README.md

Image and video analysis and processing 📷

This repository contains the code for the course Image and video analysis and processing (2023/24) of the Master in Artificial Intelligence at the Wrocław University of Science and Technology 🧑‍🎓. The course primarily focuses on classical computer vision techniques and image processing. The repository is organized into eight labs, each dedicated to a different topic. These labs leverage the OpenCV and Torchvision libraries, as detailed in the Technologies section.

Table of contents

• General info
• Technologies
• Contact
• License
• References

General info

This repository contains the code for the course Image and video analysis and processing (2023/24) of the Master in Artificial Intelligence at the Wrocław University of Science and Technology.

Notebooks:

1. Basics of openCV:

   • Podstawy_OpenCV.ipynb - Basic of openCV.

2. Binarization, image filtering:

   • lab2-zadanie.ipynb - Global thresholding & adaptive thresholding.
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3. Edge detection, histogram equalization & matching:

   • zadanie.ipynb - Edge detection, Histogram matching.
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4. Slic superpixels, image segmentation:

   • zadanie.ipynb - Slic superpixels, image segmentation.
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5. Pills detection and classification:

   • Lab5_zadanie_Hough.ipynb - Overview of parameters Hough lines detection.
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   • Lab5_zadanie.ipynb - Pills detection and classification.
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6. SIFT and Image Stitching:

   • Lab6.1_zadanie.ipynb - Overview of SIFT parameters and their impact on key point detection and matching results.
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   • Lab6.2_zadanie.ipynb - Overview of SIFT parameters and their impact on key point detection and matching results.

7. Detection using Faster-RCNN:

   • Detection and classification of chess pieces.
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   • Parameters overview.
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8. Optical Flow:

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Technologies

• Python - version 3.8.5
• OpenCV - version 4.5.1
• Numpy - version 1.19.2
• Matplotlib - version 3.3.2
• Scikit-image - version 0.17.2
• Pillow - version 8.0.1
• Jupyter Notebook - version 6.1.4

Contact

• Subject teacher: Przemysław Dolata
• Me: Szymon Leszkiewicz

License

This project is open source and available under the MIT License.

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