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236 lines
9.0 KiB
236 lines
9.0 KiB
const qiniu = require('qiniu');
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const moment = require('moment');
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const uuid = require('uuid');
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const superagent = require('superagent');
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const { reportBusinessCall, reportFastgptResponse, resolveContextUserId } = require('../services/dashboardReporter');
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module.exports.vrdemo = async (ctx, next) => {
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let {
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url,
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base64, fileName, key: keyPath, prompt
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} = ctx.request.body;
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try {
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const { qiniu: { dmn: domain, bkt: bucket, ak: accessKey, sk: secretKey } } = ctx.config;
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if (url) {
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const response = await superagent
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.get(url)
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.responseType('blob') // 或者 'arraybuffer',主要是为了拿到 buffer
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.buffer(true); // 强制 superagent 处理成 buffer
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const contentType = response.headers['content-type']; // 获取图片类型
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const buffer = response.body; // 图片数据是 buffer
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base64 = `data:${contentType};base64,` + buffer.toString('base64');
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}
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if (!base64) {
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ctx.body = { code: 400, message: '缺少base64参数' };
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return;
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}
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base64 = base64.replace(/^data:application\/octet-stream/, 'data:image/jpeg');
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const matches = base64.match(/^data:image\/(\w+);base64,(.+)$/);
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if (!matches) {
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ctx.body = { code: 400, message: 'base64格式不正确' };
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return;
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}
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const ext = matches[1];
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const data = matches[2];
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const buffer = Buffer.from(data, 'base64');
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// 配置七牛 SDK
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const mac = new qiniu.auth.digest.Mac(accessKey, secretKey);
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const options = {
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scope: bucket,
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};
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const putPolicy = new qiniu.rs.PutPolicy(options);
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const uploadToken = putPolicy.uploadToken(mac);
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const config = new qiniu.conf.Config();
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config.zone = qiniu.zone.Zone_z0; // 根据你的存储区域调整
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// 七牛接口:putBase64
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const formUploader = new qiniu.form_up.FormUploader(config);
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const putExtra = new qiniu.form_up.PutExtra();
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let keyPath_ = `ai-query${keyPath ? '/' + keyPath : ''}`;
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const key = `${keyPath_}/${fileName || uuid.v4() + '.' + ext}`;
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const putBase64 = (uploadToken, key, buffer) => {
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return new Promise((resolve, reject) => {
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formUploader.put(uploadToken, key, buffer, putExtra, function (err, body, info) {
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if (err) {
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reject(err);
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} else {
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resolve({ body, info });
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}
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});
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});
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};
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const result = await putBase64(uploadToken, key, buffer);
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if (result.info.statusCode === 200) {
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const { models } = ctx.app.fs.dc;
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const { ImageUnderstandingAppKey } = ctx.app.fs.config.fastGpt;
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const imgRes = await models.ImgRecognition.create({
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path: result.body.key,
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addTime: moment().format(),
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})
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superagent
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.post(`${ctx.app.fs.config.fastGpt.apiUrl}/api/v1/chat/completions`)
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.send({
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"stream": false,
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"detail": true,
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url": `${domain}/${result.body.key}`
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}
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},
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{
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"type": "text",
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// "text": `判断图像内有多少车辆,分别是什么车型;如果有摩托车或电动车,则为异常;将返回结果json化,并且结果可以使用 JSON.parse 进行解析;用 warning 字段标记异常,返回的json格式如下:{"warning":true,"describe":"结果描述"}`
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"text": prompt || `当前图像内容为摄像头监测画面,请对以下自然灾害及工程结构失效场景进行智能识别、分类与异常判断:
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识别目标及标准:
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1. 倒树检测:识别由于强风、洪水或老化等原因导致的树木倾倒现象.需准确区分正常直立树木与明显倒伏状态(如树干接近水平,或根部抬起).
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2. 桥梁垮塌识别:检测桥梁结构整体或局部坍塌事件,包括但不限于桥面断裂、桥墩台沉降、桥体错位、桥板断裂等典型破坏特征.
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3. 落梁事件判断:专项识别桥梁梁体脱离支座发生坠落、倾斜移位或悬挂等异常情况.需检测梁体与支座的连接状态异常.
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4. 岩体崩塌监测:监测山体边坡区域岩块剥落、滚石、大规模滑坡等现象,需结合地形坡度、岩层结构特征及崩塌物形态,判断是否存在明显地质灾害征兆.
