lenhu404 / gdv-opencv

GDV

Exercises for course "Grafische Datenverarbeitung" in winter term 2022-2023 at HS Furtwangen University. Please note that all content here is tentative and will be adopted to the students' needs during the course.

⚠️ Notice

The code structure of the tutorial code was updated in favor of tidiness. It is also now easier to distinguish the empty files (where you, the students, can work on) and the solution. For more information see here.

Prerequisites

First we need to download and install Python 3 from python.org. The recommended version is 3.10.7 or above. To inspect the currently installed version we can use the following terminal command:

python --version

Ensure that the correct version is used in the terminal as well as the selected interpreter.

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Usually the dependency management tool pip is included in the Python installation. If it is not, download and install it from pip.pypa.io. Version 19.3 or above is recommended. We can again use a terminal command to check the version:

pip --version

Getting started with Python

A great selection of useful resources are:

• https://www.python.org/about/gettingstarted/
• https://docs.python.org/3/tutorial/
• https://code.visualstudio.com/docs/python/python-tutorial

Setting up the development environment

The source code is developed using VS Code. You can use any IDE, but VS Code will be used in the lecture. You can download VS Code from https://code.visualstudio.com/. On Linux you can follow the instructions on this page. On Arch based systems you can use the OSS version code (Link) or the fully featured proprietary version visual-studio-code-bin (Link) from AUR.

VS Code extensions

• Python ms-python.python: Python support in VS Code (Link)
• Pylance ms-python.vscode-pylance: Python language server (Link)
• Python Image Preview 076923.python-image-preview: You can quickly check your Python image data during debugging (Link)
• Todo Tree Gruntfuggly.todo-tree: Provides an overview about all code lines marked with "TODO" (Link)

Python VENVs

To more easily develop Python code it is recommended to setup a VENV (virtual environment). In the project root folder execute the following terminal command:

python -m venv .venv

The will create the hidden folder .venv in your current project folder. Next we need to enable the VENV. The VENV will only be active for the current terminal session. So closing the terminal and re-opening it, will disable the VENV. Always make sure to enable it before you start developing.

# For Windows use
.venv\Scripts\Activate.ps1

# For Linux
source .venv/bin/activate

Special care is needed when using Windows. Please consult the following guide for more information.

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On both Operation Systems the VENV can be deactivated with the following terminal command:

deactivate

Installing dependencies

Installing the required dependencies is straight forward:

pip install .

This will load the defined dependencies in pyproject.toml and install them inside the VENV.

Happy coding!

Now you are ready to start coding the tutorials. The code can be found inside the src folder. The idea is to start with the <XX>-<NAME>.problem.py files and try to fulfill the TODOs. These "problem" files will guide you in the right direction. If there are any questions, you can ask for help during the lecture. The <XX>-<NAME>.solution.py files contain the solution. After you implemented your own sultion, use this file to compare and maybe add some improvements to your won code.

Helpful resources

Python

You can use https://docs.python.org/3/ as a starting point and the Library Reference or the Language Reference should contain all the needed information.

OpenCV reference

See https://docs.opencv.org/4.6.0/ for the OpenCV code reference. Here, all OpenCV methods are explained. If you want to know about parameters and flags, this is the page to look them up.

NumPy

OpenCV uses NumPy ndarrays as the common format for data exchange. It can create, operate on, and work with NumPy arrays. For some operations it makes sense to import the NumPy module and use special functions provided by NumPy. Other libraries like TensorFlow and SciPy also use NumPy. See the official docs for the API reference.