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Implementation of Gaussian sharpen

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Kovid Goyal 2016-05-06 09:10:03 +05:30
parent a244ee9da6
commit cea4a8eebf
1 changed files with 53 additions and 1 deletions
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54
src/calibre/utils/imageops/imageops.cpp
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@ -117,12 +117,13 @@ QImage grayscale(const QImage &image) { // {{{
r+=((weight))*(qRed((pixel))); g+=((weight))*(qGreen((pixel))); \
b+=((weight))*(qBlue((pixel)));
QImage convolve(QImage &img, int matrix_size, float *matrix) {
QImage convolve(const QImage &image, int matrix_size, float *matrix) {
int i, x, y, w, h, matrix_x, matrix_y;
int edge = matrix_size/2;
QRgb *dest, *s, **scanblock;
const QRgb *src = NULL;
float *m, *normalize_matrix, normalize, r, g, b;
QImage img(image);
if(!(matrix_size % 2))
throw std::out_of_range("Convolution kernel width must be an odd number");
@ -231,6 +232,57 @@ QImage convolve(QImage &img, int matrix_size, float *matrix) {
delete[] normalize_matrix;
return buffer;
}
int default_convolve_matrix_size(const float radius, const float sigma, const bool quality) {
int i, matrix_size;
float normalize, value;
float sigma2 = sigma*sigma*2.0;
float sigmaSQ2PI = M_SQ2PI*sigma;
int max = quality ? 65535 : 255;
if(sigma == 0.0) throw std::out_of_range("Zero sigma is invalid for convolution");
if(radius > 0.0)
return((int)(2.0*std::ceil(radius)+1.0));
Py_BEGIN_ALLOW_THREADS;
matrix_size = 5;
do{
normalize = 0.0;
for(i=(-matrix_size/2); i <= (matrix_size/2); ++i)
normalize += std::exp(-((float) i*i)/sigma2) / sigmaSQ2PI;
i = matrix_size/2;
value = std::exp(-((float) i*i)/sigma2) / sigmaSQ2PI / normalize;
matrix_size += 2;
} while((int)(max*value) > 0);
matrix_size-=4;
Py_END_ALLOW_THREADS;
return(matrix_size);
}
// }}}
QImage gaussian_sharpen(const QImage &img, const float radius, const float sigma, const bool high_quality=true) {
int matrix_size = default_convolve_matrix_size(radius, sigma, high_quality);
int len = matrix_size*matrix_size;
float alpha, *matrix = new float[len];
float sigma2 = sigma*sigma*2.0;
float sigmaPI2 = 2.0*M_PI*sigma*sigma;
int half = matrix_size/2;
int x, y, i=0, j=half;
float normalize=0.0;
for(y=(-half); y <= half; ++y, --j){
for(x=(-half); x <= half; ++x, ++i){
alpha = std::exp(-((float)x*x+y*y)/sigma2);
matrix[i] = alpha/sigmaPI2;
normalize += matrix[i];
}
}
matrix[i/2]=(-2.0)*normalize;
QImage result(convolve(img, matrix_size, matrix));
delete[] matrix;
return(result);
}