/*******************************************************
* Copyright (c) 2015-2019, ArrayFire
* All rights reserved.
*
* This file is distributed under 3-clause BSD license.
* The complete license agreement can be obtained at:
* http://arrayfire.com/licenses/BSD-3-Clause
********************************************************/
#include <forge.h>
#include <cuda_runtime.h>
#include <curand.h>
#include <curand_kernel.h>
#include <cuComplex.h>
#define USE_FORGE_CUDA_COPY_HELPERS
#include <ComputeCopy.h>
#include <cstdio>
const unsigned IMGW = 256;
const unsigned IMGH = 256;
const unsigned DIMX = 1000;
const unsigned DIMY = 800;
const unsigned WIN_ROWS = 1;
const unsigned WIN_COLS = 2;
const unsigned NBINS = 256;
curandState_t* state;
struct Bitmap {
unsigned char *ptr;
unsigned width;
unsigned height;
};
class PerlinNoise
{
public:
float* base;
float* perlin;
PerlinNoise();
~PerlinNoise();
void generateNoise();
};
Bitmap createBitmap(unsigned w, unsigned h);
void destroyBitmap(Bitmap& bmp);
void kernel(Bitmap& bmp, PerlinNoise& pn);
void populateBins(Bitmap& bmp, int *hist_array, const unsigned nbins, float *hist_cols);
__global__
void setupRandomKernel(curandState *states, unsigned long long seed)
{
unsigned tid = blockDim.x * blockIdx.x + threadIdx.x;
curand_init(seed, tid, 0, &states[tid]);
}
int main(void)
{
Bitmap bmp = createBitmap(IMGW, IMGH);
FORGE_CUDA_CHECK(cudaMalloc((void **)&state, NBINS*sizeof(curandState_t)));
setupRandomKernel <<< 1, NBINS >>> (state, 314567);
/*
* First Forge call should be a window creation call
* so that necessary OpenGL context is created for any
* other forge::* object to be created successfully
*/
forge::Window wnd(DIMX, DIMY, "Histogram Demo");
wnd.makeCurrent();
/*
* Split the window into grid regions
*/
wnd.grid(WIN_ROWS, WIN_COLS);
forge::Image img(IMGW, IMGH, FG_RGBA, forge::u8);
forge::Chart chart(FG_CHART_2D);
/* set x axis limits to maximum and minimum values of data
* and y axis limits to range [0, number of pixels ideally]
* but practically total number of pixels as y range will skew
* the histogram graph vertically. Therefore setting it to
* 25% of total number of pixels */
chart.setAxesLimits(0, 1, 0, IMGW*IMGH/(float)(NBINS/4.0));
/*
* Create histogram object specifying number of bins
*/
forge::Histogram hist = chart.histogram(NBINS, forge::s32);
/*
* Set histogram colors
*/
hist.setColor(FG_YELLOW);
PerlinNoise noiseGenerator;
int *histOut;
float *histColors;
FORGE_CUDA_CHECK(cudaMalloc((void**)&histOut, NBINS * sizeof(int)));
FORGE_CUDA_CHECK(cudaMalloc((void**)&histColors, 3*NBINS * sizeof(float)));
GfxHandle* handles[3];
createGLBuffer(&handles[0], img.pixels(), FORGE_IMAGE_BUFFER);
createGLBuffer(&handles[1], hist.vertices(), FORGE_VERTEX_BUFFER);
createGLBuffer(&handles[2], hist.colors(), FORGE_VERTEX_BUFFER);
unsigned frame = 0;
do {
if (frame%8==0) {
kernel(bmp, noiseGenerator);
copyToGLBuffer(handles[0], (ComputeResourceHandle)bmp.ptr, img.size());
populateBins(bmp, histOut, NBINS, histColors);
copyToGLBuffer(handles[1], (ComputeResourceHandle)histOut, hist.verticesSize());
copyToGLBuffer(handles[2], (ComputeResourceHandle)histColors, hist.colorsSize());
frame = 0;
}
wnd.draw(0, 0, img, "Dynamic Perlin Noise" );
wnd.draw(0, 1, chart, "Histogram of Noisy Image");
wnd.swapBuffers();
frame++;
} while(!wnd.close());
FORGE_CUDA_CHECK(cudaFree(histOut));
FORGE_CUDA_CHECK(cudaFree(histColors));
releaseGLBuffer(handles[0]);
releaseGLBuffer(handles[1]);
releaseGLBuffer(handles[2]);
return 0;
}
Bitmap createBitmap(unsigned w, unsigned h)
{
Bitmap retVal;
retVal.width = w;
retVal.height= h;
FORGE_CUDA_CHECK(cudaMalloc((void**)&retVal.ptr, sizeof(unsigned char)*4*w*h));
return retVal;
}
void destroyBitmap(Bitmap& bmp)
{
FORGE_CUDA_CHECK(cudaFree(bmp.ptr));
