public static int binarize_simple(Bytearray result, Bytearray image)
{
int threshold = (NarrayUtil.Max(image)/* + NarrayUtil.Min(image)*/) / 2;
result.MakeLike(image);
for (int i = 0; i < image.Length1d(); i++)
result.Put1d(i, image.At1d(i) < threshold ? (byte)0 : (byte)255);
return threshold;
}
public static void binarize_with_threshold(Bytearray result, Floatarray image, float threshold)
{
result.MakeLike(image);
for (int i = 0; i < image.Length1d(); i++)
{
result.Put1d(i, image.At1d(i) < threshold ? (byte)0 : (byte)255);
}
}
public static int binarize_simple(Bytearray result, Bytearray image)
{
int threshold = (NarrayUtil.Max(image) /* + NarrayUtil.Min(image)*/) / 2;
result.MakeLike(image);
for (int i = 0; i < image.Length1d(); i++)
{
result.Put1d(i, image.At1d(i) < threshold ? (byte)0 : (byte)255);
}
return(threshold);
}
public static void binarize_by_range(Bytearray outa, Floatarray ina, float fraction)
{
float imin = NarrayUtil.Min(ina);
float imax = NarrayUtil.Max(ina);
float thresh = (int)(imin + (imax - imin) * fraction);
outa.MakeLike(ina);
for (int i = 0; i < ina.Length1d(); i++)
{
if (ina.At1d(i) > thresh) outa.Put1d(i, 255);
else outa.Put1d(i, 0);
}
}
public static void segmentation_as_bitmap(Bytearray image, Intarray cseg)
{
image.MakeLike(cseg);
for (int i = 0; i < image.Length1d(); i++)
{
int value = cseg.At1d(i);
if (value == 0 || value == 0xffffff)
{
image.Put1d(i, 255);
}
//if (value == 0xffffff) image.Put1d(i, 255);
}
}
public static void binarize_by_range(Bytearray outa, Floatarray ina, float fraction)
{
float imin = NarrayUtil.Min(ina);
float imax = NarrayUtil.Max(ina);
float thresh = (int)(imin + (imax - imin) * fraction);
outa.MakeLike(ina);
for (int i = 0; i < ina.Length1d(); i++)
{
if (ina.At1d(i) > thresh)
{
outa.Put1d(i, 255);
}
else
{
outa.Put1d(i, 0);
}
}
}
public static void extract_holes(ref Bytearray holes, Bytearray binarized)
{
Intarray temp = new Intarray();
temp.Copy(binarized);
NarrayUtil.Sub(255, temp);
ImgLabels.label_components(ref temp);
int background = -1;
for (int i = 0; i < temp.Dim(0); i++)
{
if (temp[i, 0] != 0)
{
background = temp[i, 0];
break;
}
}
holes.MakeLike(temp);
holes.Fill((byte)0);
if (background <= 0)
{
throw new Exception("extract_holes: background must be more 0");
}
for (int i = 0; i < temp.Dim(0); i++)
{
for (int j = 0; j < temp.Dim(1); j++)
{
if (temp[i, j] > 0 && temp[i, j] != background)
{
holes[i, j] = 255;
}
}
}
/*fprintf(stderr, "segholes\n");
* dsection("segholes");
* dshow(holes, "y");*/
}
public override void Binarize(Bytearray bin_image, Bytearray gray_image)
{
w = PGeti("w");
k = (float)PGetf("k");
whalf = w >> 1;
// fprintf(stderr,"[sauvola %g %d]\n",k,w);
if (k < 0.001 || k > 0.999)
{
throw new Exception("Binarize: CHECK_ARG(k>=0.001 && k<=0.999)");
}
if (w == 0 || k >= 1000)
{
throw new Exception("Binarize: CHECK_ARG(w>0 && k<1000)");
}
if (bin_image.Length1d() != gray_image.Length1d())
{
bin_image.MakeLike(gray_image);
}
if (NarrayUtil.contains_only(gray_image, (byte)0, (byte)255))
{
bin_image.Copy(gray_image);
return;
}
int image_width = gray_image.Dim(0);
int image_height = gray_image.Dim(1);
whalf = w >> 1;
// Calculate the integral image, and integral of the squared image
Narray <long> integral_image = new Narray <long>(), rowsum_image = new Narray <long>();
Narray <long> integral_sqimg = new Narray <long>(), rowsum_sqimg = new Narray <long>();
integral_image.MakeLike(gray_image);
rowsum_image.MakeLike(gray_image);
integral_sqimg.MakeLike(gray_image);
rowsum_sqimg.MakeLike(gray_image);
int xmin, ymin, xmax, ymax;
