algorithm_system_server/app.py

from flask import Flask, render_template, request, jsonify, Response, session,g
from werkzeug.utils import secure_filename
from algorithm.people_detection import VideoPeopleDetection
from algorithm.fire_detection import FireDetection
from algorithm.smog_detection import SmogDetection
from algorithm.helmet_detection import HelmetDetection
from algorithm.mask_detection import MaskDetection
from algorithm.electromobile_detection import ElectromobileDetection
from algorithm.glove_detection import GloveDetection
from algorithm.reflective_detection import ReflectiveDetection
from algorithm.phone_detection import PhoneDetection
from algorithm.pose_detection import PoseDetection
from algorithm.image_segmentation import ImageSegmentation
from algorithm.drowsy_detection import DrowsyDetection
from algorithm.lane_detection import LaneDetection
from algorithm.trafiic_lights import TrafficLightsDetection
from algorithm.easyocr import OCR
from algorithm.detect_emotion.emotion_detection import Emotion_Detection
from algorithm.face_recognition.face_recognition import Face_Recognizer
from algorithm.Car_recognition.car_detection import CarDetection
from algorithm.pcb_detection import PCBDetection
from algorithm.Remote_sense.remote_sense import Remote_Sense
from algorithm.safe_detection import SafeDetection
from algorithm.traffic_logo_detection import TrafficLogoDetection


from algorithm.yolo_segment import YOLO_Segment


import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
import multiprocessing
from MyThreadFunc import MyThreadFunc
import json
import pymysql
import threading
import torch
from flask_cors import CORS

app = Flask(__name__)
app.secret_key = 'super_secret_key'  # Needed to use sessions
CORS(app, resources={r"/*": {"origins": "http://127.0.0.1:5173"}}) 


# Configure the upload folder and allowed extensions
UPLOAD_FOLDER = 'uploads'
ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif', 'mp4'}
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
global cameraLocation 
cameraLocation = 'rtsp://192.168.30.18:8557/h264'


#常用模型率先开启
model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/best_person_29.05.2023-yolov5s-1.pt', force_reload=True)
fire_model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/fire2.pt', force_reload=True)
face_model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/face.pt', force_reload=True)

somg_model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/smog.pt', force_reload=True)

drowsy_model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/traffic/drowsy.pt', force_reload=True)
# traffic_lights_model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/traffic/traffic.pt', force_reload=True)

global algTextNew
algTextNew = '对生产环境的行人进行检测'
global algorithm_name
global algNameNew
algNameNew = '行人检测'
if not os.path.exists(UPLOAD_FOLDER):
    os.makedirs(UPLOAD_FOLDER)

def allowed_file(filename):
    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS

def readFileTojson(input):
    with open(input, "r", encoding="utf-8") as f:
        parse_data = json.load(f)
    return parse_data

@app.route("/")
def home():

    return render_template("index.html")
# 获取数据库摄像头信息,传给前端
@app.route('/getSqlCamera', methods=['GET','POST'])
def getSqlCamera():
    conn = pymysql.connect(host='10.51.10.122', user='root', password='fate_dev', port=3306, database="camera_rtsp", charset='utf8')
    cursor = conn.cursor()
    cursor.execute("select * from camera")
    res = cursor.fetchall()
    cursor.close()
    conn.close()
    #print(res)
    return jsonify(res)
# 获取选择的算法
@app.route('/api/algname', methods=['POST','GET'])  
def get_alg_name():  
    # 从 JSON 数据中提取 algName  
    alg_name = request.json.get('algName')  
    alg_descrip = request.json.get('algText')  
    global algNameNew
    algNameNew = alg_name
    global algTextNew
    algTextNew = alg_descrip
    # 检查是否成功提取 algName  
    if alg_name is None:  
        return jsonify({"error": "algName not provided in JSON"}), 400  
    if alg_descrip is None:  
        return jsonify({"error": "alg_descrip not provided in JSON"}), 400  
  