}
PerlinNoise::PerlinNoise()
{
const size_t IMG_SIZE = IMGW*IMGH*sizeof(float);
FORGE_CUDA_CHECK(cudaMalloc((void**)&base, IMG_SIZE));
FORGE_CUDA_CHECK(cudaMalloc((void**)&perlin, IMG_SIZE));
}
PerlinNoise::~PerlinNoise()
{
FORGE_CUDA_CHECK(cudaFree(base));
FORGE_CUDA_CHECK(cudaFree(perlin));
}
inline
int divup(int a, int b)
{
return (a+b-1)/b;
}
__device__
float interp(float x0, float x1, float alpha)
{
return x0 * (1 - alpha) + alpha * x1;
}
__global__
void perlinInitKernel(float* base, float* perlin, curandState* state)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x<IMGW && y<IMGH) {
int index = y*IMGW + x;
base[index] = curand_uniform(&state[index%NBINS]);
perlin[index] = 0.0f;
}
}
__global__
void perlinComputeKernel(float* perlin, float* base, float amp, int period)
{
unsigned x = blockIdx.x * blockDim.x + threadIdx.x;
unsigned y = blockIdx.y * blockDim.y + threadIdx.y;
if (x<IMGW && y<IMGH) {
int index = y*IMGW + x;
float freq = 1.0f / period;
int si0 = (x/period) * period;
int si1 = (si0 + period) % IMGW;
float hblend = (x - si0) * freq;
int sj0 = (y/period) * period;
int sj1 = (sj0 + period) % IMGH;
float vblend = (y - sj0) * freq;
float top = interp(base[si0+IMGW*sj0], base[si1+IMGW*sj0], hblend);
float bot = interp(base[si0+IMGW*sj1], base[si1+IMGW*sj1], hblend);
perlin[index] += (amp * interp(top, bot, vblend));
}
}
__global__
void perlinNormalize(float* perlin, float tamp)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x<IMGW && y<IMGH) {
int index = y*IMGW + x;
perlin[index] = perlin[index]/tamp;
}
}
void PerlinNoise::generateNoise()
{
static dim3 threads(32, 8);
dim3 blocks(divup(IMGW, threads.x), divup(IMGH, threads.y));
float persistence = 0.5f;
float amp = 1.0f;
float tamp = 0.0f;
perlinInitKernel<<< blocks, threads >>> (base, perlin, state);
for (int octave=6; octave>=0; --octave) {
int period = 1 << octave;
perlinComputeKernel<<< blocks, threads >>>(perlin, base, amp, period);
tamp += amp;
amp *= persistence;
}
perlinNormalize<<< blocks, threads >>>(perlin, tamp);
}
__global__
void fillImageKernel(unsigned char* ptr, unsigned width, unsigned height, float* perlin)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x<width && y<height) {
int offset = x + y * width;
unsigned u = (unsigned)(IMGW*x/(float)(width));
unsigned v = (unsigned)(IMGH*y/(float)(height));
int idx = u + v*IMGW;
unsigned char val = 255 * perlin[idx];
ptr[offset*4 + 0] = val;
ptr[offset*4 + 1] = val;
ptr[offset*4 + 2] = val;
ptr[offset*4 + 3] = 255;
}
}
void kernel(Bitmap& bmp, PerlinNoise& pn)
{
static dim3 threads(32, 8);
pn.generateNoise();
dim3 blocks(divup(bmp.width, threads.x), divup(bmp.height, threads.y));
fillImageKernel<<< blocks, threads >>>(bmp.ptr, bmp.width, bmp.height, pn.perlin);
}
__global__
void histogramKernel(const unsigned char* perlinNoise, int* histOut, const unsigned nbins)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x<IMGW && y<IMGH) {
int offset = y * IMGW + x;
unsigned char noiseVal = perlinNoise[offset*4 + 0];
offset = __float2int_rd(nbins * (__int2float_rd(noiseVal)/255.f));
atomicAdd(histOut + offset , 1);
}
}
__global__
void histColorsKernel(float* histColors, curandState *states)
{
int bin = blockIdx.x * blockDim.x + threadIdx.x;
histColors[3*bin+0] = curand_uniform(&states[bin]);
histColors[3*bin+1] = curand_uniform(&states[bin]);
histColors[3*bin+2] = curand_uniform(&states[bin]);
}
void populateBins(Bitmap& bmp, int *histOut, const unsigned nbins, float *histColors)
{
static const dim3 threads(8, 8);
dim3 blocks(divup(bmp.width, threads.x),
divup(bmp.height, threads.y));
cudaMemset(histOut, 0, nbins * sizeof(int));
histogramKernel<<< blocks, threads >>>(bmp.ptr, histOut, nbins);
histColorsKernel<<< 1, nbins >>>(histColors, state);
}
1.8.12