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README.md
Apache-2.0

easyAi

软件说明:

  • 本软件对物体在图像中进行训练及识别,切割,定位的轻量级,面向小白的框架。
  • 本软件对中文输入语句,对输入语句的类别进行分类,关键词抓取,词延伸,以及集成智能客服功能在逐渐扩展中
  • 若有想扩充的功能请进群提意见,若是通用场景我会陆续补充,技术交流群:222475213
  • 本软件永久免费商业使用,但作者已拥有本软件相关的知识产权,任何个人与集体不可擅自申请本软件内的技术与代码的知识产权。

详细视频教程地址:

框架效果演示结果:

  • 因为是框架没有图像化界面,演示结果就是控制台输出的数据,只能用视频展示,想看演示结果请看教学视频
  • 详细api文档见项目html文档:https://wlhlwl.com/gw/easyAi.html

强大的自主智能客服工具,支持自动与用户对话,解答疑问,引导消费与对话,并捕捉用户对话中的需求后自动生成订单!基于easyAi算法引擎构建智脑——SayOrder

链接:https://gitee.com/dromara/sayOrder

目前拥有的核心功能(若对您的学习或生产有帮助,请留下您的STAR)

  • 对图片中得物体进行识别。
  • 对中文语言进行分类语义识别,判断用户说话的语义是什么,关键词抓取,以及要做什么
  • 游戏内交互策略机器人
  • 若有想扩充的功能请进群提意见,若是通用场景我会陆续补充,技术交流群:222475213,561433236(满)

目前市场上的AI应用的问题

  • 高门槛: 现在随着人工智能技术的兴起,很多场景需要开发人员添加相应的功能,但是大多是JAVA开发程序员,JAVA却在AI领域的开源比较匮乏。 现在比较火的大模型的应用只能帮助人去提高工作效率,而不能帮助业务系统,去自定义用户自己的业务来内嵌进系统进而帮助业务系统的智能化。
  • 高成本: 人工智能往往要求大算力,大训练样本,使得大多数中小企业和个人开发者自定义AI入手成本非常高昂。

easyAI要怎么解决

  • easyAI是百分之百Java代码编写,并且无任何依赖,开箱即用,对广大JAVA程序员排除掉任何环境配置依赖问题,做到最友善。
  • easyAI并不是对主流算法JAVA的无差别重新实现,而是根据应用场景对主流算法进行优化与魔改,让用户即便使用一台普通的电脑,就可开箱直接跑起来。并且保证普通服务器或个人电脑CPU下依然达到可用性能的流畅运行。 所以我的“easy”并不是只是指的简单,而是对算法进行了廉价,低成本方向的优化。如果我没有办法对某种算法做到廉价优化的算法实现,我也不会放入easyAI里面。
  • 只通过最简单的API调用,就可以实现部分人工智能应用,并面向覆盖面最广的JAVA程序员使用的,且能满足大部分AI业务场景实现的算法引擎。
  • 部署简单: 本引擎所有底层函数及数学库都是作者JAVA手写,不依赖任何第三方库,所以开发者只需要将本包下载到本地后,打成JAR包 引入到自己的POM文件中,就可以独立使用所有功能。
  • 功能还在扩展: 本项目现在的功能还在逐步扩展中
  • 抛错捕获暂时还没有做全,若有抛错请进群交流:222475213,561433236(满),我来做一下错误定位
  • 若您有相对复杂的人工智能业务(开源功能无法满足的,包括但不限于图像识别,自然语言)请联系作者 vx:thenk008 进行基于easyAi定制化业务情景开发(即java人工智能开发)

微信交流群

  • 加wx技术交流群
  • 加交流群

HELLO WORLD说明:

  • 以下为最简API文档,所有非必设参数都使用本引擎默认值
  • 要注意的是使用最简API,及参数默认值准确度远不能达到最佳状态

图像学习部分最简API 说明:

