MNN real-time image processing implementation scheme for live broadcasting scenarios
Live streaming scenarios are latency sensitive (need to be ≤50ms) and MNN's solution includes the following key technologies:
- pipeline optimization1) Use 'MNN::CVProcess' module to process camera YUV data directly 2) Configure 'ImageProcess::Config' to realize hardware acceleration 3) Establish double buffer mechanism to avoid lagging.
- Special effects model selection: Recommended to use MNN-converted StyleGAN-V or GFPGAN, with fast style migration enabled via the '-image_process' parameter
- Resolution Adaptation: 1) Input layer configured with 'Interpreter::resizeTensor' to 540p 2) Output layer upsampled using 'MNN::Express::CV::resize'
- Power consumption control: 1) Set 'BackendConfig::PowerMode' to Balanced 2) Dynamically adjust inference frequency to match FPS
Code Example:
MNN::CV::Matrix trans.
trans.postScale(1.0f/input_w, 1.0f/input_h);
config.filterType = MNN::CV::BILINEAR;
config.sourceFormat = MNN::CV::RGBA;
config.destFormat = MNN::CV::RGB;
This answer comes from the articleMNN-LLM-Android: MNN Multimodal Language Model for Android ApplicationsThe
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