阿里视觉AI训练营-day04-作业日-车辆保险应用

本文涉及的产品
对象存储 OSS,20GB 3个月
对象存储 OSS,恶意文件检测 1000次 1年
对象存储 OSS,内容安全 1000次 1年
简介: 车辆保险应用

阿里AI训练营【车辆保险应用】


# 前言 转载于:[【阿里云高校计划】车辆保险应用 day4 【拨云见日】](https://blog.csdn.net/weixin_42234067/article/details/106764710)
# 实施前准备工作 ## 一、本地图片上传为OSS #### 1.开通oss ![在这里插入图片描述](https://ucc.alicdn.com/images/user-upload-01/20201102173041857.png#pic_center) #### 2.创建Bucket ![在这里插入图片描述](https://ucc.alicdn.com/images/user-upload-01/20201102173108804.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5NzA2MzY5,size_16,color_FFFFFF,t_70#pic_center)![在这里插入图片描述](https://ucc.alicdn.com/images/user-upload-01/20201102173122550.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5NzA2MzY5,size_16,color_FFFFFF,t_70#pic_center) ![在这里插入图片描述](https://ucc.alicdn.com/images/user-upload-01/20201102173134702.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5NzA2MzY5,size_16,color_FFFFFF,t_70#pic_center) ## 二、开通目标检测服务 [阿里云视觉智能开放平台--目标检测](https://vision.aliyun.com/objectdet?spm=a211p3.14020179.J_7524944390.10.2c984b58Kfamiz) ![在这里插入图片描述](https://ucc.alicdn.com/images/user-upload-01/20201102173234550.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5NzA2MzY5,size_16,color_FFFFFF,t_70#pic_center)![在这里插入图片描述](https://ucc.alicdn.com/images/user-upload-01/20201102173431528.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5NzA2MzY5,size_16,color_FFFFFF,t_70#pic_center) ## 三、查看所需API 这里我们用到阿里云视觉智能开放平台提供的三个功能: - 车辆部件识别 - 车辆损伤识别 - 车险图片分类 ![在这里插入图片描述](https://ucc.alicdn.com/images/user-upload-01/20201102173511537.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5NzA2MzY5,size_16,color_FFFFFF,t_70#pic_center) 1.车辆部件识别 > 检测图片中车辆部件的位置以及名称。 2.车辆损伤识别 > 针对常见小汽车车型,识别车辆外观受损部件及损伤类型,可识别数十种车辆部件、五大类外观损伤。(刮擦、凹陷、开裂、褶皱、穿孔) 3.车险图片分类 > 对输入的车险图片进行分类。 # 具体实施 ## 一、本地图片上传至OSS的uploadPic类 #### 车辆部件识别 ###### 1.在maven中导入所需依赖 ```java com.aliyun.ossaliyun-sdk-oss3.8.0 ``` ###### 2.编写UploadPic类 ```java package com.example.demo; import com.aliyun.oss.OSS; import com.aliyun.oss.OSSClientBuilder; import java.io.File; import java.net.URL; import java.security.SecureRandom; import java.util.Date; import java.lang.*; import java.util.Scanner; public class UploadPic { public static String UploadPic(){ // Endpoint以杭州为例,其它Region请按实际情况填写。 String endpoint = "oss-cn-shanghai.aliyuncs.com"; // 阿里云主账号AccessKey。 String accessKeyId = "*************"; String accessKeySecret = "*************"; //本地文件名 System.out.println("请输入本地图片path:"); Scanner scanner = new Scanner(System.in); String fileName = scanner.nextLine(); String bucketName = "auto-insurance-pic"; // 获取文件的后缀名 String suffixName = fileName.substring(fileName.lastIndexOf(".")); // 生成上传文件名 String objectName = System.currentTimeMillis() + "" + new SecureRandom().nextInt(0x0400) + suffixName; // 创建OSSClient实例。 OSS ossClient = new OSSClientBuilder().build(endpoint, accessKeyId, accessKeySecret); // 如果需要上传时设置存储类型与访问权限,请参考以下示例代码。 // ObjectMetadata metadata = new ObjectMetadata(); // metadata.setHeader(OSSHeaders.OSS_STORAGE_CLASS, StorageClass.Standard.toString()); // metadata.setObjectAcl(CannedAccessControlList.Private); // putObjectRequest.setMetadata(metadata); // 上传文件。 ossClient.putObject(bucketName, objectName, new File(fileName)); // 设置URL过期时间为1小时。 Date expiration = new Date(System.currentTimeMillis() + 3600 * 1000); // 生成以GET方法访问的签名URL,访客可以直接通过浏览器访问相关内容。 URL url = ossClient.generatePresignedUrl(bucketName, objectName, expiration); // 关闭OSSClient。 ossClient.shutdown(); return url.toString(); } } ``` ###### 3.运行结果 ![在这里插入图片描述](https://ucc.alicdn.com/images/user-upload-01/20201102174302405.png#pic_center)![在这里插入图片描述](https://ucc.alicdn.com/images/user-upload-01/20201102174306965.png#pic_center) ## 二、车辆部件识别RecognizeVehicleParts类 #### 1.