Computer vision pipeline for class project, primarily authored by @deanljohnson and @dang3
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CS160 Computer Vision Pipeline

A video processing application using OpenFace and FFMPEG. Primarily authored by @deanljohnson.

The CVProcessor takes either a video file or RTSP stream as an input, and outputs the video with delaunay triangles applied to detected faces. It can output to a video file, RTSP stream, or STDOUT, and is capable of real-time streaming face detection.

The application was designed to work in tandem with a server and web UI, allowing video upload conversion and real-time streaming using a webcam.

Demo Videos


Clone the repo and run sudo bash This will install all required dependencies, as well as build the project. It takes roughly 20 minutes on a high end machine with a good internet connection.

The compiled output will be in CVProcessor/dist/your system info. We only tested in Debian-based systems.

Python Server

A network API comes bundled with the application (primary author @dang3).


  • Install/switch to Python 3. The server was developed and tested in Python 3.5.2.
  • The server uses the tornado framework. Install tornado pip install tornado
  • Run the server using sudo

API Routes

  1. Method: GET
    Route: /
    If the server is running, Server running should appear.

  2. Method: GET
    Route: /video/(.*)
    Searches the \done directory for the specified video file. For example, /video/12345.mp4 will search for and return 12345.mp4 if the file exists. Otherwise, a 404 response will be sent.

  3. Method: POST
    Route: /upload
    Used to upload a video to the server which will temporarily be cloned to the \to_process directory. The video file is then processed with the output file from the processing application being placed in the \done directory which can be accessed through the /video/(.*) GET request. After the processing, the uploaded file is deleted.
    Use the file key when uploading video files. Every uploaded video should have a videoid. Within the same request, the client should specify videoid as a plain string using the videoid key within the body of the request. Each unique video should have its own videoid or else video files will be overwritten.

  4. Method: POST
    Route: /status
    Returns the status of a video. Within the body of the request, use the key videoid and specify an id for the video.