yolov5项目部署+微信小程序前端展示的全过程

<view class="container">
    <view>
      <image src="{{avatarUrl}}" class=".img" bindtap="imgClick"></image>
      <button bindtap="chooseimg" class=".btn_select">选择图片</button>
      <button form-type="submit" class=".btn_submit" bindtap="submitimg">开始检测</button>
    </view>
    <view class="text-container">
      <view>{{name_and_nums}}</view>
      <view class="text" wx:for="{{names}}">{{item.name}}: {{item.value}} </view>
    </view>
  </view>

data: {
    avatarUrl: 'res.png',
    base64imgurl: null,
    name_num: '',
    names: [],
    name_and_nums: ''
 
  },

chooseimg: function() {
    var that = this
    // 选择图片
    wx.chooseImage({
      count: 1,
      sizeType: ['original', 'compressed'],
      sourceType: ['album', 'camera'],
      success(res) {
        // tempFilePath可以作为 img 标签的 src 属性显示图片
        var tempFilePaths = res.tempFilePaths
        console.log(tempFilePaths)
        that.setData({avatarUrl:tempFilePaths[0]})
        console.log(tempFilePaths[0])
    wx.setStorage({key:'img_path', data: tempFilePaths[0]})
}

submitimg: function(){
    var img_path
    var that
    wx.getImageInfo({
          src: img_path,
          success(res){
          imgtype = res.type
          console.log(imgtype)
      }
    })
    wx.uploadFile({
          filePath: img_path,
          name: 'image',
          url: '服务器地址',
          // 上传成功！
          success(res){
            console.log(res)
            var img_data =  JSON.parse(res.data).data.image
            var base64str_img = 'data:image/' + imgtype + ';base64,' + img_data
            that.setData({avatarUrl: base64str_img})
            
          // 上传失败
          fail(){
          console.log('--failed--')
    
          }
        })
      }
}

//前端：
 
wx.uploadFile({
      filePath: img_path,
      name: 'image',
      url: 服务器地址
})
 
//后端
img_file = request.files["image"]

@app.route(DETECTION_URL, methods=["POST"])
def predict():
    if request.method != "POST":
        return jsonify({"code": 2, "msg": "the request method is error!", "data": {"isSecret": "null"}})
    if request.files.get("image"):
        # 将读取的图片流转换为图片格式
        im_file = request.files["image"]
        im_bytes = im_file.read()
        im = Image.open(io.BytesIO(im_bytes))
        #将图片输入到模型中，输出的结果是一个list，带有坐标类别等信息
        results = model(im, size=640)  # reduce size=320 for faster inference
        # 预测的结果（坐标 种类 置信度）
        result = results.pandas().xyxy[0].to_json(orient="records")
 
        img_res = ''
        with open(save_path, 'rb') as f:
            img_res = f.read()
            img_res = base64.b64encode(img_res)
        
        return jsonify({ "data": {"detect_res": "yes", "image": str(img_res, 'utf-8')}})
 
if __name__ == "__main__":
    torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
 
    model = torch.hub.load("path", "custom", path="./best.pt", source="local", force_reload=False)  # force_reload to recache