ImageJ的自动二值算法C++实现
ImageJ是一款非常优秀的、跨平台的开源软件，里面的源码是用Java编写，之所以用Java是因为Java可以跨平台。常用的图像处理是基于C++和OpenCV的，所以本人对ImageJ的自动二值化算法进行修改，使其能在C++上也能运行。
ImageJ的自动二值化算法大约有16种，之前有前辈修改之后在C#上运行。
读者也可以去下载本博客的完整测试代码:https://download.csdn.net/download/wfh2015/10424253
下面是ImageJ的原始Java代码，文件名为AutoThresholder.java，有兴趣的可以去下载：
package ij.process;
import ij.IJ;
import java.util.Arrays;
/** Autothresholding methods (limited to 256 bin histograms) from the Auto_Threshold plugin
(http://fiji.sc/Auto_Threshold) by G.Landini at bham dot ac dot uk). */
public class AutoThresholder {
private static String[] mStrings;
public enum Method {
Default,
Huang,
Intermodes,
IsoData,
IJ_IsoData,
Li,
MaxEntropy,
Mean,
MinError,
Minimum,
Moments,
Otsu,
Percentile,
RenyiEntropy,
Shanbhag,
Triangle,
Yen
};
public static String[] getMethods() {
if (mStrings==null) {
Method[] mVals = Method.values();
mStrings = new String[mVals.length];
for (int i=0; ireturn mStrings;
}
/** Calculates and returns a threshold using the specified
method and 256 bin histogram. */
public int getThreshold(Method method, int[] histogram) {
if (histogram==null)
throw new IllegalArgumentException("Histogram is null");
if (histogram.length!=256)
throw new IllegalArgumentException("Histogram length not 256");
int threshold = 0;
switch (method) {
case Default: threshold = defaultIsoData(histogram); break;
case IJ_IsoData: threshold = IJIsoData(histogram); break;
case Huang: threshold = Huang(histogram); break;
case Intermodes: threshold = Intermodes(histogram); break;
case IsoData: threshold = IsoData(histogram); break;
case Li: threshold = Li(histogram); break;
case MaxEntropy: threshold = MaxEntropy(histogram); break;
case Mean: threshold = Mean(histogram); break;
case MinError: threshold = MinErrorI(histogram); break;
case Minimum: threshold = Minimum(histogram); break;
case Moments: threshold = Moments(histogram); break;
case Otsu: threshold = Otsu(histogram); break;
case Percentile: threshold = Percentile(histogram); break;
case RenyiEntropy: threshold = RenyiEntropy(histogram); break;
case Shanbhag: threshold = Shanbhag(histogram); break;
case Triangle: threshold = Triangle(histogram); break;
case Yen: threshold = Yen(histogram); break;
}
if (threshold==-1) threshold = 0;
return threshold;
}
public int getThreshold(String mString, int[] histogram) {
// throws an exception if unknown argument
int index = mString.indexOf(" ");
if (index!=-1)
mString = mString.substring(0, index);
Method method = Method.valueOf(Method.class, mString);
return getThreshold(method, histogram);
}
int Huang(int [] data ) {
// Implements Huang's fuzzy thresholding method
// Uses Shannon's entropy function (one can also use Yager's entropy function)
// Huang L.-K. and Wang M.-J.J. (1995) "Image Thresholding by Minimizing
// the Measures of Fuzziness" Pattern Recognition, 28(1): 41-51
// M. Emre Celebi 06.15.2007
// Ported to ImageJ plugin by G. Landini from E Celebi's fourier_0.8 routines
int threshold=-1;
int ih, it;
int first_bin;
int last_bin;
double sum_pix;
double num_pix;
double term;
double ent; // entropy
double min_ent; // min entropy
double mu_x;
/* Determine the first non-zero bin */
first_bin=0;
for (ih = 0; ih < 256; ih++ ) {
if ( data[ih] != 0 ) {
first_bin = ih;
break;
}
}
/* Determine the last non-zero bin */
last_bin=255;
for (ih = 255; ih >= first_bin; ih-- ) {
if ( data[ih] != 0 ) {
last_bin = ih;
break;
}
}
term = 1.0 / ( double ) ( last_bin - first_bin );
double [] mu_0 = new double[256];
sum_pix = num_pix = 0;
for ( ih = first_bin; ih < 256; ih++ ){
sum_pix += (double)ih * data[ih];
num_pix += data[ih];
/* NUM_PIX cannot be zero ! */
mu_0[ih] = sum_pix / num_pix;
}
double [] mu_1 = new double[256];
sum_pix = num_pix = 0;
for ( ih = last_bin; ih > 0; ih-- ){
sum_pix += (double)ih * data[ih];
num_pix += data[ih];
/* NUM_PIX cannot be zero ! */
mu_1[ih - 1] = sum_pix / ( double ) num_pix;
}
/* Determine the threshold that minimizes the fuzzy entropy */
threshold = -1;
min_ent = Double.MAX_VALUE;
for ( it = 0; it < 256; it++ ){
ent = 0.0;
for ( ih = 0; ih <= it; ih++ ) {
/* Equation (4) in Ref. 1 */
mu_x = 1.0 / ( 1.0 + term * Math.abs ( ih - mu_0[it] ) );
if ( !((mu_x < 1e-06 ) || ( mu_x > 0.999999))) {
/* Equation (6) & (8) in Ref. 1 */
ent += data[ih] * ( -mu_x * Math.log ( mu_x ) - ( 1.0 - mu_x ) * Math.log ( 1.0 - mu_x ) );
}
}
for ( ih = it + 1; ih < 256; ih++ ) {
/* Equation (4) in Ref. 1 */
mu_x = 1.0 / ( 1.0 + term * Math.abs ( ih - mu_1[it] ) );
if ( !((mu_x < 1e-06 ) || ( mu_x > 0.999999))) {
/* Equation (6) & (8) in Ref. 1 */
ent += data[ih] * ( -mu_x * Math.log ( mu_x ) - ( 1.0 - mu_x ) * Math.log ( 1.0 - mu_x ) );
}
}
/* No need to divide by NUM_ROWS * NUM_COLS * LOG(2) ! */
if ( ent < min_ent ) {
min_ent = ent;
threshold = it;
}
}
return threshold;
}
boolean bimodalTest(double [] y) {
int len=y.length;
boolean b = false;
int modes = 0;
for (int k=1;k1;k++){
if (y[k-1] < y[k] && y[k+1] < y[k]) {
modes++;
if (modes>2)
return false;
}
}
if (modes == 2)
b = true;
return b;
}
int Intermodes(int[] data ) {
// J. M. S. Prewitt and M. L. Mendelsohn, "The analysis of cell images," in
// Annals of the New York Academy of Sciences, vol. 128, pp. 1035-1053, 1966.
// ported to ImageJ plugin by G.Landini from Antti Niemisto's Matlab code (GPL)
// Original Matlab code Copyright (C) 2004 Antti Niemisto
// See http://www.cs.tut.fi/~ant/histthresh/ for an excellent slide presentation
// and the original Matlab code.
//
// Assumes a bimodal histogram. The histogram needs is smoothed (using a
// running average of size 3, iteratively) until there are only two local maxima.
// j and k
// Threshold t is (j+k)/2.
// Images with histograms having extremely unequal peaks or a broad and
// flat valleys are unsuitable for this method.
int minbin=-1, maxbin=-1;
for (int i=0; iif (data[i]>0) maxbin = i;
for (int i=data.length-1; i>=0; i--)
if (data[i]>0) minbin = i;
int length = (maxbin-minbin)+1;
double [] hist = new double[length];
for (int i=minbin; i<=maxbin; i++)
hist[i-minbin] = data[i];
int iter = 0;
int threshold=-1;
while (!bimodalTest(hist) ) {
//smooth with a 3 point running mean filter
double previous=0, current=0, next=hist[0];
for (int i=0; i1; i++) {
previous = current;
current = next;
next = hist[i + 1];
hist[i] = (previous+current+next)/3;
}
hist[length-1] = (current+next)/3;
iter++;
if (iter>10000) {
threshold = -1;
IJ.log("Intermodes Threshold not found after 10000 iterations.");
return threshold;
}
}
// The threshold is the mean between the two peaks.
int tt=0;
for (int i=1; i1; i++) {
if (hist[i-1] < hist[i] && hist[i+1] < hist[i]){
tt += i;
//IJ.log("mode:" +i);
}
}
threshold = (int) Math.floor(tt/2.0);
return threshold+minbin;
}
int IsoData(int[] data ) {
// Also called intermeans
// Iterative procedure based on the isodata algorithm [T.W. Ridler, S. Calvard, Picture
// thresholding using an iterative selection method, IEEE Trans. System, Man and
// Cybernetics, SMC-8 (1978) 630-632.]
// The procedure divides the image into objects and background by taking an initial threshold,
// then the averages of the pixels at or below the threshold and pixels above are computed.
// The averages of those two values are computed, the threshold is incremented and the
// process is repeated until the threshold is larger than the composite average. That is,
// threshold = (average background + average objects)/2
// The code in ImageJ that implements this function is the getAutoThreshold() method in the ImageProcessor class.
//
// From: Tim Morris ([email protected])
// Subject: Re: Thresholding method?
// posted to sci.image.processing on 1996/06/24
// The algorithm implemented in NIH Image sets the threshold as that grey
// value, G, for which the average of the averages of the grey values
// below and above G is equal to G. It does this by initialising G to the
// lowest sensible value and iterating:
// L = the average grey value of pixels with intensities < G
// H = the average grey value of pixels with intensities > G
// is G = (L + H)/2?
