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// Copyright (C) 2006 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#ifndef DLIB_THRESHOLDINg_
#define DLIB_THRESHOLDINg_
#include "../pixel.h"
#include "thresholding_abstract.h"
#include "equalize_histogram.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
const unsigned char on_pixel = 255;
const unsigned char off_pixel = 0;
// ----------------------------------------------------------------------------------------
template <
typename in_image_type,
typename out_image_type
>
void threshold_image (
const in_image_type& in_img,
out_image_type& out_img,
typename pixel_traits<typename in_image_type::type>::basic_pixel_type thresh
)
{
COMPILE_TIME_ASSERT( pixel_traits<typename in_image_type::type>::has_alpha == false );
COMPILE_TIME_ASSERT( pixel_traits<typename out_image_type::type>::has_alpha == false );
COMPILE_TIME_ASSERT(pixel_traits<typename out_image_type::type>::grayscale);
// if there isn't any input image then don't do anything
if (in_img.size() == 0)
{
out_img.clear();
return;
}
out_img.set_size(in_img.nr(),in_img.nc());
for (long r = 0; r < in_img.nr(); ++r)
{
for (long c = 0; c < in_img.nc(); ++c)
{
if (get_pixel_intensity(in_img[r][c]) >= thresh)
assign_pixel(out_img[r][c], on_pixel);
else
assign_pixel(out_img[r][c], off_pixel);
}
}
}
// ----------------------------------------------------------------------------------------
template <
typename image_type
>
void threshold_image (
image_type& img,
typename pixel_traits<typename image_type::type>::basic_pixel_type thresh
)
{
threshold_image(img,img,thresh);
}
// ----------------------------------------------------------------------------------------
template <
typename in_image_type,
typename out_image_type
>
void auto_threshold_image (
const in_image_type& in_img,
out_image_type& out_img
)
{
COMPILE_TIME_ASSERT( pixel_traits<typename in_image_type::type>::has_alpha == false );
COMPILE_TIME_ASSERT( pixel_traits<typename out_image_type::type>::has_alpha == false );
COMPILE_TIME_ASSERT( pixel_traits<typename in_image_type::type>::is_unsigned == true );
COMPILE_TIME_ASSERT( pixel_traits<typename out_image_type::type>::is_unsigned == true );
COMPILE_TIME_ASSERT(pixel_traits<typename out_image_type::type>::grayscale);
// if there isn't any input image then don't do anything
if (in_img.size() == 0)
{
out_img.clear();
return;
}
unsigned long thresh;
// find the threshold we should use
matrix<unsigned long,1> hist;
get_histogram(in_img,hist);
// Start our two means (a and b) out at the ends of the histogram
long a = 0;
long b = hist.size()-1;
bool moved_a = true;
bool moved_b = true;
while (moved_a || moved_b)
{
moved_a = false;
moved_b = false;
// catch the degenerate case where the histogram is empty
if (a >= b)
break;
if (hist(a) == 0)
{
++a;
moved_a = true;
}
if (hist(b) == 0)
{
--b;
moved_b = true;
}
}
// now do k-means clustering with k = 2 on the histogram.
