QAction *Act_Detect_SSD = new QAction(tr("SSD模型"), this); // 菜单项
connect(Act_Detect_SSD, SIGNAL(triggered()), I_MainWindow, SLOT(deep_detect_ssd())); // 绑定点击菜单项的信号槽， deep_detect_ssd函数就是点击该项去执行的槽函数

// 菜单项添加到一级菜单中
menu_deep_detect = new QMenu();  // 此处为头文件中定义的指针，用来添加最后一级的菜单项，比如“SSD模型”、“YOLO模型”等
QAction *Act_detects = new QAction(tr("目标检测"), this);
//action添加menu 当且仅当菜单项有子选项时，才需要setMenu
Act_detects->setMenu(menu_deep_detect);
// “目标检测”将一级菜单项“SSD模型”添加进来
menu_deep_detect->addAction(Act_Detect_SSD);

QMenu *deep = addMenu(tr("深度处理"));
deep->addAction(Act_detects);

DeepProcess *ssd;
ssd = new DeepProcess("ssd", "detect/ssd", this);// detect/ssd为服务器端的ssd方法的url

QImage current = imgLabel->getImage(); // 此处使用此方法获取当前图像

connect(ssd, SIGNAL(responseImage(QImage, QString)), this, SLOT(process_response(QImage, QString)));
ssd->post(current, "hello"); // "hello"是方法提供的额外参数，可以扩展为 向服务器发送的客户机id、密钥等信息，供服务器验证

import cv2
import base64
import numpy as np

def cv2_base64(image):
    base64_str = cv2.imencode('.jpg', image)[1].tostring()
    base64_str = base64.b64encode(base64_str)
    return str(base64_str).lstrip("b'")


##base64转cv2
def base64_cv2(base64_str):
    imgString = base64.b64decode(base64_str)
    nparr = np.fromstring(imgString, np.uint8)
    image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
    return image

from flask import Flask, jsonify, request
import utils
import cv2

app = Flask(__name__)
@app.route("/detect/ssd", methods=['POST'])
def ssd():
    data = request.json
    img = data['image'] # 获取post过来的图像和msg信息
    msg = data['msg']
    
    try:
        # 将图像转化
        image = utils.base64_cv2(img)
        # 模型处理
        image = ssd(image)    # 返回的应该是cv2 ndarrray的图像
        # 将结果返回
        return jsonify({"image": utils.cv2_base64(image), "msg": "OK", "code": 200})
    except Exception as e:
        print(e)
        return jsonify({"code": 500, "msg": str(e)})

if __name__ == '__main__':
    app.run(debug=True)