double diagsum, idiagsum, diff, sqdiagsum, sqidiagsum, sqdiff, area;
double mean, std, threshold;
for (int j = 0; j < image_height; j++)
{
rowsum_image[0, j] = gray_image[0, j];
rowsum_sqimg[0, j] = gray_image[0, j] * gray_image[0, j];
}
for (int i = 1; i < image_width; i++)
{
for (int j = 0; j < image_height; j++)
{
rowsum_image[i, j] = rowsum_image[i - 1, j] + gray_image[i, j];
rowsum_sqimg[i, j] = rowsum_sqimg[i - 1, j] + gray_image[i, j] * gray_image[i, j];
}
}
for (int i = 0; i < image_width; i++)
{
integral_image[i, 0] = rowsum_image[i, 0];
integral_sqimg[i, 0] = rowsum_sqimg[i, 0];
}
for (int i = 0; i < image_width; i++)
{
for (int j = 1; j < image_height; j++)
{
integral_image[i, j] = integral_image[i, j - 1] + rowsum_image[i, j];
integral_sqimg[i, j] = integral_sqimg[i, j - 1] + rowsum_sqimg[i, j];
}
}
//Calculate the mean and standard deviation using the integral image
for (int i = 0; i < image_width; i++)
{
for (int j = 0; j < image_height; j++)
{
xmin = Math.Max(0, i - whalf);
ymin = Math.Max(0, j - whalf);
xmax = Math.Min(image_width - 1, i + whalf);
ymax = Math.Min(image_height - 1, j + whalf);
area = (xmax - xmin + 1) * (ymax - ymin + 1);
// area can't be 0 here
// proof (assuming whalf >= 0):
// we'll prove that (xmax-xmin+1) > 0,
// (ymax-ymin+1) is analogous
// It's the same as to prove: xmax >= xmin
// image_width - 1 >= 0 since image_width > i >= 0
// i + whalf >= 0 since i >= 0, whalf >= 0
// i + whalf >= i - whalf since whalf >= 0
// image_width - 1 >= i - whalf since image_width > i
// --IM
if (area <= 0)
{
throw new Exception("Binarize: area can't be 0 here");
}
if (xmin == 0 && ymin == 0)
{ // Point at origin
diff = integral_image[xmax, ymax];
sqdiff = integral_sqimg[xmax, ymax];
}
else if (xmin == 0 && ymin > 0)
{ // first column
diff = integral_image[xmax, ymax] - integral_image[xmax, ymin - 1];
sqdiff = integral_sqimg[xmax, ymax] - integral_sqimg[xmax, ymin - 1];
}
else if (xmin > 0 && ymin == 0)
{ // first row
diff = integral_image[xmax, ymax] - integral_image[xmin - 1, ymax];
sqdiff = integral_sqimg[xmax, ymax] - integral_sqimg[xmin - 1, ymax];
}
else
{ // rest of the image
diagsum = integral_image[xmax, ymax] + integral_image[xmin - 1, ymin - 1];
idiagsum = integral_image[xmax, ymin - 1] + integral_image[xmin - 1, ymax];
diff = diagsum - idiagsum;
sqdiagsum = integral_sqimg[xmax, ymax] + integral_sqimg[xmin - 1, ymin - 1];
sqidiagsum = integral_sqimg[xmax, ymin - 1] + integral_sqimg[xmin - 1, ymax];
sqdiff = sqdiagsum - sqidiagsum;
}
mean = diff / area;
std = Math.Sqrt((sqdiff - diff * diff / area) / (area - 1));
threshold = mean * (1 + k * ((std / 128) - 1));
if (gray_image[i, j] < threshold)
{
bin_image[i, j] = 0;
}
else
{
bin_image[i, j] = (byte)(MAXVAL - 1);
}
}
}
if (PGeti("debug_binarize") > 0)
{
ImgIo.write_image_gray("debug_binarize.png", bin_image);
}
}
public override void Binarize(Bytearray bin_image, Bytearray gray_image)
{
w = PGeti("w");
k = (float)PGetf("k");
whalf = w >> 1;
// fprintf(stderr,"[sauvola %g %d]\n",k,w);
if(k<0.001 || k>0.999)
throw new Exception("Binarize: CHECK_ARG(k>=0.001 && k<=0.999)");
if(w==0 || k>=1000)
throw new Exception("Binarize: CHECK_ARG(w>0 && k<1000)");
if(bin_image.Length1d() != gray_image.Length1d())
bin_image.MakeLike(gray_image);
if(NarrayUtil.contains_only(gray_image, (byte)0, (byte)255))
{
bin_image.Copy(gray_image);
return;
}
int image_width = gray_image.Dim(0);
int image_height = gray_image.Dim(1);
whalf = w >> 1;
// Calculate the integral image, and integral of the squared image
Narray<long> integral_image = new Narray<long>(), rowsum_image = new Narray<long>();
