微信小程序前端如何调用python后端的模型详解

Page({
    data: {
        SHOW_TOP: true,
        canRecordStart: false,
    },
    data: {
        tempFilePaths:'',
        sourceType: ['camera', 'album']
      },
    isSpeaking: false,
    accessToken: "",
    onLoad: function (options) {
        
        console.log("onLoad！");
        this.setHeader();
        var that=this
        wx.showShareMenu({
            withShareTicket: true //要求小程序返回分享目标信息
        });
        var isShowed = wx.getStorageSync("tip");
        if (isShowed != 1) {
            setTimeout(() => {
                this.setData({
                    SHOW_TOP: false
                })
                wx.setStorageSync("tip", 1)
            }, 3 * 1000)
        } else {
            this.setData({
                SHOW_TOP: false
            })
        };
    },
    },
    
 //头像点击处理事件，使用wx.showActionSheet()调用菜单栏
 buttonclick: function () {
    const that = this
    wx.showActionSheet({
      itemList: ['拍照', '相册'],
      itemColor: '',
      //成功时回调
      success: function (res) {
        if (!res.cancel) {
          /*
           res.tapIndex返回用户点击的按钮序号，从上到下的顺序，从0开始
           比如用户点击本例中的拍照就返回0，相册就返回1
           我们res.tapIndex的值传给chooseImage()
          */
          that.chooseImage(res.tapIndex)
        }
      },
      
setHeader(){
    const tempFilePaths = wx.getStorageSync('tempFilePaths');
    if (tempFilePaths) {
      this.setData({
        tempFilePaths: tempFilePaths
      })
    } else {
      this.setData({
        tempFilePaths: '/images/camera.png'
      })
    }
  },

  chooseImage(tapIndex) {
    const checkeddata = true
    const that = this
    wx.chooseImage({
    //count表示一次可以选择多少照片
      count: 1,
      //sizeType所选的图片的尺寸，original原图，compressed压缩图
      sizeType: ['original', 'compressed'],
      //如果sourceType为camera则调用摄像头，为album时调用相册
      sourceType: [that.data.sourceType[tapIndex]],
      success(res) {
        // tempFilePath可以作为img标签的src属性显示图片
        console.log(res);
        const tempFilePaths = res.tempFilePaths
        //将选择到的图片缓存到本地storage中
        wx.setStorageSync('tempFilePaths', tempFilePaths)
        /*
		由于在我们选择图片后图片只是保存到storage中，所以我们需要调用一次   	        setHeader()方法来使页面上的头像更新
		*/
        that.setHeader();
        // wx.showToast({
        //   title: '设置成功',
        //   icon: 'none',
        // //   duration: 2000
        // })
        wx.showLoading({
            title: '识别中...',
        })
        
        var team_image = wx.getFileSystemManager().readFileSync(res.tempFilePaths[0], "base64")
        wx.request({
          url: 'http://127.0.0.1:5000/upload', //API地址，upload是我给路由起的名字，参照下面的python代码
          　　　　　 　　　　　method: "POST",
          header: {
            　　　　　　　　　'content-type': "application/x-www-form-urlencoded",
            　　　　　　　　},
          data: {image: team_image},//将数据传给后端
     
        success: function (res) {
            console.log(res.data);  //控制台输出返回数据  
            wx.hideLoading()
            wx.showModal({

                title: '识别结果', 
                confirmText: "识别正确",
                cancelText:"识别错误",
                content: res.data, 
                success: function(res) { 
                if (res.confirm) {
                console.log('识别正确')
                } else if (res.cancel) {
                console.log('重新识别')
                }
                }
                })     
          }
        })
      }
    })
  },
});

# coding=utf-8
from flask import Flask, render_template, request, jsonify
from werkzeug.utils import secure_filename
from datetime import timedelta
from flask import Flask, render_template, request
import torchvision.transforms as transforms
from PIL import Image
from torchvision import models
import os
import torch
import json
import numpy as np
import torch.nn as nn
import matplotlib.pyplot as plt
import base64

app = Flask(__name__)

def softmax(x):
    exp_x = np.exp(x)
    softmax_x = exp_x / np.sum(exp_x, 0)
    return softmax_x

with open('dir_label.txt', 'r', encoding='utf-8') as f:
    labels = f.readlines()
    print("oldlabels:",labels)
    labels = list(map(lambda x: x.strip().split('\t'), labels))
    print("newlabels:",labels)

def padding_black(img):
    w, h = img.size

    scale = 224. / max(w, h)
    img_fg = img.resize([int(x) for x in [w * scale, h * scale]])

    size_fg = img_fg.size
    size_bg = 224

    img_bg = Image.new("RGB", (size_bg, size_bg))

    img_bg.paste(img_fg, ((size_bg - size_fg[0]) // 2,
                              (size_bg - size_fg[1]) // 2))

    img = img_bg
    return img
# 输出
@app.route('/')
def hello_world():
    return 'Hello World!'

# 设置允许的文件格式
ALLOWED_EXTENSIONS = set(['png', 'jpg', 'JPG', 'PNG', 'bmp'])
def allowed_file(filename):
    return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS

# 设置静态文件缓存过期时间
app.send_file_max_age_default = timedelta(seconds=1)

# 添加路由
@app.route('/upload', methods=['POST', 'GET'])
def upload():
    if request.method == 'POST':
        # 通过file标签获取文件
        team_image = base64.b64decode(request.form.get("image"))  # 队base64进行解码还原。
        with open("static/111111.jpg", "wb") as f:
            f.write(team_image)
        image = Image.open("static/111111.jpg")
        # image = Image.open('laji.jpg')
        image = image.convert('RGB')
        image = padding_black(image)
        transform1 = transforms.Compose([
            transforms.Resize(224),
            transforms.ToTensor(),
        ])
        image = transform1(image)
        image = image.unsqueeze(0)
        # image = torch.unsqueeze(image, dim=0).float()
        print(image.shape)
        model = models.resnet50(pretrained=False)
        fc_inputs = model.fc.in_features
        model.fc = nn.Linear(fc_inputs, 214)
        # model = model.cuda()
        # 加载训练好的模型
        checkpoint = torch.load('model_best_checkpoint_resnet50.pth.tar')
        model.load_state_dict(checkpoint['state_dict'])
        model.eval()

        src = image.numpy()
        src = src.reshape(3, 224, 224)
        src = np.transpose(src, (1, 2, 0))
        # image = image.cuda()
        # label = label.cuda()
        pred = model(image)
        pred = pred.data.cpu().numpy()[0]

        score = softmax(pred)
        pred_id = np.argmax(score)

        plt.imshow(src)
        print('预测结果：', labels[pred_id][0])
        # return labels[pred_id][0];
        return json.dumps(labels[pred_id][0], ensure_ascii=False)//将预测结果传回给前端
        # plt.show()
    #     return render_template('upload_ok.html')
    #     重新返回上传界面
    # return render_template('upload.html')

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