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人脸聚类

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使用人脸聚类功能，您可以将数据集中存在相似人脸的多张图片进行分组，适用于网盘的人脸相册、家庭监控的陌生人检测、甚至新零售的顾客管理等场景。人脸聚类后，您可以根据人脸分组查询对应人员的所有图片信息。

应用场景

网盘人脸相册

将网盘中的照片进行人脸聚类，按人脸进行分组，产生个性化的人脸相册。

家庭监控

将家庭成员照片进行人脸聚类，当出现不能聚类的陌生人照片时及时预警，可有效管理环境安全，及时识别并处理危险人员及事件，保障家庭群体人身安全。

新零售顾客管理

将采集到的照片通过人脸聚类和去重可以获得准确的客流量数据，分析顾客购买偏好进行精准营销。

前提条件

创建人脸聚类任务

您可以调用CreateFigureClusteringTask - 创建人物聚类任务接口创建一个人脸聚类任务，在您已索引到数据集的图片中，将属于不同人物的人脸进行聚类分组。如下以对数据集test-dataset中的图片进行人脸聚类分组为例。

说明

调用该接口创建分组任务不会对保存的文件做更改，仅生成分组数据。

重要

任务开始执行后，任务信息只保存7天，超过7天则无法再获取。您可以通过以下几种方式及时获取任务信息：

请求示例

{
    "ProjectName": "test-project",
    "DatasetName": "test-dataset"
}

返回示例

{
    "TaskId": "CreateFigureClusteringTask-ba5784b8-f61e-485d-8ea0-****",
    "RequestId": "42F4F8FD-006D-0EF0-8F2A-****",
    "EventId": "140-1L5dh6eSUErqdxV1ZvJ****"
}
说明

返回如上所示的示例信息，表示人脸聚类任务创建成功。

示例代码

# -*- coding: utf-8 -*-

import os
from alibabacloud_imm20200930.client import Client as imm20200930Client
from alibabacloud_tea_openapi import models as open_api_models
from alibabacloud_imm20200930 import models as imm_20200930_models
from alibabacloud_tea_util import models as util_models
from alibabacloud_tea_util.client import Client as UtilClient


class Sample:
    def __init__(self):
        pass

    @staticmethod
    def create_client(
        access_key_id: str,
        access_key_secret: str,
    ) -> imm20200930Client:
        """
        使用AccessKey ID&AccessKey Secret初始化账号Client。
        @param access_key_id:
        @param access_key_secret:
        @return: Client
        @throws Exception
        """
        config = open_api_models.Config(
            access_key_id=access_key_id,
            access_key_secret=access_key_secret
        )
        # 填写访问的域名。
        config.endpoint = f'imm.cn-beijing.aliyuncs.com'
        return imm20200930Client(config)

    @staticmethod
    def main() -> None:
        # 阿里云账号AccessKey拥有所有API的访问权限，建议您使用RAM用户进行API访问或日常运维。
        # 强烈建议不要把AccessKey ID和AccessKey Secret保存到工程代码里，否则可能导致AccessKey泄露，威胁您账号下所有资源的安全。
        # 本示例通过从环境变量中读取AccessKey，来实现API访问的身份验证。如何配置环境变量，请参见https://help.aliyun.com/document_detail/2361894.html。
        imm_access_key_id = os.getenv("AccessKeyId")
        imm_access_key_secret = os.getenv("AccessKeySecret")
        # 初始化客户端。
        client = Sample.create_client(imm_access_key_id, imm_access_key_secret)
        # 构造请求。
        create_figure_clustering_task_request = imm_20200930_models.CreateFigureClusteringTaskRequest(
            # 填写IMM项目名称。
            project_name='test-project',
            # 填写数据集名称.
            dataset_name='test-dataset'
        )
        runtime = util_models.RuntimeOptions()
        try:
            # 复制代码运行请自行打印API的返回值.
            response = client.create_figure_clustering_task_with_options(
                create_figure_clustering_task_request, runtime)
            print(response.body.to_map())
        except Exception as error:
            # 如有需要，请打印错误信息。
            UtilClient.assert_as_string(error.message)
            print(error)


if __name__ == '__main__':
    Sample.main()

查询人脸分组信息

人脸聚类任务创建成功后，您可以调用QueryFigureClusters - 查询人物聚类接口，查询分组的信息，包括分组数量、各分组的图片数量等。如下以查询数据集test-dataset中的人脸聚类分组信息为例。

