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add rgbtoint

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Dennis Eichhorn 2022-08-14 20:25:49 +02:00
parent 246b77ca08
commit 0d7b5ec5a0
2 changed files with 127 additions and 0 deletions
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10
Image/ImageUtils.h
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@ -34,6 +34,16 @@ namespace Image {
return lStar / 100.0;
}
static inline
int rgbToInt(int r, int g, int b)
{
int rgb = r;
rgb = (rgb << 8) + g;
rgb = (rgb << 8) + b;
return rgb;
}
};
}

117
Image/Kernel.h Normal file
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@ -0,0 +1,117 @@
/**
* Karaka
*
* @package Image
* @copyright Dennis Eichhorn
* @license OMS License 1.0
* @version 1.0.0
* @link https://karaka.app
*/
#ifndef IMAGE_KERNEL_H
#define IMAGE_KERNEL_H
#include <stdio.h>
#include <opencv2/opencv.hpp>
#include "ImageUtils.h"
#include "../Utils/MathUtils.h"
namespace Image {
const float KERNEL_RIDGE_1[3][3] = {
{0.0, -1.0, 0.0},
{-1.0, 4.0, -1.0},
{0.0, -1.0, 0.0}
};
const float KERNEL_RIDGE_2[3][3] = {
{-1.0, -1.0, -1.0},
{-1.0, 8.0, -1.0},
{-1.0, -1.0, -1.0}
};
const float KERNEL_SHARPEN[3][3] = {
{0.0, -1.0, 0.0},
{-1.0, 5.0, -1.0},
{0.0, -1.0, 0.0}
};
const float KERNEL_BOX_BLUR[3][3] = {
{1.0 / 9.0, 1.0 / 9.0, 1.0 / 9.0},
{1.0 / 9.0, 1.0 / 9.0, 1.0 / 9.0},
{1.0 / 9.0, 1.0 / 9.0, 1.0 / 9.0}
};
const float KERNEL_GAUSSUAN_BLUR_3[3][3] = {
{1.0 / 16.0, 2.0 / 16.0, 1.0 / 16.0},
{2.0 / 16.0, 4.0 / 16.0, 2.0 / 16.0},
{1.0 / 16.0, 2.0 / 16.0, 1.0 / 16.0}
};
const float KERNEL_EMBOSS[3][3] = {
{-2.0, -1.0, 0.0},
{-1.0, 1.0, 1.0},
{0.0, 1.0, 2.0}
};
const float KERNEL_UNSHARP_MASKING[5][5] = {
{-1.0 / 256.0, -4.0 / 256.0, -6.0 / 256.0, -4.0 / 256.0, -1.0 / 256.0},
{-4.0 / 256.0, -16.0 / 256.0, -24.0 / 256.0, -16.0 / 256.0, -4.0 / 256.0},
{-6.0 / 256.0, -24.0 / 256.0, 476.0 / 256.0, -24.0 / 256.0, -6.0 / 256.0},
{-4.0 / 256.0, -16.0 / 256.0, -24.0 / 256.0, -16.0 / 256.0, -4.0 / 256.0},
{-1.0 / 256.0, -4.0 / 256.0, -6.0 / 256.0, -4.0 / 256.0, -1.0 / 256.0},
};
class Kernel {
private:
public:
/**
* @see https://en.wikipedia.org/wiki/Kernel_(image_processing)
* @see https://towardsdatascience.com/image-processing-with-python-blurring-and-sharpening-for-beginners-3bcebec0583a
* @see https://web.eecs.umich.edu/~jjcorso/t/598F14/files/lecture_0924_filtering.pdf
*/
static
cv::Mat convolve(cv::Mat in, const float kernel[][3])
{
cv::Size dim = in.size();
cv::Mat out(in.size(), in.type());
int kernel_dimension = 3;
int kWidthRadius = (unsigned int) kernel_dimension >> 1;
int kHeightRadius = (unsigned int) kernel_dimension >> 1;
int newPixel;
cv::Vec3b p;
// @todo: implement @see https://rosettacode.org/wiki/Image_convolution
// @todo: not working yet
for (int i = dim.width - 1; i >= 0; --i) {
for (int j = dim.height - 1; j >= 0; --j) {
newPixel = 0;
for (int ki = kernel_dimension - 1; ki >= 0; --ki) {
for (int kj = kernel_dimension - 1; kj >= 0; --kj) {
p = in.at<cv::Vec3b>(
oms_min(oms_max(j + kj - kHeightRadius, 0), dim.height - 1),
oms_min(oms_max(i + ki - kWidthRadius, 0), dim.width - 1)
);
newPixel += kernel[ki][kj] * Image::ImageUtils::rgbToInt(p[2], p[1], p[0]);
}
}
// bgr vs rgb
out.at<cv::Vec3b>(j, i)[0] = newPixel & 255;
out.at<cv::Vec3b>(j, i)[1] = (newPixel >> 8) & 255;
out.at<cv::Vec3b>(j, i)[2] = (newPixel >> 16) & 255;
}
}
return out;
}
};
}
#endif