使用 AWS SDK 將人臉索引到 Amazon Rekognition 集合

下列程式碼範例示範如何索引映像中的人臉，並將其新增至 Amazon Rekognition 集合。

如需詳細資訊，請參閱將人臉新增至集合。

.NET

AWS SDK for .NET

注意

還有更多關於 GitHub。尋找完整範例，並了解如何在AWS 設定和執行程式碼範例儲存庫。

    using System;
    using System.Collections.Generic;
    using System.Threading.Tasks;
    using Amazon.Rekognition;
    using Amazon.Rekognition.Model;

    /// <summary>
    /// Uses the Amazon Rekognition Service to detect faces in an image
    /// that has been uploaded to an Amazon Simple Storage Service (Amazon S3)
    /// bucket and then adds the information to a collection.
    /// </summary>
    public class AddFaces
    {
        public static async Task Main()
        {
            string collectionId = "MyCollection2";
            string bucket = "doc-example-bucket";
            string photo = "input.jpg";

            var rekognitionClient = new AmazonRekognitionClient();

            var image = new Image
            {
                S3Object = new S3Object
                {
                    Bucket = bucket,
                    Name = photo,
                },
            };

            var indexFacesRequest = new IndexFacesRequest
            {
                Image = image,
                CollectionId = collectionId,
                ExternalImageId = photo,
                DetectionAttributes = new List<string>() { "ALL" },
            };

            IndexFacesResponse indexFacesResponse = await rekognitionClient.IndexFacesAsync(indexFacesRequest);

            Console.WriteLine($"{photo} added");
            foreach (FaceRecord faceRecord in indexFacesResponse.FaceRecords)
            {
                Console.WriteLine($"Face detected: Faceid is {faceRecord.Face.FaceId}");
            }
        }
    }

• 如需 API 詳細資訊，請參閱 AWS SDK for .NETAPI 參考IndexFaces中的。

CLI

AWS CLI

將面加入至集合的步驟

以下指index-faces令會將在影像中找到的面加入至指定的集合。

aws rekognition index-faces \
    --image '{"S3Object":{"Bucket":"MyVideoS3Bucket","Name":"MyPicture.jpg"}}' \
    --collection-id MyCollection \
    --max-faces 1 \
    --quality-filter "AUTO" \
    --detection-attributes "ALL" \
    --external-image-id "MyPicture.jpg"