moved_a = true;
moved_b = true;
while (moved_a || moved_b)
{
moved_a = false;
moved_b = false;
int64 a_hits = 0;
int64 b_hits = 0;
int64 a_mass = 0;
int64 b_mass = 0;
for (long i = 0; i < hist.size(); ++i)
{
// if i is closer to a
if (std::abs(i-a) < std::abs(i-b))
{
a_mass += hist(i)*i;
a_hits += hist(i);
}
else // if i is closer to b
{
b_mass += hist(i)*i;
b_hits += hist(i);
}
}
long new_a = (a_mass + a_hits/2)/a_hits;
long new_b = (b_mass + b_hits/2)/b_hits;
if (new_a != a)
{
moved_a = true;
a = new_a;
}
if (new_b != b)
{
moved_b = true;
b = new_b;
}
}
// put the threshold between the two means we found
thresh = (a + b)/2;
// now actually apply the threshold
threshold_image(in_img,out_img,thresh);
}
template <
typename image_type
>
void auto_threshold_image (
image_type& img
)
{
auto_threshold_image(img,img);
}
// ----------------------------------------------------------------------------------------
template <
typename in_image_type,
typename out_image_type
>
void hysteresis_threshold (
const in_image_type& in_img,
out_image_type& out_img,
typename pixel_traits<typename in_image_type::type>::basic_pixel_type lower_thresh,
typename pixel_traits<typename in_image_type::type>::basic_pixel_type upper_thresh
)
{
COMPILE_TIME_ASSERT( pixel_traits<typename in_image_type::type>::has_alpha == false );
COMPILE_TIME_ASSERT( pixel_traits<typename out_image_type::type>::has_alpha == false );
COMPILE_TIME_ASSERT(pixel_traits<typename out_image_type::type>::grayscale);
DLIB_ASSERT( lower_thresh <= upper_thresh && is_same_object(in_img, out_img) == false,
"\tvoid hysteresis_threshold(in_img, out_img, lower_thresh, upper_thresh)"
<< "\n\tYou can't use an upper_thresh that is less than your lower_thresh"
<< "\n\tlower_thresh: " << lower_thresh
<< "\n\tupper_thresh: " << upper_thresh
<< "\n\tis_same_object(in_img,out_img): " << is_same_object(in_img,out_img)
);
// if there isn't any input image then don't do anything
if (in_img.size() == 0)
{
out_img.clear();
return;
}
out_img.set_size(in_img.nr(),in_img.nc());
const long size = 100;
long rstack[size];
long cstack[size];
// now do the thresholding
for (long r = 0; r < in_img.nr(); ++r)
{
for (long c = 0; c < in_img.nc(); ++c)
{
typename pixel_traits<typename in_image_type::type>::basic_pixel_type p;
assign_pixel(p,in_img[r][c]);
if (p >= upper_thresh)
{
// now do line following for pixels >= lower_thresh.
// set the stack position to 0.
long pos = 1;
rstack[0] = r;
cstack[0] = c;
while (pos > 0)
{
--pos;
const long r = rstack[pos];
const long c = cstack[pos];
// This is the base case of our recursion. We want to stop if we hit a
// pixel we have already visited.
if (out_img[r][c] == on_pixel)
continue;
out_img[r][c] = on_pixel;
// put the neighbors of this pixel on the stack if they are bright enough
if (r-1 >= 0)
{
if (pos < size && get_pixel_intensity(in_img[r-1][c]) >= lower_thresh)
{
rstack[pos] = r-1;
cstack[pos] = c;
++pos;
}
if (pos < size && c-1 >= 0 && get_pixel_intensity(in_img[r-1][c-1]) >= lower_thresh)
{
rstack[pos] = r-1;
cstack[pos] = c-1;
++pos;
}
if (pos < size && c+1 < in_img.nc() && get_pixel_intensity(in_img[r-1][c+1]) >= lower_thresh)
{
rstack[pos] = r-1;
cstack[pos] = c+1;
++pos;
}
}
if (pos < size && c-1 >= 0 && get_pixel_intensity(in_img[r][c-1]) >= lower_thresh)
{
rstack[pos] = r;
cstack[pos] = c-1;
++pos;
}
if (pos < size && c+1 < in_img.nc() && get_pixel_intensity(in_img[r][c+1]) >= lower_thresh)
{
rstack[pos] = r;
cstack[pos] = c+1;
++pos;
}
if (r+1 < in_img.nr())
{
if (pos < size && get_pixel_intensity(in_img[r+1][c]) >= lower_thresh)
{
rstack[pos] = r+1;
cstack[pos] = c;
++pos;
}
if (pos < size && c-1 >= 0 && get_pixel_intensity(in_img[r+1][c-1]) >= lower_thresh)
{
rstack[pos] = r+1;
cstack[pos] = c-1;
++pos;
}
if (pos < size && c+1 < in_img.nc() && get_pixel_intensity(in_img[r+1][c+1]) >= lower_thresh)
{
rstack[pos] = r+1;
cstack[pos] = c+1;
++pos;
}
}
} // end while (pos >= 0)
}
else
{
out_img[r][c] = off_pixel;
}
}
}
}
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_THRESHOLDINg_
|