// yes => exit
// no => increment G and repeat
//
int i, l, totl, g=0;
double toth, h;
for (i = 1; i < 256; i++) {
if (data[i] > 0){
g = i + 1;
break;
}
}
while (true){
l = 0;
totl = 0;
for (i = 0; i < g; i++) {
totl = totl + data[i];
l = l + (data[i] * i);
}
h = 0;
toth = 0;
for (i = g + 1; i < 256; i++){
toth += data[i];
h += ((double)data[i]*i);
}
if (totl > 0 && toth > 0){
l /= totl;
h /= toth;
if (g == (int) Math.round((l + h) / 2.0))
break;
}
g++;
if (g > 254)
return -1;
}
return g;
}
int defaultIsoData(int[] data) {
// This is the modified IsoData method used by the "Threshold" widget in "Default" mode
int n = data.length;
int[] data2 = new int[n];
int mode=0, maxCount=0;
for (int i=0; iint count = data[i];
data2[i] = data[i];
if (data2[i]>maxCount) {
maxCount = data2[i];
mode = i;
}
}
int maxCount2 = 0;
for (int i = 0; iif ((data2[i]>maxCount2) && (i!=mode))
maxCount2 = data2[i];
}
int hmax = maxCount;
if ((hmax>(maxCount2*2)) && (maxCount2!=0)) {
hmax = (int)(maxCount2 * 1.5);
data2[mode] = hmax;
}
return IJIsoData(data2);
}
int IJIsoData(int[] data) {
// This is the original ImageJ IsoData implementation, here for backward compatibility.
int level;
int maxValue = data.length - 1;
double result, sum1, sum2, sum3, sum4;
int count0 = data[0];
data[0] = 0; //set to zero so erased areas aren't included
int countMax = data[maxValue];
data[maxValue] = 0;
int min = 0;
while ((data[min]==0) && (minint max = maxValue;
while ((data[max]==0) && (max>0))
max--;
if (min>=max) {
data[0]= count0; data[maxValue]=countMax;
level = data.length/2;
return level;
}
int movingIndex = min;
int inc = Math.max(max/40, 1);
do {
sum1=sum2=sum3=sum4=0.0;
for (int i=min; i<=movingIndex; i++) {
sum1 += (double)i*data[i];
sum2 += data[i];
}
for (int i=(movingIndex+1); i<=max; i++) {
sum3 += (double)i*data[i];
sum4 += data[i];
}
result = (sum1/sum2 + sum3/sum4)/2.0;
movingIndex++;
} while ((movingIndex+1)<=result && movingIndex1);
data[0]= count0; data[maxValue]=countMax;
level = (int)Math.round(result);
return level;
}
int Li(int [] data ) {
// Implements Li's Minimum Cross Entropy thresholding method
// This implementation is based on the iterative version (Ref. 2) of the algorithm.
// 1) Li C.H. and Lee C.K. (1993) "Minimum Cross Entropy Thresholding"
// Pattern Recognition, 26(4): 617-625
// 2) Li C.H. and Tam P.K.S. (1998) "An Iterative Algorithm for Minimum
// Cross Entropy Thresholding"Pattern Recognition Letters, 18(8): 771-776
// 3) Sezgin M. and Sankur B. (2004) "Survey over Image Thresholding
// Techniques and Quantitative Performance Evaluation" Journal of
// Electronic Imaging, 13(1): 146-165
// http://citeseer.ist.psu.edu/sezgin04survey.html
// Ported to ImageJ plugin by G.Landini from E Celebi's fourier_0.8 routines
int threshold;
double num_pixels;
double sum_back; /* sum of the background pixels at a given threshold */
double sum_obj; /* sum of the object pixels at a given threshold */
double num_back; /* number of background pixels at a given threshold */
double num_obj; /* number of object pixels at a given threshold */
double old_thresh;
double new_thresh;
double mean_back; /* mean of the background pixels at a given threshold */
double mean_obj; /* mean of the object pixels at a given threshold */
double mean; /* mean gray-level in the image */
double tolerance; /* threshold tolerance */
double temp;
tolerance=0.5;
num_pixels = 0;
for (int ih = 0; ih < 256; ih++ )
num_pixels += data[ih];
/* Calculate the mean gray-level */
mean = 0.0;
for (int ih = 0 + 1; ih < 256; ih++ ) //0 + 1?
mean += (double)ih * data[ih];
mean /= num_pixels;
/* Initial estimate */
new_thresh = mean;
do {
old_thresh = new_thresh;
threshold = (int) (old_thresh + 0.5); /* range */
/* Calculate the means of background and object pixels */
/* Background */
sum_back = 0;
num_back = 0;
for (int ih = 0; ih <= threshold; ih++ ) {
sum_back += (double)ih * data[ih];
num_back += data[ih];
}
mean_back = ( num_back == 0 ? 0.0 : ( sum_back / ( double ) num_back ) );
/* Object */
sum_obj = 0;
num_obj = 0;
for (int ih = threshold + 1; ih < 256; ih++ ) {
sum_obj += (double)ih * data[ih];
num_obj += data[ih];
}
mean_obj = ( num_obj == 0 ? 0.0 : ( sum_obj / ( double ) num_obj ) );
/* Calculate the new threshold: Equation (7) in Ref. 2 */
//new_thresh = simple_round ( ( mean_back - mean_obj ) / ( Math.log ( mean_back ) - Math.log ( mean_obj ) ) );
//simple_round ( double x ) {
// return ( int ) ( IS_NEG ( x ) ? x - .5 : x + .5 );
//}
//
//#define IS_NEG( x ) ( ( x ) < -DBL_EPSILON )
//DBL_EPSILON = 2.220446049250313E-16
temp = ( mean_back - mean_obj ) / ( Math.log ( mean_back ) - Math.log ( mean_obj ) );
if (temp < -2.220446049250313E-16)
new_thresh = (int) (temp - 0.5);
else
new_thresh = (int) (temp + 0.5);
/* Stop the iterations when the difference between the
new and old threshold values is less than the tolerance */
}
while ( Math.abs ( new_thresh - old_thresh ) > tolerance );
return threshold;
}
int MaxEntropy(int [] data ) {
// Implements Kapur-Sahoo-Wong (Maximum Entropy) thresholding method
// Kapur J.N., Sahoo P.K., and Wong A.K.C. (1985) "A New Method for
// Gray-Level Picture Thresholding Using the Entropy of the Histogram"
// Graphical Models and Image Processing, 29(3): 273-285
// M. Emre Celebi
// 06.15.2007
// Ported to ImageJ plugin by G.Landini from E Celebi's fourier_0.8 routines
int threshold=-1;
int ih, it;
int first_bin;
int last_bin;
double tot_ent; /* total entropy */
double max_ent; /* max entropy */
double ent_back; /* entropy of the background pixels at a given threshold */
double ent_obj; /* entropy of the object pixels at a given threshold */
double [] norm_histo = new double[256]; /* normalized histogram */
double [] P1 = new double[256]; /* cumulative normalized histogram */
double [] P2 = new double[256];
double total =0;
for (ih = 0; ih < 256; ih++ )
total+=data[ih];
for (ih = 0; ih < 256; ih++ )
norm_histo[ih] = data[ih]/total;
P1[0]=norm_histo[0];
P2[0]=1.0-P1[0];
for (ih = 1; ih < 256; ih++ ){
P1[ih]= P1[ih-1] + norm_histo[ih];
P2[ih]= 1.0 - P1[ih];
}
/* Determine the first non-zero bin */
first_bin=0;
for (ih = 0; ih < 256; ih++ ) {
if ( !(Math.abs(P1[ih])<2.220446049250313E-16)) {
first_bin = ih;
break;
}
}
/* Determine the last non-zero bin */
last_bin=255;
for (ih = 255; ih >= first_bin; ih-- ) {
if ( !(Math.abs(P2[ih])<2.220446049250313E-16)) {
last_bin = ih;
break;
}
}
// Calculate the total entropy each gray-level
// and find the threshold that maximizes it
max_ent = Double.MIN_VALUE;
for ( it = first_bin; it <= last_bin; it++ ) {
/* Entropy of the background pixels */
ent_back = 0.0;
for ( ih = 0; ih <= it; ih++ ) {
if ( data[ih] !=0 ) {
ent_back -= ( norm_histo[ih] / P1[it] ) * Math.log ( norm_histo[ih] / P1[it] );
}
}
/* Entropy of the object pixels */
ent_obj = 0.0;
for ( ih = it + 1; ih < 256; ih++ ){
if (data[ih]!=0){
ent_obj -= ( norm_histo[ih] / P2[it] ) * Math.log ( norm_histo[ih] / P2[it] );
}
}
/* Total entropy */
tot_ent = ent_back + ent_obj;
// IJ.log(""+max_ent+" "+tot_ent);
if ( max_ent < tot_ent ) {
max_ent = tot_ent;
threshold = it;
}
}
return threshold;
}
int Mean(int [] data ) {
// C. A. Glasbey, "An analysis of histogram-based thresholding algorithms,"
// CVGIP: Graphical Models and Image Processing, vol. 55, pp. 532-537, 1993.
//
// The threshold is the mean of the greyscale data
int threshold = -1;
double tot=0, sum=0;
for (int i=0; i<256; i++){
tot+= data[i];
sum+=((double)i*data[i]);
}
threshold =(int) Math.floor(sum/tot);
return threshold;
}
int MinErrorI(int [] data ) {
// Kittler and J. Illingworth, "Minimum error thresholding," Pattern Recognition, vol. 19, pp. 41-47, 1986.
// C. A. Glasbey, "An analysis of histogram-based thresholding algorithms," CVGIP: Graphical Models and Image Processing, vol. 55, pp. 532-537, 1993.
