144 lines
4.5 KiB
Markdown
144 lines
4.5 KiB
Markdown
---
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layout: default
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title: MediaPipe in Python
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parent: Getting Started
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has_children: true
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has_toc: false
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nav_order: 3
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---
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# MediaPipe in Python
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{: .no_toc }
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1. TOC
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{:toc}
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---
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## Ready-to-use Python Solutions
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MediaPipe offers ready-to-use yet customizable Python solutions as a prebuilt
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Python package. MediaPipe Python package is available on
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[PyPI](https://pypi.org/project/mediapipe/) for Linux, macOS and Windows.
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You can, for instance, activate a Python virtual environment:
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```bash
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$ python3 -m venv mp_env && source mp_env/bin/activate
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```
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Install MediaPipe Python package and start Python interpreter:
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```bash
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(mp_env)$ pip install mediapipe
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(mp_env)$ python3
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```
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In Python interpreter, import the package and start using one of the solutions:
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```python
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import mediapipe as mp
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mp_face_mesh = mp.solutions.face_mesh
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```
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Tip: Use command `deactivate` to later exit the Python virtual environment.
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To learn more about configuration options and usage examples, please find
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details in each solution via the links below:
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* [MediaPipe Face Detection](../solutions/face_detection#python-solution-api)
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* [MediaPipe Face Mesh](../solutions/face_mesh#python-solution-api)
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* [MediaPipe Hands](../solutions/hands#python-solution-api)
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* [MediaPipe Holistic](../solutions/holistic#python-solution-api)
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* [MediaPipe Objectron](../solutions/objectron#python-solution-api)
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* [MediaPipe Pose](../solutions/pose#python-solution-api)
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* [MediaPipe Selfie Segmentation](../solutions/selfie_segmentation#python-solution-api)
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## MediaPipe on Google Colab
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* [MediaPipe Face Detection Colab](https://mediapipe.page.link/face_detection_py_colab)
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* [MediaPipe Face Mesh Colab](https://mediapipe.page.link/face_mesh_py_colab)
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* [MediaPipe Hands Colab](https://mediapipe.page.link/hands_py_colab)
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* [MediaPipe Holistic Colab](https://mediapipe.page.link/holistic_py_colab)
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* [MediaPipe Objectron Colab](https://mediapipe.page.link/objectron_py_colab)
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* [MediaPipe Pose Colab](https://mediapipe.page.link/pose_py_colab)
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* [MediaPipe Pose Classification Colab (Basic)](https://mediapipe.page.link/pose_classification_basic)
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* [MediaPipe Pose Classification Colab (Extended)](https://mediapipe.page.link/pose_classification_extended)
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* [MediaPipe Selfie Segmentation Colab](https://mediapipe.page.link/selfie_segmentation_py_colab)
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## MediaPipe Python Framework
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The ready-to-use solutions are built upon the MediaPipe Python framework, which
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can be used by advanced users to run their own MediaPipe graphs in Python.
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Please see [here](./python_framework.md) for more info.
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## Building MediaPipe Python Package
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Follow the steps below only if you have local changes and need to build the
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Python package from source. Otherwise, we strongly encourage our users to simply
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run `pip install mediapipe` to use the ready-to-use solutions, more convenient
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and much faster.
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MediaPipe PyPI currently doesn't provide aarch64 Python wheel
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files. For building and using MediaPipe Python on aarch64 Linux systems such as
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Nvidia Jetson and Raspberry Pi, please read
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[here](https://github.com/jiuqiant/mediapipe-python-aarch64).
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1. Make sure that Bazel and OpenCV are correctly installed and configured for
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MediaPipe. Please see [Installation](./install.md) for how to setup Bazel
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and OpenCV for MediaPipe on Linux and macOS.
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2. Install the following dependencies.
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Debian or Ubuntu:
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```bash
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$ sudo apt install python3-dev
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$ sudo apt install python3-venv
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$ sudo apt install -y protobuf-compiler
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# If you need to build opencv from source.
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$ sudo apt install cmake
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```
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macOS:
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```bash
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$ brew install protobuf
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# If you need to build opencv from source.
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$ brew install cmake
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```
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Windows:
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Download the latest protoc win64 zip from
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[the Protobuf GitHub repo](https://github.com/protocolbuffers/protobuf/releases),
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unzip the file, and copy the protoc.exe executable to a preferred location.
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Please ensure that location is added into the Path environment variable.
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3. Activate a Python virtual environment.
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```bash
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$ python3 -m venv mp_env && source mp_env/bin/activate
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```
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4. In the virtual environment, go to the MediaPipe repo directory.
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5. Install the required Python packages.
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```bash
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(mp_env)mediapipe$ pip3 install -r requirements.txt
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```
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6. Build and install MediaPipe package.
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```bash
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(mp_env)mediapipe$ python3 setup.py install --link-opencv
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```
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or
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```bash
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(mp_env)mediapipe$ python3 setup.py bdist_wheel
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```
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