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ES-DE/external/CImg/examples/radon_transform2d.cpp

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/*
#
# File : radon_transform2d.cpp
# ( C++ source file )
#
# Description : An implementation of the Radon Transform.
# This file is a part of the CImg Library project.
# ( http://cimg.eu )
#
# Copyright : David G. Starkweather
# ( starkdg@sourceforge.net - starkweatherd@cox.net )
#
# License : CeCILL v2.0
# ( http://www.cecill.info/licences/Licence_CeCILL_V2-en.html )
#
# This software is governed by the CeCILL license under French law and
# abiding by the rules of distribution of free software. You can use,
# modify and/ or redistribute the software under the terms of the CeCILL
# license as circulated by CEA, CNRS and INRIA at the following URL
# "http://www.cecill.info".
#
# As a counterpart to the access to the source code and rights to copy,
# modify and redistribute granted by the license, users are provided only
# with a limited warranty and the software's author, the holder of the
# economic rights, and the successive licensors have only limited
# liability.
#
# In this respect, the user's attention is drawn to the risks associated
# with loading, using, modifying and/or developing or reproducing the
# software by the user in light of its specific status of free software,
# that may mean that it is complicated to manipulate, and that also
# therefore means that it is reserved for developers and experienced
# professionals having in-depth computer knowledge. Users are therefore
# encouraged to load and test the software's suitability as regards their
# requirements in conditions enabling the security of their systems and/or
# data to be ensured and, more generally, to use and operate it in the
# same conditions as regards security.
#
# The fact that you are presently reading this means that you have had
# knowledge of the CeCILL license and that you accept its terms.
#
*/
#include "CImg.h"
using namespace cimg_library;
#ifndef cimg_imagepath
#define cimg_imagepath "img/"
#endif
#define ROUNDING_FACTOR(x) (((x) >= 0) ? 0.5 : -0.5)
CImg<double> GaussianKernel(double rho);
CImg<float> ApplyGaussian(CImg<unsigned char> im,double rho);
CImg<unsigned char> RGBtoGrayScale(CImg<unsigned char> &im);
int GetAngle(int dy,int dx);
CImg<unsigned char> CannyEdges(CImg<float> im, double T1, double T2,bool doHysteresis);
CImg<> RadonTransform(CImg<unsigned char> im,int N);
// Main procedure
//----------------
int main(int argc,char **argv) {
cimg_usage("Illustration of the Radon Transform");
const char *file = cimg_option("-f",cimg_imagepath "parrot.ppm","path and file name");
const double sigma = cimg_option("-r",1.0,"blur coefficient for gaussian low pass filter (lpf)"),
thresh1 = cimg_option("-t1",0.50,"lower threshold for canny edge detector"),
thresh2 = cimg_option("-t2",1.25,"upper threshold for canny edge detector");;
const int N = cimg_option("-n",64,"number of angles to consider in the Radon transform - should be a power of 2");
//color to draw lines
const unsigned char green[] = {0,255,0};
CImg<unsigned char> src(file);
int rhomax = (int)std::sqrt((double)(src.width()*src.width() + src.height()*src.height()))/2;
if (cimg::dialog(cimg::basename(argv[0]),
"Instructions:\n"
"Click on space bar or Enter key to display Radon transform of given image\n"
