https://github.com/lamaabdeldayem/furniture_legs_detection
Custom-trained object detection model to identify and track furniture legs in images and videos
https://github.com/lamaabdeldayem/furniture_legs_detection
ai object-detection opencv python roboflow yolov8 yolov9
Last synced: 7 months ago
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Custom-trained object detection model to identify and track furniture legs in images and videos
- Host: GitHub
- URL: https://github.com/lamaabdeldayem/furniture_legs_detection
- Owner: lamaabdeldayem
- Created: 2024-09-17T11:35:34.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-17T13:02:38.000Z (9 months ago)
- Last Synced: 2025-02-01T18:16:42.434Z (9 months ago)
- Topics: ai, object-detection, opencv, python, roboflow, yolov8, yolov9
- Language: Python
- Homepage:
- Size: 5.86 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🛋️ Furniture Leg Detection Project
## 🚀 Overview
Welcome to the **Furniture Leg Detection Project**! This project leverages advanced **object detection** to identify and track furniture legs in images and videos. Whether you're looking to automate **inventory management**, enhance **quality control**, or implement **augmented reality** applications, this system provides robust solutions! 💡
### 🔑 Key Features
- 📦 **Custom Dataset**: Annotated with **Roboflow** for high accuracy.
- 🔍 **YOLOv8 & YOLOv9**: Leveraging the power of state-of-the-art object detection frameworks for precise performance.
- 🎥 **Real-Time Detection**: Supports both **image** and **video** input, perfect for live applications.
- 🎯 **Confidence Threshold Adjustment**: Fine-tune detection results for optimal accuracy.
- 💾 **Auto-Save Results**: Detected items are automatically saved for further analysis.
---
## ⚙️ Technologies Used
- **Python**: The programming language that powers the project. 🐍
- **YOLOv8 & YOLOv9**: Cutting-edge object detection frameworks for accuracy and speed. ⚡
- **OpenCV**: Real-time image processing library for video capture and manipulation. 📸
- **Roboflow**: Tool for dataset annotation and preprocessing, enhancing model quality. 📝
---
## 🛠️ How It Works
1. **Dataset Preparation**: Annotate the dataset of furniture legs using **Roboflow** and export it for training. 🖼️
2. **Model Training**: Train the **YOLO** model with the annotated data, teaching it to detect patterns of furniture legs. 💡
3. **Detection**: Use the trained model to detect furniture legs in images and videos, displaying bounding boxes and tracking objects in real time. 🎯
---
## 💻 Code Example
Here’s a quick look at how the detection works in action:
```python
from ultralytics import YOLO
# Load the pre-trained model
model = YOLO('C:/Users/dell/Downloads/Nike/legs_wout_whole/legs_wout_whole.pt')
# Perform detection on live video (e.g., webcam feed)
results = model(source=0, show=True, conf=0.6, save=True)
```
---
## 🏃♂️ How to Run
1. **Install Dependencies**:
Install the required libraries via pip:
```bash
pip install ultralytics opencv-python
```
2. **Download the Model**:
- Get the trained model [here](https://drive.google.com/file/d/1X4YulpSKUdRvk0FL24E-lGNJq1mTnhwF/view?usp=sharing).
- Save the `.pt` file to a known directory. 🗂️
3. **Run the Script**:
- Update the script with the correct path to your downloaded model.
- Execute the script with:
```bash
python detect_furniture_legs.py
```
4. **Results**:
- The model will display **live detections** on your video feed! 📹
- Detected frames will be saved to the output directory. 📂
---
## 🌍 Example Use Case
**Input**: An image or video of a room filled with furniture. 🛏️🪑
**Output**:
- Bounding boxes around the detected furniture legs.
- Saved results with annotated images or videos. 📸
---
## ⚠️ Notes
- Ensure that your **input source** (camera or video file) is properly configured. 🎥
- Adjust the **confidence threshold** (`conf` parameter) for fine-tuning detection. 🎯
---
## 🚀 Future Improvements
- Expand the model to detect **other furniture components** (e.g., tables, chairs). 🪑
- Integrate with **IoT devices** for **automated inventory tracking**. 🏠
- Develop a **user-friendly interface** for non-technical users to easily interact with the model. 💻