       训练过程
       Picture picture = new Picture();//图片解析类
        Config config = new Config();//配置文件
        config.setTypeNub(2);//设置训练种类数
        config.setBoxSize(125);//设置物体大致大小 单位像素 即 125*125 的矩形
        config.setPictureNumber(5);//设置每个种类训练图片数量 某个类别有几张照片,注意所有种类照片数量要保持一致
        config.setPth(0.7);//设置可信概率,只有超过可信概率阈值,得出的结果才是可信的 数值为0-1之间
        config.setShowLog(true);//输出学习时打印数据
        Distinguish distinguish = new Distinguish(config);//创建识别类
        distinguish.setBackGround(picture.getThreeMatrix("E:\\ls\\fp15\\back.jpg"));//设置识别的背景图片(该api为固定背景)
        List<FoodPicture> foodPictures = new ArrayList<>();//创建训练模板集合
        for (int i = 1; i < 3; i++) {
            FoodPicture foodPicture = new FoodPicture();//创建每一类图片的训练模板类
            foodPictures.add(foodPicture);//将该类模板加入集合
            List<PicturePosition> picturePositionList = new ArrayList<>();//创建该类模板的训练集合类
            foodPicture.setId(i + 1);//设置该图片类别id
            foodPicture.setPicturePositionList(picturePositionList);
            for (int j = 1; j < 6; j++) {//训练图片数量为 每种五张 注意跟config 中的 pictureNumber 要一致
                String name;
                if (i == 1) {//加载图片url地址名称
                    name = "a";
                } else {
                    name = "b";
                }
                PicturePosition picturePosition = new PicturePosition();
                picturePosition.setUrl("E:\\ls\\fp15\\" + name + i + ".jpg");//加载该类别图片地址
                picturePosition.setNeedCut(false);//是否需要剪切,若训练素材为充满全图图片,则充满全图不需要剪切 写false
                picturePositionList.add(picturePosition);//加载
            }
        }
        distinguish.studyImage(foodPictures);//进行学习
        System.out.println(JSON.toJSONString(distinguish.getModel()));//输出模型保存,将模型实体类序列化为json保存
       ///////////////////////////////////////////////////////////////////////
       初始化过程
        Picture picture = new Picture();//图片解析类
        Config config = new Config();//配置文件
        config.setTypeNub(2);//设置类别数量
        config.setBoxSize(125);//设置物体大小 单位像素
        config.setPictureNumber(5);//设置每个种类训练图片数量
        config.setPth(0.7);//设置可信概率,只有超过可信概率阈值,得出的结果才是可信的
        config.setShowLog(true);//输出学习时打印数据
        Distinguish distinguish = new Distinguish(config);//识别类
        distinguish.insertModel(JSONObject.parseObject(ModelData.DATA, Model.class));//将之前训练时保存的训练模型反序列化为实体类后,注入模型
        完成后请单例Distinguish类即完成系统启动时初始化过程
        ///////////////////////////////////////////////////////////////////////
        识别过程
        Distinguish distinguish; 此识别类为系统启动时已经初始化的 单例distinguish识别过程请不要 "new" 这个类
         for (int i = 1; i < 8; i++) {
            System.out.println("i====" + i);
            ThreeChannelMatrix t = picture.getThreeMatrix("E:\\ls\\fp15\\t" + i + ".jpg");//将识别图片转化为矩阵
            Map<Integer, Double> map = distinguish.distinguish(t);//识别结果
            for (Map.Entry<Integer, Double> entry : map.entrySet()) {
                System.out.println(entry.getKey() + ":" + entry.getValue());//识别结果打印
            }
        }
        ////////////////////////////////////////////////////////////////////////////////////
        识别结果打印说明(此为本包提供的测试图片样本的 输出结果说明在之前的训练中橘子设置的id为2苹果为3)
        i====1//第一张图 结果为 橘子,出现2:代表类别。:0.8874306751020916,带表该类别权重,权重越高说明该类别的物品在当前 图片中数量越多或者面积越大。
        2:0.8874306751020916 说明图1有橘子权重为0.8874306751020916
        i====2
        2:0.8878192183606407
        i====3
        3:0.7233916245920673说明图3有苹果权重为0.7233916245920673
        i====4
        2:0.9335699571468958说明图4有橘子权重为0.9335699571468958
        3:0.7750825597199661说明图4有苹果权重为0.7750825597199661
        i====5
        3:0.8481590575557582
        i====6
        2:0.7971025523095067
        i====7
        2:1.5584968376080388图7有橘子权重为1.5584968376080388
        3:0.8754957897385587图7有苹果权重为0.8754957897385587
        本演示样例代码位置在 src/test/java/org/wlld/ImageTest.java
        本演示训练素材位置在 src/test/image
        注意以上图片识别代码样例为训练素材为物品全图充满图片(自己看能看到橘子训练图片为全图充满苹果也是).自行开发时用以上代码样例时请也使用全图充满训练物品的图片来做训练非全图充满训练素材图训练api有变化

通过给图片生成摘要id进行快速相似度对比

//参数分别为:
//第一个参数:threeChannelMatrix,图片矩阵(图片矩阵如何提取,上文有讲不在阐述)
//第二个参数:boxSize,将一张图片横纵各分为几个区域提取特征
参数说明该值越大摘要id敏感度越高该参数有最大值最大值为图片图片最小边长/5,超过会报错数组越界
//第三个参数:regionSize,相似特征区域分区种类数量
参数说明该值越大摘要id敏感度越高
//返回name 即为该图片摘要id,通过id逐位对比即可对比相似程度
//什么是id敏感度:
//id敏感度越高,对图片变化越敏感,越适合越大的检索区域匹配,即特征越细致,但缺点id长度越长。
//id敏感度越低,对图片变化越不敏感,越适合越小的检索区域匹配,特征越粗,优点是id长度越短。
FastPictureExcerpt fastPictureExcerpt = new FastPictureExcerpt();
String name = fastPictureExcerpt.creatImageName(threeChannelMatrix, 5, 10);