在maven中导入所需依赖 [阿里maven私有仓库服务](https://maven.aliyun.com/mvn/search) ![在这里插入图片描述](https://ucc.alicdn.com/images/user-upload-01/20201102175052583.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5NzA2MzY5,size_16,color_FFFFFF,t_70#pic_center) ```java com.aliyunaliyun-java-sdk-core4.4.8com.alibabafastjson1.2.52com.aliyunaliyun-java-sdk-objectdet1.0.7 ``` #### 2.编写RecognizeVehicleParts类 ```java package com.example.demo; import com.aliyuncs.DefaultAcsClient; import com.aliyuncs.IAcsClient; import com.aliyuncs.exceptions.ClientException; import com.aliyuncs.exceptions.ServerException; import com.aliyuncs.profile.DefaultProfile; import com.example.demo.UploadPic; import com.google.gson.Gson; import java.util.*; import com.aliyuncs.objectdet.model.v20191230.*; public class RecognizeVehicleParts { public static void main(String[] args) { DefaultProfile profile = DefaultProfile.getProfile("cn-shanghai", "accessKeyId", "accessKeySecret"); IAcsClient client = new DefaultAcsClient(profile); RecognizeVehiclePartsRequest request = new RecognizeVehiclePartsRequest(); request.setRegionId("cn-shanghai"); request.setImageURL(UploadPic.UploadPic()); try { RecognizeVehiclePartsResponse response = client.getAcsResponse(request); System.out.println(new Gson().toJson(response)); } catch (ServerException e) { e.printStackTrace(); } catch (ClientException e) { System.out.println("ErrCode:" + e.getErrCode()); System.out.println("ErrMsg:" + e.getErrMsg()); System.out.println("RequestId:" + e.getRequestId()); } } } ``` #### 3.运行结果 ![在这里插入图片描述](https://ucc.alicdn.com/images/user-upload-01/20201102175250466.png#pic_center)返回值为: > {"requestId":"BBFB102D-5EAC-483F-9A94-9DA79A06E1F6","data":{"elements":[{"score":0.98788995,"type":"left_tail_light","boxes":[132,274,862,607]},{"score":0.952229,"type":"left_rear_wing","boxes":[4,162,365,750]},{"score":0.74785864,"type":"rear_bumper","boxes":[60,456,987,760]}],"originShapes":[768,1024]}} ## 三.车辆损伤识别 #### 1.在maven中导入所需依赖 见本文二-1 #### 2.编写RecognizeVehicleDamage类 ```java package com.example.demo; import com.aliyuncs.DefaultAcsClient; import com.aliyuncs.IAcsClient; import com.aliyuncs.exceptions.ClientException; import com.aliyuncs.exceptions.ServerException; import com.aliyuncs.profile.DefaultProfile; import com.google.gson.Gson; import java.util.*; import com.aliyuncs.objectdet.model.v20191230.*; public class RecognizeVehicleDamage { public static void main(String[] args) { DefaultProfile profile = DefaultProfile.getProfile("cn-shanghai", "accessKeyId", "accessKeySecret"); IAcsClient client = new DefaultAcsClient(profile); RecognizeVehicleDamageRequest request = new RecognizeVehicleDamageRequest(); request.setRegionId("cn-shanghai"); request.setImageURL(UploadPic.UploadPic()); try { RecognizeVehicleDamageResponse response = client.getAcsResponse(request); System.out.println(new Gson().toJson(response)); } catch (ServerException e) { e.printStackTrace(); } catch (ClientException e) { System.out.println("ErrCode:" + e.getErrCode()); System.out.println("ErrMsg:" + e.getErrMsg()); System.out.println("RequestId:" + e.getRequestId()); } } } ``` #### 3.