// Ported to ImageJ plugin by G.Landini from Antti Niemisto's Matlab code (GPL)
// Original Matlab code Copyright (C) 2004 Antti Niemisto
// See http://www.cs.tut.fi/~ant/histthresh/ for an excellent slide presentation
// and the original Matlab code.
int threshold = Mean(data); //Initial estimate for the threshold is found with the MEAN algorithm.
int Tprev =-2;
double mu, nu, p, q, sigma2, tau2, w0, w1, w2, sqterm, temp;
//int counter=1;
while (threshold!=Tprev){
//Calculate some statistics.
mu = B(data, threshold)/A(data, threshold);
nu = (B(data, data.length - 1)-B(data, threshold))/(A(data, data.length - 1)-A(data, threshold));
p = A(data, threshold)/A(data, data.length - 1);
q = (A(data, data.length - 1)-A(data, threshold)) / A(data, data.length - 1);
sigma2 = C(data, threshold)/A(data, threshold)-(mu*mu);
tau2 = (C(data, data.length - 1)-C(data, threshold)) / (A(data, data.length - 1)-A(data, threshold)) - (nu*nu);
//The terms of the quadratic equation to be solved.
w0 = 1.0/sigma2-1.0/tau2;
w1 = mu/sigma2-nu/tau2;
w2 = (mu*mu)/sigma2 - (nu*nu)/tau2 + Math.log10((sigma2*(q*q))/(tau2*(p*p)));
//If the next threshold would be imaginary, return with the current one.
sqterm = (w1*w1)-w0*w2;
if (sqterm < 0) {
IJ.log("MinError(I): not converging.");
return threshold;
}
//The updated threshold is the integer part of the solution of the quadratic equation.
Tprev = threshold;
temp = (w1+Math.sqrt(sqterm))/w0;
if (Double.isNaN(temp))
threshold = Tprev;
else
threshold =(int) Math.floor(temp);
}
return threshold;
}
private double A(int[] y, int j) {
if (j>=y.length) j=y.length-1;
double x = 0;
for (int i=0;i<=j;i++)
x+=y[i];
return x;
}
private double B(int[] y, int j) {
if (j>=y.length) j=y.length-1;
double x = 0;
for (int i=0;i<=j;i++)
x+=i*y[i];
return x;
}
private double C(int[] y, int j) {
if (j>=y.length) j=y.length-1;
double x = 0;
for (int i=0;i<=j;i++)
x+=i*i*y[i];
return x;
}
int Minimum(int [] data ) {
// J. M. S. Prewitt and M. L. Mendelsohn, "The analysis of cell images," in
// Annals of the New York Academy of Sciences, vol. 128, pp. 1035-1053, 1966.
// ported to ImageJ plugin by G.Landini from Antti Niemisto's Matlab code (GPL)
// Original Matlab code Copyright (C) 2004 Antti Niemisto
// See http://www.cs.tut.fi/~ant/histthresh/ for an excellent slide presentation
// and the original Matlab code.
//
// Assumes a bimodal histogram. The histogram needs is smoothed (using a
// running average of size 3, iteratively) until there are only two local maxima.
// Threshold t is such that yt-1 > yt <= yt+1.
// Images with histograms having extremely unequal peaks or a broad and
// flat valleys are unsuitable for this method.
int iter =0;
int threshold = -1;
double [] iHisto = new double [256];
for (int i=0; i<256; i++)
iHisto[i]=(double) data[i];
double [] tHisto = new double[iHisto.length] ;
while (!bimodalTest(iHisto) ) {
//smooth with a 3 point running mean filter
for (int i=1; i<255; i++)
tHisto[i]= (iHisto[i-1] + iHisto[i] +iHisto[i+1])/3;
tHisto[0] = (iHisto[0]+iHisto[1])/3; //0 outside
tHisto[255] = (iHisto[254]+iHisto[255])/3; //0 outside
System.arraycopy(tHisto, 0, iHisto, 0, iHisto.length) ;
iter++;
if (iter>10000) {
threshold = -1;
IJ.log("Minimum: threshold not found after 10000 iterations.");
return threshold;
}
}
// The threshold is the minimum between the two peaks.
for (int i=1; i<255; i++) {
if (iHisto[i-1] > iHisto[i] && iHisto[i+1] >= iHisto[i]) {
threshold = i;
break;
}
}
return threshold;
}
int Moments(int [] data ) {
// W. Tsai, "Moment-preserving thresholding: a new approach," Computer Vision,
// Graphics, and Image Processing, vol. 29, pp. 377-393, 1985.
// Ported to ImageJ plugin by G.Landini from the the open source project FOURIER 0.8
// by M. Emre Celebi , Department of Computer Science, Louisiana State University in Shreveport
// Shreveport, LA 71115, USA
// http://sourceforge.net/projects/fourier-ipal
// http://www.lsus.edu/faculty/~ecelebi/fourier.htm
double total =0;
double m0=1.0, m1=0.0, m2 =0.0, m3 =0.0, sum =0.0, p0=0.0;
double cd, c0, c1, z0, z1; /* auxiliary variables */
int threshold = -1;
double [] histo = new double [256];
for (int i=0; i<256; i++)
total+=data[i];
for (int i=0; i<256; i++)
histo[i]=(double)(data[i]/total); //normalised histogram
/* Calculate the first, second, and third order moments */
for ( int i = 0; i < 256; i++ ) {
double di = i;
m1 += di * histo[i];
m2 += di * di * histo[i];
m3 += di * di * di * histo[i];
}
/*
First 4 moments of the gray-level image should match the first 4 moments
of the target binary image. This leads to 4 equalities whose solutions
are given in the Appendix of Ref. 1
*/
cd = m0 * m2 - m1 * m1;
c0 = ( -m2 * m2 + m1 * m3 ) / cd;
c1 = ( m0 * -m3 + m2 * m1 ) / cd;
z0 = 0.5 * ( -c1 - Math.sqrt ( c1 * c1 - 4.0 * c0 ) );
z1 = 0.5 * ( -c1 + Math.sqrt ( c1 * c1 - 4.0 * c0 ) );
p0 = ( z1 - m1 ) / ( z1 - z0 ); /* Fraction of the object pixels in the target binary image */
// The threshold is the gray-level closest
// to the p0-tile of the normalized histogram
sum=0;
for (int i=0; i<256; i++){
sum+=histo[i];
if (sum>p0) {
threshold = i;
break;
}
}
return threshold;
}
int Otsu(int [] data ) {
// Otsu's threshold algorithm
// C++ code by Jordan Bevik double)k * data[k]; // Total histogram intensity
N += data[k]; // Total number of data points
}
Sk = 0;
N1 = data[0]; // The entry for zero intensity
BCV = 0;
BCVmax=0;
kStar = 0;
// Look at each possible threshold value,
// calculate the between-class variance, and decide if it's a max
for (k=1; k1; k++) { // No need to check endpoints k = 0 or k = L-1
Sk += (double)k * data[k];
N1 += data[k];
// The float casting here is to avoid compiler warning about loss of precision and
// will prevent overflow in the case of large saturated images
denom = (double)( N1) * (N - N1); // Maximum value of denom is (N^2)/4 = approx. 3E10
if (denom != 0 ){
// Float here is to avoid loss of precision when dividing
num = ( (double)N1 / N ) * S - Sk; // Maximum value of num = 255*N = approx 8E7
BCV = (num * num) / denom;
}
else
BCV = 0;
if (BCV >= BCVmax){ // Assign the best threshold found so far
BCVmax = BCV;
kStar = k;
}
}
// kStar += 1; // Use QTI convention that intensity -> 1 if intensity >= k
// (the algorithm was developed for I-> 1 if I <= k.)
return kStar;
}
int Percentile(int [] data ) {
// W. Doyle, "Operation useful for similarity-invariant pattern recognition,"
// Journal of the Association for Computing Machinery, vol. 9,pp. 259-267, 1962.
// ported to ImageJ plugin by G.Landini from Antti Niemisto's Matlab code (GPL)
// Original Matlab code Copyright (C) 2004 Antti Niemisto
// See http://www.cs.tut.fi/~ant/histthresh/ for an excellent slide presentation
// and the original Matlab code.
int iter =0;
int threshold = -1;
double ptile= 0.5; // default fraction of foreground pixels
double [] avec = new double [256];
for (int i=0; i<256; i++)
avec[i]=0.0;
double total =partialSum(data, 255);
double temp = 1.0;
for (int i=0; i<256; i++){
avec[i]=Math.abs((partialSum(data, i)/total)-ptile);
//IJ.log("Ptile["+i+"]:"+ avec[i]);
if (avec[i]return threshold;
}
double partialSum(int [] y, int j) {
double x = 0;
for (int i=0;i<=j;i++)
x+=y[i];
return x;
}
int RenyiEntropy(int [] data ) {
// Kapur J.N., Sahoo P.K., and Wong A.K.C. (1985) "A New Method for
// Gray-Level Picture Thresholding Using the Entropy of the Histogram"
// Graphical Models and Image Processing, 29(3): 273-285
// M. Emre Celebi
// 06.15.2007
// Ported to ImageJ plugin by G.Landini from E Celebi's fourier_0.8 routines
int threshold;
int opt_threshold;
int ih, it;
int first_bin;
int last_bin;
int tmp_var;
int t_star1, t_star2, t_star3;
int beta1, beta2, beta3;
double alpha;/* alpha parameter of the method */
double term;
double tot_ent; /* total entropy */
double max_ent; /* max entropy */
double ent_back; /* entropy of the background pixels at a given threshold */
double ent_obj; /* entropy of the object pixels at a given threshold */
double omega;
double [] norm_histo = new double[256]; /* normalized histogram */
double [] P1 = new double[256]; /* cumulative normalized histogram */
double [] P2 = new double[256];
double total =0;
for (ih = 0; ih < 256; ih++ )
total+=data[ih];
for (ih = 0; ih < 256; ih++ )
norm_histo[ih] = data[ih]/total;
P1[0]=norm_histo[0];
P2[0]=1.0-P1[0];
for (ih = 1; ih < 256; ih++ ){
P1[ih]= P1[ih-1] + norm_histo[ih];
P2[ih]= 1.0 - P1[ih];
}
/* Determine the first non-zero bin */
first_bin=0;
for (ih = 0; ih < 256; ih++ ) {
if ( !(Math.abs(P1[ih])<2.220446049250313E-16)) {
first_bin = ih;
break;
}
}
/* Determine the last non-zero bin */
last_bin=255;
for (ih = 255; ih >= first_bin; ih-- ) {
if ( !(Math.abs(P2[ih])<2.220446049250313E-16)) {
last_bin = ih;
break;
}
}
/* Maximum Entropy Thresholding - BEGIN */
/* ALPHA = 1.0 */
/* Calculate the total entropy each gray-level
and find the threshold that maximizes it
*/
threshold =0; // was MIN_INT in original code, but if an empty image is processed it gives an error later on.
