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https://github.com/ashishtukaral/fire-detection-system
https://github.com/ashishtukaral/fire-detection-system
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- Host: GitHub
- URL: https://github.com/ashishtukaral/fire-detection-system
- Owner: AshishTukaral
- Created: 2024-04-19T13:40:21.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-04-19T16:29:22.000Z (8 months ago)
- Last Synced: 2024-04-20T15:09:10.405Z (8 months ago)
- Language: Python
- Size: 10.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Fire Detection System
## Introduction:
This project aims to develop a fire detection system using the YOLOv8 model for real-time detection and tracking of fires in video streams. The system draws bounding boxes around detected fires and displays a "Danger: Fire Detected" message on the video feed.
## Fire Dataset Link:
The dataset used for training the YOLOv8 model can be accessed at [Fire Dataset](https://mega.nz/file/MgVhQSoS#kOcuJFezOwU_9F46GZ1KJnX1STNny-tlD5oaJ9Hv0gY).
## Instructions to run the script
1. Install Required Libraries:
- Ensure that you have Python installed on your system.
- Navigate to the project directory in your terminal.
- Install the required libraries using the provided `requirements.txt` file by running the following command:
```
pip install -r requirements.txt
```
2. Train the Model:
- Before making predictions, you need to train the YOLOv8 model using the provided dataset and configuration.
- Execute the model training script using the following command:
```
python train_model.py
```
- Adjust training parameters such as epochs, batch size, and model name as needed in the script.
3. Make Predictions:
- After training the model, you can make predictions on video streams or images to detect fires.
- Run the prediction script using the following command:
```
python fire_detection.py
```
- Ensure that the trained model weights (e.g., `best.pt`) are correctly placed in the designated directory.
- The script will capture video frames from the webcam, perform fire detection using the trained model, and display the results in real-time.
## Training the YOLOv8 Model:
1. Load the YOLOv8 model using the following code:
```
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
```
2. Start training the model using the `train` method:
```
results = model.train(
data='data.yaml',
epochs=35,
batch=8,
name='yolov8n_custom_all'
)
```
3. Adjust training parameters such as epochs, batch size, and model name as needed in the script.
4. Ensure that the `data.yaml` file contains information about the dataset, including the class labels. In this project, there is only one class, which is 'fire'.
## Making Predictions:
To make predictions using the trained YOLOv8 model, follow these steps:
1. Load the YOLOv8 model with the trained weights:
```
from ultralytics import YOLO
model = YOLO("model/weights/best.pt")
```
2. Start capturing video from the webcam using OpenCV:
```
cap = cv2.VideoCapture(0)
```
3. Extract frames from the video feed and perform object detection using the YOLOv8 model:
```
ret, frame = cap.read()
results = model.predict(source=frame, line_width=1, show_conf=False)
```
4. Draw bounding boxes around detected fires and display a "Danger: Fire Detected" message on the video feed:
```
for box, cls, conf in zip(boxes, clss, confs):
x, y, w, h = box
percentage = int(conf * 100) # Calculate detection percentage
# Display the bounding box and label on the frame
cv2.rectangle(frame, (int(x - w / 2), int(y - h / 2)), (int(x + w / 2), int(y + h / 2)), (0, 0, 255), 2)
# Display the danger message at the bottom center of the screen
cv2.putText(frame, 'Danger', (225, screen_height - 38), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 4,
cv2.LINE_AA)
cv2.putText(frame, 'Fire Detected', (241, screen_height - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255),
2, cv2.LINE_AA)
```
5. Display the processed frame with bounding boxes and messages using OpenCV:
```
cv2.imshow('Fire Detection', frame)
```
6. Press 'q' to exit the video feed.