https://github.com/kasraskari/finding-objects
finding objects with OpenCV
https://github.com/kasraskari/finding-objects
computer-vision flann homography jupyter-notebook opencv python ransac-algorithm
Last synced: 9 months ago
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finding objects with OpenCV
- Host: GitHub
- URL: https://github.com/kasraskari/finding-objects
- Owner: KasrAskari
- License: mit
- Created: 2024-09-06T20:09:44.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-05T08:20:39.000Z (9 months ago)
- Last Synced: 2025-04-05T09:24:06.592Z (9 months ago)
- Topics: computer-vision, flann, homography, jupyter-notebook, opencv, python, ransac-algorithm
- Language: Jupyter Notebook
- Homepage:
- Size: 5.15 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🔍 Finding Objects with OpenCV
## 📌 Overview
This project demonstrates **object detection** using **OpenCV**, employing feature-matching techniques such as **SIFT**, **FLANN**, **RANSAC**, and **Homography**. The objective is to extract key features from an image and match them against another to achieve **accurate object localization**.
## ✨ Features
✅ **Feature Extraction & Matching** – Uses **SIFT** to detect and match key points across images.
✅ **Robust Matching Algorithm** – Implements **FLANN** and **RANSAC** for precise and efficient feature matching.
✅ **Object Localization** – Utilizes **Homography** to determine the object's position in different images.
✅ **Interactive Experimentation** – Includes a **Jupyter Notebook** for hands-on testing and visualization.
## 📁 Project Structure
```
Finding-Objects/
│
├── finding-objects.ipynb # Jupyter Notebook with full implementation
├── images/ # Sample images for object detection
├── README.md # Project documentation
└── requirements.txt # List of dependencies
```
## 🛠 Technologies Used
- **Python** – Core programming language
- **OpenCV** – Computer vision library for image processing
- **NumPy** – Efficient numerical operations
- **Jupyter Notebook** – Interactive coding environment
## 📊 Results
This project successfully detects objects by:
🔹 Extracting **key features** using **SIFT**
🔹 Matching **features** with **FLANN** and refining results with **RANSAC**
🔹 **Transforming** detected objects using **Homography**
## 📜 License
This project is open-source and available under the **MIT License**.
## 🙌 Acknowledgments
- **Libraries Used:** OpenCV, NumPy
- **Techniques Implemented:** SIFT, FLANN, RANSAC, Homography