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Image convolution in C++ + Gaussian blur
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#include <iostream> | |
#include <vector> | |
#include <assert.h> | |
#include <cmath> | |
#include <png++/png.hpp> | |
using namespace std; | |
typedef vector<double> Array; | |
typedef vector<Array> Matrix; | |
typedef vector<Matrix> Image; | |
Matrix getGaussian(int height, int width, double sigma) | |
{ | |
Matrix kernel(height, Array(width)); | |
double sum=0.0; | |
int i,j; | |
for (i=0 ; i<height ; i++) { | |
for (j=0 ; j<width ; j++) { | |
kernel[i][j] = exp(-(i*i+j*j)/(2*sigma*sigma))/(2*M_PI*sigma*sigma); | |
sum += kernel[i][j]; | |
} | |
} | |
for (i=0 ; i<height ; i++) { | |
for (j=0 ; j<width ; j++) { | |
kernel[i][j] /= sum; | |
} | |
} | |
return kernel; | |
} | |
Image loadImage(const char *filename) | |
{ | |
png::image<png::rgb_pixel> image(filename); | |
Image imageMatrix(3, Matrix(image.get_height(), Array(image.get_width()))); | |
int h,w; | |
for (h=0 ; h<image.get_height() ; h++) { | |
for (w=0 ; w<image.get_width() ; w++) { | |
imageMatrix[0][h][w] = image[h][w].red; | |
imageMatrix[1][h][w] = image[h][w].green; | |
imageMatrix[2][h][w] = image[h][w].blue; | |
} | |
} | |
return imageMatrix; | |
} | |
void saveImage(Image &image, const char *filename) | |
{ | |
assert(image.size()==3); | |
int height = image[0].size(); | |
int width = image[0][0].size(); | |
int x,y; | |
png::image<png::rgb_pixel> imageFile(width, height); | |
for (y=0 ; y<height ; y++) { | |
for (x=0 ; x<width ; x++) { | |
imageFile[y][x].red = image[0][y][x]; | |
imageFile[y][x].green = image[1][y][x]; | |
imageFile[y][x].blue = image[2][y][x]; | |
} | |
} | |
imageFile.write(filename); | |
} | |
Image applyFilter(Image &image, Matrix &filter){ | |
assert(image.size()==3 && filter.size()!=0); | |
int height = image[0].size(); | |
int width = image[0][0].size(); | |
int filterHeight = filter.size(); | |
int filterWidth = filter[0].size(); | |
int newImageHeight = height-filterHeight+1; | |
int newImageWidth = width-filterWidth+1; | |
int d,i,j,h,w; | |
Image newImage(3, Matrix(newImageHeight, Array(newImageWidth))); | |
for (d=0 ; d<3 ; d++) { | |
for (i=0 ; i<newImageHeight ; i++) { | |
for (j=0 ; j<newImageWidth ; j++) { | |
for (h=i ; h<i+filterHeight ; h++) { | |
for (w=j ; w<j+filterWidth ; w++) { | |
newImage[d][i][j] += filter[h-i][w-j]*image[d][h][w]; | |
} | |
} | |
} | |
} | |
} | |
return newImage; | |
} | |
Image applyFilter(Image &image, Matrix &filter, int times) | |
{ | |
Image newImage = image; | |
for(int i=0 ; i<times ; i++) { | |
newImage = applyFilter(newImage, filter); | |
} | |
return newImage; | |
} | |
int main() | |
{ | |
Matrix filter = getGaussian(5, 5, 10.0); | |
cout << "Loading image..." << endl; | |
Image image = loadImage("image.png"); | |
cout << "Applying filter..." << endl; | |
Image newImage = applyFilter(image, filter); | |
cout << "Saving image..." << endl; | |
saveImage(newImage, "newImage.png"); | |
cout << "Done!" << endl; | |
} |
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output size will be the same as input if you do padding with the input.
same formula for width.