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需求：
小程序端拍照调用python训练好的图片分类模型。实现图片分类识别的功能。
微信小程序端：
重点在chooseImage函数中，根据图片路径获取到图片传递给flask的url;
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) { 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) 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') } } }) } }) } }) },});flask端：
将图片裁剪，填充，调用自己训练保存最优的模型，用softmax处理结果矩阵，最后得到预测种类
# coding=utf-8from flask import Flask, render_template, request, jsonifyfrom werkzeug.utils import secure_filenamefrom datetime import timedeltafrom flask import Flask, render_template, requestimport torchvision.transforms as transformsfrom PIL import Imagefrom torchvision import modelsimport osimport torchimport jsonimport numpy as npimport torch.nn as nnimport matplotlib.pyplot as pltimport base64app = Flask(__name__)def softmax(x): exp_x = np.exp(x) softmax_x = exp_x / np.sum(exp_x, 0) return softmax_xwith 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)大致的效果：
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