使用 Yolov8 Flask 自定義訓(xùn)練實時火災(zāi)和煙霧檢測
作者：二旺
本文開發(fā)了一個專門用于火災(zāi)和煙霧檢測的自定義訓(xùn)練YOLOv8模型。用于此訓(xùn)練的數(shù)據(jù)集可在Kaggle上找到，如果需要重新訓(xùn)練模型，訓(xùn)練腳本也可供使用。
近年來，人工智能和機器學(xué)習(xí)的進步徹底改變了包括公共安全在內(nèi)的各個行業(yè)。這些技術(shù)在火災(zāi)和煙霧檢測方面取得了顯著進展，這對于早期預(yù)警系統(tǒng)和高效的應(yīng)急響應(yīng)至關(guān)重要。實現(xiàn)這一目標(biāo)的最有效方法之一是將YOLOv8強大的目標(biāo)檢測能力與基于Python的輕量級Web框架Flask的靈活性相結(jié)合。它們共同構(gòu)成了一個通過視頻流實現(xiàn)的強大實時火災(zāi)和煙霧檢測解決方案。
本文開發(fā)了一個專門用于火災(zāi)和煙霧檢測的自定義訓(xùn)練YOLOv8模型。用于此訓(xùn)練的數(shù)據(jù)集可在Kaggle上找到，如果需要重新訓(xùn)練模型，訓(xùn)練腳本也可供使用。
數(shù)據(jù)集：
https://www.kaggle.com/code/deepaknr/yolov8-fire-and-smoke-detection?source=post_page-----79058b024b09--------------------------------
訓(xùn)練腳本：
實際示例：使用YOLOv8和Flask進行火災(zāi)和煙霧檢測
假設(shè)一個實際場景，您需要監(jiān)控一個有火災(zāi)風(fēng)險的工業(yè)場地。通過攝像頭建立實時視頻流并利用YOLOv8模型的火災(zāi)檢測功能，您可以及早識別火災(zāi)或煙霧，從而預(yù)防潛在的災(zāi)難。以下是一個Python代碼片段，展示了如何將YOLOv8與Flask集成以實現(xiàn)火災(zāi)和煙霧檢測。
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)主要函數(shù)說明：
- def generate_frames()：此函數(shù)從上傳的視頻中提取幀，并利用YOLOv8模型進行目標(biāo)檢測，特別是針對火災(zāi)和煙霧等元素。幀上會渲染帶有相應(yīng)類別標(biāo)簽（火災(zāi)、煙霧）的邊界框。為了增強可見性，在檢測到物體的區(qū)域應(yīng)用了半透明的白色覆蓋層。處理后的幀被轉(zhuǎn)換為JPEG格式，并持續(xù)輸出以生成視頻流。
- def video_feed()：此路由使用generate_frames函數(shù)將處理后的視頻幀作為HTTP響應(yīng)流式傳輸。它使用MIME類型multipart/x-mixed-replace向Web客戶端發(fā)送JPEG圖像流。
應(yīng)用程序啟動：
if __name__ == '__main__':
os.makedirs('uploads', exist_ok=True)
app.run(host='0.0.0.0', port=5000, debug=True)如果直接運行腳本，它會確保uploads目錄存在，然后在端口5000上啟動Flask應(yīng)用程序，并監(jiān)聽所有接口（0.0.0.0）。
責(zé)任編輯：趙寧寧
來源：
小白玩轉(zhuǎn)Python
