83 lines
3.3 KiB
C++
83 lines
3.3 KiB
C++
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//
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// Copyright 2005-2007 Adobe Systems Incorporated
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// Copyright 2021 Pranam Lashkari <plashkari628@gmail.com>
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//
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// Distributed under the Boost Software License, Version 1.0
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// See accompanying file LICENSE_1_0.txt or copy at
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// http://www.boost.org/LICENSE_1_0.txt
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#include <boost/gil.hpp>
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#include <boost/gil/extension/io/jpeg.hpp>
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#include <boost/gil/image_processing/kernel.hpp>
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#include <boost/gil/image_processing/convolve.hpp>
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// Convolves the image with a Gaussian kernel.
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// Note that the kernel can be fixed or resizable:
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// kernel_1d_fixed<float, N> k(matrix, centre) produces a fixed kernel
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// kernel_1d<float> k(matrix, size, centre) produces a resizable kernel
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// Work can be done row by row and column by column, as in this example,
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// using the functions convolve_rows and convolve_cols (or their _fixed counterpart)
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// but the header boost/gil/image_processing/convolve.hpp also offers the function convolve_1d which combines the two.
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// See also:
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// convolve2d.cpp - Convolution with 2d kernels
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int main() {
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using namespace boost::gil;
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rgb8_image_t img;
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read_image("test.jpg", img, jpeg_tag{});
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// Convolve the rows and the columns of the image with a fixed kernel
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rgb8_image_t convolved(img);
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// radius-1 Gaussian kernel, size 9
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float gaussian_1[]={0.00022923296f,0.0059770769f,0.060597949f,0.24173197f,0.38292751f,
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0.24173197f,0.060597949f,0.0059770769f,0.00022923296f};
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/*
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// radius-2 Gaussian kernel, size 15
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float gaussian_2[]={
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0.00048869418f,0.0024031631f,0.0092463447f,
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0.027839607f,0.065602221f,0.12099898f,0.17469721f,
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0.19744757f,
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0.17469721f,0.12099898f,0.065602221f,0.027839607f,
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0.0092463447f,0.0024031631f,0.00048869418f
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};
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//radius-3 Gaussian kernel, size 23
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float gaussian_3[]={
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0.00016944126f,0.00053842377f,0.0015324751f,0.0039068931f,
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0.0089216027f,0.018248675f,0.033434924f,0.054872241f,
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0.080666073f,0.10622258f,0.12529446f,
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0.13238440f,
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0.12529446f,0.10622258f,0.080666073f,
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0.054872241f,0.033434924f,0.018248675f,0.0089216027f,
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0.0039068931f,0.0015324751f,0.00053842377f,0.00016944126f
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};
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//radius-4 Gaussian kernel, size 29
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float gaussian_4[]={
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0.00022466264f,0.00052009715f,0.0011314391f,0.0023129794f,
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0.0044433107f,0.0080211498f,0.013606987f,0.021691186f,
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0.032493830f,0.045742013f,0.060509924f,0.075220309f,
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0.087870099f,0.096459411f,0.099505201f,0.096459411f,0.087870099f,
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0.075220309f,0.060509924f,0.045742013f,0.032493830f,
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0.021691186f,0.013606987f,0.0080211498f,0.0044433107f,
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0.0023129794f,0.0011314391f,0.00052009715f,0.00022466264f,
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};
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*/
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kernel_1d_fixed<float,9> kernel(gaussian_1,4);
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convolve_rows_fixed<rgb32f_pixel_t>(const_view(convolved),kernel,view(convolved));
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convolve_cols_fixed<rgb32f_pixel_t>(const_view(convolved),kernel,view(convolved));
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write_view("out-convolution.jpg", view(convolved), jpeg_tag{});
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// This is how to use a resizable kernel
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kernel_1d<float> kernel2(gaussian_1,9,4);
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convolve_rows<rgb32f_pixel_t>(const_view(img),kernel2,view(img));
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convolve_cols<rgb32f_pixel_t>(const_view(img),kernel2,view(img));
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write_view("out-convolution2.jpg", view(img), jpeg_tag{});
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return 0;
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}
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