Narray<long> integral_sqimg = new Narray<long>(), rowsum_sqimg = new Narray<long>();
integral_image.MakeLike(gray_image);
rowsum_image.MakeLike(gray_image);
integral_sqimg.MakeLike(gray_image);
rowsum_sqimg.MakeLike(gray_image);
int xmin,ymin,xmax,ymax;
double diagsum,idiagsum,diff,sqdiagsum,sqidiagsum,sqdiff,area;
double mean,std,threshold;
for (int j = 0; j < image_height; j++)
{
rowsum_image[0, j] = gray_image[0, j];
rowsum_sqimg[0, j] = gray_image[0, j] * gray_image[0, j];
}
for (int i = 1; i < image_width; i++)
{
for (int j = 0; j < image_height; j++)
{
rowsum_image[i, j] = rowsum_image[i - 1, j] + gray_image[i, j];
rowsum_sqimg[i, j] = rowsum_sqimg[i - 1, j] + gray_image[i, j] * gray_image[i, j];
}
}
for (int i = 0; i < image_width; i++)
{
integral_image[i, 0] = rowsum_image[i, 0];
integral_sqimg[i, 0] = rowsum_sqimg[i, 0];
}
for (int i = 0; i < image_width; i++)
{
for (int j = 1; j < image_height; j++)
{
integral_image[i, j] = integral_image[i, j - 1] + rowsum_image[i, j];
integral_sqimg[i, j] = integral_sqimg[i, j - 1] + rowsum_sqimg[i, j];
}
}
//Calculate the mean and standard deviation using the integral image
for(int i=0; i<image_width; i++){
for(int j=0; j<image_height; j++){
xmin = Math.Max(0,i-whalf);
ymin = Math.Max(0, j - whalf);
xmax = Math.Min(image_width - 1, i + whalf);
ymax = Math.Min(image_height - 1, j + whalf);
area = (xmax-xmin+1)*(ymax-ymin+1);
// area can't be 0 here
// proof (assuming whalf >= 0):
// we'll prove that (xmax-xmin+1) > 0,
// (ymax-ymin+1) is analogous
// It's the same as to prove: xmax >= xmin
// image_width - 1 >= 0 since image_width > i >= 0
// i + whalf >= 0 since i >= 0, whalf >= 0
// i + whalf >= i - whalf since whalf >= 0
// image_width - 1 >= i - whalf since image_width > i
// --IM
if (area <= 0)
throw new Exception("Binarize: area can't be 0 here");
if (xmin == 0 && ymin == 0)
{ // Point at origin
diff = integral_image[xmax, ymax];
sqdiff = integral_sqimg[xmax, ymax];
}
else if (xmin == 0 && ymin > 0)
{ // first column
diff = integral_image[xmax, ymax] - integral_image[xmax, ymin - 1];
sqdiff = integral_sqimg[xmax, ymax] - integral_sqimg[xmax, ymin - 1];
}
else if (xmin > 0 && ymin == 0)
{ // first row
diff = integral_image[xmax, ymax] - integral_image[xmin - 1, ymax];
sqdiff = integral_sqimg[xmax, ymax] - integral_sqimg[xmin - 1, ymax];
}
else
{ // rest of the image
diagsum = integral_image[xmax, ymax] + integral_image[xmin - 1, ymin - 1];
idiagsum = integral_image[xmax, ymin - 1] + integral_image[xmin - 1, ymax];
diff = diagsum - idiagsum;
sqdiagsum = integral_sqimg[xmax, ymax] + integral_sqimg[xmin - 1, ymin - 1];
sqidiagsum = integral_sqimg[xmax, ymin - 1] + integral_sqimg[xmin - 1, ymax];
sqdiff = sqdiagsum - sqidiagsum;
}
mean = diff/area;
std = Math.Sqrt((sqdiff - diff*diff/area)/(area-1));
threshold = mean*(1+k*((std/128)-1));
if(gray_image[i,j] < threshold)
bin_image[i,j] = 0;
else
bin_image[i,j] = (byte)(MAXVAL-1);
}
}
if(PGeti("debug_binarize") > 0) {
ImgIo.write_image_gray("debug_binarize.png", bin_image);
}
}
public override void Binarize(Bytearray bin_image, Bytearray gray_image)
{
if(bin_image.Length1d() != gray_image.Length1d())
bin_image.MakeLike(gray_image);
if(NarrayUtil.contains_only(gray_image, (byte)0, (byte)255))
{
bin_image.Copy(gray_image);
return;
}
int image_width = gray_image.Dim(0);
int image_height = gray_image.Dim(1);
int[] hist = new int[MAXVAL];
double[] pdf = new double[MAXVAL]; //probability distribution
double[] cdf = new double[MAXVAL]; //cumulative probability distribution