识别结果 返回值为: > {"requestId":"7FFBD390-7019-4B85-9FFA-779C912A9CEB","data":{"elements":[{"score":0.414995,"type":"1","scores":[0.414995,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"boxes":[343,390,473,542]},{"score":0.408405,"type":"1","scores":[0.408405,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"boxes":[541,442,659,545]},{"score":0.348472,"type":"1","scores":[0.348472,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"boxes":[273,293,423,400]},{"score":0.378637,"type":"2","scores":[0.0,0.378637,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"boxes":[261,26,496,142]},{"score":0.873101,"type":"5","scores":[0.0,0.0,0.0,0.0,0.873101,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"boxes":[91,4,555,267]},{"score":0.815785,"type":"6","scores":[0.0,0.0,0.0,0.0,0.0,0.815785,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"boxes":[564,270,869,441]},{"score":0.845525,"type":"8","scores":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.845525,0.0,0.0,0.0,0.0,0.0,0.0,0.0],"boxes":[230,234,529,313]},{"score":0.411336,"type":"11","scores":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.411336,0.0,0.0,0.0,0.0],"boxes":[632,425,922,563]},{"score":0.334054,"type":"11","scores":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.334054,0.0,0.0,0.0,0.0],"boxes":[538,91,733,194]},{"score":0.333818,"type":"11","scores":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.333818,0.0,0.0,0.0,0.0],"boxes":[694,157,899,286]},{"score":0.32519,"type":"11","scores":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.32519,0.0,0.0,0.0,0.0],"boxes":[523,87,902,278]}]}} ## 四.车险图片分类 #### 1.在maven中导入所需依赖 见本文二-1 #### 2.编写ClassifyVehicleInsurance类 ```java package com.example.demo; import com.aliyuncs.DefaultAcsClient; import com.aliyuncs.IAcsClient; import com.aliyuncs.exceptions.ClientException; import com.aliyuncs.exceptions.ServerException; import com.aliyuncs.profile.DefaultProfile; import com.google.gson.Gson; import java.util.*; import com.aliyuncs.objectdet.model.v20191230.*; public class ClassifyVehicleInsurance { public static void main(String[] args) { DefaultProfile profile = DefaultProfile.getProfile("cn-shanghai", "accessKeyId", "accessKeySecret"); IAcsClient client = new DefaultAcsClient(profile); ClassifyVehicleInsuranceRequest request = new ClassifyVehicleInsuranceRequest(); request.setRegionId("cn-shanghai"); request.setImageURL(UploadPic.UploadPic()); try { ClassifyVehicleInsuranceResponse response = client.getAcsResponse(request); System.out.println(new Gson().toJson(response)); } catch (ServerException e) { e.printStackTrace(); } catch (ClientException e) { System.out.println("ErrCode:" + e.getErrCode()); System.out.println("ErrMsg:" + e.getErrMsg()); System.out.println("RequestId:" + e.getRequestId()); } } } ``` #### 3.识别结果 ![在这里插入图片描述](https://ucc.alicdn.com/images/user-upload-01/20201102175903416.png#pic_center)返回值为: > {"requestId":"87BEADED-F581-4C38-9F5F-E6F8DF0A1BA5","data":{"threshold":0.0,"labels":[{"score":0.0046,"name":"others"},{"score":0.0164,"name":"detail"},{"score":0.1934,"name":"component"},{"score":0.0,"name":"vin"},{"score":8.0E-4,"name":"people"},{"score":2.0E-4,"name":"motor"},{"score":0.1439,"name":"semi-car"},{"score":0.0027,"name":"panoramic"},{"score":3.0E-4,"name":"license"},{"score":0.0169,"name":"CT-scan"},{"score":5.0E-4,"name":"truck"},{"score":0.0144,"name":"disassembly"},{"score":0.6059,"name":"scene"}]}} ![在这里插入图片描述](https://ucc.alicdn.com/images/user-upload-01/20201102175927853.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5NzA2MzY5,size_16,color_FFFFFF,t_70#pic_center) # 总结 转载于:[【阿里云高校计划】车辆保险应用 day4 【拨云见日】](https://blog.csdn.net/weixin_42234067/article/details/106764710)
相关实践学习
借助OSS搭建在线教育视频课程分享网站
本教程介绍如何基于云服务器ECS和对象存储OSS,搭建一个在线教育视频课程分享网站。
目录
相关文章
|
1月前
|
消息中间件 人工智能 Cloud Native
|
1月前
|
机器学习/深度学习 人工智能 算法
使用 NVIDIA TAO Toolkit 5.0 体验最新的视觉 AI 模型开发工作流程
NVIDIA TAO Toolkit 5.0 提供低代码框架,支持从新手到专家级别的用户快速开发视觉AI模型。新版本引入了开源架构、基于Transformer的预训练模型、AI辅助数据标注等功能,显著提升了模型开发效率和精度。TAO Toolkit 5.0 还支持多平台部署,包括GPU、CPU、MCU等,简化了模型训练和优化流程,适用于广泛的AI应用场景。
57 0
使用 NVIDIA TAO Toolkit 5.0 体验最新的视觉 AI 模型开发工作流程
|
1月前
|
人工智能 编解码 文字识别
阿里国际AI开源Ovis1.6,多项得分超GPT-4o-mini!
