MediaPipe in Python

1. Ready-to-use Python Solutions
2. MediaPipe on Google Colab
3. MediaPipe Python Framework
4. Building MediaPipe Python Package

Ready-to-use Python Solutions

MediaPipe offers ready-to-use yet customizable Python solutions as a prebuilt Python package. MediaPipe Python package is available on PyPI for Linux, macOS and Windows.

You can, for instance, activate a Python virtual environment:

$ python3 -m venv mp_env && source mp_env/bin/activate

Install MediaPipe Python package and start Python interpreter:

(mp_env)$ pip install mediapipe
(mp_env)$ python3

In Python interpreter, import the package and start using one of the solutions:

import mediapipe as mp
mp_face_mesh = mp.solutions.face_mesh

Tip: Use command deactivate to later exit the Python virtual environment.

To learn more about configuration options and usage examples, please find details in each solution via the links below:

• MediaPipe Face Detection
• MediaPipe Face Mesh
• MediaPipe Hands
• MediaPipe Holistic
• MediaPipe Objectron
• MediaPipe Pose
• MediaPipe Selfie Segmentation

MediaPipe on Google Colab

• MediaPipe Face Detection Colab
• MediaPipe Face Mesh Colab
• MediaPipe Hands Colab
• MediaPipe Holistic Colab
• MediaPipe Objectron Colab
• MediaPipe Pose Colab
• MediaPipe Pose Classification Colab (Basic)
• MediaPipe Pose Classification Colab (Extended)
• MediaPipe Selfie Segmentation Colab

MediaPipe Python Framework

The ready-to-use solutions are built upon the MediaPipe Python framework, which can be used by advanced users to run their own MediaPipe graphs in Python. Please see here for more info.

Building MediaPipe Python Package

Follow the steps below only if you have local changes and need to build the Python package from source. Otherwise, we strongly encourage our users to simply run pip install mediapipe to use the ready-to-use solutions, more convenient and much faster.

MediaPipe PyPI currently doesn’t provide aarch64 Python wheel files. For building and using MediaPipe Python on aarch64 Linux systems such as Nvidia Jetson and Raspberry Pi, please read here.

1. Make sure that Bazel and OpenCV are correctly installed and configured for MediaPipe. Please see Installation for how to setup Bazel and OpenCV for MediaPipe on Linux and macOS.

2. Install the following dependencies.

   Debian or Ubuntu:

   $ sudo apt install python3-dev
   $ sudo apt install python3-venv
   $ sudo apt install -y protobuf-compiler
   
   # If you need to build opencv from source.
   $ sudo apt install cmake
   

   macOS:

   $ brew install protobuf
   
   # If you need to build opencv from source.
   $ brew install cmake
   

   Windows:

   Download the latest protoc win64 zip from the Protobuf GitHub repo, unzip the file, and copy the protoc.exe executable to a preferred location. Please ensure that location is added into the Path environment variable.

3. Activate a Python virtual environment.

   $ python3 -m venv mp_env && source mp_env/bin/activate
   

4. In the virtual environment, go to the MediaPipe repo directory.

5. Install the required Python packages.

   (mp_env)mediapipe$ pip3 install -r requirements.txt
   

6. Generate and install MediaPipe package.

   (mp_env)mediapipe$ python3 setup.py gen_protos
   (mp_env)mediapipe$ python3 setup.py install --link-opencv
   

   or

   (mp_env)mediapipe$ python3 setup.py gen_protos
   (mp_env)mediapipe$ python3 setup.py bdist_wheel