"Click on anywhere in the transform window to display a \n"
"corresponding green line in the original image\n",
"Start", "Quit",0,0,0,0,
src.get_resize(100,100,1,3),true)) std::exit(0);
//retrieve a grayscale from the image
CImg<unsigned char> grayScaleIm;
if ((src.spectrum() == 3) && (src.width() > 0) && (src.height() > 0) && (src.depth() == 1))
grayScaleIm = (CImg<unsigned char>)src.get_norm(0).quantize(255,false);
else if ((src.spectrum() == 1)&&(src.width() > 0) && (src.height() > 0) && (src.depth() == 1))
grayScaleIm = src;
else { // image in wrong format
if (cimg::dialog(cimg::basename("wrong file format"),
"Incorrect file format\n","OK",0,0,0,0,0,
src.get_resize(100,100,1,3),true)) std::exit(0);
}
//blur the image with a Gaussian lpf to remove spurious edges (e.g. noise)
CImg<float> blurredIm = ApplyGaussian(grayScaleIm,sigma);
//use canny edge detection algorithm to get edge map of the image
//- the threshold values are used to perform hysteresis in the edge detection process
CImg<unsigned char> cannyEdgeMap = CannyEdges(blurredIm,thresh1,thresh2,false);
CImg<unsigned char> radonImage(500,400,1,1,0);
//display the two windows
CImgDisplay dispImage(src,"original image");
dispImage.move(CImgDisplay::screen_width()/8,CImgDisplay::screen_height()/8);
CImgDisplay dispRadon(radonImage,"Radon Transform");
dispRadon.move(CImgDisplay::screen_width()/4,CImgDisplay::screen_height()/4);
CImgDisplay dispCanny(cannyEdgeMap,"canny edges");
//start main display loop
while (!dispImage.is_closed() && !dispRadon.is_closed() &&
!dispImage.is_keyQ() && !dispRadon.is_keyQ() &&
!dispImage.is_keyESC() && !dispRadon.is_keyESC()) {
CImgDisplay::wait(dispImage,dispRadon);
if (dispImage.is_keySPACE() || dispRadon.is_keySPACE()) {
radonImage = (CImg<unsigned char>)RadonTransform(cannyEdgeMap,N).quantize(255,false).resize(500,400);
radonImage.display(dispRadon);
}
//when clicking on dispRadon window, draw line in original image window
if (dispRadon.button())
{
const double rho = dispRadon.mouse_y()*rhomax/dispRadon.height(),
theta = (dispRadon.mouse_x()*N/dispRadon.width())*2*cimg::PI/N,
x = src.width()/2 + rho*std::cos(theta),
y = src.height()/2 + rho*std::sin(theta);
const int x0 = (int)(x + 1000*std::cos(theta + cimg::PI/2)),
y0 = (int)(y + 1000*std::sin(theta + cimg::PI/2)),
x1 = (int)(x - 1000*std::cos(theta + cimg::PI/2)),
y1 = (int)(y - 1000*std::sin(theta + cimg::PI/2));
src.draw_line(x0,y0,x1,y1,green,1.0f,0xF0F0F0F0).display(dispImage);
}
}
return 0;
}
/**
* PURPOSE: create a 5x5 gaussian kernel matrix
* PARAM rho - gaussiam equation parameter (default = 1.0)
* RETURN CImg<double> the gaussian kernel
**/
CImg<double> GaussianKernel(double sigma = 1.0)
{
CImg<double> resultIm(5,5,1,1,0);
int midX = 3, midY = 3;
cimg_forXY(resultIm,X,Y) {
resultIm(X,Y) = std::ceil(256.0*(std::exp(-(midX*midX + midY*midY)/(2*sigma*sigma)))/(2*cimg::PI*sigma*sigma));
}
return resultIm;
}
/*
* PURPOSE: convolve a given image with the gaussian kernel
* PARAM CImg<unsigned char> im - image to be convolved upon
* PARAM double sigma - gaussian equation parameter
* RETURN CImg<float> image resulting from the convolution
* */
CImg<float> ApplyGaussian(CImg<unsigned char> im,double sigma)
{
CImg<float> smoothIm(im.width(),im.height(),1,1,0);
//make gaussian kernel
CImg<float> gk = GaussianKernel(sigma);
//apply gaussian
CImg_5x5(I,int);
cimg_for5x5(im,X,Y,0,0,I,int) {
