中文README | VideoPipe Website | VideoPipe tutorials(视频教程)
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--- ## Introduction `VideoPipe` is a framework for video analysis and structuring, written in C++. It has minimal dependencies and is easy to use. It operates like a pipeline, where each node is independent and can be combined in various ways. `VideoPipe` can be used to build different types of video analysis applications, suitable for scenarios such as video structuring, image search, face recognition, and behavior analysis in traffic/security fields (such as traffic incident detection). ## Advantages and Features `VideoPipe` is similar to NVIDIA's DeepStream and Huawei's mxVision frameworks, but it is easier to use and more portable. Here is a comparison table: | **Name** | **Open Source** | **Learning Curve** | **Supported Platforms** | **Performance** | **Third-Party Dependencies** | |---------------|-----------------|---------------------|--------------------------|-----------------|-------------------------------| | DeepStream | No | High | NVIDIA only | High | Many | | mxVision | No | High | Huawei only | High | Many | | VideoPipe | Yes | Low | Any platform | Medium | Few | `VideoPipe` uses a plugin-oriented coding style that allows for flexible configuration based on different needs. We can use independent plugins (referred to as `Node` types within the framework) to build various types of video analysis applications. You only need to prepare the model and understand how to parse its output. Inference can be implemented using different backends, such as OpenCV::DNN (default), TensorRT, PaddleInference, ONNXRuntime, or any other backend you prefer. ## Demonstration https://github.com/sherlockchou86/video_pipe_c/assets/13251045/b1289faa-e2c7-4d38-871e-879ae36f6d50 To watch in fullscreen, use the button in the bottom right corner of the player，[more video demos](./SAMPLES.md) ## Functions `VideoPipe` is a framework that simplifies the integration of computer vision algorithm models. It is important to note that it is not a deep learning framework like TensorFlow or TensorRT. The main features of `VideoPipe` are as follows: - **Stream Reading**: Supports mainstream video stream protocols such as UDP, RTSP, RTMP, file, and application. It also supports image reading. - **Video Decoding**: Supports video and image decoding based on OpenCV/GStreamer (with hardware acceleration). - **Algorithm Inference**: Supports multi-level inference based on deep learning algorithms, such as object detection, image classification, feature extraction, and image generation. It also supports the integration of traditional image algorithms. **Support mLLM(Multimodal Large Language Model) integration now (update 2025/8/12)** - **Object Tracking**: Supports object tracking, such as IOU and SORT tracking algorithms. - **Behavior Analysis (BA)**: Supports behavior analysis based on tracking, such as traffic behavior detection like line-crossing, parking, and violations. - **Business Logic**: Allows integration of any custom business logic, which can be closely related to specific business requirements. - **Data Proxy**: Supports pushing structured data (in JSON, XML, or custom formats) to the cloud, files, or other third-party platforms via methods like Kafka or Soc ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/sherlockchou86-videopipe`](/api/graphcanon/tools/sherlockchou86-videopipe) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_