輸出：

{
    "FaceRecords": [
        {
            "FaceDetail": {
                "Confidence": 99.993408203125,
                "Eyeglasses": {
                    "Confidence": 99.11750030517578,
                    "Value": false
                },
                "Sunglasses": {
                    "Confidence": 99.98249053955078,
                    "Value": false
                },
                "Gender": {
                    "Confidence": 99.92769622802734,
                    "Value": "Male"
                },
                "Landmarks": [
                    {
                        "Y": 0.26750367879867554,
                        "X": 0.6202793717384338,
                        "Type": "eyeLeft"
                    },
                    {
                        "Y": 0.26642778515815735,
                        "X": 0.6787431836128235,
                        "Type": "eyeRight"
                    },
                    {
                        "Y": 0.31361380219459534,
                        "X": 0.6421601176261902,
                        "Type": "nose"
                    },
                    {
                        "Y": 0.3495299220085144,
                        "X": 0.6216195225715637,
                        "Type": "mouthLeft"
                    },
                    {
                        "Y": 0.35194727778434753,
                        "X": 0.669899046421051,
                        "Type": "mouthRight"
                    },
                    {
                        "Y": 0.26844894886016846,
                        "X": 0.6210268139839172,
                        "Type": "leftPupil"
                    },
                    {
                        "Y": 0.26707562804222107,
                        "X": 0.6817160844802856,
                        "Type": "rightPupil"
                    },
                    {
                        "Y": 0.24834522604942322,
                        "X": 0.6018546223640442,
                        "Type": "leftEyeBrowLeft"
                    },
                    {
                        "Y": 0.24397172033786774,
                        "X": 0.6172008514404297,
                        "Type": "leftEyeBrowUp"
                    },
                    {
                        "Y": 0.24677404761314392,
                        "X": 0.6339119076728821,
                        "Type": "leftEyeBrowRight"
                    },
                    {
                        "Y": 0.24582654237747192,
                        "X": 0.6619398593902588,
                        "Type": "rightEyeBrowLeft"
                    },
                    {
                        "Y": 0.23973053693771362,
                        "X": 0.6804757118225098,
                        "Type": "rightEyeBrowUp"
                    },
                    {
                        "Y": 0.24441994726657867,
                        "X": 0.6978968977928162,
                        "Type": "rightEyeBrowRight"
                    },
                    {
                        "Y": 0.2695908546447754,
                        "X": 0.6085202693939209,
                        "Type": "leftEyeLeft"
                    },
                    {
                        "Y": 0.26716896891593933,
                        "X": 0.6315826177597046,
                        "Type": "leftEyeRight"
                    },
                    {
                        "Y": 0.26289820671081543,
                        "X": 0.6202316880226135,
                        "Type": "leftEyeUp"
                    },
                    {
                        "Y": 0.27123287320137024,
                        "X": 0.6205548048019409,
                        "Type": "leftEyeDown"
                    },
                    {
                        "Y": 0.2668408751487732,
                        "X": 0.6663622260093689,
                        "Type": "rightEyeLeft"
                    },
                    {
                        "Y": 0.26741549372673035,
                        "X": 0.6910083889961243,
                        "Type": "rightEyeRight"
                    },
                    {
                        "Y": 0.2614026665687561,
                        "X": 0.6785826086997986,
                        "Type": "rightEyeUp"
                    },
                    {
                        "Y": 0.27075251936912537,
                        "X": 0.6789616942405701,
                        "Type": "rightEyeDown"
                    },
                    {
                        "Y": 0.3211299479007721,
                        "X": 0.6324167847633362,
                        "Type": "noseLeft"
                    },
                    {
                        "Y": 0.32276326417922974,
                        "X": 0.6558475494384766,
                        "Type": "noseRight"
                    },
                    {
                        "Y": 0.34385165572166443,
                        "X": 0.6444970965385437,
                        "Type": "mouthUp"
                    },
                    {
                        "Y": 0.3671635091304779,
                        "X": 0.6459195017814636,
                        "Type": "mouthDown"
                    }
                ],
                "Pose": {
                    "Yaw": -9.54541015625,
                    "Roll": -0.5709401965141296,
                    "Pitch": 0.6045494675636292
                },
                "Emotions": [
                    {
                        "Confidence": 39.90074157714844,
                        "Type": "HAPPY"
                    },
                    {
                        "Confidence": 23.38753890991211,
                        "Type": "CALM"
                    },
                    {
                        "Confidence": 5.840933322906494,
                        "Type": "CONFUSED"
                    }
                ],
                "AgeRange": {
                    "High": 63,
                    "Low": 45
                },
                "EyesOpen": {
                    "Confidence": 99.80887603759766,
                    "Value": true
                },
                "BoundingBox": {
                    "Width": 0.18562500178813934,
                    "Top": 0.1618015021085739,
                    "Left": 0.5575000047683716,
                    "Height": 0.24770642817020416
                },
                "Smile": {
                    "Confidence": 99.69740295410156,
                    "Value": false
                },
                "MouthOpen": {
                    "Confidence": 99.97393798828125,
                    "Value": false
                },
                "Quality": {
                    "Sharpness": 95.54405975341797,
                    "Brightness": 63.867706298828125
                },
                "Mustache": {
                    "Confidence": 97.05007934570312,
                    "Value": false
                },
                "Beard": {
                    "Confidence": 87.34505462646484,
                    "Value": false
                }
            },
            "Face": {
                "BoundingBox": {
                    "Width": 0.18562500178813934,
                    "Top": 0.1618015021085739,
                    "Left": 0.5575000047683716,
                    "Height": 0.24770642817020416
                },
                "FaceId": "ce7ed422-2132-4a11-ab14-06c5c410f29f",
                "ExternalImageId": "example-image.jpg",
                "Confidence": 99.993408203125,
                "ImageId": "8d67061e-90d2-598f-9fbd-29c8497039c0"
            }
        }
    ],
    "UnindexedFaces": [],
    "FaceModelVersion": "3.0",
    "OrientationCorrection": "ROTATE_0"
}

