https://github.com/Awrsha/Image-Processing-Course
Some of the topics, algorithms and projects in image processing and computer vision that I have worked on and become familiar with.
https://github.com/Awrsha/Image-Processing-Course
cnn computer-vision detection homography image-processing localization mask pose-estimation recognition rnn segmentation tracker
Last synced: 20 days ago
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Some of the topics, algorithms and projects in image processing and computer vision that I have worked on and become familiar with.
- Host: GitHub
- URL: https://github.com/Awrsha/Image-Processing-Course
- Owner: Awrsha
- License: mit
- Created: 2024-04-07T23:16:47.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-08-18T16:46:39.000Z (about 1 year ago)
- Last Synced: 2024-08-18T18:08:35.025Z (about 1 year ago)
- Topics: cnn, computer-vision, detection, homography, image-processing, localization, mask, pose-estimation, recognition, rnn, segmentation, tracker
- Language: Jupyter Notebook
- Homepage:
- Size: 45.1 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Image Processing Course 🚀
Welcome to the **Image-Processing-Course** repository!
This collection is a comprehensive compilation of practical and advanced image processing projects implemented in Python using OpenCV, TensorFlow, PyTorch, and other relevant libraries. It serves as a hands-on guide for learning, experimenting, and mastering computer vision techniques.
---
## 🧠 Purpose
The goal of this repository is to provide a rich set of **mini-projects** and **notebooks** that teach fundamental to advanced concepts in image processing and computer vision. Ideal for students, researchers, and engineers.
---
## 🗂️ Project Structure
Each project is self-contained in its own directory or notebook. Here's an overview of the available modules:
| Project | Description |
|--------|-------------|
| **Adaptive Threshold for Image/Webcam** | Dynamic binarization for images and real-time streams |
| **Advanced Histogram Equalization** | CLAHE and other histogram improvement techniques |
| **Basic Yolov8n and MTCNN for Face Recognition** | Simple implementation of YOLO and MTCNN for detecting and recognizing faces |
| **Better Coin Detection** | Improved contour-based coin recognition |
| **Cartooning Image** | Convert photos to cartoon-style images |
| **Dominant Colors** | K-Means-based dominant color extraction |
| **Homography** | Perspective transformation between images |
| **Image Compression** | Image compression using K-Means clustering |
| **Image Cropping** | Manual and automatic image cropping tools |
| **Image Deblurring** | Restore blurred images using filters |
| **Image Denoising** | Noise reduction using Gaussian and median filters |
| **Image Inpainting** | Restore missing regions of images |
| **Image Restoration** | Degradation + restoration pipeline |
| **Image Rotation** | Image rotation using affine transforms |
| **Image Segmentation with GrabCut** | Foreground-background segmentation with GrabCut |
| **Otsu Binarization for Car Plate** | License plate binarization with Otsu's method |
| **Pose Estimation** | Skeleton pose detection using pre-trained models |
| **Simple BoundingBox** | Draw bounding boxes on detected objects |
| **Simple Coin Counter** | Count circular objects (coins) in images |
| **Simple Color Tracker** | Real-time color tracking using HSV thresholding |
| **Simple Deep Face Detection** | Deep learning model to detect faces |
| **Simple Deep Face Recognition** | Deep learning-based facial recognition |
| **Simple Mask RCNN** | Instance segmentation using Mask R-CNN |
| **Simple Open Pose** | Body joint detection using OpenPose |
| **Simple Parking Space Counter** | Detect and count parking spaces |
| **Simple Skin Detection** | Detect skin areas in images |
| **Simple Tensorflow Object Detection** | Basic object detection using TensorFlow models |
| **Simple Word Detection** | Text recognition using image pre-processing |
---
## 📦 Requirements
Most projects use the following packages:
```bash
opencv-python
numpy
matplotlib
scikit-image
scikit-learn
tensorflow
torch
mediapipe
ultralytics
````
Install all dependencies:
```bash
pip install -r requirements.txt
```
> Some projects may require specific versions. Check each notebook or `.py` file for details.
---
## 🧪 Usage
You can run each `.ipynb` notebook directly in Jupyter or Colab.
For Python scripts:
```bash
python project_name.py
```
For real-time webcam-based projects, make sure your system has an accessible camera.
---
## 📝 License
This repository is licensed under the [MIT License](LICENSE).
---
## 🙋♂️ Author
**Awrsha**
Follow me on GitHub for more machine learning and image processing projects.
---
## ⭐️ Show Your Support
If you find this repository useful, please consider giving it a ⭐️ and sharing it with your peers.
---
## 🔗 Contributions
Feel free to open issues or pull requests to improve or add more projects!