https://github.com/pawlo77/computer-vision
Tutorial on computer vision with python. Developed for Data Science Scientific Circle at Faculty of Mathematics and Information Science, Warsaw University of Technology
https://github.com/pawlo77/computer-vision
computer-vision cv2 machine-learning python skimage tensorflow
Last synced: 2 months ago
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Tutorial on computer vision with python. Developed for Data Science Scientific Circle at Faculty of Mathematics and Information Science, Warsaw University of Technology
- Host: GitHub
- URL: https://github.com/pawlo77/computer-vision
- Owner: Pawlo77
- Created: 2023-10-25T21:06:49.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-17T15:11:24.000Z (over 2 years ago)
- Last Synced: 2025-03-20T12:15:26.071Z (about 1 year ago)
- Topics: computer-vision, cv2, machine-learning, python, skimage, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 42.7 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Computer Vision
Computer vision is a multidisciplinary field focused on enabling machines to interpret and understand visual information from the world. It leverages artificial intelligence and image processing techniques to give computers the ability to "see" and make decisions based on visual data.
This set of tutorials provides end-to-end examples on efficiently working with images. The tutorials are structured to guide you through the essential steps, ensuring a comprehensive understanding of image processing and augmentation techniques.
Computer vision plays a pivotal role in various industries, transforming the way machines interact with the visual world. Its applications range from enhancing daily tasks to revolutionizing entire sectors, making it a key component of the evolving landscape of artificial intelligence.
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## Course overview
### 1. [How to work with python like a pro?](./intro/intro.ipynb)
The introductory tutorial lays the foundation for working proficiently with Python—a fundamental skill applicable throughout the entire course. This initial step ensures that student knows how to properly format code in python, how to document it and how to effortlessly work with anaconda.
### 2. [What is Computer Vision](./intro/intro.pdf)
This is first lecture of the course. It aims to present what is computer vision, which challenges it encounters, crushial parts of its history and modern applications.
### 2. [Image Preprocessing and Augmentation](./processing/preprocessing.ipynb)
This tutorial serves as an introduction to computer vision, focusing on tasks that should be performed before delving into machine learning. It covers crucial subjects that significantly impact a model's ability to learn and perform well. The content spans image preprocessing techniques and augmentation strategies, providing essential knowledge for enhancing the quality of your image datasets.
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Follow these tutorials in the suggested order to build a strong foundation and seamlessly progress through the course, gaining valuable skills in image processing and computer vision.
*This tutorial series is developed for *Koło Naukowe Data Science at Warsaw University of Technology*, as preparation for larger computer-vision based project described **[here](./descGeoa.pdf)***