MNN-CV is a lightweight computer vision library built into the framework, providing similar functionality to OpenCV but focusing more on mobile optimization:
Core Features Comparison::
- Basic operations: color space conversion (RGB/YUV), affine transformation, histogram equalization
- Feature processing: edge detection (Sobel/Canny), corner detection (Harris)
- Compared to traditional OpenCV: 651 TP3T in size and 501 TP3T in running memory.
Typical usage examples::
#include <MNN/ImageProcess.hpp>
MNN::CV::ImageProcess::Config config;
config.destFormat = MNN::CV::BGR;
auto processor = MNN::CV::ImageProcess::create(config);
// 执行图像resize和归一化
processor->convert(srcData, width, height, 0, dstData);
Performance Optimization Tips::
- Reusing ImageProcess instances to avoid duplicate creation overheads
- Reduce conditional judgments with setPaddingValue pre-padding boundaries
- Processing pipelining in conjunction with MNN-Express
The module has been applied to scenes such as real-time beauty face on mobile Taobao and video frame analysis on Youku, with 1080P image processing latency <8ms.
This answer comes from the articleMNN: A Lightweight and Efficient Deep Learning Inference FrameworkThe
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