stim/cuda/ivote/vote.cuh

#ifndef STIM_CUDA_VOTE_H
#define STIM_CUDA_VOTE_H
# include <iostream>
# include <cuda.h>
#include <stim/cuda/cudatools.h>
#include <stim/cuda/sharedmem.cuh>
namespace stim{
	namespace cuda{
		// this kernel calculates the vote value by adding up the gradient magnitudes of every voter that this pixel is located in their voting area
		template<typename T>
		__global__ void cuda_vote(T* gpuVote, cudaTextureObject_t in, T* gpuTable, T phi, int rmax, int x, int y){
			//generate a pointer to shared memory (size will be specified as a kernel parameter)
			extern __shared__ float2 s_grad[];
			//calculate the start point for this block
			int bxi = blockIdx.x * blockDim.x;
			
			// calculate the 2D coordinates for this current thread.
			int xi = bxi + threadIdx.x;
			int yi = blockIdx.y * blockDim.y + threadIdx.y;
			// convert 2D coordinates to 1D
			int i = yi * x + xi;
						
			// define a local variable to sum the votes from the voters
			float sum = 0;
			
			//calculate the width of the shared memory block
			int swidth = 2 * rmax + blockDim.x;
			
			// compute the size of window which will be checked for finding the proper voters for this pixel
			int x_table = 2*rmax +1;
			int rmax_sq = rmax * rmax;
			int tx_rmax = threadIdx.x + rmax;
			int bxs = bxi - rmax;
			
			//for every line (along y)
			for(int yr = -rmax; yr <= rmax; yr++){
				//copy the portion of the image necessary for this block to shared memory
				__syncthreads();
				stim::cuda::sharedMemcpy_tex2D<float2>(s_grad, in, bxs, yi + yr , swidth, 1, threadIdx, blockDim);
				__syncthreads();
				
				if(xi < x && yi < y){
					for(int xr = -rmax; xr <= rmax; xr++){
					
							//find the location of this voter in the atan2 table
							int id_t = (yr + rmax) * x_table + xr + rmax;
							// calculate the angle between the pixel and the current voter in x and y directions
							float atan_angle = gpuTable[id_t];
												
							// calculate the voting direction based on the grtadient direction
							int idx_share = xr + tx_rmax ;
							float2 g = s_grad[idx_share];
							float theta = g.x;
							// check if the current voter is located in the voting area of this pixel.
							if (((xr * xr + yr *yr)< rmax_sq) && (abs(atan_angle - theta) <phi)){
								sum += g.y;		
							}
					}
				
				}
			}
			if(xi < x && yi < y)
				gpuVote[i] = sum;
			
		}
		template<typename T>
		void gpu_vote(T* gpuVote, T* gpuGrad, T* gpuTable, T phi, unsigned int rmax, unsigned int x, unsigned int y){
			//get the number of pixels in the image
			unsigned int pixels = x * y;
			unsigned int bytes = sizeof(T) * pixels;
						
			unsigned int max_threads = stim::maxThreadsPerBlock();
			dim3 threads(max_threads, 1);
			dim3 blocks(x/threads.x + (x %threads.x == 0 ? 0:1) , y);
			
			// Allocate CUDA array in device memory
			
			//define a channel descriptor for a single 32-bit channel
			cudaChannelFormatDesc channelDesc =
					   cudaCreateChannelDesc(32, 32, 0, 0,
											 cudaChannelFormatKindFloat);
			cudaArray* cuArray;												//declare the cuda array
			cudaMallocArray(&cuArray, &channelDesc, x, y);			//allocate the cuda array
			// Copy the image data from global memory to the array
			cudaMemcpyToArray(cuArray, 0, 0, gpuGrad, bytes*2,
							  cudaMemcpyDeviceToDevice);
			// Specify texture
			struct cudaResourceDesc resDesc;				//create a resource descriptor
			memset(&resDesc, 0, sizeof(resDesc));			//set all values to zero
			resDesc.resType = cudaResourceTypeArray;		//specify the resource descriptor type
			resDesc.res.array.array = cuArray;				//add a pointer to the cuda array
			// Specify texture object parameters
			struct cudaTextureDesc texDesc;							//create a texture descriptor
			memset(&texDesc, 0, sizeof(texDesc));					//set all values in the texture descriptor to zero
			texDesc.addressMode[0]   = cudaAddressModeWrap;			//use wrapping (around the edges)
			texDesc.addressMode[1]   = cudaAddressModeWrap;
			texDesc.filterMode       = cudaFilterModePoint;		//use linear filtering
			texDesc.readMode         = cudaReadModeElementType;		//reads data based on the element type (32-bit floats)
			texDesc.normalizedCoords = 0;							//not using normalized coordinates
			// Create texture object
			cudaTextureObject_t texObj = 0;
			cudaCreateTextureObject(&texObj, &resDesc, &texDesc, NULL);
			// specify  share memory
			unsigned int share_bytes = (2*rmax + threads.x)*(1)*2*4;
			
			//call the kernel to do the voting
			
			cuda_vote <<< blocks, threads,share_bytes >>>(gpuVote, texObj, gpuTable, phi, rmax, x , y);
			cudaDestroyTextureObject(texObj);
			cudaFreeArray(cuArray);
		}
		template<typename T>
		void cpu_vote(T* cpuVote, T* cpuGrad,T* cpuTable, T phi, unsigned int rmax, unsigned int x, unsigned int y){
			//calculate the number of bytes in the array
			unsigned int bytes = x * y * sizeof(T);
			//calculate the number of bytes in the atan2 table
			unsigned int bytes_table = (2*rmax+1) * (2*rmax+1) * sizeof(T);
			//allocate space on the GPU for the Vote Image
			T* gpuVote;
			cudaMalloc(&gpuVote, bytes);		
			//allocate space on the GPU for the input Gradient image
			T* gpuGrad;
			HANDLE_ERROR(cudaMalloc(&gpuGrad, bytes*2));
			//copy the Gradient Magnitude data to the GPU
			HANDLE_ERROR(cudaMemcpy(gpuGrad, cpuGrad, bytes*2, cudaMemcpyHostToDevice));
			//allocate space on the GPU for the atan2 table
			T* gpuTable;
			HANDLE_ERROR(cudaMalloc(&gpuTable, bytes_table));
			//copy the atan2 values to the GPU
			HANDLE_ERROR(cudaMemcpy(gpuTable, cpuTable, bytes_table, cudaMemcpyHostToDevice));
						
			//call the GPU version of the vote calculation function
			gpu_vote<T>(gpuVote, gpuGrad, gpuTable, phi, rmax, x , y);
							
			//copy the Vote Data back to the CPU
			cudaMemcpy(cpuVote, gpuVote, bytes, cudaMemcpyDeviceToHost) ;
			//free allocated memory
			cudaFree(gpuTable);
			cudaFree(gpuVote);
			cudaFree(gpuGrad);
		}
		
	}
}
#endif