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Image convolution function ImageKernelConvolution (#3528) 

* Added image convultion ImageKernelConvolution

* comment changes

* spelling changes and change to kernel size

* removed kernel normalization inside function

* fix to formating
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Karim 2023-11-18 14:05:45 -05:00 committed by GitHub
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1
examples/Makefile
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@ -490,6 +490,7 @@ TEXTURES = \
textures/textures_gif_player \
textures/textures_image_drawing \
textures/textures_image_generation \
textures/textures_image_kernel \
textures/textures_image_loading \
textures/textures_image_processing \
textures/textures_image_rotate \

127
examples/textures/textures_image_kernel.c Normal file
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@ -0,0 +1,127 @@
/*******************************************************************************************
*
* raylib [textures] example - Image loading and texture creation
*
* NOTE: Images are loaded in CPU memory (RAM); textures are loaded in GPU memory (VRAM)
*
* Example originally created with raylib 1.3, last time updated with raylib 1.3
*
* Example licensed under an unmodified zlib/libpng license, which is an OSI-certified,
* BSD-like license that allows static linking with closed source software
*
* Copyright (c) 2015-2023 Karim Salem (@kimo-s)
*
********************************************************************************************/
#include "raylib.h"
//------------------------------------------------------------------------------------
// Program main entry point
//------------------------------------------------------------------------------------
void normalizeKernel(float *kernel, int size){
float sum = 0.0f;
for(int i = 0; i < size; i++)
{
sum += kernel[i];
}
if(sum != 0.0f)
{
for(int i = 0; i < size; i++)
{
kernel[i] /= sum;
}
}
}
int main(void)
{
// Initialization
//--------------------------------------------------------------------------------------
Image image = LoadImage("resources/cat.png"); // Loaded in CPU memory (RAM)
const int screenWidth = 800;
const int screenHeight = 450;
InitWindow(screenWidth, screenHeight, "raylib [textures] example - image convolution");
float gaussiankernel[] = {1.0, 2.0, 1.0,
2.0, 4.0, 2.0,
1.0, 2.0, 1.0};
float sobelkernel[] = {1.0, 0.0, -1.0,
2.0, 0.0, -2.0,
1.0, 0.0, -1.0};
float sharpenkernel[] = {0.0, -1.0, 0.0,
-1.0, 5.0, -1.0,
0.0, -1.0, 0.0};
normalizeKernel(gaussiankernel, 9);
normalizeKernel(sharpenkernel, 9);
normalizeKernel(sobelkernel, 9);
Image catSharpend = ImageCopy(image);
ImageKernelConvolution(&catSharpend, sharpenkernel, 9);
Image catSobel = ImageCopy(image);
ImageKernelConvolution(&catSobel, sobelkernel, 9);
Image catGaussian = ImageCopy(image);
for(int i = 0; i < 6; i++)
{
ImageKernelConvolution(&catGaussian, gaussiankernel, 9);
}
ImageCrop(&image, (Rectangle){ 0, 0, (float)200, (float)450 });
ImageCrop(&catGaussian, (Rectangle){ 0, 0, (float)200, (float)450 });
ImageCrop(&catSobel, (Rectangle){ 0, 0, (float)200, (float)450 });
ImageCrop(&catSharpend, (Rectangle){ 0, 0, (float)200, (float)450 });
Texture2D texture = LoadTextureFromImage(image); // Image converted to texture, GPU memory (VRAM)
Texture2D catSharpendTexture = LoadTextureFromImage(catSharpend);
Texture2D catSobelTexture = LoadTextureFromImage(catSobel);
Texture2D catGaussianTexture = LoadTextureFromImage(catGaussian);
UnloadImage(image); // Once image has been converted to texture and uploaded to VRAM, it can be unloaded from RAM
UnloadImage(catGaussian);
UnloadImage(catSobel);
UnloadImage(catSharpend);
SetTargetFPS(60); // Set our game to run at 60 frames-per-second
//---------------------------------------------------------------------------------------
// Main game loop
while (!WindowShouldClose()) // Detect window close button or ESC key
{
// Update
//----------------------------------------------------------------------------------
// TODO: Update your variables here
//----------------------------------------------------------------------------------
// Draw
//----------------------------------------------------------------------------------
BeginDrawing();
ClearBackground(RAYWHITE);
DrawTexture(catSharpendTexture, 0, 0, WHITE);
DrawTexture(catSobelTexture, 200, 0, WHITE);
DrawTexture(catGaussianTexture, 400, 0, WHITE);
DrawTexture(texture, 600, 0, WHITE);
EndDrawing();
//----------------------------------------------------------------------------------
}
// De-Initialization
//--------------------------------------------------------------------------------------
UnloadTexture(texture); // Texture unloading
UnloadTexture(catGaussianTexture);
UnloadTexture(catSobelTexture);
UnloadTexture(catSharpendTexture);
CloseWindow(); // Close window and OpenGL context
//--------------------------------------------------------------------------------------
return 0;
}