max_ent = 0.0;
for ( it = first_bin; it <= last_bin; it++ ) {
/* Entropy of the background pixels */
ent_back = 0.0;
for ( ih = 0; ih <= it; ih++ ) {
if ( data[ih] !=0 ) {
ent_back -= ( norm_histo[ih] / P1[it] ) * Math.log ( norm_histo[ih] / P1[it] );
}
}
/* Entropy of the object pixels */
ent_obj = 0.0;
for ( ih = it + 1; ih < 256; ih++ ){
if (data[ih]!=0){
ent_obj -= ( norm_histo[ih] / P2[it] ) * Math.log ( norm_histo[ih] / P2[it] );
}
}
/* Total entropy */
tot_ent = ent_back + ent_obj;
// IJ.log(""+max_ent+" "+tot_ent);
if ( max_ent < tot_ent ) {
max_ent = tot_ent;
threshold = it;
}
}
t_star2 = threshold;
/* Maximum Entropy Thresholding - END */
threshold =0; //was MIN_INT in original code, but if an empty image is processed it gives an error later on.
max_ent = 0.0;
alpha = 0.5;
term = 1.0 / ( 1.0 - alpha );
for ( it = first_bin; it <= last_bin; it++ ) {
/* Entropy of the background pixels */
ent_back = 0.0;
for ( ih = 0; ih <= it; ih++ )
ent_back += Math.sqrt ( norm_histo[ih] / P1[it] );
/* Entropy of the object pixels */
ent_obj = 0.0;
for ( ih = it + 1; ih < 256; ih++ )
ent_obj += Math.sqrt ( norm_histo[ih] / P2[it] );
/* Total entropy */
tot_ent = term * ( ( ent_back * ent_obj ) > 0.0 ? Math.log ( ent_back * ent_obj ) : 0.0);
if ( tot_ent > max_ent ){
max_ent = tot_ent;
threshold = it;
}
}
t_star1 = threshold;
threshold = 0; //was MIN_INT in original code, but if an empty image is processed it gives an error later on.
max_ent = 0.0;
alpha = 2.0;
term = 1.0 / ( 1.0 - alpha );
for ( it = first_bin; it <= last_bin; it++ ) {
/* Entropy of the background pixels */
ent_back = 0.0;
for ( ih = 0; ih <= it; ih++ )
ent_back += ( norm_histo[ih] * norm_histo[ih] ) / ( P1[it] * P1[it] );
/* Entropy of the object pixels */
ent_obj = 0.0;
for ( ih = it + 1; ih < 256; ih++ )
ent_obj += ( norm_histo[ih] * norm_histo[ih] ) / ( P2[it] * P2[it] );
/* Total entropy */
tot_ent = term *( ( ent_back * ent_obj ) > 0.0 ? Math.log(ent_back * ent_obj ): 0.0 );
if ( tot_ent > max_ent ){
max_ent = tot_ent;
threshold = it;
}
}
t_star3 = threshold;
/* Sort t_star values */
if ( t_star2 < t_star1 ){
tmp_var = t_star1;
t_star1 = t_star2;
t_star2 = tmp_var;
}
if ( t_star3 < t_star2 ){
tmp_var = t_star2;
t_star2 = t_star3;
t_star3 = tmp_var;
}
if ( t_star2 < t_star1 ) {
tmp_var = t_star1;
t_star1 = t_star2;
t_star2 = tmp_var;
}
/* Adjust beta values */
if ( Math.abs ( t_star1 - t_star2 ) <= 5 ) {
if ( Math.abs ( t_star2 - t_star3 ) <= 5 ) {
beta1 = 1;
beta2 = 2;
beta3 = 1;
}
else {
beta1 = 0;
beta2 = 1;
beta3 = 3;
}
}
else {
if ( Math.abs ( t_star2 - t_star3 ) <= 5 ) {
beta1 = 3;
beta2 = 1;
beta3 = 0;
}
else {
beta1 = 1;
beta2 = 2;
beta3 = 1;
}
}
//IJ.log(""+t_star1+" "+t_star2+" "+t_star3);
/* Determine the optimal threshold value */
omega = P1[t_star3] - P1[t_star1];
opt_threshold = (int) (t_star1 * ( P1[t_star1] + 0.25 * omega * beta1 ) + 0.25 * t_star2 * omega * beta2 + t_star3 * ( P2[t_star3] + 0.25 * omega * beta3 ));
return opt_threshold;
}
int Shanbhag(int [] data ) {
// Shanhbag A.G. (1994) "Utilization of Information Measure as a Means of
// Image Thresholding" Graphical Models and Image Processing, 56(5): 414-419
// Ported to ImageJ plugin by G.Landini from E Celebi's fourier_0.8 routines
int threshold;
int ih, it;
int first_bin;
int last_bin;
double term;
double tot_ent; /* total entropy */
double min_ent; /* max entropy */
double ent_back; /* entropy of the background pixels at a given threshold */
double ent_obj; /* entropy of the object pixels at a given threshold */
double [] norm_histo = new double[256]; /* normalized histogram */
double [] P1 = new double[256]; /* cumulative normalized histogram */
double [] P2 = new double[256];
double total =0;
for (ih = 0; ih < 256; ih++ )
total+=data[ih];
for (ih = 0; ih < 256; ih++ )
norm_histo[ih] = data[ih]/total;
P1[0]=norm_histo[0];
P2[0]=1.0-P1[0];
for (ih = 1; ih < 256; ih++ ){
P1[ih]= P1[ih-1] + norm_histo[ih];
P2[ih]= 1.0 - P1[ih];
}
/* Determine the first non-zero bin */
first_bin=0;
for (ih = 0; ih < 256; ih++ ) {
if ( !(Math.abs(P1[ih])<2.220446049250313E-16)) {
first_bin = ih;
break;
}
}
/* Determine the last non-zero bin */
last_bin=255;
for (ih = 255; ih >= first_bin; ih-- ) {
if ( !(Math.abs(P2[ih])<2.220446049250313E-16)) {
last_bin = ih;
break;
}
}
// Calculate the total entropy each gray-level
// and find the threshold that maximizes it
threshold =-1;
min_ent = Double.MAX_VALUE;
for ( it = first_bin; it <= last_bin; it++ ) {
/* Entropy of the background pixels */
ent_back = 0.0;
term = 0.5 / P1[it];
for ( ih = 1; ih <= it; ih++ ) { //0+1?
ent_back -= norm_histo[ih] * Math.log ( 1.0 - term * P1[ih - 1] );
}
ent_back *= term;
/* Entropy of the object pixels */
ent_obj = 0.0;
term = 0.5 / P2[it];
for ( ih = it + 1; ih < 256; ih++ ){
ent_obj -= norm_histo[ih] * Math.log ( 1.0 - term * P2[ih] );
}
ent_obj *= term;
/* Total entropy */
tot_ent = Math.abs ( ent_back - ent_obj );
if ( tot_ent < min_ent ) {
min_ent = tot_ent;
threshold = it;
}
}
return threshold;
}
int Triangle(int [] data ) {
// Zack, G. W., Rogers, W. E. and Latt, S. A., 1977,
// Automatic Measurement of Sister Chromatid Exchange Frequency,
// Journal of Histochemistry and Cytochemistry 25 (7), pp. 741-753
//
// modified from Johannes Schindelin plugin
//
// find min and max
int min = 0, dmax=0, max = 0, min2=0;
for (int i = 0; i < data.length; i++) {
if (data[i]>0){
min=i;
break;
}
}
if (min>0) min--; // line to the (p==0) point, not to data[min]
// The Triangle algorithm cannot tell whether the data is skewed to one side or another.
// This causes a problem as there are 2 possible thresholds between the max and the 2 extremes
// of the histogram.
// Here I propose to find out to which side of the max point the data is furthest, and use that as
// the other extreme.