float sum = 0;
sum += gk(0,0)*Ibb + gk(0,1)*Ibp + gk(0,2)*Ibc + gk(0,3)*Ibn + gk(0,4)*Iba;
sum += gk(1,0)*Ipb + gk(1,1)*Ipp + gk(1,2)*Ipc + gk(1,3)*Ipn + gk(1,4)*Ipa;
sum += gk(2,0)*Icb + gk(2,1)*Icp + gk(2,2)*Icc + gk(2,3)*Icn + gk(2,4)*Ica;
sum += gk(3,0)*Inb + gk(3,1)*Inp + gk(3,2)*Inc + gk(3,3)*Inn + gk(3,4)*Ina;
sum += gk(4,0)*Iab + gk(4,1)*Iap + gk(4,2)*Iac + gk(4,3)*Ian + gk(4,4)*Iaa;
smoothIm(X,Y) = sum/256;
}
return smoothIm;
}
/**
* PURPOSE: convert a given rgb image to a MxNX1 single vector grayscale image
* PARAM: CImg<unsigned char> im - rgb image to convert
* RETURN: CImg<unsigned char> grayscale image with MxNx1x1 dimensions
**/
CImg<unsigned char> RGBtoGrayScale(CImg<unsigned char> &im)
{
CImg<unsigned char> grayImage(im.width(),im.height(),im.depth(),1,0);
if (im.spectrum() == 3) {
cimg_forXYZ(im,X,Y,Z) {
grayImage(X,Y,Z,0) = (unsigned char)(0.299*im(X,Y,Z,0) + 0.587*im(X,Y,Z,1) + 0.114*im(X,Y,Z,2));
}
}
grayImage.quantize(255,false);
return grayImage;
}
/**
* PURPOSE: aux. function used by CannyEdges to quantize an angle theta given by gradients, dx and dy
* into 0 - 7
* PARAM: dx,dy - gradient magnitudes
* RETURN int value between 0 and 7
**/
int GetAngle(int dy,int dx)
{
double angle = cimg::abs(std::atan2((double)dy,(double)dx));
if ((angle >= -cimg::PI/8)&&(angle <= cimg::PI/8))//-pi/8 to pi/8 => 0
return 0;
else if ((angle >= cimg::PI/8)&&(angle <= 3*cimg::PI/8))//pi/8 to 3pi/8 => pi/4
return 1;
else if ((angle > 3*cimg::PI/8)&&(angle <= 5*cimg::PI/8))//3pi/8 to 5pi/8 => pi/2
return 2;
else if ((angle > 5*cimg::PI/8)&&(angle <= 7*cimg::PI/8))//5pi/8 to 7pi/8 => 3pi/4
return 3;
else if (((angle > 7*cimg::PI/8) && (angle <= cimg::PI)) ||
((angle <= -7*cimg::PI/8)&&(angle >= -cimg::PI))) //-7pi/8 to -pi OR 7pi/8 to pi => pi
return 4;
else return 0;
}
/**
* PURPOSE: create an edge map of the given image with hysteresis using thresholds T1 and T2
* PARAMS: CImg<float> im the image to perform edge detection on
* T1 lower threshold
* T2 upper threshold
* RETURN CImg<unsigned char> edge map
**/
CImg<unsigned char> CannyEdges(CImg<float> im, double T1, double T2, bool doHysteresis=false)
{
CImg<unsigned char> edges(im);
CImg<float> secDerivs(im);
secDerivs.fill(0);
edges.fill(0);
CImgList<float> gradients = im.get_gradient("xy",1);
int image_width = im.width();
int image_height = im.height();
cimg_forXY(im,X,Y) {
double Gr = std::sqrt(std::pow((double)gradients[0](X,Y),2.0) + std::pow((double)gradients[1](X,Y),2.0));
double theta = GetAngle(Y,X);
//if Gradient magnitude is positive and X,Y within the image
//take the 2nd deriv in the appropriate direction
if ((Gr > 0)&&(X < image_width - 2)&&(Y < image_height - 2)) {
if (theta == 0)
secDerivs(X,Y) = im(X + 2,Y) - 2*im(X + 1,Y) + im(X,Y);
else if (theta == 1)
secDerivs(X,Y) = im(X + 2,Y + 2) - 2*im(X + 1,Y + 1) + im(X,Y);
else if (theta == 2)
secDerivs(X,Y) = im(X,Y + 2) - 2*im(X,Y + 1) + im(X,Y);
else if (theta == 3)
secDerivs(X,Y) = im(X + 2,Y + 2) - 2*im(X + 1,Y + 1) + im(X,Y);
else if (theta == 4)
secDerivs(X,Y) = im(X + 2,Y) - 2*im(X + 1,Y) + im(X,Y);
}
}
//for each 2nd deriv that crosses a zero point and magnitude passes the upper threshold.
//Perform hysteresis in the direction of the gradient, rechecking the gradient
//angle for each pixel that meets the threshold requirement. Stop checking when
//the lower threshold is not reached.
