基于tensorflow.js的自动填充zf教务系统验证码脚本.zip

// ==UserScript== // @name 基于tf的zf验证码填充脚本 // @version 1.0 // @author 1248289414 // @namespace https://github.com/1248289414 // @description 基于tensorflow.js的自动填充教务处验证码的脚本 // @match https://jwc.scnu.edu.cn/ // @match https://jwc.scnu.edu.cn/default2.aspx // @run-at document-end // @require https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.1.2/dist/tf.min.js // @grant GM_registerMenuCommand // ==/UserScript== // 结果跟实际字母的对照表 const Alphabet = "012345678abcdefghijklmnpqrstuvwxy"; const DBname = "jwc-captcha-model-1"; class imgData { async load(img) { // 整个图片的像素个数 let IMAGE_SIZE = img.width * img.height; // 获取canvas用于绘图 let canvas = document.createElement('canvas'); canvas.width = img.width; canvas.height = img.height; // 绘图 let ctx = canvas.getContext('2d'); ctx.drawImage(img, 0, 0); // 将提取像素信息 let imageData = ctx.getImageData(0, 0, canvas.width, canvas.height); // 单独提取red通道值，建立灰度图 let grayImageData = new Float32Array(IMAGE_SIZE); for (let i = 0; i < IMAGE_SIZE; i++) { grayImageData[i] = imageData.data[i * 4] / 255.0; } // 在cut_x的切割点处将，切割图像为4个18*23的小块 let cut_x = [0, 13, 27, 41]; let block_width = 18; let block_height = 23; let block_size = block_width * block_height; this.data = new Float32Array(block_size * 4); for (let i = 0; i < 4; i++) { let index = cut_x[i]; for (let j = 0; j < block_height; j++) { let row = grayImageData.slice(j * img.width + index, j * img.width + index + block_width); this.data.set(row, i * block_size + j * block_width) } } } get() { return tf.tensor4d(this.data, [4, 23, 18, 1]); } } async function run() { // 获取验证码图片，判断是否加载成功 let img = document.getElementById("icode"); if (img.height != 27 || img.width != 72) { return } let data = new imgData(); await data.load(img); await indexedDB.open(DBname); // 打开模型，失败则退出 let model; try { model = await tf.loadLayersModel("indexeddb://" + DBname); } catch (e) { console.log("模型加载失败，你可能没有导入模型"); return } // 预测验证码的值 let score = await model.predict(data.get()).argMax([-1]); // 将结果转换为字符串 let arr = Array.from(score.dataSync()); let captcha = ''; for (let i = 0; i < 4; i++) { captcha = captcha + Alphabet[arr[i]]; } // 填入验证码 let txtSecretCode = document.getElementById("txtSecretCode"); txtSecretCode.value = captcha; } // 上传模型并保存 async function get_and_storage_model() { let json_upload = document.createElement("input"); json_upload.id = "json_upload"; json_upload.type = "file"; let weights_upload = document.createElement("input"); weights_upload.id = "weights_upload"; weights_upload.type = "file"; json_upload.onchange = async function () { confirm("传入权重文件，后缀名为.bin") weights_upload.click() } weights_upload.onchange = async function () { if (json_upload.files.length <= 0 && weights_upload.files.length <= 0) { confirm("你没有选择文件"); return } await indexedDB.open(DBname); let model = await tf.loadLayersModel(tf.io.browserFiles([json_upload.files[0], weights_upload.files[0]])); await model.save("indexeddb://" + DBname); run(); } confirm("传入模型文件，后缀名为.json") json_upload.click() } (async function () { 'use strict'; GM_registerMenuCommand("导入模型", get_and_storage_model); // 运行预测 run(); let img = document.getElementById("icode"); img.onload = run; })();