double[] myu = new double[MAXVAL]; // mean value for separation
double max_sigma;
double[] sigma = new double[MAXVAL]; // inter-class variance
/* Histogram generation */
for(int i=0; i<MAXVAL; i++){
hist[i] = 0;
}
for(int x=0; x<image_width; x++){
for(int y=0; y<image_height; y++){
hist[gray_image[x,y]]++;
}
}
/* calculation of probability density */
for(int i=0; i<MAXVAL; i++){
pdf[i] = (double)hist[i] / (image_width * image_height);
}
/* cdf & myu generation */
cdf[0] = pdf[0];
myu[0] = 0.0; /* 0.0 times prob[0] equals zero */
for(int i=1; i<MAXVAL; i++){
cdf[i] = cdf[i-1] + pdf[i];
myu[i] = myu[i-1] + i*pdf[i];
}
/* sigma maximization
sigma stands for inter-class variance
and determines optimal threshold value */
int threshold = 0;
max_sigma = 0.0;
for(int i=0; i<MAXVAL-1; i++){
if(cdf[i] != 0.0 && cdf[i] != 1.0){
double p1p2 = cdf[i]*(1.0 - cdf[i]);
double mu1mu2diff = myu[MAXVAL-1]*cdf[i]-myu[i];
sigma[i] = mu1mu2diff * mu1mu2diff / p1p2;
}
else
sigma[i] = 0.0;
if(sigma[i] > max_sigma){
max_sigma = sigma[i];
threshold = i;
}
}
for(int x=0; x<image_width; x++){
for(int y=0; y<image_height; y++){
if (gray_image[x,y] > threshold)
bin_image[x,y] = (byte)(MAXVAL-1);
else
bin_image[x,y] = 0;
}
}
if(PGeti("debug_otsu") > 0) {
Logger.Default.Format("Otsu threshold value = {0}\n", threshold);
//ImgIo.write_image_gray("debug_otsu.png", bin_image);
}
}
public static void binarize_with_threshold(Bytearray result, Floatarray image, float threshold)
{
result.MakeLike(image);
for (int i = 0; i < image.Length1d(); i++)
result.Put1d(i, image.At1d(i) < threshold ? (byte)0 : (byte)255);
}
public override void Binarize(Bytearray bin_image, Bytearray gray_image)
{
if (bin_image.Length1d() != gray_image.Length1d())
{
bin_image.MakeLike(gray_image);
}
if (NarrayUtil.contains_only(gray_image, (byte)0, (byte)255))
{
bin_image.Copy(gray_image);
return;
}
int image_width = gray_image.Dim(0);
int image_height = gray_image.Dim(1);
int[] hist = new int[MAXVAL];
double[] pdf = new double[MAXVAL]; //probability distribution
double[] cdf = new double[MAXVAL]; //cumulative probability distribution
double[] myu = new double[MAXVAL]; // mean value for separation
double max_sigma;
double[] sigma = new double[MAXVAL]; // inter-class variance
/* Histogram generation */
for (int i = 0; i < MAXVAL; i++)
{
hist[i] = 0;
}
for (int x = 0; x < image_width; x++)
{
for (int y = 0; y < image_height; y++)
{
hist[gray_image[x, y]]++;
}
}
/* calculation of probability density */
for (int i = 0; i < MAXVAL; i++)
{
pdf[i] = (double)hist[i] / (image_width * image_height);
}
/* cdf & myu generation */
cdf[0] = pdf[0];
myu[0] = 0.0; /* 0.0 times prob[0] equals zero */
for (int i = 1; i < MAXVAL; i++)
{
cdf[i] = cdf[i - 1] + pdf[i];
myu[i] = myu[i - 1] + i * pdf[i];
}
/* sigma maximization
* sigma stands for inter-class variance
* and determines optimal threshold value */
int threshold = 0;
max_sigma = 0.0;
for (int i = 0; i < MAXVAL - 1; i++)
{
if (cdf[i] != 0.0 && cdf[i] != 1.0)
{
double p1p2 = cdf[i] * (1.0 - cdf[i]);
double mu1mu2diff = myu[MAXVAL - 1] * cdf[i] - myu[i];
sigma[i] = mu1mu2diff * mu1mu2diff / p1p2;
}
else
{
sigma[i] = 0.0;
}
if (sigma[i] > max_sigma)
{
max_sigma = sigma[i];
threshold = i;
}
}
for (int x = 0; x < image_width; x++)
{
for (int y = 0; y < image_height; y++)
{
if (gray_image[x, y] > threshold)
{
bin_image[x, y] = (byte)(MAXVAL - 1);
}
else
{
bin_image[x, y] = 0;
}
}
}
if (PGeti("debug_otsu") > 0)
{
Logger.Default.Format("Otsu threshold value = {0}\n", threshold);
//ImgIo.write_image_gray("debug_otsu.png", bin_image);
}
}
