AI 宠物面板开发模版与流程（二）-优快云博客

本文链接：https://blog.youkuaiyun.com/Ms_Smart/article/details/149543295

7. 宠物特征分析

首先使用 App 端侧 AI 能力 对宠物图像进行宠物识别和异物过滤，完成上传图像质量判断
- 1. 宠物图像资源导入
  - 调用 chooseImage 方法，支持用户从本地相册选择图片或使用相机拍照。
  - 调用 resizeImage 方法，保持原图长宽比裁剪至目标尺寸，再根据文件大小限制进行质量压缩。
- 2. 宠物图像质量检测
  - 调用 petsDetectCreate 方法，初始化 AI 宠物图像质量检测实例。
  - 调用 petsPictureQualityDetectForImage 方法，输入参数进行质量检测并返回结果。
  - 页面销毁时调用 petsDetectDestory 方法销毁实例，避免内存泄漏。
若图像通过 端侧 AI 质量判定，则上传至云端分析宠物体型、毛色、表情等特征，实现进食时的精准识别
- 1. 请求云端获取文件上传签名及对应 objectKey
- 2. 图片上传与分析流程
  - 将图片上传至指定服务器，完成后传递 objectKey 至特征分析接口。
  - 接口返回 taskId，通过轮询获取分析进度：
    - 若 analysisResult 为 2，表示分析成功。
    - 若 analysisResult 为 1，表示分析失败。

功能展示

相关代码段

export async function pictureQualityDetect(pathUrl: string) {
  const result = await new Promise<{
    imagePath: string;
    lowQuality: boolean;
    lowQualityReason: number;
  }>((resolve, reject) => {
    petsPictureQualityDetectForImage({
      inputImagePath: pathUrl,
      labelAllow: 1,
      objectAreaPercent: 30,
      objectFaceRotationAngle: 40,
      objectFaceSideAngle: 45,
      maximumPictureBrightness: 80,
      minimumPictureBrightness: 0,
      success: res => {
        resolve(res);
      },
      fail: error => {
        reject(error);
      },
    });
  });

  return result;
}

  const handleChooseImg = async () => {
    let paths = [];

    try {
      // paths = await chooseImage(3);
      paths = await chooseImage(3, () => enter('analyzing'));
    } catch (err) {
      return;
    }

    // 先进入下一阶段
    // enter('analyzing');

    setState({
      analyzingText: Strings.getLang('add_pet_analytics_upload_img'),
    });

    const controller = new AbortController();
    controllerRef.current = controller;
    let tempPaths: Array<{ imagePath: string; lowQuality: boolean; lowQualityReason: number }> = [];
    let pathList: Array<{ imagePath: string; lowQuality: boolean; lowQualityReason: number }> = [];

    if (petType === 'cat') {
      try {
        tempPaths = (await Promise.all(paths.map(url => pictureQualityDetect(url)))).map(d => d);

        pathList = tempPaths.filter(item => !item.lowQuality) as Array<{
          imagePath: string;
          lowQuality: boolean;
          lowQualityReason: number;
        }>;
        const lowQualityList = tempPaths
          .filter(item => item.lowQuality)
          .map(item => item.lowQualityReason) as Array<number>;

        if (pathList.length === 0) {
          const errorText = Array.from(new Set(lowQualityList)).map((lowQualityItem, index) => {
            return `${Strings.getLang(`dsc_${lowQualityItem}`)}`;
          });
          setPicErrorTips(errorText.join('、'));
          setTimeout(() => {
            enter('failed');
          }, 1000);

          return;
        }
      } catch (error) {
        enter('failed');
        return;
      }
    }

    let images: Array<{ imageDisplayUrl: string; objectKey: string }> = [];

    const pathSourceList = petType === 'cat' ? pathList.map(element => element.imagePath) : paths;
    try {
      images = (
        await Promise.all(pathSourceList.map(p => uploadImageCat(p, ANALYTICS_BIZ_TYPE)))
      ).map(d => ({
        imageDisplayUrl: d.publicUrl,
        objectKey: d.cloudKey,
      }));
      if (controller.signal.aborted) {
        return;
      }
    } catch (error) {
      setTimeout(() => {
        enter('failed');
      }, 1000);
      return;
    }

    let idx = 0;
    const tips = [
      Strings.getLang('add_pet_analytics_upload_tip1'),
      Strings.getLang('add_pet_analytics_upload_tip2'),
      Strings.getLang('add_pet_analytics_upload_tip3'),
      Strings.getLang('add_pet_analytics_upload_tip4'),
      Strings.getLang('add_pet_analytics_upload_tip5'),
    ];

    const id = setInterval(() => {
      setState({
        analyzingText: tips[idx++ % tips.length],
      });
    }, 3000);

    try {
      const taskId = await analyzePetFeature({
        ownerId: homeId,
        images,
        miniAppId,
        agentId: AGENT_ID,
      });
      const [infoRes] = await Promise.all([
        loopGetAnalysisResult({
          taskId,
          controller,
          type: AnalysType.Profile,
        }),
      ]);
      if (infoRes) {
        setState({
          similarShow: true,
          petResInfo: infoRes,
        });
      } else if (infoRes) {
        emitter.emit('selectProfile', infoRes);
        enter('success');
      } else {
        // 超时
        enter('failed');
      }
    } catch (error) {
      enter('failed');
      return;
    } finally {
      clearInterval(id);
    }
  };

// 上传图片
export async function uploadImage(filePath: string, bizType: UploadFileBizType) {
  const fileName = parseFileName(filePath);
  const signInfo = await getPetUploadSign({ bizType, fileName });
  const { url, objectKey } = signInfo;

  await uploadFile(url, filePath, fileName);

  return { cloudKey: objectKey };
}