for (int i = 255; i >0; i-- ) {
if (data[i]>0){
min2=i;
break;
}
}
if (min2<255) min2++; // line to the (p==0) point, not to data[min]
for (int i =0; i < 256; i++) {
if (data[i] >dmax) {
max=i;
dmax=data[i];
}
}
// find which is the furthest side
//IJ.log(""+min+" "+max+" "+min2);
boolean inverted = false;
if ((max-min)<(min2-max)){
// reverse the histogram
//IJ.log("Reversing histogram.");
inverted = true;
int left = 0; // index of leftmost element
int right = 255; // index of rightmost element
while (left < right) {
// exchange the left and right elements
int temp = data[left];
data[left] = data[right];
data[right] = temp;
// move the bounds toward the center
left++;
right--;
}
min=255-min2;
max=255-max;
}
if (min == max){
//IJ.log("Triangle: min == max.");
return min;
}
// describe line by nx * x + ny * y - d = 0
double nx, ny, d;
// nx is just the max frequency as the other point has freq=0
nx = data[max]; //-min; // data[min]; // lowest value bmin = (p=0)% in the image
ny = min - max;
d = Math.sqrt(nx * nx + ny * ny);
nx /= d;
ny /= d;
d = nx * min + ny * data[min];
// find split point
int split = min;
double splitDistance = 0;
for (int i = min + 1; i <= max; i++) {
double newDistance = nx * i + ny * data[i] - d;
if (newDistance > splitDistance) {
split = i;
splitDistance = newDistance;
}
}
split--;
if (inverted) {
// The histogram might be used for something else, so let's reverse it back
int left = 0;
int right = 255;
while (left < right) {
int temp = data[left];
data[left] = data[right];
data[right] = temp;
left++;
right--;
}
return (255-split);
}
else
return split;
}
int Yen(int [] data ) {
// Implements Yen thresholding method
// 1) Yen J.C., Chang F.J., and Chang S. (1995) "A New Criterion
// for Automatic Multilevel Thresholding" IEEE Trans. on Image
// Processing, 4(3): 370-378
// 2) Sezgin M. and Sankur B. (2004) "Survey over Image Thresholding
// Techniques and Quantitative Performance Evaluation" Journal of
// Electronic Imaging, 13(1): 146-165
// http://citeseer.ist.psu.edu/sezgin04survey.html
//
// M. Emre Celebi
// 06.15.2007
// Ported to ImageJ plugin by G.Landini from E Celebi's fourier_0.8 routines
int threshold;
int ih, it;
double crit;
double max_crit;
double [] norm_histo = new double[256]; /* normalized histogram */
double [] P1 = new double[256]; /* cumulative normalized histogram */
double [] P1_sq = new double[256];
double [] P2_sq = new double[256];
double total =0;
for (ih = 0; ih < 256; ih++ )
total+=data[ih];
for (ih = 0; ih < 256; ih++ )
norm_histo[ih] = data[ih]/total;
P1[0]=norm_histo[0];
for (ih = 1; ih < 256; ih++ )
P1[ih]= P1[ih-1] + norm_histo[ih];
P1_sq[0]=norm_histo[0]*norm_histo[0];
for (ih = 1; ih < 256; ih++ )
P1_sq[ih]= P1_sq[ih-1] + norm_histo[ih] * norm_histo[ih];
P2_sq[255] = 0.0;
for ( ih = 254; ih >= 0; ih-- )
P2_sq[ih] = P2_sq[ih + 1] + norm_histo[ih + 1] * norm_histo[ih + 1];
/* Find the threshold that maximizes the criterion */
threshold = -1;
max_crit = Double.MIN_VALUE;
for ( it = 0; it < 256; it++ ) {
crit = -1.0 * (( P1_sq[it] * P2_sq[it] )> 0.0? Math.log( P1_sq[it] * P2_sq[it]):0.0) + 2 * ( ( P1[it] * ( 1.0 - P1[it] ) )>0.0? Math.log( P1[it] * ( 1.0 - P1[it] ) ): 0.0);
if ( crit > max_crit ) {
max_crit = crit;
threshold = it;
}
}
return threshold;
}
}
本人修改后的源码头文件，文件名称为auto_thresholder.h
/** @file
* @brief 自动计算单通道图像二值化阈值
* @author ImageJ fh
*/
#ifndef AUTO_THRESHOLDER
#define AUTO_THRESHOLDER
#include
ported to ImageJ plugin by G.Landini
*/
static int Otsu(const std::vector<int>& data);
/**
W. Doyle, "Operation useful for similarity-invariant pattern recognition,"
Journal of the Association for Computing Machinery, vol. 9,pp. 259-267, 1962.
ported to ImageJ plugin by G.Landini from Antti Niemisto's Matlab code (GPL)
Original Matlab code Copyright (C) 2004 Antti Niemisto
See http://www.cs.tut.fi/~ant/histthresh/ for an excellent slide presentation
and the original Matlab code.
*/
static int Percentile(const std::vector<int>& data);
/**
Kapur J.N., Sahoo P.K., and Wong A.K.C. (1985) "A New Method for
Gray-Level Picture Thresholding Using the Entropy of the Histogram"
Graphical Models and Image Processing, 29(3): 273-285
M. Emre Celebi
06.15.2007
Ported to ImageJ plugin by G.Landini from E Celebi's fourier_0.8 routines
*/
static int RenyiEntropy(const std::vector<int>& data);
/**
Shanhbag A.G. (1994) "Utilization of Information Measure as a Means of
Image Thresholding" Graphical Models and Image Processing, 56(5): 414-419
Ported to ImageJ plugin by G.Landini from E Celebi's fourier_0.8 routines
*/
static int Shanbhag(const std::vector<int>& data);
/**
Zack, G. W., Rogers, W. E. and Latt, S. A., 1977,
Automatic Measurement of Sister Chromatid Exchange Frequency,
Journal of Histochemistry and Cytochemistry 25 (7), pp. 741-753
modified from Johannes Schindelin plugin
函数有可能会修改data
*/
static int Triangle(std::vector<int>& data);
/** @brief Implements Yen thresholding method
1) Yen J.C., Chang F.J., and Chang S. (1995) "A New Criterion for Automatic Multilevel Thresholding"
IEEE Trans. on Image Processing, 4(3): 370-378
2) Sezgin M. and Sankur B. (2004) "Survey over Image Thresholding
Techniques and Quantitative Performance Evaluation" Journal of
Electronic Imaging, 13(1): 146-165
http://citeseer.ist.psu.edu/sezgin04survey.html
M. Emre Celebi
06.15.2007
Ported to ImageJ plugin by G.Landini from E Celebi's fourier_0.8 routin
*/
static int Yen(const std::vector<int>& data);
};
#endif // !AUTO_THRESHOLDER
具体算法实现cpp为auto_thresholder.cpp
/** @file
* @brief 自动计算单通道图像二值化阈值
* @author ImageJ fh
* 由于VC编译器Max, Min函数可能会出现问题, 所以有关求最值函数全部自定义在cpp文件中
*/
#include "auto_thresholder.h"
/*****************************************常用函数*********************************************/
static int CalcMaxValue(int a, int b)
{
return (a > b) ? a : b;
}
static double CalcMaxValue(double a, double b)
{
return (a > b) ? a : b;
}
static int CalcMinValue(int a, int b)
{
return (a < b) ? a : b;
}
static double CalcMinValue(double a, double b)
{
return (a < b) ? a : b;
}
void AutoThresholder::CalcHist(const cv::Mat& src, std::vector<int>& data)
{
CV_Assert(CV_8UC1 == src.type());
data.clear();
// 初始化数组
const int GRAY_LEVEL = 256;
for (int i = 0; i < GRAY_LEVEL; ++i)
{
data.push_back(0);
}
// 统计每个灰度级及其对应的像素个数
for (int y = 0; y < src.rows; ++y)
{
for (int x = 0; x < src.cols; ++x)
{
int value = src.at(y, x);
data.at(value) += 1;
}
}
}
int AutoThresholder::DefaultIsoData(const std::vector<int>& data)
{
// This is the modified IsoData method used by the "Threshold" widget in "Default" mode
int n = data.size();
std::vector<int> data2(n, 0);
int mode = 0, maxCount = 0;
for (int i = 0; i < n; i++)
{
int count = data[i];
data2[i] = data[i];
if (data2[i] > maxCount)
{
maxCount = data2[i];
mode = i;
}
}
int maxCount2 = 0;
for (int i = 0; i < n; i++)
{
if ((data2[i] > maxCount2) && (i != mode))
maxCount2 = data2[i];
}
int hmax = maxCount;
if ((hmax > (maxCount2 * 2)) && (maxCount2 != 0))
{
hmax = (int)(maxCount2 * 1.5);
data2[mode] = hmax;
}
return IJIsoData(data2);
}
int AutoThresholder::IJIsoData(std::vector<int>& data)
{
// This is the original ImageJ IsoData implementation, here for backward compatibility.
int level;
int maxValue = data.size() - 1;
double result, sum1, sum2, sum3, sum4;
int count0 = data[0];
data[0] = 0; //set to zero so erased areas aren't included
int countMax = data[maxValue];
data[maxValue] = 0;
int min = 0;
while ((data[min] == 0) && (min < maxValue))
min++;
int max = maxValue;
while ((data[max] == 0) && (max > 0))
max--;
if (min >= max)
{
data[0] = count0; data[maxValue] = countMax;
level = data.size() / 2;
return level;
}
int movingIndex = min;
int inc = CalcMaxValue(max / 40, 1);
do
{
sum1 = sum2 = sum3 = sum4 = 0.0;
for (int i = min; i <= movingIndex; i++)
{
sum1 += (double)i*data[i];
sum2 += data[i];
}
for (int i = (movingIndex + 1); i <= max; i++)
{
sum3 += (double)i*data[i];
sum4 += data[i];
}
result = (sum1 / sum2 + sum3 / sum4) / 2.0;
movingIndex++;
} while ((movingIndex + 1) <= result && movingIndex < max - 1);
data[0] = count0; data[maxValue] = countMax;
level = (int)std::round(result);
return level;
}
int AutoThresholder::Huang(const std::vector<int>& data)
{
const int GRAY_LEVELS = 256;
int threshold = -1;
int ih = 0;
int it = 0;
int first_bin = 0;
int last_bin = 0;
double sum_pix = 0.0;
double num_pix = 0.0;
double term = 0.0;
double ent = 0.0; // entropy
double min_ent = 0.0; // min entropy
double mu_x = 0.0;
/* Determine the first non-zero bin */
first_bin = 0;
for (ih = 0; ih < GRAY_LEVELS; ih++)
{
if (data[ih] != 0)
{
first_bin = ih;
break;
}
}
/* Determine the last non-zero bin */
last_bin = 255;
for (ih = 255; ih >= first_bin; ih--)
{
if (data[ih] != 0)
{
last_bin = ih;
break;
}
}
term = 1.0 / (double)(last_bin - first_bin);
std::vector<double> mu_0(GRAY_LEVELS, 0.0);
sum_pix = num_pix = 0;
for (ih = first_bin; ih < GRAY_LEVELS; ih++)
{
sum_pix += (double)ih * data[ih];
num_pix += data[ih];
/* NUM_PIX cannot be zero ! */
mu_0[ih] = sum_pix / num_pix;
}
//double[] mu_1 = new double[256];
std::vector<double> mu_1(GRAY_LEVELS, 0.0);
sum_pix = num_pix = 0;
for (ih = last_bin; ih > 0; ih--)
{
sum_pix += (double)ih * data[ih];
num_pix += data[ih];
/* NUM_PIX cannot be zero ! */
mu_1[ih - 1] = sum_pix / (double)num_pix;
}
/* Determine the threshold that minimizes the fuzzy entropy */
threshold = -1;
min_ent = DBL_MAX;
for (it = 0; it < GRAY_LEVELS; it++)
{
ent = 0.0;
for (ih = 0; ih <= it; ih++)
{
/* Equation (4) in Ref. 1 */
mu_x = 1.0 / (1.0 + term * std::abs(ih - mu_0[it]));
if (!((mu_x < 1e-06) || (mu_x > 0.999999)))
{
/* Equation (6) & (8) in Ref. 1 */
ent += data[ih] * (-mu_x * std::log(mu_x) - (1.0 - mu_x) * std::log(1.0 - mu_x));
}
}
for (ih = it + 1; ih < GRAY_LEVELS; ih++)
{
/* Equation (4) in Ref. 1 */
mu_x = 1.0 / (1.0 + term * std::abs(ih - mu_1[it]));
if (!((mu_x < 1e-06) || (mu_x > 0.999999)))
{
/* Equation (6) & (8) in Ref. 1 */
ent += data[ih] * (-mu_x * std::log(mu_x) - (1.0 - mu_x) * std::log(1.0 - mu_x));
}
}
/* No need to divide by NUM_ROWS * NUM_COLS * LOG(2) ! */
if (ent < min_ent)
{
min_ent = ent;
threshold = it;
}
}
return threshold;
}
static bool BimodalTest(std::vector<double>& y)
{
int len = y.size();
bool b = false;
int modes = 0;
for (int k = 1; k < len - 1; k++)
{
if (y[k - 1] < y[k] && y[k + 1] < y[k])
{
modes++;
if (modes > 2) return false;
}
}
if (modes == 2) b = true;
return b;
}
int AutoThresholder::Intermodes(const std::vector<int>& data)
{
int maxbin = -1;
for (int i = 0; i < data.size(); i++)
{
if (data[i] > 0) maxbin = i;
}
int minbin = -1;
for (int i = data.size() - 1; i >= 0; i--)
{
if (data[i] > 0) minbin = i;
}
int length = (maxbin - minbin) + 1;
std::vector<double> hist(data.size(), 0);
for (int i = minbin; i <= maxbin; i++)
{
hist[i - minbin] = data[i];
}
int iter = 0;
int threshold = -1;
while (!BimodalTest(hist))
{
//smooth with a 3 point running mean filter
double previous = 0;
double current = 0;
double next = hist[0];
for (int i = 0; i < length - 1; i++)
{
previous = current;
current = next;
next = hist[i + 1];
hist[i] = (previous + current + next) / 3;
}
hist[length - 1] = (current + next) / 3;
iter++;
if (iter > 10000)
{
threshold = -1;
return threshold;
}
}
// The threshold is the mean between the two peaks.