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返回结果要求:
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当检测到以上任一异常现象,结果标记 'warning:true',并在 'describe' 字段中简要描述具体异常类型与场景.
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当未检测到异常,结果标记 'warning:false',并在 'describe' 字段说明当前状态正常.
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返回JSON格式示例:
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{"warning":true,"describe":""}
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该结果应支持 JSON.parse 解析.`
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}
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]
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}
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]
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})
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.set({
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Authorization: `Bearer ${ImageUnderstandingAppKey}`,
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"Content-Type": "application/json",
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}).then((res) => {
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reportFastgptResponse({
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ctx,
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applicationId: 'exp-scene',
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actionId: `scene:${imgRes.id}`,
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responseBody: res.body,
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appKey: ImageUnderstandingAppKey,
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});
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const contentMd = res.body.choices[0].message
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.content?.replaceAll('\n', '')
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// .replaceAll('```', '')
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// .replaceAll('json', '')
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.replaceAll(' ', '');
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const match = contentMd.match(/```json\s*([\s\S]*?)```/)[1];
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const content = JSON.parse(match);
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const { warning, describe } = content
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imgRes.update({
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recognitionTime: moment().format(),
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warning,
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describe,
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})
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}).catch((err) => {
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ctx.logger.log(err);
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imgRes.update({
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warning: false,
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describe: '图像理解失败',
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})
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})
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ctx.body = {
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code: 200,
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message: '上传成功',
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url: `${domain}/${result.body.key}`,
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};
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} else {
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ctx.body = { code: 500, message: '上传失败', detail: result.info };
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}
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} catch (error) {
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ctx.body = { code: 500, message: '服务器异常', error };
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}
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}
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module.exports.vrReslt = async (ctx, next) => {
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try {
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const { models, ORM: { Op } } = ctx.app.fs.dc;
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const { startTime } = ctx.query;
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let findOption = {}
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if (startTime) {
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findOption.addTime = {
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[Op.gte]: startTime,
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}
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}
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const recognitionRes = await models.ImgRecognition.findAll({
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where: findOption,
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order: [['addTime', 'DESC']],
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raw: true
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})
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ctx.body = recognitionRes;
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} catch (error) {
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ctx.body = { code: 400, message: '查询失败', error };
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}
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}
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/**
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* 功能：上报视频识别模块的播放业务调用。
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* 使用场景：前端点击“播放视频”并成功创建播放器后调用。
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*
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* 入参：deviceSerial 摄像头序列号；hasPlayUrl 是否已获取播放地址。
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* 返回：上报是否成功。
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* 注意：该接口只做业务调用上报，不保存 accessToken。
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*/
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module.exports.reportPlay = async (ctx, next) => {
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try {
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const { deviceSerial, hasPlayUrl, userId } = ctx.request.body || {};
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const normalizedDeviceSerial = String(deviceSerial || '').trim();
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if (!normalizedDeviceSerial) {
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ctx.status = 400;
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ctx.body = { message: '缺少摄像头序列号' };
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return;
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}
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const eventId = `exp-scene:video-play:${uuid.v4()}`;
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const resolvedUserId = resolveContextUserId(ctx, userId);
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const analyticsConfig = ctx.app.fs.config?.analytics || {};
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const hasAnalyticsConfig = Boolean(
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(analyticsConfig.centerUrl || process.env.AI_CENTER_URL) &&
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(analyticsConfig.keyId || process.env.ANALYTICS_HMAC_KEY_ID) &&
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(analyticsConfig.secret || process.env.ANALYTICS_HMAC_SECRET)
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);
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const reported = await reportBusinessCall({
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ctx,
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applicationId: 'exp-scene',
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eventId,
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userId: resolvedUserId,
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traceId: eventId,
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reportContext: {
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deviceSerial: normalizedDeviceSerial,
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hasPlayUrl: Boolean(hasPlayUrl),
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},
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});
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ctx.body = {
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reported,
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reason: reported
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? ''
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: (!resolvedUserId ? 'missing_user_id' : (!hasAnalyticsConfig ? 'missing_analytics_config' : 'report_failed')),
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};
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} catch (error) {
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ctx.logger.error('[videoRecognition] 视频播放上报失败', error);
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ctx.status = 400;
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ctx.body = { message: '视频播放上报失败' };
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}
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}
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