https://github.com/zhenglinlei/fireguardml
Open Source Fire and Smoke AI detector trained with Yolov8
https://github.com/zhenglinlei/fireguardml
deep-learning machine-learning object-recognition yolov8
Last synced: over 1 year ago
JSON representation
Open Source Fire and Smoke AI detector trained with Yolov8
- Host: GitHub
- URL: https://github.com/zhenglinlei/fireguardml
- Owner: ZhengLinLei
- License: other
- Created: 2024-03-12T16:53:31.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-03-12T17:59:37.000Z (over 2 years ago)
- Last Synced: 2025-01-29T22:01:36.216Z (over 1 year ago)
- Topics: deep-learning, machine-learning, object-recognition, yolov8
- Language: Jupyter Notebook
- Homepage:
- Size: 2.51 MB
- Stars: 1
- Watchers: 1
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# FireGuard Machine Learning AI
Fireguard is a machine learning Artificial Intelligent trained with Yolov8 to detect **Fire** and **Smokes** from pictures.
For this project you have two options:
* Use pre-trained model in release, check **Model**
* Train model by yourself, check **Train**
## Table of Contents
1. [Model](#model)
2. [Train](#todel)
--------------------------
## Model
If you want to use the model directly without training your own model, download the `FireGuardBestModel.pt` or `FireGuardLastModel.pt` from [release](https://github.com/ZhengLinLei/FireGuardML/releases/)
### Usage
```python
import cv2
from ultralytics import YOLO
import math
import os
# Cargar el modelo
model = YOLO('./FireGuardBestModel.pt')
# Leer las clases
classnames = ['fire', 'neutral', 'smoke']
# Crear un directorio para guardar las imágenes procesadas
os.makedirs('../tmp', exist_ok=True)
# Lista de imágenes a procesar
# Coger las imagenes de ./img sin . .. y otros que no sean .jpg
images = [img for img in os.listdir('../img') if img.endswith('.jpg')]
for image_path in images:
# Leer la imagen
frame = cv2.imread(f'../img/{image_path}')
frame = cv2.resize(frame, (640, 480))
# Procesar la imagen con YOLO
result = model(frame, stream=True)
# Get confidence and class names
# print(result[0])
# Obtener la información de bbox, confianza y nombres de clases para trabajar con ella
for info in result:
boxes = info.boxes
for box in boxes:
confidence = box.conf[0]
confidence = math.ceil(confidence * 100)
Class = int(box.cls[0])
if confidence > 10:
x1, y1, x2, y2 = box.xyxy[0]
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
print(f'Class: {classnames[Class]}, Confidence: {confidence}%')
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
# Write the class name and confidence
cv2.putText(frame, f'{classnames[Class]} {confidence}%', (x1 - 8, y1 - 8), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 1)
# Guardar la imagen procesada
processed_image_path = os.path.join('../tmp', os.path.basename(image_path))
cv2.imwrite(processed_image_path, frame)
```
--------------------------
## Train
If you have enough time or you want to include custom data to train your own AI model, you can choose from
1. Train in Google colab (recommended)
2. Train in your machine
You can find the datasets from [release](https://github.com/ZhengLinLei/FireGuardML/releases/)
### Google colab
#### Dowload dataset and import to drive
To train the model in Google colab, be sure to have the datasets installed and uploaded to your Google drive with this name `~/D-Fire-datasets`. And check if you have this file branch
```
📁 D-Fire-datasets
|
\-----> 📁 fire
|
|----> 📁 test
| |
| |---> 📁 images (images)
| \---> 📁 labels (txt)
|
|----> 📁 train
| |
| |---> 📁 images (images)
| |---> 📁 labels (txt)
| \---> 📄 labels.cache
|
|----> 📁 train
| |
| |---> 📁 images (images)
| |---> 📁 labels (txt)
| \---> 📄 labels.cache
|
\---> 📄 data.yaml
```
### Download Colab file
Donwload the source file from `./src/FireGuardML_Train.ipynb` and import it to google colab. **Be sure to enable GPU**
-------
### Own Machine
#### Installation
Go to `src/` and install dependecies
```bash
python3 -m pip install -r requirements.txt
```
#### Download datasets
Down load the dataset from [release](https://github.com/ZhengLinLei/FireGuardML/releases/) and decompress it inside `./data` folder
#### Usage
To train the model, execute the file `./src/setup.sh` inside `./src/context`
```bash
cd src
./setup.sh
```
----------------------------------------------------------------------------
## Information
FireGuardML by Zheng Lin Lei is licensed under CC BY-NC 4.0
