本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

# 在串流影片中搜尋人臉。
<a name="rekognition-video-stream-processor-search-faces"></a>

**注意**  
串流影片和大量影像分析不再提供給新客戶。如需詳細資訊，請參閱[Amazon Rekognition 功能可用性變更](rekognition-availability-changes.md)。  
**此變更不會影響其他 Amazon Rekognition 功能的可用性。**

Amazon Rekognition Video 可在集合中搜尋與串流影片中偵測到之人臉相符的人臉。如需集合的詳細資訊，請參閱 [在集合中搜尋人臉](collections.md)。

**Topics**
+ [建立 Amazon Rekognition Video 人臉搜尋串流處理器](#streaming-video-creating-stream-processor)
+ [啟動 Amazon Rekognition Video 人臉搜尋串流處理器](#streaming-video-starting-stream-processor)
+ [使用串流處理器進行人臉搜尋 (Java V2 範例)](#using-stream-processors-v2)
+ [使用串流處理器進行人臉搜尋 (Java V1 範例)](#using-stream-processors)
+ [讀取串流影片分析結果](streaming-video-kinesis-output.md)
+ [使用 Kinesis Video Streams 本機顯示 Rekognition 結果](displaying-rekognition-results-locally.md)
+ [了解 Kinesis 人臉辨識 JSON 影格記錄](streaming-video-kinesis-output-reference.md)

下圖顯示 Amazon Rekognition Video 如何偵測及辨識串流影片中的人臉。

![使用 Amazon Rekognition Video 從 Amazon Kinesis 處理影片串流的工作流程圖表。](http://docs.aws.amazon.com/zh_tw/rekognition/latest/dg/images/VideoRekognitionStream.png)


## 建立 Amazon Rekognition Video 人臉搜尋串流處理器
<a name="streaming-video-creating-stream-processor"></a>

您必須先建立 Amazon Rekognition Video 串流處理器 ([CreateStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateStreamProcessor.html))，才能分析串流影片。串流處理器包含 Kinesis 資料串流和 Kinesis 影片串流的相關資訊。其也會包含集合的識別符，該集合含有您要在輸入串流影片中辨識的人臉。您也可以指定串流處理器的名稱。以下是 `CreateStreamProcessor` 要求的 JSON 範例。

```
{
       "Name": "streamProcessorForCam",
       "Input": {
              "KinesisVideoStream": {
                     "Arn": "arn:aws:kinesisvideo:us-east-1:nnnnnnnnnnnn:stream/inputVideo"
              }
       },
       "Output": {
              "KinesisDataStream": {
                     "Arn": "arn:aws:kinesis:us-east-1:nnnnnnnnnnnn:stream/outputData"
              }
       },
       "RoleArn": "arn:aws:iam::nnnnnnnnnnn:role/roleWithKinesisPermission",
       "Settings": {
              "FaceSearch": {
                     "CollectionId": "collection-with-100-faces",
                     "FaceMatchThreshold": 85.5
              }
       }
}
```

以下是 `CreateStreamProcessor` 的回應範例。

```
{
       “StreamProcessorArn”: “arn:aws:rekognition:us-east-1:nnnnnnnnnnnn:streamprocessor/streamProcessorForCam”
}
```

## 啟動 Amazon Rekognition Video 人臉搜尋串流處理器
<a name="streaming-video-starting-stream-processor"></a>

您可以透過呼叫 [StartStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartStreamProcessor.html) 並提供您在 `CreateStreamProcessor` 中指定的串流處理器名稱，來開始分析串流影片。以下是 `StartStreamProcessor` 要求的 JSON 範例。

```
{
       "Name": "streamProcessorForCam"
}
```

如果串流處理器成功啟動，就會傳回 HTTP 200 回應，以及空的 JSON 內文。

## 使用串流處理器進行人臉搜尋 (Java V2 範例)
<a name="using-stream-processors-v2"></a>

下列範例程式碼示範如何使用適用於 Java 的 AWS SDK 第 2 版呼叫各種串流處理器操作，例如 [CreateStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateStreamProcessor.html) 和 [StartStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartStreamProcessor.html)。