如需詳細資訊，請參閱 Amazon Rekognition 開發人員指南中的將人臉新增至集合。

• 如需 API 詳細資訊，請參閱AWS CLI命令參考IndexFaces中的。

Java

適用於 Java 2.x 的 SDK

注意

還有更多關於 GitHub。尋找完整範例，並了解如何在AWS 設定和執行程式碼範例儲存庫。

import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.IndexFacesResponse;
import software.amazon.awssdk.services.rekognition.model.IndexFacesRequest;
import software.amazon.awssdk.services.rekognition.model.Image;
import software.amazon.awssdk.services.rekognition.model.QualityFilter;
import software.amazon.awssdk.services.rekognition.model.Attribute;
import software.amazon.awssdk.services.rekognition.model.FaceRecord;
import software.amazon.awssdk.services.rekognition.model.UnindexedFace;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.Reason;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.InputStream;
import java.util.List;

/**
 * Before running this Java V2 code example, set up your development
 * environment, including your credentials.
 *
 * For more information, see the following documentation topic:
 *
 * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
 */
public class AddFacesToCollection {
    public static void main(String[] args) {

        final String usage = """

                Usage:     <collectionId> <sourceImage>

                Where:
                    collectionName - The name of the collection.
                    sourceImage - The path to the image (for example, C:\\AWS\\pic1.png).\s
                """;

        if (args.length != 2) {
            System.out.println(usage);
            System.exit(1);
        }

        String collectionId = args[0];
        String sourceImage = args[1];
        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        addToCollection(rekClient, collectionId, sourceImage);
        rekClient.close();
    }

    public static void addToCollection(RekognitionClient rekClient, String collectionId, String sourceImage) {
        try {
            InputStream sourceStream = new FileInputStream(sourceImage);
            SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream);
            Image souImage = Image.builder()
                    .bytes(sourceBytes)
                    .build();

            IndexFacesRequest facesRequest = IndexFacesRequest.builder()
                    .collectionId(collectionId)
                    .image(souImage)
                    .maxFaces(1)
                    .qualityFilter(QualityFilter.AUTO)
                    .detectionAttributes(Attribute.DEFAULT)
                    .build();

            IndexFacesResponse facesResponse = rekClient.indexFaces(facesRequest);
            System.out.println("Results for the image");
            System.out.println("\n Faces indexed:");
            List<FaceRecord> faceRecords = facesResponse.faceRecords();
            for (FaceRecord faceRecord : faceRecords) {
                System.out.println("  Face ID: " + faceRecord.face().faceId());
                System.out.println("  Location:" + faceRecord.faceDetail().boundingBox().toString());
            }

            List<UnindexedFace> unindexedFaces = facesResponse.unindexedFaces();
            System.out.println("Faces not indexed:");
            for (UnindexedFace unindexedFace : unindexedFaces) {
                System.out.println("  Location:" + unindexedFace.faceDetail().boundingBox().toString());
                System.out.println("  Reasons:");
                for (Reason reason : unindexedFace.reasons()) {
                    System.out.println("Reason:  " + reason);
                }
            }

        } catch (RekognitionException | FileNotFoundException e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
    }
}

