近年来，人工智能和机器学习的进步彻底改变了包括公共安全在内的各个行业。这些技术在火灾和烟雾检测方面取得了显著进展，这对于早期预警系统和高效的应急响应至关重要。实现这一目标的最有效方法之一是将YOLOv8强大的目标检测能力与基于Python的轻量级Web框架Flask的灵活性相结合。它们共同构成了一个通过视频流实现的强大实时火灾和烟雾检测解决方案。
本文开发了一个专门用于火灾和烟雾检测的自定义训练YOLOv8模型。用于此训练的数据集可在Kaggle上找到，如果需要重新训练模型，训练脚本也可供使用。
数据集：
https://www.kaggle.com/code/deepaknr/yolov8-fire-and-smoke-detection?source=post_page-----79058b024b09--------------------------------
训练脚本：
实际示例：使用YOLOv8和Flask进行火灾和烟雾检测
假设一个实际场景，您需要监控一个有火灾风险的工业场地。通过摄像头建立实时视频流并利用YOLOv8模型的火灾检测功能，您可以及早识别火灾或烟雾，从而预防潜在的灾难。以下是一个Python代码片段，展示了如何将YOLOv8与Flask集成以实现火灾和烟雾检测。
import os
import cv2
import numpy as np
from flask import Flask, render_template, Response, request
from werkzeug.utils import secure_filename
from ultralytics import YOLO
app = Flask(__name__)
YOLOV8_MODEL_PATH = 'path-to-yolov8-model'
ALLOWED_EXTENSIONS = {'mp4', 'avi', 'mov'}
video_path = None
def allowed_file(filename):
return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
model = YOLO(YOLOV8_MODEL_PATH)
@app.route('/')
def index():
return render_template('index.html')
@app.route('/upload', methods=['POST'])
def upload():
global video_path
if 'file' not in request.files:
return 'No file part', 400
file = request.files['file']
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
filepath = os.path.join('uploads', filename)
file.save(filepath)
video_path = filepath
return render_template('index.html')
return 'Invalid file type', 400
def generate_frames():
global video_path
if video_path is None:
return None
cap = cv2.VideoCapture(video_path)
alpha = 0.4
while True:
success, frame = cap.read()
if not success:
break
result = model(frame, verbose=False, conf=0.35)[0]
bboxes = np.array(result.boxes.xyxy.cpu(), dtype="int")
classes = np.array(result.boxes.cls.cpu(), dtype="int")
confidence = np.array(result.boxes.conf.cpu(), dtype="float")
for cls, bbox, conf in zip(classes, bboxes, confidence):
(x1, y1, x2, y2) = bbox
object_name = model.names[cls]
if object_name == 'fire':
color = (19, 127, 240)
else:
color = (145, 137, 132)
cropped_image = frame[int(y1):int(y2), int(x1):int(x2)]
white_layer = np.ones(cropped_image.shape, dtype=np.uint8) * 255
cropped_image = cv2.addWeighted(cropped_image, 1 - alpha, white_layer, alpha, 0)
frame[int(y1):int(y2), int(x1):int(x2)] = cropped_image
cv2.rectangle(frame, (x1, y1 -30), (x1 + 200, y1), color, -1)
cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
cv2.putText(frame, f"{object_name.capitalize()}: {conf * 100:.2f}%", (x1, y1 - 5), cv2.FONT_HERSHEY_DUPLEX,
0.8, (255, 255, 255), 1)
ret, buffer = cv2.imencode('.jpg', frame)
frame = buffer.tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
cap.release()
@app.route('/video_feed')
def video_feed():
return Response(generate_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
if __name__ == '__main__':
os.makedirs('uploads', exist_ok=True)
app.run(host='0.0.0.0', port=5000, debug=True)import os
import cv2
import numpy as np
from flask import Flask, render_template, Response, request
from werkzeug.utils import secure_filename
from ultralytics import YOLO
app = Flask(__name__)
YOLOV8_MODEL_PATH = 'path-to-yolov8-model'
ALLOWED_EXTENSIONS = {'mp4', 'avi', 'mov'}
video_path = None
def allowed_file(filename):
return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
model = YOLO(YOLOV8_MODEL_PATH)
@app.route('/')
def index():
return render_template('index.html')
@app.route('/upload', methods=['POST'])
def upload():
global video_path
if 'file' not in request.files:
return 'No file part', 400
file = request.files['file']
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
filepath = os.path.join('uploads', filename)
file.save(filepath)
video_path = filepath
return render_template('index.html')
return 'Invalid file type', 400
def generate_frames():
global video_path
if video_path is None:
return None
cap = cv2.VideoCapture(video_path)
alpha = 0.4
while True:
success, frame = cap.read()
if not success:
break
result = model(frame, verbose=False, conf=0.35)[0]
bboxes = np.array(result.boxes.xyxy.cpu(), dtype="int")
classes = np.array(result.boxes.cls.cpu(), dtype="int")
confidence = np.array(result.boxes.conf.cpu(), dtype="float")
for cls, bbox, conf in zip(classes, bboxes, confidence):
(x1, y1, x2, y2) = bbox
object_name = model.names[cls]
if object_name == 'fire':
color = (19, 127, 240)
else:
color = (145, 137, 132)
cropped_image = frame[int(y1):int(y2), int(x1):int(x2)]
white_layer = np.ones(cropped_image.shape, dtype=np.uint8) * 255
cropped_image = cv2.addWeighted(cropped_image, 1 - alpha, white_layer, alpha, 0)
frame[int(y1):int(y2), int(x1):int(x2)] = cropped_image
cv2.rectangle(frame, (x1, y1 -30), (x1 + 200, y1), color, -1)
cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
cv2.putText(frame, f"{object_name.capitalize()}: {conf * 100:.2f}%", (x1, y1 - 5), cv2.FONT_HERSHEY_DUPLEX,
0.8, (255, 255, 255), 1)
ret, buffer = cv2.imencode('.jpg', frame)
frame = buffer.tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
cap.release()
@app.route('/video_feed')
def video_feed():
return Response(generate_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
if __name__ == '__main__':
os.makedirs('uploads', exist_ok=True)
app.run(host='0.0.0.0', port=5000, debug=True)主要函数说明：
- def generate_frames()：此函数从上传的视频中提取帧，并利用YOLOv8模型进行目标检测，特别是针对火灾和烟雾等元素。帧上会渲染带有相应类别标签（火灾、烟雾）的边界框。为了增强可见性，在检测到物体的区域应用了半透明的白色覆盖层。处理后的帧被转换为JPEG格式，并持续输出以生成视频流。
- def video_feed()：此路由使用generate_frames函数将处理后的视频帧作为HTTP响应流式传输。它使用MIME类型multipart/x-mixed-replace向Web客户端发送JPEG图像流。
应用程序启动：
if __name__ == '__main__':
os.makedirs('uploads', exist_ok=True)
app.run(host='0.0.0.0', port=5000, debug=True)如果直接运行脚本，它会确保uploads目录存在，然后在端口5000上启动Flask应用程序，并监听所有接口（0.0.0.0）。