请求示例

{
    "ProjectName": "test-project",
    "DatasetName": "test-dataset"
}

返回示例

{
    "FigureClusters": [
        {
            "AverageAge": 27.125,
            "Cover": {
                "Addresses": [],
                "AudioCovers": [],
                "AudioStreams": [],
                "CroppingSuggestions": [],
                "Figures": [
                    {
                        "Attractive": 0.9980000257492065,
                        "Beard": "none",
                        "BeardConfidence": 0.9959999918937683,
                        "Boundary": {
                            "Height": 270,
                            "Left": 573,
                            "Top": 104,
                            "Width": 202
                        },
                        "FaceQuality": 1.0,
                        "FigureId": "d7365ab8-1378-4bec-83cb-eccad8d11e0b",
                        "FigureType": "face",
                        "Glasses": "none",
                        "GlassesConfidence": 0.9990000128746033,
                        "Hat": "none",
                        "HatConfidence": 1.0,
                        "HeadPose": {
                            "Pitch": -0.7369999885559082,
                            "Roll": 2.5399999618530273,
                            "Yaw": 9.138999938964844
                        },
                        "Mask": "none",
                        "MaskConfidence": 0.7269999980926514,
                        "Mouth": "open",
                        "MouthConfidence": 0.9959999918937683,
                        "Sharpness": 1.0
                    }
                ],
                "ImageHeight": 683,
                "ImageWidth": 1024,
                "Labels": [],
                "OCRContents": [],
                "ObjectId": "170ffdeb36cec846f4214c78a0f3a0d4b7e37d0305370216ae780f7b8c72f871",
                "Subtitles": [],
                "URI": "oss://bucket1/photos/2.jpg",
                "VideoStreams": []
            },
            "CreateTime": "2022-07-12T16:41:19.336825716+08:00",
            "DatasetName": "dataset1",
            "FaceCount": 16,
            "Gender": "female",
            "ImageCount": 16,
            "MaxAge": 30.0,
            "MinAge": 23.0,
            "ObjectId": "Cluster-7bdbcedb-bd79-42e7-a1e2-b29a48532bd6",
            "ObjectType": "figure-cluster",
            "OwnerId": "*****",
            "ProjectName": "test-project",
            "UpdateTime": "2022-09-19T17:08:59.374781532+08:00",
            "VideoCount": 0
        },
        {
            "AverageAge": 24.200000762939453,
            "Cover": {
                "Addresses": [],
                "AudioCovers": [],
                "AudioStreams": [],
                "CroppingSuggestions": [],
                "Figures": [
                    {
                        "Attractive": 0.9990000128746033,
                        "Beard": "none",
                        "BeardConfidence": 0.9990000128746033,
                        "Boundary": {
                            "Height": 266,
                            "Left": 301,
                            "Top": 218,
                            "Width": 196
                        },
                        "FaceQuality": 0.8859999775886536,
                        "FigureId": "f58bbdce-f3d1-4674-be6b-43d4b47c08e1",
                        "FigureType": "face",
                        "Glasses": "none",
                        "GlassesConfidence": 1.0,
                        "Hat": "none",
                        "HatConfidence": 1.0,
                        "HeadPose": {
                            "Pitch": 13.963000297546387,
                            "Roll": -12.21399974822998,
                            "Yaw": -6.2210001945495605
                        },
                        "Mask": "none",
                        "MaskConfidence": 0.7490000128746033,
                        "Mouth": "open",
                        "MouthConfidence": 0.9940000176429749,
                        "Sharpness": 1.0
                    }
                ],
                "ImageHeight": 1024,
                "ImageWidth": 683,
                "Labels": [],
                "OCRContents": [],
                "ObjectId": "b9c80e51aa95072413e2a0a6e5262644bc3cba14a4754f54f3fa9850c4d244f1",
                "Subtitles": [],
                "URI": "oss://bucket1/photos/11.jpg",
                "VideoStreams": []
            },
            "CreateTime": "2022-09-19T17:08:59.374932448+08:00",
            "DatasetName": "test-dataset",
            "FaceCount": 5,
            "Gender": "female",
            "ImageCount": 5,
            "MaxAge": 26.0,
            "MinAge": 22.0,
            "ObjectId": "Cluster-856be781-bf5a-46d7-8494-8d7c44f5e282",
            "ObjectType": "figure-cluster",
            "OwnerId": "*****",
            "ProjectName": "test-project",
            "UpdateTime": "2022-09-19T17:08:59.374932448+08:00",
            "VideoCount": 0
        }
    ],
    "NextToken": "",
    "TotalCount": 2,
    "RequestId": "42B3DD92-FE0D-09B7-B582-*****"
}
说明

