Miguel A. Cabrera Minagorri


Pipeless: Computer vision with Python without building media pipelines

Pipeless is an easy to use open source multimedia framework with a focus on AI. Developers can build and deploy apps that manipulate audio and video in just minutes by allowing them to concentrate on the code, not on building and maintaining processing pipelines.

For example, just with these simple lines of code:


You can build an application that can recognize cats in any video:

The rest of this article explains in detail what Pipeless is and how to use it. Do you prefer playing with the code to reading? Here is the repo with ready to run examples, including the examples/cats directory.

The Problem

AI/ML has taken the programming world by storm in the past few years. The number of public, freely available Computer Vision (CV) models is increasing really fast. Training over custom datasets is becoming easier every day, with transfer learning techniques and libraries like Keras or PyTorch everyone can train a model within hours. However, the actual processing of multimedia is still really hard. For example, all these CV models use RGB/BGR arrays as image input. If you want to use them with a picture, it is straightforward to convert it to an RGB array. In the case of a video, things start to get complicated really fast. They get relatively complex when developing locally, but it becomes even harder if you try to deploy a service that, for example, waits for a stream and when it arrives processes it in real-time to produce some data or modify the stream itself. Not to mention about handling different input media formats, streams encoded with different codecs, etc.

How Pipeless works

Let's use the computer vision example to explain in more detail the pain outlined in the previous section. Imagine you are building an app that detects cats on a video (this is one of the ready-to-run examples). You will need to:

  1. Demux the video: open the container (for example mp4) to extract the individual streams (video, audio, subtitles, etc)
  2. Decode each stream: convert the encoded stream into a raw stream. In the case of a mp4 video stream this is equivalent to take the H.264 encoded frames and convert them into RGB frames.
  3. Process the video: you can now provide the RGB image as input of our computer vision model, for example, to obtain bounding boxes and then modify the frame by drawing a square over the cat face.
  4. Encode the stream: exactly the opposite of the decoding step. You have to pass the frames through an H.264 codec.
  5. Mux the new video: take our H.264 encoded video stream, take the audio stream again and create a mp4 container for them, including presentation timestamps for syncing the streams.

The steps outlined above are the most basic pipeline you can build to work with video locally. There are some tools already, like OpenCV, that make it easy to iterate over the frames of a video within a for loop. That's great for learning and experimenting, however, not suitable for real apps running in production.

At some point, you will need to deploy your app. Let's imagine you have a garden and a camera with a motion sensor and you want to know where your cat is at any moment. The camera will send video when it detects some motion. You will need to extend the basic pipeline above so it is able to recognize when there is a new stream to process. You also need to deploy your cat recognition model and maybe deploy a RTSP server that handles the input of the video stream and pass it to the processing pipeline, and finally send you the edited video. What started by simply using a public cat recognition model, ended up becoming really hard by virtue of having to set up ‘everything else’ around it. Here is where Pipeless comes in. It takes care of all the grunt work, automatically. By using Pipeless all you need to do for the above case is to take an RGB image, pass it through the model, draw the square and voila. You just saved some weeks of your life! This is exactly what was shown at the beginning of the article under the demo section.

Pipeless Architecture

Pipeless is designed to be easy to deploy in the cloud but also being able to run it locally with a single command. It has three main independent components, and all the components are communicated via messages using the lightweight Nanomsg Next generation Library (NNG):

  • Input: takes care of reading/receiving the streams, decode and demux them into single frames. It sends raw frames to the workers to be processed following a round robin schedule.

  • Worker: takes care of executing the user’s applications (your applications). You can deploy any number of workers to process frames in parallel, this helps to increase the stream framerate when processing in real-time and distribute the “heavy” load over different computers.

  • Output: takes care of receiving the processed raw streams, encode them and mux them into the output container format, for example mp4. It allows you to send the video to an external URI, play it directly on the device screen or store it on a file.

In addition, Pipeless doesn’t have too many dependencies, you just need a Gstreamer and Python installed in your system. Find the detailed list of requirements and versions here.


Right now Pipeless is supporting a limited set of functionality, however, we are actively working to add more every day.

You can learn more about the currently supported input and output formats on the Current State section of the readme. Do you need a new one? Create a pull request or ask for it creating a GitHub issue.

The current version is the very first one, and doesn’t support automatically deploying apps to production, however, this is one of the main features we will bring very soon. So, stay tuned!

Do you like Pipeless? We would love to get your feedback and contributions. Comment your thoughts directly here. You can also help us by starring the repository and sharing it with your friends!

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