1
projects/Notepad++/raylib_npp_parser/raylib_to_parse.h
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@ -375,6 +375,7 @@ RLAPI void ImageAlphaClear(Image *image, Color color, float threshold);
RLAPI void ImageAlphaMask(Image *image, Image alphaMask); // Apply alpha mask to image
RLAPI void ImageAlphaPremultiply(Image *image); // Premultiply alpha channel
RLAPI void ImageBlurGaussian(Image *image, int blurSize); // Apply Gaussian blur using a box blur approximation
RLAPI void ImageKernelConvolution(Image *image, float* kernel, int kernelSize); // Apply Custom Square image convolution kernel
RLAPI void ImageResize(Image *image, int newWidth, int newHeight); // Resize image (Bicubic scaling algorithm)
RLAPI void ImageResizeNN(Image *image, int newWidth,int newHeight); // Resize image (Nearest-Neighbor scaling algorithm)
RLAPI void ImageResizeCanvas(Image *image, int newWidth, int newHeight, int offsetX, int offsetY, Color fill); // Resize canvas and fill with color

1
src/raylib.h
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@ -1329,6 +1329,7 @@ RLAPI void ImageAlphaClear(Image *image, Color color, float threshold);
RLAPI void ImageAlphaMask(Image *image, Image alphaMask); // Apply alpha mask to image
RLAPI void ImageAlphaPremultiply(Image *image); // Premultiply alpha channel
RLAPI void ImageBlurGaussian(Image *image, int blurSize); // Apply Gaussian blur using a box blur approximation
RLAPI void ImageKernelConvolution(Image *image, float* kernel, int kernelSize); // Apply Custom Square image convolution kernel
RLAPI void ImageResize(Image *image, int newWidth, int newHeight); // Resize image (Bicubic scaling algorithm)
RLAPI void ImageResizeNN(Image *image, int newWidth,int newHeight); // Resize image (Nearest-Neighbor scaling algorithm)
RLAPI void ImageResizeCanvas(Image *image, int newWidth, int newHeight, int offsetX, int offsetY, Color fill); // Resize canvas and fill with color

