# **NanoTrack**
**NanoTrack** is a lightweight and efficient object detection and tracking library designed for seamless integration with **YOLOv5** and **YOLOv8** models. It delivers **real-time tracking** with minimal resource usage, making it ideal for edge devices and systems with limited performance.
---
## **Features**
- 🚀 **Lightweight**: Optimized for minimal computational overhead.
- 🎯 **Seamless Integration**: Fully compatible with YOLOv5 and YOLOv8.
- ⚡ **Real-Time Performance**: Fast and accurate tracking for video streams.
- 🛠️ **Simple API**: Easy-to-use interfaces for rapid development.
- 📹 **Video & Stream Support**: Works with video files and live camera streams.
---
## **Installation**
### **Install via PyPI**
To install NanoTrack from PyPI, run:
```bash
pip install nanotrack
```
### **Install from GitHub**
For the latest version directly from the source:
```bash
pip install git+https://github.com/ragultv/nanotrack.git
```
---
## **Usage**
### **1. Import and Initialize**
```python
from nanotrack import YOLOv8Detector, NanoTrack
import cv2
# Initialize the YOLOv8 Detector
detector = YOLOv8Detector(model_path="yolov8n.pt") # Replace with your model path
# Initialize the NanoTrack Tracker
tracker = NanoTrack()
```
### **2. Process Video for Detection and Tracking**
```python
# Load video file or webcam input
cap = cv2.VideoCapture("path_to_video.mp4") # Replace with your video file path
while True:
ret, frame = cap.read()
if not ret:
break
# Perform object detection
detections = detector.detect(frame)
# Update tracker with detections
tracks = tracker.update(detections)
# Draw bounding boxes and track IDs
for track in tracks:
x1, y1, x2, y2, _, _, track_id = track[:7]
cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
cv2.putText(frame, f"ID: {track_id}", (int(x1), int(y1) - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1)
# Display the results
cv2.imshow("NanoTrack", frame)
# Press 'q' to exit
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
```
---
## **Supported Models**
NanoTrack seamlessly works with:
- **YOLOv5**: Optimized and reliable object detection.
- **YOLOv8**: Cutting-edge detection accuracy and performance.
---
## **Example Output**
Here is an example of NanoTrack in action:
---
## **Contributing**
We welcome contributions to NanoTrack!
To contribute:
1. **Fork** the repository.
2. **Create a branch**:
```bash
git checkout -b feature-branch
```
3. Make your changes and test thoroughly.
4. **Submit a Pull Request** with a clear description.
---
## **License**
This project is licensed under the **MIT License**. See the `LICENSE` file for details.
---
## **Support**
For issues, feature requests, or questions, feel free to:
- Open an issue on our [GitHub repository](https://github.com/ragultv/nanotrack).
- Reach out with feedback or suggestions.
---
### **Let’s Track Smarter, Faster, and Lighter with NanoTrack!** 🚀
---
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