psrc[j] = (unsigned char)clip( c, 0, 255 );
}
}
free (dst);
}
/*******************************************************************************
OpenCV돨暠獗뇹잿쌈왯변鑒
*******************************************************************************/
# include
# include
# include
IplImage* process(IplImage* orig)
{
//IplImage * orig = NULL;
IplImage * dst = NULL;
unsigned char * sImage, * dImage;
int x, y;
int nWidth, nHeight, step;
nWidth = orig->width;
nHeight = orig->height;
step = orig->widthStep/sizeof( unsigned char );
dst = cvCreateImage( cvSize(nWidth,nHeight), IPL_DEPTH_8U, 3 );
sImage = new unsigned char[nHeight*nWidth*3];
dImage = new unsigned char[nHeight*nWidth*3];
//cvNamedWindow( "Original Video", CV_WINDOW_AUTOSIZE );
//cvNamedWindow( "Result Video", CV_WINDOW_AUTOSIZE );
// 혤暠獗쏵契뇹잿
//cvShowImage( "Original Video", orig );
if ( orig->nChannels == 3 )
{
for ( y = 0; y < nHeight; y++ )
for ( x = 0; x < nWidth; x++ )
{
sImage[(y*nWidth+x)*orig->nChannels] = orig->imageData[y*step+x*orig->nChannels];
sImage[(y*nWidth+x)*orig->nChannels+1] = orig->imageData[y*step+x*orig->nChannels+1];
sImage[(y*nWidth+x)*orig->nChannels+2] = orig->imageData[y*step+x*orig->nChannels+2];
}
}
memcpy( dImage, sImage, nWidth*nHeight*orig->nChannels );
/* 꽈暠獗藤퓻 */
MSRCR( dImage, nWidth, nHeight, orig->nChannels );
for ( y = 0; y < nHeight; y++ )
for ( x = 0; x < nWidth; x++ )
{
dst->imageData[y*step+x*3] = dImage[(y*nWidth+x)*3];
dst->imageData[y*step+x*3+1] = dImage[(y*nWidth+x)*3+1];
dst->imageData[y*step+x*3+2] = dImage[(y*nWidth+x)*3+2];
}
/************** 鞫刻뇹잿暠獗 ****************/
//cvShowImage( "Result Video", dst );
//if ( rFilename != NULL )
// cvSaveImage( rFilename, dst ); // 깻괏닸써벎匡숭
//cvWaitKey( 0 );
// 헌뇜
//cvReleaseImage( &orig );
//cvReleaseImage( &dst );
delete [] sImage; // 헌뇜暠獗buffer
delete [] dImage;
return dst;
}
