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先 完整阅读文档!!!别忘记点star

项目由来

  1. 在调用深度学习训练好的AI模型时,如果使用python调用非常简单,甚至不用编写代码,大部分深度学习框架就是python编写的,自带有推理逻辑文件和方法
  2. 但是不是每个同学都会python,不是每个项目都是python语言开发,不是每个岗位都会深度学习
  3. 由于大部分服务器项目还是由java语言居多,之前java方向的同学也多,由于找遍全网也没有找到java调用AI模型的例子
  4. 所以特意编写一个java调用AI模型的方法(全网独这一份,目前市面上出来的java调用onnx的项目和框架基本上思路和代码都来源我这个)。思路是通用的,只需要替换不同的模型即可达到不同效果
  5. 极其轻量,两个依赖,一个java主文件即可运行
  6. 不懂项目有什么用作?不知道用在什么地方?没关系,先下载运行看效果后立马就明白了!

环境

  • master分支:面向过程写法,dp分支:面向对象写法。第一次先运行master分支代码
  • 只需要java环境不需要安装其它!JDK大于等于11,不能用1.8。 代码目录不能含有中文!
  • maven源记得改为国内源,否则下载依赖需要2天。
  • CPU建议i7十一代以上,自己测试可以不用GPU,实际项目必须GPU,尽量3060以上(图片检测无所谓,视频流实时检测必须GPU)
  • 本项目相当于最基础工具处理方法,不包含和结合业务逻辑,项目使用时视频流需要多线程,队列等等,需要自己处理。
  • 不包含视频流处理以及存储,转发等功能,具体实现搜索关键字:流媒体服务器,rtmp 等等。 思路如下:一个线程拉流,一个或多个线程识别,一个线程推流,一个共变量存储最新画面防止堆积,拉流线程只负责更新最新画面,识别线程只负责识别最新画面,识别后放到队列等待推流线程推流(注意帧率)

紧接着下载运行看效果再研究代码

看不懂也要先运行

  1. 下载代码可直接运行主文件:CameraDetection.java(优先),ObjectDetection_1_25200_n.java , ObjectDetection_n_7.java,ObjectDetection_1_n_8400.java可以直接运行不会报错
  2. CameraDetection.java,是实时视频流识别检测,也可直接运行,三个文件完全独立,不互相依赖,如果有GPU帧率会更高,需要开启调用GPU。images目录下有视频文件也可以改为路径预览视频识别效果,根据视频实时识别demo,其他文件都可以改为实时识别
  3. 多个主文件是为了支持不用网络结构的模型,即使是onnx模型,输出的结果参数也不一样,目前支持三种结构,下面有讲解
  4. 可以封装为http controller api接口,也可以结合摄像头实时分析视频流,进行识别后预览和告警
  5. 支持yolov7 , yolov5yolov8,paddlepaddle后处理稍微改一下也可以支持, 代码中自带的onnx模型仅仅为了演示,准确率非常低,实际应用需要自己训练
  6. 训练出来的模型成为基础模型,可以用于测试。生产环境的模型需要经过模型压缩,量化,剪枝,蒸馏,才可以使用(当然这不是java开发者的工作)。会提升视频华民啊帧率达到60-120帧左右。点击查看:百度压缩模型工具基础概念参考文章
  7. 视频流检测用小模型,接口图片检测用大模型
  8. 替换model目录下的onnx模型文件,可以识别检测任何物体(烟火,跌倒,抽烟,安全帽,口罩,人,打架,计数,攀爬,垃圾,开关,状态,分类,等等),有模型即可
  9. 模型不是onnx格式怎么办?不要紧张,主流AI框架模型都可以转为onnx格式。怎么转?自己搜!

ObjectDetection_1_25200_n.java

  • yolov5
  • 85:每一行85个数值,5个center_x,center_y, width, height,score ,80个标签类别得分(不一定是80要看模型标签数量)
  • 25200:三个尺度上的预测框总和 ( 80∗80∗3 + 40∗40∗3 + 20∗20∗3 ),每个网格三个预测框,后续需要非极大值抑制NMS处理
  • 1:没有批量预测推理,即每次输入推理一张图片 输入图片说明

ObjectDetection_n_7.java

  • yolov7
  • Concatoutput_dim_0 :变量,表示当前图像中预测目标的数量,
  • 7:表示每个目标的七个参数:batch_id,x0,y0,x1,y1,cls_id,score 输入图片说明

ObjectDetection_1_n_8400.java

  • yolov8
    输入图片说明

暂不直接支持输出结果是三个数组参数的模型(因为不常用)

  • 但是这种结构模型可以导出为[1,25200,85][n,7]输出结构,然后就可以使用已有代码调用。
  • yolov5 :导出onnx时增加参数 inplace=True,simplify=True(ObjectDetection_1_25200_n.java)
  • yolov7 :导出onnx时增加参数 grid=True,simplify=True(ObjectDetection_1_25200_n.java) 或者 grid=True,simplify=True,end2end=True(ObjectDetection_n_7.java) 输入图片说明 输入图片说明

ONNX

Open Neural Network Exchange(ONNX,开放神经网络交换)格式,是一个用于表示深度学习模型的标准,可使模型在不同框架之间进行转移. 是一种针对机器学习所设计的开放式的文件格式,用于存储训练好的模型。它使得不同的人工智能框架(如Pytorch,TensorFlow,PaddlePaddle,MXNet)可以采用相同格式存储模型数据并交互。 ONNX的规范及代码主要由微软,亚马逊 ,Facebook 和 IBM 等公司共同开发,以开放源代码的方式托管在Github.

图片效果

输入图片说明 输入图片说明 输入图片说明

视频效果(必看)

扫码备注:onnx

  • 无备注不通过
  • 进群1小时内发运行成功代码截图,不然踢出群,真踢。 输入图片说明

有用链接

使用GPU前提

  • 对于图片处理,不是必须使用GPU,处理视频建议使用
  • 更新显卡驱动,显卡驱动一定要最新版本
  • 安装对应版本的:cuda 和 cudnn,版本需要和自己电脑上的GPU型号对应,和项目无关
  • 并测试是否安装成功,一定要测试: nvcc -V
  • 版本不要高于:cuda11.8
  • 安装环境要有耐心,初学者可能需要2周左右才能安装好,别着急

中文解决方案

输入图片说明

输入图片说明 输入图片说明 输入图片说明 输入图片说明 输入图片说明 输入图片说明 输入图片说明 输入图片说明 输入图片说明 输入图片说明 输入图片说明 输入图片说明 输入图片说明 输入图片说明 输入图片说明 输入图片说明 输入图片说明 输入图片说明 输入图片说明

旋转目标检测(定位方向和角度:摆放电池和抓取螺丝等等)

输入图片说明

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简介

java 调用 python yolo onnx 模型 AI 视频 识别 支持 yolov5 yolov8 yolov7,包含 预处理 和 后处理 。java 目标检测 目标识别,可集成 rtsp rtmp 展开 收起
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