    # 在这里处理 algName，例如保存到数据库、执行算法等  
    # ...  
    # 返回一个成功的响应  
    return jsonify(alg_name,alg_descrip ), 200 
    
@app.route('/getSelectAlg', methods=['GET','POST'])
def getSelectAlg():
    res = [algNameNew, algTextNew]
    #print(res)
    return jsonify(res)
# 获取前端用户选择的摄像头信息
@app.route('/getFrontCamera', methods=['GET','POST'])
def getFrontCamera():
    
    cameraId= request.form['camID']
    
    # print(cameraId)
    conn = pymysql.connect(host='10.51.10.122', user='root', password='fate_dev', port=3306, database="camera_rtsp", charset='utf8')
    cursor = conn.cursor()
    cursor.execute("select * from camera where cameraID =" +cameraId)
    chsCamera  = cursor.fetchall()
    # 摄像头IP
    global cameraLocation 
    cameraLocation = chsCamera[0][1]
    print(cameraLocation)

    cursor.close()
    conn.close()
    #print(chsCamera[0][1])
    return jsonify(message='successfully'), 200


# 获取数据库算法信息,传给前端
@app.route('/getSqlAlg', methods=['GET','POST'])
def getSqlAlg():
    conn = pymysql.connect(host='10.51.10.122', user='root', password='fate_dev', port=3306, database="camera_rtsp", charset='utf8')
    cursor = conn.cursor()
    cursor.execute("select * from algorithm")
    res = cursor.fetchall()
    cursor.close()
    conn.close()
    #print(res)
    return jsonify(res)

# 获取前端选择的算法信息
@app.route('/getSelectAlgorithm', methods=['GET','POST'])
def getSelectAlgorithm():
    global algorithm_name
    algorithm_name = request.form['selectAlgorithm']
    print(algorithm_name+"-----")
    return jsonify(message='successfully'), 200


last_uploaded_filename = None
@app.route('/upload', methods=['POST'])
def upload_file():
    if 'file' not in request.files:
        return jsonify(message='No file part'), 400
    file = request.files['file']
    if file.filename == '':
        return jsonify(message='No selected file'), 400
    if file and allowed_file(file.filename):
        filename = secure_filename(file.filename)
        file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
        file.save(file_path)
        session['uploaded_file_path'] = file_path  # Save the file path to the session
        return jsonify(message=f'File {filename} uploaded successfully'), 200
    else:
        return jsonify(message='File type not allowed'), 400
        
def gen(camera):

    while True:
        frame, resText = camera.get_frame()

        # 将 JSON 字符串发送给前端
        yield (b'--frame\r\n'
            b'Content-Type: image/jpeg\r\n\r\n' + frame
            + b'\r\n\r\n' + b'--resText\r\n'
            b'Content-Type: application/json\r\n\r\n'
            + json.dumps({'resText': resText}).encode('utf-8')
            + b'\r\n\r\n')
        
@app.route("/use_webcam")
def use_webcam():

    global cameraLocation
    source = cameraLocation
    print("使用RTSP", source)
    if algNameNew == '行人检测':
        camera = VideoPeopleDetection(model=model)
    elif algNameNew == '火焰识别':
        camera = FireDetection(model=fire_model)
    elif algNameNew == '人脸识别':
        camera = Face_Recognizer(model=face_model)
            
    elif algNameNew == '表情识别':
        camera = Emotion_Detection(model=face_model)
                    
    elif algNameNew == 'OCR':
        camera = OCR()
        
    elif algNameNew == '车辆检测':
        camera = CarDetection()
  
    elif algNameNew == '实例分割':
        camera = YOLO_Segment()
        
    elif algNameNew == '疲劳检测':
        camera = DrowsyDetection(model=drowsy_model)    
    elif algNameNew == '车道线检测':
        camera = LaneDetection()
    elif algNameNew == '红绿灯检测':
        camera = TrafficLightsDetection()

    elif algNameNew == 'PCB缺陷检测':
        camera = PCBDetection()

    elif algNameNew == '遥感目标检测':
        camera = Remote_Sense()   
                      
    elif algNameNew == '电动车头盔识别':
        camera = HelmetDetection()

    elif algNameNew == '戴口罩识别':
        camera = MaskDetection()
   
    elif algNameNew == '电动车检测':
        camera = ElectromobileDetection()
     # 
    elif algNameNew == '反光衣识别':
        camera = ReflectiveDetection()
    