该返回示例显示，此数据集中的人脸图片被分为2组，其中一个分组ID为Cluster-7bdbcedb-bd79-42e7-a1e2-b29a48532bd6，包含16张图片；另一个分组ID为Cluster-856be781-bf5a-46d7-8494-8d7c44f5e282，包含5张图片。

示例代码

# -*- coding: utf-8 -*-

import os
from alibabacloud_imm20200930.client import Client as imm20200930Client
from alibabacloud_tea_openapi import models as open_api_models
from alibabacloud_imm20200930 import models as imm_20200930_models
from alibabacloud_tea_util import models as util_models
from alibabacloud_tea_util.client import Client as UtilClient


class Sample:
    def __init__(self):
        pass

    @staticmethod
    def create_client(
        access_key_id: str,
        access_key_secret: str,
    ) -> imm20200930Client:
        """
        使用AccessKey ID&AccessKey Secret初始化账号Client。
        @param access_key_id:
        @param access_key_secret:
        @return: Client
        @throws Exception
        """
        config = open_api_models.Config(
            access_key_id=access_key_id,
            access_key_secret=access_key_secret
        )
        # 填写访问的域名。
        config.endpoint = f'imm.cn-beijing.aliyuncs.com'
        return imm20200930Client(config)

    @staticmethod
    def main() -> None:
        # 阿里云账号AccessKey拥有所有API的访问权限，建议您使用RAM用户进行API访问或日常运维。
        # 强烈建议不要把AccessKey ID和AccessKey Secret保存到工程代码里，否则可能导致AccessKey泄露，威胁您账号下所有资源的安全。
        # 本示例通过从环境变量中读取AccessKey，来实现API访问的身份验证。如何配置环境变量，请参见https://help.aliyun.com/document_detail/2361894.html。
        imm_access_key_id = os.getenv("AccessKeyId")
        imm_access_key_secret = os.getenv("AccessKeySecret")
        # 初始化客户端。
        client = Sample.create_client(imm_access_key_id, imm_access_key_secret)
        # 构造请求。
        query_figure_clusters_request = imm_20200930_models.QueryFigureClustersRequest(
            # 填写IMM项目名称。
            project_name='test-project',
            # 填写数据集名称。
            dataset_name='test-dataset'
        )
        runtime = util_models.RuntimeOptions()
        try:
            # 打印API的返回值。
            response = client.query_figure_clusters_with_options(query_figure_clusters_request, runtime)
            print(response.body.to_map())
        except Exception as error:
            # 如有需要，请打印错误信息。
            UtilClient.assert_as_string(error.message)
            print(error)


if __name__ == '__main__':
    Sample.main()

查询人脸分组中的图片列表

查询完分组信息之后，您可以调用SimpleQuery - 简单查询接口，通过分组ID查询某个分组中包含的所有图片。如下以查询数据集test-dataset中分组ID为Cluster-7bdbcedb-bd79-42e7-a1e2-b29a48532bd6的人脸分组图片信息为例。

请求示例

{
    "ProjectName": "test-project",
    "DatasetName": "test-dataset",
    "Query": "{\"Field\": \"Figures.FigureClusterId\", \"Operation\": \"eq\", \"Value\": \"Cluster-7bdbcedb-bd79-42e7-a1e2-b29a48532bd6\"}",
    "MaxResults": 100
}

