Digital Imaging / Visual Computing

This is the repository for the Digital Imaging / Visual Computing course (05_DVC4IL) at the FH Hagenberg.

E-Learning course

Contact: David C. Schedl.

Tutorials:

#	Tutorial (link to .ipynb)	Open in Colab
1	Python for Computer Vision
2	Introduction to OpenCV
3	Histograms
4	Filters
5	Edges
6	Thresh
7	Lines
8	Machine Learning
9	Neural Networks
10	Object Detection with YOLO

Homework Tasks:

#	Homework (link to .ipynb)	Open in Colab
I	Point Operations & Histograms
II	Hybrid Images
III	Leaf Classification
IV	Hockey Dataset Analysis

Python Setup:

Students have the option to run the code online with Google Colab (requires a Google account) or locally with your own installation of Python.

Online:

Everything runs on a Google machine, so you don't need to set up anything on your computer. Furthermore, the machines come with the most popular libraries preinstalled. Just click on the corresponding Open in Colab badge: .

Local:

Install Python on your computer via Conda/Miniconda or the Python Installer. Use Python3, as Python2 is not supported anymore. Furthermore, you need an Editor that supports Jupyter (.ipynb) notebooks. I recommend using Visual Studio Code. Optionally, you can also use a local server and open Notebooks in your browser (Visual Studio simplifies this).

Required packages are listed in requirements.txt. Install them with:

pip install -r requirements.txt

Useful Links:

• Python Documentation
• OpenCV Tutorial
• Ultralytics YOLO Documentation
• Roboflow Documentation
• If you know Matlab, you can find the differences between Matlab and Python here.

Course Grading:

This course will be graded based on your performance in the course homeworks. The homework tasks will be announced while we progress through the course.