int tt = 0;
for (int i = 1; i < length - 1; i++)
{
if (hist[i - 1] < hist[i] && hist[i + 1] < hist[i])
{
tt += i;
}
}
threshold = (int)std::floor(tt / 2.0);
return threshold + minbin;
}
int AutoThresholder::IsoData(const std::vector<int>& data)
{
int i = 0;
int l = 0;
int totl = 0;
int g = 0;
double toth = 0.0;
double h = 0.0;
const int GRAY_LEVELS = 256;
for (i = 1; i < GRAY_LEVELS; i++)
{
if (data[i] > 0)
{
g = i + 1;
break;
}
}
while (true)
{
l = 0;
totl = 0;
for (i = 0; i < g; i++)
{
totl = totl + data[i];
l = l + (data[i] * i);
}
h = 0;
toth = 0;
for (i = g + 1; i < GRAY_LEVELS; i++)
{
toth += data[i];
h += ((double)data[i] * i);
}
if (totl > 0 && toth > 0)
{
l /= totl;
h /= toth;
if (g == (int)std::round((l + h) / 2.0))
break;
}
g++;
if (g > 254) return -1;
}
return g;
}
int AutoThresholder::Li(const std::vector<int>& data)
{
int threshold = 0;
double num_pixels = 0;
double sum_back = 0; /* sum of the background pixels at a given threshold */
double sum_obj = 0; /* sum of the object pixels at a given threshold */
double num_back = 0; /* number of background pixels at a given threshold */
double num_obj = 0; /* number of object pixels at a given threshold */
double old_thresh = 0;
double new_thresh = 0;
double mean_back = 0; /* mean of the background pixels at a given threshold */
double mean_obj = 0; /* mean of the object pixels at a given threshold */
double mean = 0; /* mean gray-level in the image */
double tolerance = 0; /* threshold tolerance */
double temp = 0;
tolerance = 0.5;
num_pixels = 0;
for (int ih = 0; ih < 256; ih++)
num_pixels += data[ih];
/* Calculate the mean gray-level */
mean = 0.0;
for (int ih = 0 + 1; ih < 256; ih++) //0 + 1?
mean += (double)ih * data[ih];
mean /= num_pixels;
/* Initial estimate */
new_thresh = mean;
do
{
old_thresh = new_thresh;
threshold = (int)(old_thresh + 0.5); /* range */
/* Calculate the means of background and object pixels */
/* Background */
sum_back = 0;
num_back = 0;
for (int ih = 0; ih <= threshold; ih++)
{
sum_back += (double)ih * data[ih];
num_back += data[ih];
}
mean_back = (num_back == 0 ? 0.0 : (sum_back / (double)num_back));
/* Object */
sum_obj = 0;
num_obj = 0;
for (int ih = threshold + 1; ih < 256; ih++)
{
sum_obj += (double)ih * data[ih];
num_obj += data[ih];
}
mean_obj = (num_obj == 0 ? 0.0 : (sum_obj / (double)num_obj));
/* Calculate the new threshold: Equation (7) in Ref. 2 */
//new_thresh = simple_round ( ( mean_back - mean_obj ) / ( Math.log ( mean_back ) - Math.log ( mean_obj ) ) );
//simple_round ( double x ) {
// return ( int ) ( IS_NEG ( x ) ? x - .5 : x + .5 );
//}
//
//#define IS_NEG( x ) ( ( x ) < -DBL_EPSILON )
//DBL_EPSILON = 2.220446049250313E-16
temp = (mean_back - mean_obj) / (std::log(mean_back) - std::log(mean_obj));
if (temp < -2.220446049250313E-16)
new_thresh = (int)(temp - 0.5);
else
new_thresh = (int)(temp + 0.5);
/* Stop the iterations when the difference between the
new and old threshold values is less than the tolerance */
} while (std::abs(new_thresh - old_thresh) > tolerance);
return threshold;
}
int AutoThresholder::MaxEntropy(const std::vector<int>& data)
{
const int GRAY_LEVELS = 256;
int threshold = -1;
int ih = 0;
int it = 0;
int first_bin = 0;
int last_bin = 0;
double tot_ent = 0.0; /* total entropy */
double max_ent = 0.0; /* max entropy */
double ent_back = 0.0; /* entropy of the background pixels at a given threshold */
double ent_obj = 0.0; /* entropy of the object pixels at a given threshold */
std::vector<double> norm_histo(GRAY_LEVELS, 0.0);/* normalized histogram */
std::vector<double> P1(GRAY_LEVELS, 0.0);/* cumulative normalized histogram */
std::vector<double> P2(GRAY_LEVELS, 0.0);
double total = 0;
for (ih = 0; ih < GRAY_LEVELS; ih++)
total += data[ih];
for (ih = 0; ih < GRAY_LEVELS; ih++)
norm_histo[ih] = data[ih] / total;
P1[0] = norm_histo[0];
P2[0] = 1.0 - P1[0];
for (ih = 1; ih < GRAY_LEVELS; ih++)
{
P1[ih] = P1[ih - 1] + norm_histo[ih];
P2[ih] = 1.0 - P1[ih];
}
/* Determine the first non-zero bin */
first_bin = 0;
for (ih = 0; ih < GRAY_LEVELS; ih++)
{
if (!(std::abs(P1[ih]) < 2.220446049250313E-16))
{
first_bin = ih;
break;
}
}
/* Determine the last non-zero bin */
last_bin = 255;
for (ih = 255; ih >= first_bin; ih--)
{
if (!(std::abs(P2[ih]) < 2.220446049250313E-16))
{
last_bin = ih;
break;
}
}
// Calculate the total entropy each gray-level
// and find the threshold that maximizes it
max_ent = DBL_MIN;
for (it = first_bin; it <= last_bin; it++)
{
/* Entropy of the background pixels */
ent_back = 0.0;
for (ih = 0; ih <= it; ih++)
{
if (data[ih] != 0)
{
ent_back -= (norm_histo[ih] / P1[it]) * std::log(norm_histo[ih] / P1[it]);
}
}
/* Entropy of the object pixels */
ent_obj = 0.0;
for (ih = it + 1; ih < 256; ih++)
{
if (data[ih] != 0)
{
ent_obj -= (norm_histo[ih] / P2[it]) * std::log(norm_histo[ih] / P2[it]);
}
}
/* Total entropy */
tot_ent = ent_back + ent_obj;
if (max_ent < tot_ent)
{
max_ent = tot_ent;
threshold = it;
}
}
return threshold;
}
int AutoThresholder::Mean(const std::vector<int>& data)
{
const int GRAY_LEVELS = 256;
int threshold = -1;
double tot = 0, sum = 0;
for (int i = 0; i < GRAY_LEVELS; i++)
{
tot += data[i];
sum += ((double)i*data[i]);
}
threshold = (int)std::floor(sum / tot);
return threshold;
}
/***************************************MinErrorI***************************************************/
static double A(const std::vector<int>& y, int j)
{
if (j >= y.size()) j = y.size() - 1;
double x = 0;
for (int i = 0; i <= j; i++)
x += y[i];
return x;
}
static double B(const std::vector<int>& y, int j)
{
if (j >= y.size()) j = y.size() - 1;
double x = 0;
for (int i = 0; i <= j; i++)
x += i*y[i];
return x;
}
static double C(const std::vector<int>& y, int j)
{
if (j >= y.size()) j = y.size() - 1;
double x = 0;
for (int i = 0; i <= j; i++)
x += i*i*y[i];
return x;
}
int AutoThresholder::MinErrorI(const std::vector<int>& data)
{
int threshold = Mean(data); //Initial estimate for the threshold is found with the MEAN algorithm.
int Tprev = -2;
double mu, nu, p, q, sigma2, tau2, w0, w1, w2, sqterm, temp;
while (threshold != Tprev)
{
//Calculate some statistics.
mu = B(data, threshold) / A(data, threshold);
nu = (B(data, data.size() - 1) - B(data, threshold)) / (A(data, data.size() - 1) - A(data, threshold));
p = A(data, threshold) / A(data, data.size() - 1);
q = (A(data, data.size() - 1) - A(data, threshold)) / A(data, data.size() - 1);
sigma2 = C(data, threshold) / A(data, threshold) - (mu*mu);
tau2 = (C(data, data.size() - 1) - C(data, threshold)) / (A(data, data.size() - 1) - A(data, threshold)) - (nu*nu);
//The terms of the quadratic equation to be solved.
w0 = 1.0 / sigma2 - 1.0 / tau2;
w1 = mu / sigma2 - nu / tau2;
w2 = (mu*mu) / sigma2 - (nu*nu) / tau2 + std::log10((sigma2*(q*q)) / (tau2*(p*p)));
//If the next threshold would be imaginary, return with the current one.
sqterm = (w1*w1) - w0*w2;
if (sqterm < 0)
{
return threshold;
}
//The updated threshold is the integer part of the solution of the quadratic equation.