    elif algNameNew == '玩手机识别':
        camera = PhoneDetection()
        
    elif algNameNew == '姿态检测':
        camera = PoseDetection()    
        
        
    elif algNameNew == '安全检测':
        camera = SafeDetection()    
        
    elif algNameNew == '交通标志检测':
        camera = TrafficLogoDetection()    
        
    camera.use_webcam(source)

    return Response(gen(camera),
                    mimetype="multipart/x-mixed-replace; boundary=frame")
# @app.route("/getDetectResult")
# def getDetectResult():
#     camera = VideoPeopleDetection()
#     numPeople, accuracy=camera.getDetectResult()
#     return jsonify(numPeople,accuracy)

        
@app.route("/video_feed")
def video_feed():
    # Check if a file has been uploaded and saved in the session
    if 'uploaded_file_path' in session:
        # Initialize VideoPeopleDetection with the uploaded file path
        if algNameNew == '行人检测':
            camera = VideoPeopleDetection(video_path = session['uploaded_file_path'], model=model)
            
        elif algNameNew == '火焰识别':
            camera = FireDetection(video_path = session['uploaded_file_path'], model=fire_model)

        elif algNameNew == '烟雾识别':
            camera = SmogDetection(video_path = session['uploaded_file_path'], model=fire_model)
            
        elif algNameNew == '人脸识别':
            camera = Face_Recognizer(video_path = session['uploaded_file_path'], model=face_model)
                       
        elif algNameNew == '表情识别':
            camera = Emotion_Detection(video_path = session['uploaded_file_path'], model=face_model)
            
        elif algNameNew == 'OCR':
            camera = OCR(video_path = session['uploaded_file_path'])
            
        elif algNameNew == '车辆检测':
            camera = CarDetection(video_path = session['uploaded_file_path'])
            
        elif algNameNew == '实例分割':
            camera = YOLO_Segment(video_path = session['uploaded_file_path'])
            
        elif algNameNew == '疲劳检测':
            camera = DrowsyDetection(video_path = session['uploaded_file_path'], model=drowsy_model)
            
        elif algNameNew == '车道线检测':
            camera = LaneDetection(video_path=session['uploaded_file_path'])
            
        elif algNameNew == '红绿灯检测':
            camera = TrafficLightsDetection(video_path=session['uploaded_file_path']) 
        elif algNameNew == 'PCB缺陷检测':
            camera = PCBDetection(video_path=session['uploaded_file_path'])
   
        elif algNameNew == '遥感目标检测':
            camera = Remote_Sense(video_path=session['uploaded_file_path'])   
    
        elif algNameNew == '电动车头盔识别':
            camera = HelmetDetection(video_path=session['uploaded_file_path'])

        elif algNameNew == '戴口罩识别':
            camera = MaskDetection(video_path=session['uploaded_file_path'])
            
        elif algNameNew == '电动车检测':
            camera = ElectromobileDetection(video_path=session['uploaded_file_path'])
            
        elif algNameNew == '手套检测':
            camera = GloveDetection(video_path=session['uploaded_file_path'])   
            
        elif algNameNew == '反光衣识别':
            camera = ReflectiveDetection(video_path=session['uploaded_file_path'])   
        
        elif algNameNew == '玩手机识别':
            camera = PhoneDetection(video_path=session['uploaded_file_path'])         
            
        elif algNameNew == '姿态检测':
            camera = PoseDetection(video_path=session['uploaded_file_path'])       
    
        elif algNameNew == '肝脏图像分割':
            camera = ImageSegmentation(video_path = session['uploaded_file_path'])
            
            
        elif algNameNew == '安全检测':
            camera = SafeDetection(video_path = session['uploaded_file_path'])
            
        
        elif algNameNew == '交通标志检测':
            camera = TrafficLogoDetection(video_path = session['uploaded_file_path'])    
    
        return Response(gen(camera),
                        mimetype="multipart/x-mixed-replace; boundary=frame")
    else:
        # If no file has been uploaded, return a default message or empty feed
        return "No video uploaded yet", 200

if __name__ == "__main__":
    
    app.run(host='10.51.10.122',debug=True, port=5001)