• 如需 API 詳細資訊，請參閱 AWS SDK for Java 2.xAPI 參考IndexFaces中的。

Kotlin

適用於 Kotlin 的 SDK

注意

還有更多關於 GitHub。尋找完整範例，並了解如何在AWS 設定和執行程式碼範例儲存庫。

suspend fun addToCollection(collectionIdVal: String?, sourceImage: String) {

    val souImage = Image {
        bytes = (File(sourceImage).readBytes())
    }

    val request = IndexFacesRequest {
        collectionId = collectionIdVal
        image = souImage
        maxFaces = 1
        qualityFilter = QualityFilter.Auto
        detectionAttributes = listOf(Attribute.Default)
    }

    RekognitionClient { region = "us-east-1" }.use { rekClient ->
        val facesResponse = rekClient.indexFaces(request)

        // Display the results.
        println("Results for the image")
        println("\n Faces indexed:")
        facesResponse.faceRecords?.forEach { faceRecord ->
            println("Face ID: ${faceRecord.face?.faceId}")
            println("Location: ${faceRecord.faceDetail?.boundingBox}")
        }

        println("Faces not indexed:")
        facesResponse.unindexedFaces?.forEach { unindexedFace ->
            println("Location: ${unindexedFace.faceDetail?.boundingBox}")
            println("Reasons:")

            unindexedFace.reasons?.forEach { reason ->
                println("Reason:  $reason")
            }
        }
    }
}

• 有關 API 的詳細信息，請參閱 AWSSDK IndexFaces中的 Kotlin API 參考。

Python

適用於 Python (Boto3) 的 SDK

注意

還有更多關於 GitHub。尋找完整範例，並了解如何在AWS 設定和執行程式碼範例儲存庫。

class RekognitionCollection:
    """
    Encapsulates an Amazon Rekognition collection. This class is a thin wrapper
    around parts of the Boto3 Amazon Rekognition API.
    """

    def __init__(self, collection, rekognition_client):
        """
        Initializes a collection object.

        :param collection: Collection data in the format returned by a call to
                           create_collection.
        :param rekognition_client: A Boto3 Rekognition client.
        """
        self.collection_id = collection["CollectionId"]
        self.collection_arn, self.face_count, self.created = self._unpack_collection(
            collection
        )
        self.rekognition_client = rekognition_client

    @staticmethod
    def _unpack_collection(collection):
        """
        Unpacks optional parts of a collection that can be returned by
        describe_collection.

        :param collection: The collection data.
        :return: A tuple of the data in the collection.
        """
        return (
            collection.get("CollectionArn"),
            collection.get("FaceCount", 0),
            collection.get("CreationTimestamp"),
        )


    def index_faces(self, image, max_faces):
        """
        Finds faces in the specified image, indexes them, and stores them in the
        collection.

        :param image: The image to index.
        :param max_faces: The maximum number of faces to index.
        :return: A tuple. The first element is a list of indexed faces.
                 The second element is a list of faces that couldn't be indexed.
        """
        try:
            response = self.rekognition_client.index_faces(
                CollectionId=self.collection_id,
                Image=image.image,
                ExternalImageId=image.image_name,
                MaxFaces=max_faces,
                DetectionAttributes=["ALL"],
            )
            indexed_faces = [
                RekognitionFace({**face["Face"], **face["FaceDetail"]})
                for face in response["FaceRecords"]
            ]
            unindexed_faces = [
                RekognitionFace(face["FaceDetail"])
                for face in response["UnindexedFaces"]
            ]
            logger.info(
                "Indexed %s faces in %s. Could not index %s faces.",
                len(indexed_faces),
                image.image_name,
                len(unindexed_faces),
            )
        except ClientError:
            logger.exception("Couldn't index faces in image %s.", image.image_name)
            raise
        else:
            return indexed_faces, unindexed_faces

• 如需 API 的詳細資訊，請參閱AWS開發套件IndexFaces中的 Python (博托 3) API 參考。