Tprev = threshold;
temp = (w1 + std::sqrt(sqterm)) / w0;
if (std::isnan(temp))
threshold = Tprev;
else
threshold = (int)std::floor(temp);
}
return threshold;
}
int AutoThresholder::Minimum(const std::vector<int>& data)
{
const int GRAY_LEVELS = 256;
int iter = 0;
int threshold = -1;
std::vector<double> iHisto(GRAY_LEVELS, 0.0);
for (int i = 0; i < GRAY_LEVELS; i++)
{
iHisto[i] = (double)data[i];
}
std::vector<double> tHisto(iHisto.size(), 0.0);
while (!BimodalTest(iHisto))
{
//smooth with a 3 point running mean filter
for (int i = 1; i < 255; i++)
{
tHisto[i] = (iHisto[i - 1] + iHisto[i] + iHisto[i + 1]) / 3;
}
tHisto[0] = (iHisto[0] + iHisto[1]) / 3; //0 outside
tHisto[255] = (iHisto[254] + iHisto[255]) / 3; //0 outside
std::copy(tHisto.begin(), tHisto.end(), iHisto.begin());
iter++;
if (iter > 10000)
{
threshold = -1;
return threshold;
}
}
// The threshold is the minimum between the two peaks.
for (int i = 1; i < 255; i++)
{
if (iHisto[i - 1] > iHisto[i] && iHisto[i + 1] >= iHisto[i])
{
threshold = i;
break;
}
}
return threshold;
}
int AutoThresholder::Moments(const std::vector<int>& data)
{
const int GRAY_LEVELS = 256;
double total = 0;
double m0 = 1.0, m1 = 0.0, m2 = 0.0, m3 = 0.0, sum = 0.0, p0 = 0.0;
double cd, c0, c1, z0, z1; /* auxiliary variables */
int threshold = -1;
//double[] histo = new double[256];
std::vector<double> histo(GRAY_LEVELS, 0.0);
for (int i = 0; i < GRAY_LEVELS; i++)
{
total += data[i];
}
for (int i = 0; i < GRAY_LEVELS; i++)
{
histo[i] = (double)(data[i] / total); //normalised histogram
}
/* Calculate the first, second, and third order moments */
for (int i = 0; i < GRAY_LEVELS; i++)
{
double di = i;
m1 += di * histo[i];
m2 += di * di * histo[i];
m3 += di * di * di * histo[i];
}
/*
First 4 moments of the gray-level image should match the first 4 moments
of the target binary image. This leads to 4 equalities whose solutions
are given in the Appendix of Ref. 1
*/
cd = m0 * m2 - m1 * m1;
c0 = (-m2 * m2 + m1 * m3) / cd;
c1 = (m0 * -m3 + m2 * m1) / cd;
z0 = 0.5 * (-c1 - std::sqrt(c1 * c1 - 4.0 * c0));
z1 = 0.5 * (-c1 + std::sqrt(c1 * c1 - 4.0 * c0));
p0 = (z1 - m1) / (z1 - z0); /* Fraction of the object pixels in the target binary image */
// The threshold is the gray-level closest
// to the p0-tile of the normalized histogram
sum = 0;
for (int i = 0; i < GRAY_LEVELS; i++)
{
sum += histo[i];
if (sum > p0)
{
threshold = i;
break;
}
}
return threshold;
}
int AutoThresholder::Otsu(const std::vector<int>& data)
{
int k, kStar; // k = the current threshold; kStar = optimal threshold
double N1, N; // N1 = # points with intensity <=k; N = total number of points
double BCV, BCVmax; // The current Between Class Variance and maximum BCV
double num, denom; // temporary bookeeping
double Sk; // The total intensity for all histogram points <=k
double S, L = 256; // The total intensity of the image
// Initialize values:
S = N = 0;
for (k = 0; k < L; k++)
{
S += (double)k * data[k]; // Total histogram intensity
N += data[k]; // Total number of data points
}
Sk = 0;
N1 = data[0]; // The entry for zero intensity
BCV = 0;
BCVmax = 0;
kStar = 0;
// Look at each possible threshold value,
// calculate the between-class variance, and decide if it's a max
for (k = 1; k < L - 1; k++)
{
// No need to check endpoints k = 0 or k = L-1
Sk += (double)k * data[k];
N1 += data[k];
// The float casting here is to avoid compiler warning about loss of precision and
// will prevent overflow in the case of large saturated images
denom = (double)(N1) * (N - N1); // Maximum value of denom is (N^2)/4 = approx. 3E10
if (denom != 0)
{
// Float here is to avoid loss of precision when dividing
num = ((double)N1 / N) * S - Sk; // Maximum value of num = 255*N = approx 8E7
BCV = (num * num) / denom;
}
else
{
BCV = 0;
}
if (BCV >= BCVmax)
{ // Assign the best threshold found so far
BCVmax = BCV;
kStar = k;
}
}
// kStar += 1; // Use QTI convention that intensity -> 1 if intensity >= k
// (the algorithm was developed for I-> 1 if I <= k.)
return kStar;
}
static double PartialSum(const std::vector<int>& y, int j)
{
double x = 0;
for (int i = 0; i <= j; i++)
x += y[i];
return x;
}
int AutoThresholder::Percentile(const std::vector<int>& data)
{
int iter = 0;
int threshold = -1;
double ptile = 0.5; // default fraction of foreground pixels
std::vector<double> avec(256, 0.0);
for (int i = 0; i < 256; i++)
{
avec[i] = 0.0;
}
double total = PartialSum(data, 255);
double temp = 1.0;
for (int i = 0; i < 256; i++)
{
avec[i] = std::abs((PartialSum(data, i) / total) - ptile);
if (avec[i] < temp)
{
temp = avec[i];
threshold = i;
}
}
return threshold;
}
int AutoThresholder::RenyiEntropy(const std::vector<int>& data)
{
int threshold;
int opt_threshold;
int ih, it;
int first_bin;
int last_bin;
int tmp_var;
int t_star1, t_star2, t_star3;
int beta1, beta2, beta3;
double alpha;/* alpha parameter of the method */
double term;
double tot_ent; /* total entropy */
double max_ent; /* max entropy */
double ent_back; /* entropy of the background pixels at a given threshold */
double ent_obj; /* entropy of the object pixels at a given threshold */
double omega;
std::vector<double> norm_histo(256, 0.0);/* normalized histogram */
std::vector<double> P1(256, 0.0);/* cumulative normalized histogram */
std::vector<double> P2(256, 0.0);
double total = 0;
for (ih = 0; ih < 256; ih++)
total += data[ih];
for (ih = 0; ih < 256; ih++)
norm_histo[ih] = data[ih] / total;
P1[0] = norm_histo[0];
P2[0] = 1.0 - P1[0];
for (ih = 1; ih < 256; ih++)
{
P1[ih] = P1[ih - 1] + norm_histo[ih];
P2[ih] = 1.0 - P1[ih];
}
/* Determine the first non-zero bin */
first_bin = 0;
for (ih = 0; ih < 256; ih++)
{
if (!(std::abs(P1[ih]) < 2.220446049250313E-16))
{
first_bin = ih;
break;
}
}
/* Determine the last non-zero bin */
last_bin = 255;
for (ih = 255; ih >= first_bin; ih--)
{
if (!(std::abs(P2[ih]) < 2.220446049250313E-16))
{
last_bin = ih;
break;
}
}
/* Maximum Entropy Thresholding - BEGIN */
/* ALPHA = 1.0 */
/* Calculate the total entropy each gray-level
and find the threshold that maximizes it
*/
threshold = 0; // was MIN_INT in original code, but if an empty image is processed it gives an error later on.
max_ent = 0.0;
for (it = first_bin; it <= last_bin; it++)
{
/* Entropy of the background pixels */
ent_back = 0.0;
for (ih = 0; ih <= it; ih++)
{
if (data[ih] != 0) {
ent_back -= (norm_histo[ih] / P1[it]) * std::log(norm_histo[ih] / P1[it]);
}
}
/* Entropy of the object pixels */
ent_obj = 0.0;
for (ih = it + 1; ih < 256; ih++)
{
if (data[ih] != 0) {
ent_obj -= (norm_histo[ih] / P2[it]) * std::log(norm_histo[ih] / P2[it]);
}
}
/* Total entropy */
tot_ent = ent_back + ent_obj;
if (max_ent < tot_ent)
{
max_ent = tot_ent;
threshold = it;
}
}
t_star2 = threshold;
/* Maximum Entropy Thresholding - END */
threshold = 0; //was MIN_INT in original code, but if an empty image is processed it gives an error later on.