自然语言分类最简API 说明(已过时,自然语言处理请移步myJecs 链接:https://gitee.com/ldp_dpsmax/my-jecs ):

         //通过txt默认格式进行读取
        TemplateReader templateReader = new TemplateReader();
        WordTemple wordTemple = new WordTemple();//初始化语言模版,该语言模板训练结束后一定要static出来,在内存中长期持有,Talk识别构造参数进行复用
        //wordTemple.setTreeNub(9);
        //wordTemple.setTrustPunishment(0.5);
        //读取语言模版,第一个参数是模版地址,第二个参数是编码方式 (教程里的第三个参数已经省略)
        //同时也是学习过程
        templateReader.read("/Users/lidapeng/Desktop/myDocument/model.txt", "UTF-8", wordTemple);
        Talk talk = new Talk(wordTemple);
        //输入语句进行识别,若有标点符号会形成LIST中的每个元素
        //返回的集合中每个值代表了输入语句,每个标点符号前语句的分类
        List<Integer> list = talk.talk("空调坏了,帮我修一修");
        System.out.println(list);
        /////////////////////////////////自定义输入训练语句
        WordTemple wordTemple = new WordTemple();//初始化语言模版,该语言模板训练结束后一定要static出来,在内存中长期持有,Talk识别构造参数进行复用
        Tokenizer tokenizer = new Tokenizer(wordTemple);//学习类
        //训练模板 主键为类别id,值为该类别id的语句集合
        //注意
        //1,若训练量不足,建议训练语句通过标点符号拆分为若干句,且不要将标点符号带入训练语句
        //2,包含数字的语句用统一的占位符代替 例如 35,3,36% 变为 #,#,#%
        Map<Integer, List<String>> model = new HashMap<>();
        //开始训练
        tokenizer.start(model);
        ///////////////////////////////////单纯对输入语句进行切词结果返回,不进行识别
        wordTemple.setSplitWord(true);//将模板设置成纯切词模式
        List<List<String>> lists = talk.getSplitWord("空调坏了,帮我修一修");
        for (List<String> list : lists) {
            System.out.println(list);
        }

神经网络最简API说明

     //创建一个DNN神经网络管理器
     NerveManager nerveManager = new NerveManager(...);
     //构造参数
     //sensoryNerveNub 感知神经元数量 即输入特征数量
     //hiddenNerverNub  每一层隐层神经元的数量
     //outNerveNub 输出神经元数量 即分类的类别
     //hiddenDepth 隐层神经元深度,即学习深度
     //activeFunction 激活函数
     //isDynamic 是否启用动态神经元数量(没有特殊需求建议为静态,动态需要专业知识)
     public NerveManager(int sensoryNerveNub, int hiddenNerverNub, int outNerveNub, int hiddenDepth, ActiveFunction activeFunction, boolean isDynamic)
     nerveManager.getSensoryNerves()获取感知神经元集合
     //eventId:事件ID
     //parameter:输入特征值
     //isStudy:是否是学习
     //E:特征标注
     //OutBack 回调类
     SensoryNerv.postMessage(long eventId, double parameter, boolean isStudy, Map<Integer, Double> E, OutBack outBack)
     //每一次输出结果都会返回给回调类,通过回调类拿取输出结果,并通过eventId来对应事件

随机森林最简API说明

        //创建一个内存中的数据表
        DataTable dataTable = new DataTable(column);
        //构造参数是列名集合
        public DataTable(Set<String> key)
        //指定主列名集合中该表的主键
        dataTable.setKey("point");
        //创建一片随机森林
        RandomForest randomForest = new RandomForest(7);
        //构造参数为森林里的树木数量
        public RandomForest(int treeNub)
        //唤醒随机森林里的树木
        randomForest.init(dataTable);
        //将加入数据的实体类一条条插入森林中
        randomForest.insert(Object object);
        //森林进行学习
        randomForest.study();
        //插入特征数据,森林对该数据的最终分类结果进行判断
        randomForest.forest(Object objcet);

如果该项目对你有用,请赞助一下作者的劳动力支持开源,请作者吃一顿早饭就好!给作者持续更新easyAi引擎,同时封装更多依赖easyAi的常用应用提供动力!为大家低成本部署AI应用添砖加瓦!万谢!好心人!

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java傻瓜ai框架,无需任何算法知识,通过简单的api调用就可以实现 常用的图像内物体的识别,定位等图像ai服务,及自然语言分类处理服务。面向java开发程序员,不依赖任何第三方库,第三方接口,独立包。视频教程地址如下 expand collapse
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