返回示例

说明

由于图片数量和信息较多，如下示例仅列举分组中的一张图片信息。

{
    "Aggregations": [],
    "Files": [
        {
            "Addresses": [],
            "AudioCovers": [],
            "AudioStreams": [],
            "ContentMd5": "ViAbCBHAZgNU4zvs5****==",
            "ContentType": "image/jpeg",
            "CreateTime": "2022-07-12T15:57:47.792615815+08:00",
            "CroppingSuggestions": [],
            "DatasetName": "test-dataset",
            "ETag": "\"56201B0811C0660354E33BECE4C****\"",
            "EXIF": "****",
            "Figures": [
                {
                    "FaceQuality": 1.0,
                    "FigureClusterId": "Cluster-7bdbcedb-bd79-42e7-a1e2-b29a48532bd6",
                    "FigureConfidence": 1.0,
                    "FigureId": "cd9139bf-f339-4ec2-b5fd-****",
                    "FigureType": "face",
                    "Glasses": "none",
                    "GlassesConfidence": 0.9990000128746033,
                    "Hat": "none",
                    "HatConfidence": 1.0,
                    "HeadPose": {
                        "Pitch": -0.8999999761581421,
                        "Roll": 1.1660000085830688,
                        "Yaw": 7.932000160217285
                    },
                    "Mask": "none",
                    "MaskConfidence": 0.6830000281333923,
                    "Mouth": "close",
                    "MouthConfidence": 0.7879999876022339,
                    "Sharpness": 1.0,
                    ...
                }
            ],
            "FileHash": "\"56201B0811C0660354E33BECE****\"",
            "FileModifiedTime": "2022-07-12T15:56:41+08:00",
            "Filename": "3.jpg",
            "ImageHeight": 1024,
            "ImageScore": {
                "OverallQualityScore": 0.7490000128746033
            },
            "ImageWidth": 683,
            "Labels": [
                {
                    "CentricScore": 0.8349999785423279,
                    "LabelConfidence": 1.0,
                    "LabelLevel": 2,
                    "LabelName": "\u7167\u7247\u62cd\u6444",
                    "Language": "zh-Hans",
                    "ParentLabelName": "\u827a\u672f\u54c1"
                },
                ...
            ],
            "MediaType": "image",
            "OCRContents": [],
            "OSSCRC64": "3400224321778591044",
            "OSSObjectType": "Normal",
            "OSSStorageClass": "Standard",
            "OSSTaggingCount": 0,
            "ObjectACL": "default",
            "ObjectId": "d132a61122c659f6fc1b42ecee1662aff358c7f1720027bead225****",
            "ObjectType": "file",
            "Orientation": 1,
            "OwnerId": "****",
            "ProduceTime": "2014-02-21T00:03:36+08:00",
            "ProjectName": "test-project",
            "Size": 187674,
            "Subtitles": [],
            "URI": "oss://bucket1/1.jpg",
            "UpdateTime": "2022-07-12T16:41:19.336736388+08:00",
            "VideoStreams": []
        },
        ...
    ],
    "NextToken": "",
    "RequestId": "84E4D242-8D15-0312-B976-****"
}
说明

返回示例显示，该分组中包含一张OSS地址为oss://bucket1/1.jpg的图片。

示例代码

# -*- coding: utf-8 -*-

import os
from alibabacloud_imm20200930.client import Client as imm20200930Client
from alibabacloud_tea_openapi import models as open_api_models
from alibabacloud_imm20200930 import models as imm_20200930_models
from alibabacloud_tea_util import models as util_models
from alibabacloud_tea_util.client import Client as UtilClient


class Sample:
    def __init__(self):
        pass

    @staticmethod
    def create_client(
        access_key_id: str,
        access_key_secret: str,
    ) -> imm20200930Client:
        """
        使用AccessKey ID&AccessKey Secret初始化账号Client。
        @param access_key_id:
        @param access_key_secret:
        @return: Client
        @throws Exception
        """
        config = open_api_models.Config(
            access_key_id=access_key_id,
            access_key_secret=access_key_secret
        )
        # 填写访问的域名。
        config.endpoint = f'imm.cn-beijing.aliyuncs.com'
        return imm20200930Client(config)

    @staticmethod
    def main() -> None:
        # 阿里云账号AccessKey拥有所有API的访问权限，建议您使用RAM用户进行API访问或日常运维。
        # 强烈建议不要把AccessKey ID和AccessKey Secret保存到工程代码里，否则可能导致AccessKey泄露，威胁您账号下所有资源的安全。
        # 本示例通过从环境变量中读取AccessKey，来实现API访问的身份验证。如何配置环境变量，请参见https://help.aliyun.com/document_detail/2361894.html。
        imm_access_key_id = os.getenv("AccessKeyId")
        imm_access_key_secret = os.getenv("AccessKeySecret")
        # 初始化客户端。
        client = Sample.create_client(imm_access_key_id, imm_access_key_secret)
        # 构造请求。
        request = imm_20200930_models.SimpleQueryRequest()
        params = {
            # 设置查询条件。
            "Query": {"Field": "Figures.FigureClusterId", "Operation": "eq", "Value": "Cluster-7bdbcedb-bd79-42e7-a1e2-b29a48532bd6"},
            # 填写IMM项目名称。
            "ProjectName": "test-project",
            # 填写数据集名称。
            "DatasetName": "test-dataset",
            # 设置最多返回100条查询结果。
            "MaxResults": 100
        }
        request.from_map(params)
        runtime = util_models.RuntimeOptions()
        try:
            # 打印API的返回值。
            response = client.simple_query_with_options(request, runtime)
            print(response.body.to_map())
        except Exception as error:
            # 如有需要，请打印错误信息。
            UtilClient.assert_as_string(error.message)
            print(error)


if __name__ == '__main__':
    Sample.main()
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