max_ent = 0.0;
alpha = 0.5;
term = 1.0 / (1.0 - alpha);
for (it = first_bin; it <= last_bin; it++)
{
/* Entropy of the background pixels */
ent_back = 0.0;
for (ih = 0; ih <= it; ih++)
ent_back += std::sqrt(norm_histo[ih] / P1[it]);
/* Entropy of the object pixels */
ent_obj = 0.0;
for (ih = it + 1; ih < 256; ih++)
ent_obj += std::sqrt(norm_histo[ih] / P2[it]);
/* Total entropy */
tot_ent = term * ((ent_back * ent_obj) > 0.0 ? std::log(ent_back * ent_obj) : 0.0);
if (tot_ent > max_ent)
{
max_ent = tot_ent;
threshold = it;
}
}
t_star1 = threshold;
threshold = 0; //was MIN_INT in original code, but if an empty image is processed it gives an error later on.
max_ent = 0.0;
alpha = 2.0;
term = 1.0 / (1.0 - alpha);
for (it = first_bin; it <= last_bin; it++)
{
/* Entropy of the background pixels */
ent_back = 0.0;
for (ih = 0; ih <= it; ih++)
ent_back += (norm_histo[ih] * norm_histo[ih]) / (P1[it] * P1[it]);
/* Entropy of the object pixels */
ent_obj = 0.0;
for (ih = it + 1; ih < 256; ih++)
ent_obj += (norm_histo[ih] * norm_histo[ih]) / (P2[it] * P2[it]);
/* Total entropy */
tot_ent = term *((ent_back * ent_obj) > 0.0 ? std::log(ent_back * ent_obj) : 0.0);
if (tot_ent > max_ent)
{
max_ent = tot_ent;
threshold = it;
}
}
t_star3 = threshold;
/* Sort t_star values */
if (t_star2 < t_star1)
{
tmp_var = t_star1;
t_star1 = t_star2;
t_star2 = tmp_var;
}
if (t_star3 < t_star2)
{
tmp_var = t_star2;
t_star2 = t_star3;
t_star3 = tmp_var;
}
if (t_star2 < t_star1)
{
tmp_var = t_star1;
t_star1 = t_star2;
t_star2 = tmp_var;
}
/* Adjust beta values */
if (std::abs(t_star1 - t_star2) <= 5)
{
if (std::abs(t_star2 - t_star3) <= 5)
{
beta1 = 1;
beta2 = 2;
beta3 = 1;
}
else
{
beta1 = 0;
beta2 = 1;
beta3 = 3;
}
}
else
{
if (std::abs(t_star2 - t_star3) <= 5)
{
beta1 = 3;
beta2 = 1;
beta3 = 0;
}
else
{
beta1 = 1;
beta2 = 2;
beta3 = 1;
}
}
/* Determine the optimal threshold value */
omega = P1[t_star3] - P1[t_star1];
opt_threshold = (int)(t_star1 * (P1[t_star1] + 0.25 * omega * beta1) + 0.25 * t_star2 * omega * beta2 + t_star3 * (P2[t_star3] + 0.25 * omega * beta3));
return opt_threshold;
}
int AutoThresholder::Shanbhag(const std::vector<int>& data)
{
int threshold;
int ih, it;
int first_bin;
int last_bin;
double term;
double tot_ent; /* total entropy */
double min_ent; /* max entropy */
double ent_back; /* entropy of the background pixels at a given threshold */
double ent_obj; /* entropy of the object pixels at a given threshold */
std::vector<double> norm_histo(256, 0.0);/* normalized histogram */
std::vector<double> P1(256, 0.0);/* cumulative normalized histogram */
std::vector<double> P2(256, 0.0);
double total = 0;
for (ih = 0; ih < 256; ih++)
total += data[ih];
for (ih = 0; ih < 256; ih++)
norm_histo[ih] = data[ih] / total;
P1[0] = norm_histo[0];
P2[0] = 1.0 - P1[0];
for (ih = 1; ih < 256; ih++) {
P1[ih] = P1[ih - 1] + norm_histo[ih];
P2[ih] = 1.0 - P1[ih];
}
/* Determine the first non-zero bin */
first_bin = 0;
for (ih = 0; ih < 256; ih++)
{
if (!(std::abs(P1[ih]) < 2.220446049250313E-16))
{
first_bin = ih;
break;
}
}
/* Determine the last non-zero bin */
last_bin = 255;
for (ih = 255; ih >= first_bin; ih--)
{
if (!(std::abs(P2[ih]) < 2.220446049250313E-16))
{
last_bin = ih;
break;
}
}
// Calculate the total entropy each gray-level
// and find the threshold that maximizes it
threshold = -1;
min_ent = DBL_MAX;
for (it = first_bin; it <= last_bin; it++)
{
/* Entropy of the background pixels */
ent_back = 0.0;
term = 0.5 / P1[it];
for (ih = 1; ih <= it; ih++)
{ //0+1?
ent_back -= norm_histo[ih] * std::log(1.0 - term * P1[ih - 1]);
}
ent_back *= term;
/* Entropy of the object pixels */
ent_obj = 0.0;
term = 0.5 / P2[it];
for (ih = it + 1; ih < 256; ih++)
{
ent_obj -= norm_histo[ih] * std::log(1.0 - term * P2[ih]);
}
ent_obj *= term;
/* Total entropy */
tot_ent = std::abs(ent_back - ent_obj);
if (tot_ent < min_ent)
{
min_ent = tot_ent;
threshold = it;
}
}
return threshold;
}
int AutoThresholder::Triangle(std::vector<int>& data)
{
const int GRAY_LEVELS = 256;
// find min and max
int min = 0, dmax = 0, max = 0, min2 = 0;
for (int i = 0; i < data.size(); i++)
{
if (data[i] > 0)
{
min = i;
break;
}
}
if (min > 0) min--; // line to the (p==0) point, not to data[min]
// The Triangle algorithm cannot tell whether the data is skewed to one side or another.
// This causes a problem as there are 2 possible thresholds between the max and the 2 extremes
// of the histogram.
// Here I propose to find out to which side of the max point the data is furthest, and use that as
// the other extreme.
for (int i = 255; i > 0; i--)
{
if (data[i] > 0)
{
min2 = i;
break;
}
}
if (min2 < 255) min2++; // line to the (p==0) point, not to data[min]
for (int i = 0; i < GRAY_LEVELS; i++)
{
if (data[i] > dmax)
{
max = i;
dmax = data[i];
}
}
// find which is the furthest side
//IJ.log(""+min+" "+max+" "+min2);
bool inverted = false;
if ((max - min) < (min2 - max))
{
// reverse the histogram
inverted = true;
int left = 0; // index of leftmost element
int right = 255; // index of rightmost element
while (left < right)
{
// exchange the left and right elements
int temp = data[left];
data[left] = data[right];
data[right] = temp;
// move the bounds toward the center
left++;
right--;
}
min = 255 - min2;
max = 255 - max;
}
if (min == max)
{
return min;
}
// describe line by nx * x + ny * y - d = 0
double nx, ny, d;
// nx is just the max frequency as the other point has freq=0
nx = data[max]; //-min; // data[min]; // lowest value bmin = (p=0)% in the image
ny = min - max;
d = std::sqrt(nx * nx + ny * ny);
nx /= d;
ny /= d;
d = nx * min + ny * data[min];
// find split point
int split = min;
double splitDistance = 0;
for (int i = min + 1; i <= max; i++)
{
double newDistance = nx * i + ny * data[i] - d;
if (newDistance > splitDistance)
{
split = i;
splitDistance = newDistance;
}
}
split--;
if (inverted)
{
// The histogram might be used for something else, so let's reverse it back
int left = 0;
int right = 255;
while (left < right)
{
int temp = data[left];
data[left] = data[right];
data[right] = temp;
left++;
right--;
}
return (255 - split);
}
else
return split;
}
int AutoThresholder::Yen(const std::vector<int>& data)
{
const int GRAY_LEVELS = 256;
int threshold = 0;
int ih = 0;
int it=0;
double crit = 0.0;
double max_crit = 0.0;
std::vector<double> norm_histo(GRAY_LEVELS, 0.0);/* normalized histogram */
std::vector<double> P1(GRAY_LEVELS, 0.0);
std::vector<double> P1_sq(GRAY_LEVELS, 0.0);
std::vector<double> P2_sq(GRAY_LEVELS, 0.0);
double total = 0;
for (ih = 0; ih < GRAY_LEVELS; ih++)
{
total += data[ih];
}
for (ih = 0; ih < GRAY_LEVELS; ih++)
{
norm_histo[ih] = data[ih] / total;
}
P1[0] = norm_histo[0];
for (ih = 1; ih < GRAY_LEVELS; ih++)
{
P1[ih] = P1[ih - 1] + norm_histo[ih];
}
P1_sq[0] = norm_histo[0] * norm_histo[0];
for (ih = 1; ih < GRAY_LEVELS; ih++)
{
P1_sq[ih] = P1_sq[ih - 1] + norm_histo[ih] * norm_histo[ih];
}
P2_sq[255] = 0.0;
for (ih = 254; ih >= 0; ih--)
{
P2_sq[ih] = P2_sq[ih + 1] + norm_histo[ih + 1] * norm_histo[ih + 1];
}
/* Find the threshold that maximizes the criterion */
threshold = -1;
max_crit = DBL_MIN;
for (it = 0; it < GRAY_LEVELS; it++)
{
crit = -1.0 * ((P1_sq[it] * P2_sq[it]) > 0.0 ? std::log(P1_sq[it] * P2_sq[it]) : 0.0) + 2 * ((P1[it] * (1.0 - P1[it])) > 0.0 ? std::log(P1[it] * (1.0 - P1[it])) : 0.0);
if (crit > max_crit)
{
max_crit = crit;
threshold = it;
}
}
return threshold;
}
另外，本人所使用的测试平台如下:
OpenCV3.4.0- VS2015 x32
- Windows 7
OpenCV主要是用到cv::Mat数据结构以及测试的cv::threshold和cv::imshow等函数