阿里国际AI团队提出了一种名为Ovis (Open VISion)的新型多模态大模型的架构。
|
1月前
|
人工智能 Ubuntu Linux
安装阿里图文融合AI - AnyText心路历程(安装失败告终,心痛!)
安装阿里图文融合AI - AnyText心路历程(安装失败告终,心痛!)
|
2月前
|
人工智能 前端开发 Java
Spring Cloud Alibaba AI,阿里AI这不得玩一下
🏀闪亮主角: 大家好,我是JavaDog程序狗。今天分享Spring Cloud Alibaba AI,基于Spring AI并提供阿里云通义大模型的Java AI应用。本狗用SpringBoot+uniapp+uview2对接Spring Cloud Alibaba AI,带你打造聊天小AI。 📘故事背景: 🎁获取源码: 关注公众号“JavaDog程序狗”,发送“alibaba-ai”即可获取源码。 🎯主要目标:
97 0
|
3月前
|
人工智能 前端开发 Java
【实操】Spring Cloud Alibaba AI,阿里AI这不得玩一下(含前后端源码)
本文介绍了如何使用 **Spring Cloud Alibaba AI** 构建基于 Spring Boot 和 uni-app 的聊天机器人应用。主要内容包括:Spring Cloud Alibaba AI 的概念与功能,使用前的准备工作(如 JDK 17+、Spring Boot 3.0+ 及通义 API-KEY),详细实操步骤(涵盖前后端开发工具、组件选择、功能分析及关键代码示例)。最终展示了如何成功实现具备基本聊天功能的 AI 应用,帮助读者快速搭建智能聊天系统并探索更多高级功能。
1396 2
【实操】Spring Cloud Alibaba AI,阿里AI这不得玩一下(含前后端源码)
|
2月前
|
消息中间件 人工智能 运维
|
2月前
|
人工智能 自然语言处理 Linux
Llama 3.2:开源可定制视觉模型,引领边缘AI革命
Llama 3.2 系列 11B 和 90B 视觉LLM,支持图像理解,例如文档级理解(包括图表和图形)、图像字幕以及视觉基础任务(例如基于自然语言描述在图像中精确定位对象)。
|
4天前
|
机器学习/深度学习 人工智能 算法
AI技术在医疗诊断中的应用及前景展望
本文旨在探讨人工智能(AI)技术在医疗诊断领域的应用现状、挑战与未来发展趋势。通过分析AI技术如何助力提高诊断准确率、缩短诊断时间以及降低医疗成本,揭示了其在现代医疗体系中的重要价值。同时,文章也指出了当前AI医疗面临的数据隐私、算法透明度等挑战,并对未来的发展方向进行了展望。
|
12天前
|
机器学习/深度学习 人工智能 自然语言处理
当前AI大模型在软件开发中的创新应用与挑战
2024年,AI大模型在软件开发领域的应用正重塑传统流程,从自动化编码、智能协作到代码审查和测试,显著提升了开发效率和代码质量。然而,技术挑战、伦理安全及模型可解释性等问题仍需解决。未来,AI将继续推动软件开发向更高效、智能化方向发展。
下一篇
无影云桌面