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Raspberry Pi 4 BrailleSense Project Setup

This guide outlines setting up a virtual environment for your Raspberry Pi 4 camera project, considering two approaches:

1. Using System-Wide Libraries for Camera and GPIO Control

Requirements:

  • Raspberry Pi 4
  • Raspberry Pi Camera Module 2 (compatible with Pi 4)

1.1. Create Virtual Environment (System-Site Packages):

Here, we create a virtual environment named my-env that utilizes system-wide libraries for camera control and GPIO pins. This might be necessary if your project requires specific system-wide libraries:

python -m venv --system-site-packages my-env

1.2. Activate Virtual Environment:

Activate the newly created environment:

source my-env/bin/activate

1.3. Install Dependencies:

Install required packages from your requirements.txt file:

pip install -r requirements.txt

2. Exploring picamera2 Features

For projects requiring extensive camera functionalities or for learning purposes, you can choose to install picamera2 specifically within the virtual environment.

2.1. Install picamera2 (Optional):

Within the activated virtual environment, install picamera2:

pip install picamera2

3. Resources:

For an in-depth understanding of picamera2 features, refer to the official documentation:

Notes:

  • Remember to adjust the virtual environment name (my-env) and package names in requirements.txt according to your project needs.
  • Using system-wide libraries comes with potential risks of conflicts. Consider isolated virtual environments if possible.
  • Always exercise caution when modifying system-wide libraries and packages.

By following these steps, you can effectively set up your Raspberry Pi 4 camera project with a virtual environment, catering to both system-wide library usage and exploring picamera2 specifically.

About

BrailleSense is a proof of concept for a device that can process real-time, ad hoc information and transcribe it into Braille. The device is a decent baseline for continuing research into real-time Braille transcription using machine learning of adaptive systems, and shows that the approach is effective with room for improvement.

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