Optimizing Video Workflows with CUDA Libraries

#include #include // Sample kernel for FFT-based filtering __global__ void filterKernel(cufftComplex *d_input, cufftComplex *d_output, int N) { int idx = threadIdx.x + blockIdx.x * blockDim.x; if (idx < N) { d_output[idx].x = d_input[idx].x * 0.5; // Example filter: half amplitude d_output[idx].y = d_input[idx].y * 0.5; } } int main() { cufftHandle plan; cufftComplex *d_input, *d_output; int N = 1024; // Example FFT size // Allocate memory for FFT input and output cudaMalloc((void**)&d_input, N * sizeof(cufftComplex)); cudaMalloc((void**)&d_output, N * sizeof(cufftComplex)); // Create FFT plan cufftPlan1d(&plan, N, CUFFT_C2C, 1); // Perform FFT cufftExecC2C(plan, d_input, d_output, CUFFT_FORWARD); // Apply a custom filter using CUDA kernel filterKernel<<<(N + 255) / 256, 256>>>(d_input, d_output, N); // Cleanup cufftDestroy(plan); cudaFree(d_input); cudaFree(d_output); return 0; } This code snippet demonstrates how to use cuFFT to apply a simple filter to video frames by manipulating the frequency domain. While cuFFT can be complex, it is a powerful tool for custom video filtering tasks. Why Use cuFFT for Filtering? Custom Filters : cuFFT allows developers to create custom filters based on the frequency domain of video frames. High-Speed Processing : cuFFT performs fast Fourier Transforms, enabling efficient signal processing for video analysis and manipulation.

Boost Campaign Performance Through Video