142
src/rtextures.c
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@ -2082,6 +2082,148 @@ void ImageBlurGaussian(Image *image, int blurSize) {
ImageFormat(image, format);
}
// The kernel matrix is assumed to be square. Only supply the width of the kernel.
void ImageKernelConvolution(Image *image, float* kernel, int kernelSize){
if ((image->data == NULL) || (image->width == 0) || (image->height == 0) || kernel == NULL) return;
int kernelWidth = (int)sqrtf((float)kernelSize);
if (kernelWidth*kernelWidth != kernelSize)
{
TRACELOG(LOG_WARNING, "IMAGE: Convolution kernel must be square to be applied");
return;
}
Color *pixels = LoadImageColors(*image);
Vector4 *imageCopy2 = RL_MALLOC((image->height)*(image->width)*sizeof(Vector4));
Vector4 *temp = RL_MALLOC(kernelSize*sizeof(Vector4));
for(int i = 0; i < kernelSize; i++){
temp[i].x = 0.0f;
temp[i].y = 0.0f;
temp[i].z = 0.0f;
temp[i].w = 0.0f;
}
float rRes = 0.0f;
float gRes = 0.0f;
float bRes = 0.0f;
float aRes = 0.0f;
int startRange, endRange;
if(kernelWidth % 2 == 0)
{
startRange = -kernelWidth/2;
endRange = kernelWidth/2;
} else
{
startRange = -kernelWidth/2;
endRange = kernelWidth/2+1;
}
for(int x = 0; x < image->height; x++)
{
for(int y = 0; y < image->width; y++)
{
for(int xk = startRange; xk < endRange; xk++)
{
for(int yk = startRange; yk < endRange; yk++)
{
int xkabs = xk + kernelWidth/2;
int ykabs = yk + kernelWidth/2;
size_t imgindex = image->width * (x+xk) + (y+yk);
if(imgindex < 0 || imgindex >= image->width * image->height){
temp[kernelWidth * xkabs + ykabs].x = 0.0f;
temp[kernelWidth * xkabs + ykabs].y = 0.0f;
temp[kernelWidth * xkabs + ykabs].z = 0.0f;
temp[kernelWidth * xkabs + ykabs].w = 0.0f;
} else {
temp[kernelWidth * xkabs + ykabs].x = ((float)pixels[imgindex].r)/255.0f * kernel[kernelWidth * xkabs + ykabs];
temp[kernelWidth * xkabs + ykabs].y = ((float)pixels[imgindex].g)/255.0f * kernel[kernelWidth * xkabs + ykabs];
temp[kernelWidth * xkabs + ykabs].z = ((float)pixels[imgindex].b)/255.0f * kernel[kernelWidth * xkabs + ykabs];
temp[kernelWidth * xkabs + ykabs].w = ((float)pixels[imgindex].a)/255.0f * kernel[kernelWidth * xkabs + ykabs];
}
}
}
for(int i = 0; i < kernelSize; i++)
{
rRes += temp[i].x;
gRes += temp[i].y;
bRes += temp[i].z;
aRes += temp[i].w;
}
if(rRes < 0.0f)
{
rRes = 0.0f;
}
if(gRes < 0.0f)
{
gRes = 0.0f;
}
if(bRes < 0.0f)
{
bRes = 0.0f;
}
if(rRes > 1.0f)
{
rRes = 1.0f;
}
if(gRes > 1.0f)
{
gRes = 1.0f;
}
if(bRes > 1.0f)
{
bRes = 1.0f;
}
imageCopy2[image->width * (x) + (y)].x = rRes;
imageCopy2[image->width * (x) + (y)].y = gRes;
imageCopy2[image->width * (x) + (y)].z = bRes;
imageCopy2[image->width * (x) + (y)].w = aRes;
rRes = 0.0f;
gRes = 0.0f;
bRes = 0.0f;
aRes = 0.0f;
for(int i = 0; i < kernelSize; i++)
{
temp[i].x = 0.0f;
temp[i].y = 0.0f;
temp[i].z = 0.0f;
temp[i].w = 0.0f;
}
}
}
for (int i = 0; i < (image->width) * (image->height); i++)
{
float alpha = (float)imageCopy2[i].w;
pixels[i].r = (unsigned char)((imageCopy2[i].x)*255.0f);
pixels[i].g = (unsigned char)((imageCopy2[i].y)*255.0f);
pixels[i].b = (unsigned char)((imageCopy2[i].z)*255.0f);
pixels[i].a = (unsigned char)((alpha)*255.0f);
// printf("pixels[%d] = %d", i, pixels[i].r);
}
int format = image->format;
RL_FREE(image->data);
RL_FREE(imageCopy2);
RL_FREE(temp);
image->data = pixels;
image->format = PIXELFORMAT_UNCOMPRESSED_R8G8B8A8;
ImageFormat(image, format);
}
// Generate all mipmap levels for a provided image
// NOTE 1: Supports POT and NPOT images
// NOTE 2: image.data is scaled to include mipmap levels