opencv/modules/gpu/src/cuda/blend.cu

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#include "internal_shared.hpp"

namespace cv { namespace gpu { namespace device 
{
    namespace blend 
    {
        template <typename T>
        __global__ void blendLinearKernel(int rows, int cols, int cn, const PtrStep<T> img1, const PtrStep<T> img2,
                                          const PtrStepf weights1, const PtrStepf weights2, PtrStep<T> result)
        {
            int x = blockIdx.x * blockDim.x + threadIdx.x;
            int y = blockIdx.y * blockDim.y + threadIdx.y;

            if (y < rows && x < cols)
            {
                int x_ = x / cn;
                float w1 = weights1.ptr(y)[x_];
                float w2 = weights2.ptr(y)[x_];
                T p1 = img1.ptr(y)[x];
                T p2 = img2.ptr(y)[x];
                result.ptr(y)[x] = (p1 * w1 + p2 * w2) / (w1 + w2 + 1e-5f);
            }
        }	

        template <typename T>
        void blendLinearCaller(int rows, int cols, int cn, PtrStep<T> img1, PtrStep<T> img2, PtrStepf weights1, PtrStepf weights2, PtrStep<T> result, cudaStream_t stream)
        {
            dim3 threads(16, 16);
            dim3 grid(divUp(cols * cn, threads.x), divUp(rows, threads.y));
            
            blendLinearKernel<<<grid, threads, 0, stream>>>(rows, cols * cn, cn, img1, img2, weights1, weights2, result);
            cudaSafeCall( cudaGetLastError() );

            if (stream == 0)
                cudaSafeCall(cudaDeviceSynchronize());
        }

        template void blendLinearCaller<uchar>(int, int, int, PtrStep<uchar>, PtrStep<uchar>, PtrStepf, PtrStepf, PtrStep<uchar>, cudaStream_t stream);
        template void blendLinearCaller<float>(int, int, int, PtrStep<float>, PtrStep<float>, PtrStepf, PtrStepf, PtrStep<float>, cudaStream_t stream);


        __global__ void blendLinearKernel8UC4(int rows, int cols, const PtrStepb img1, const PtrStepb img2,
                                              const PtrStepf weights1, const PtrStepf weights2, PtrStepb result)
        {
            int x = blockIdx.x * blockDim.x + threadIdx.x;
            int y = blockIdx.y * blockDim.y + threadIdx.y;

            if (y < rows && x < cols)
            {
                float w1 = weights1.ptr(y)[x];
                float w2 = weights2.ptr(y)[x];
                float sum_inv = 1.f / (w1 + w2 + 1e-5f);
                w1 *= sum_inv;
                w2 *= sum_inv;
                uchar4 p1 = ((const uchar4*)img1.ptr(y))[x];
                uchar4 p2 = ((const uchar4*)img2.ptr(y))[x];
                ((uchar4*)result.ptr(y))[x] = make_uchar4(p1.x * w1 + p2.x * w2, p1.y * w1 + p2.y * w2,
                                                          p1.z * w1 + p2.z * w2, p1.w * w1 + p2.w * w2);
            }
        }

        void blendLinearCaller8UC4(int rows, int cols, PtrStepb img1, PtrStepb img2, PtrStepf weights1, PtrStepf weights2, PtrStepb result, cudaStream_t stream)
        {
            dim3 threads(16, 16);
            dim3 grid(divUp(cols, threads.x), divUp(rows, threads.y));
            
            blendLinearKernel8UC4<<<grid, threads, 0, stream>>>(rows, cols, img1, img2, weights1, weights2, result);
            cudaSafeCall( cudaGetLastError() );

            if (stream == 0)
                cudaSafeCall(cudaDeviceSynchronize());
        }
    } // namespace blend 
}}} // namespace cv { namespace gpu { namespace device