CImg_5x5(I,float);
cimg_for5x5(secDerivs,X,Y,0,0,I,float) {
if ( (Ipp*Ibb < 0) ||
(Ipc*Ibc < 0)||
(Icp*Icb < 0) ) {
double Gr = std::sqrt(std::pow((double)gradients[0](X,Y),2.0) + std::pow((double)gradients[1](X,Y),2.0));
int dir = GetAngle(Y,X);
int Xt = X, Yt = Y, delta_x = 0, delta_y=0;
double GRt = Gr;
if (Gr >= T2)
edges(X,Y) = 255;
//work along the gradient in one direction
if (doHysteresis) {
while ((Xt > 0) && (Xt < image_width - 1) && (Yt > 0) && (Yt < image_height - 1)) {
switch (dir){
case 0 : delta_x=0;delta_y=1;break;
case 1 : delta_x=1;delta_y=1;break;
case 2 : delta_x=1;delta_y=0;break;
case 3 : delta_x=1;delta_y=-1;break;
case 4 : delta_x=0;delta_y=1;break;
}
Xt += delta_x;
Yt += delta_y;
GRt = std::sqrt(std::pow((double)gradients[0](Xt,Yt),2.0) + std::pow((double)gradients[1](Xt,Yt),2.0));
dir = GetAngle(Yt,Xt);
if (GRt >= T1)
edges(Xt,Yt) = 255;
}
//work along gradient in other direction
Xt = X; Yt = Y;
while ((Xt > 0) && (Xt < image_width - 1) && (Yt > 0) && (Yt < image_height - 1)) {
switch (dir){
case 0 : delta_x=0;delta_y=1;break;
case 1 : delta_x=1;delta_y=1;break;
case 2 : delta_x=1;delta_y=0;break;
case 3 : delta_x=1;delta_y=-1;break;
case 4 : delta_x=0;delta_y=1;break;
}
Xt -= delta_x;
Yt -= delta_y;
GRt = std::sqrt(std::pow((double)gradients[0](Xt,Yt),2.0) + std::pow((double)gradients[1](Xt,Yt),2.0));
dir = GetAngle(Yt,Xt);
if (GRt >= T1)
edges(Xt,Yt) = 255;
}
}
}
}
return edges;
}
/**
* PURPOSE: perform radon transform of given image
* PARAM: CImg<unsigned char> im - image to detect lines
* int N - number of angles to consider (should be a power of 2)
* (the values of N will be spread over 0 to 2PI)
* RETURN CImg<unsigned char> - transform of given image of size, N x D
* D = rhomax = sqrt(dimx*dimx + dimy*dimy)/2
**/
CImg<> RadonTransform(CImg<unsigned char> im,int N) {
int image_width = im.width();
int image_height = im.height();
//calc offsets to center the image
float xofftemp = image_width/2.0f - 1;
float yofftemp = image_height/2.0f - 1;
int xoffset = (int)std::floor(xofftemp + ROUNDING_FACTOR(xofftemp));
int yoffset = (int)std::floor(yofftemp + ROUNDING_FACTOR(yofftemp));
float dtemp = (float)std::sqrt((double)(xoffset*xoffset + yoffset*yoffset));
int D = (int)std::floor(dtemp + ROUNDING_FACTOR(dtemp));
CImg<> imRadon(N,D,1,1,0);
//for each angle k to consider
for (int k= 0 ; k < N; k++) {
//only consider from PI/8 to 3PI/8 and 5PI/8 to 7PI/8
//to avoid computational complexity of a steep angle
if (k == 0){k = N/8;continue;}
else if (k == (3*N/8 + 1)){ k = 5*N/8;continue;}
else if (k == 7*N/8 + 1){k = N; continue;}
//for each rho length, determine linear equation and sum the line
//sum is to sum the values along the line at angle k2pi/N
//sum2 is to sum the values along the line at angle k2pi/N + N/4
//The sum2 is performed merely by swapping the x,y axis as if the image were rotated 90 degrees.
for (int d=0; d < D; d++) {
double theta = 2*k*cimg::PI/N;//calculate actual theta
double alpha = std::tan(theta + cimg::PI/2);//calculate the slope
double beta_temp = -alpha*d*std::cos(theta) + d*std::sin(theta);//y-axis intercept for the line
int beta = (int)std::floor(beta_temp + ROUNDING_FACTOR(beta_temp));
//for each value of m along x-axis, calculate y
//if the x,y location is within the boundary for the respective image orientations, add to the sum
unsigned int sum1 = 0,
sum2 = 0;
int M = (image_width >= image_height) ? image_width : image_height;
for (int m=0;m < M; m++) {
//interpolate in-between values using nearest-neighbor approximation
//using m,n as x,y indices into image
double n_temp = alpha*(m - xoffset) + beta;
int n = (int)std::floor(n_temp + ROUNDING_FACTOR(n_temp));
if ((m < image_width) && (n + yoffset >= 0) && (n + yoffset < image_height))
{
sum1 += im(m, n + yoffset);
}
n_temp = alpha*(m - yoffset) + beta;
n = (int)std::floor(n_temp + ROUNDING_FACTOR(n_temp));
if ((m < image_height)&&(n + xoffset >= 0)&&(n + xoffset < image_width))
{
sum2 += im(-(n + xoffset) + image_width - 1, m);
}
}
//assign the sums into the result matrix
imRadon(k,d) = (float)sum1;
//assign sum2 to angle position for theta + PI/4
imRadon(((k + N/4)%N),d) = (float)sum2;
}
}
return imRadon;
}
/* references:
* 1. See Peter Toft's thesis on the Radon transform: http://petertoft.dk/PhD/index.html
* While I changed his basic algorithm, the main idea is still the same and provides an excellent explanation.
*
* */
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