此程式碼取自 AWS 文件開發套件範例 GitHub 儲存庫。請參閱[此處](https://github.com/awsdocs/aws-doc-sdk-examples/blob/master/javav2/example_code/rekognition/src/main/java/com/example/rekognition/CreateStreamProcessor.java)的完整範例。

```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.CreateStreamProcessorRequest;
import software.amazon.awssdk.services.rekognition.model.CreateStreamProcessorResponse;
import software.amazon.awssdk.services.rekognition.model.FaceSearchSettings;
import software.amazon.awssdk.services.rekognition.model.KinesisDataStream;
import software.amazon.awssdk.services.rekognition.model.KinesisVideoStream;
import software.amazon.awssdk.services.rekognition.model.ListStreamProcessorsRequest;
import software.amazon.awssdk.services.rekognition.model.ListStreamProcessorsResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.StreamProcessor;
import software.amazon.awssdk.services.rekognition.model.StreamProcessorInput;
import software.amazon.awssdk.services.rekognition.model.StreamProcessorSettings;
import software.amazon.awssdk.services.rekognition.model.StreamProcessorOutput;
import software.amazon.awssdk.services.rekognition.model.StartStreamProcessorRequest;
import software.amazon.awssdk.services.rekognition.model.DescribeStreamProcessorRequest;
import software.amazon.awssdk.services.rekognition.model.DescribeStreamProcessorResponse;

/**
 * Before running this Java V2 code example, set up your development
 * environment, including your credentials.
 * <p>
 * For more information, see the following documentation topic:
 * <p>
 * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
 */
public class CreateStreamProcessor {
    public static void main(String[] args) {
        final String usage = """
                
                Usage:    <role> <kinInputStream> <kinOutputStream> <collectionName> <StreamProcessorName>
                
                Where:
                   role - The ARN of the AWS Identity and Access Management (IAM) role to use. \s
                   kinInputStream - The ARN of the Kinesis video stream.\s
                   kinOutputStream - The ARN of the Kinesis data stream.\s
                   collectionName - The name of the collection to use that contains content. \s
                   StreamProcessorName - The name of the Stream Processor. \s
                """;

        if (args.length != 5) {
            System.out.println(usage);
            System.exit(1);
        }

        String role = args[0];
        String kinInputStream = args[1];
        String kinOutputStream = args[2];
        String collectionName = args[3];
        String streamProcessorName = args[4];

        Region region = Region.US_EAST_1;
        RekognitionClient rekClient = RekognitionClient.builder()
                .region(region)
                .build();

        processCollection(rekClient, streamProcessorName, kinInputStream, kinOutputStream, collectionName,
                role);
        startSpecificStreamProcessor(rekClient, streamProcessorName);
        listStreamProcessors(rekClient);
        describeStreamProcessor(rekClient, streamProcessorName);
        deleteSpecificStreamProcessor(rekClient, streamProcessorName);
    }

    public static void listStreamProcessors(RekognitionClient rekClient) {
        ListStreamProcessorsRequest request = ListStreamProcessorsRequest.builder()
                .maxResults(15)
                .build();

        ListStreamProcessorsResponse listStreamProcessorsResult = rekClient.listStreamProcessors(request);
        for (StreamProcessor streamProcessor : listStreamProcessorsResult.streamProcessors()) {
            System.out.println("StreamProcessor name - " + streamProcessor.name());
            System.out.println("Status - " + streamProcessor.status());
        }
    }

    private static void describeStreamProcessor(RekognitionClient rekClient, String StreamProcessorName) {
        DescribeStreamProcessorRequest streamProcessorRequest = DescribeStreamProcessorRequest.builder()
                .name(StreamProcessorName)
                .build();

        DescribeStreamProcessorResponse describeStreamProcessorResult = rekClient
                .describeStreamProcessor(streamProcessorRequest);
        System.out.println("Arn - " + describeStreamProcessorResult.streamProcessorArn());
        System.out.println("Input kinesisVideo stream - "
                + describeStreamProcessorResult.input().kinesisVideoStream().arn());
        System.out.println("Output kinesisData stream - "
                + describeStreamProcessorResult.output().kinesisDataStream().arn());
        System.out.println("RoleArn - " + describeStreamProcessorResult.roleArn());
        System.out.println(
                "CollectionId - "
                        + describeStreamProcessorResult.settings().faceSearch().collectionId());
        System.out.println("Status - " + describeStreamProcessorResult.status());
        System.out.println("Status message - " + describeStreamProcessorResult.statusMessage());
        System.out.println("Creation timestamp - " + describeStreamProcessorResult.creationTimestamp());
        System.out.println("Last update timestamp - " + describeStreamProcessorResult.lastUpdateTimestamp());
    }

    private static void startSpecificStreamProcessor(RekognitionClient rekClient, String StreamProcessorName) {
        try {
            StartStreamProcessorRequest streamProcessorRequest = StartStreamProcessorRequest.builder()
                    .name(StreamProcessorName)
                    .build();

            rekClient.startStreamProcessor(streamProcessorRequest);
            System.out.println("Stream Processor " + StreamProcessorName + " started.");

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

    private static void processCollection(RekognitionClient rekClient, String StreamProcessorName,
                                          String kinInputStream, String kinOutputStream, String collectionName, String role) {
        try {
            KinesisVideoStream videoStream = KinesisVideoStream.builder()
                    .arn(kinInputStream)
                    .build();

            KinesisDataStream dataStream = KinesisDataStream.builder()
                    .arn(kinOutputStream)
                    .build();

            StreamProcessorOutput processorOutput = StreamProcessorOutput.builder()
                    .kinesisDataStream(dataStream)
                    .build();

            StreamProcessorInput processorInput = StreamProcessorInput.builder()
                    .kinesisVideoStream(videoStream)
                    .build();

            FaceSearchSettings searchSettings = FaceSearchSettings.builder()
                    .faceMatchThreshold(75f)
                    .collectionId(collectionName)
                    .build();

            StreamProcessorSettings processorSettings = StreamProcessorSettings.builder()
                    .faceSearch(searchSettings)
                    .build();

            CreateStreamProcessorRequest processorRequest = CreateStreamProcessorRequest.builder()
                    .name(StreamProcessorName)
                    .input(processorInput)
                    .output(processorOutput)
                    .roleArn(role)
                    .settings(processorSettings)
                    .build();

            CreateStreamProcessorResponse response = rekClient.createStreamProcessor(processorRequest);
            System.out.println("The ARN for the newly create stream processor is "
                    + response.streamProcessorArn());

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

    private static void deleteSpecificStreamProcessor(RekognitionClient rekClient, String StreamProcessorName) {
        rekClient.stopStreamProcessor(a -> a.name(StreamProcessorName));
        rekClient.deleteStreamProcessor(a -> a.name(StreamProcessorName));
        System.out.println("Stream Processor " + StreamProcessorName + " deleted.");
    }
}
```

## 使用串流處理器進行人臉搜尋 (Java V1 範例)
<a name="using-stream-processors"></a>

下列範例程式碼示範如何使用 Java V1 呼叫各種串流處理器作業，例如，例如[CreateStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateStreamProcessor.html)和[StartStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartStreamProcessor.html)。範例包含串流處理器管理員類別 (StreamManager)，可提供方法呼叫串流處理器操作。入門課程 (Starter) 建立 StreamManager 物件並呼叫多個操作。

**設定範例：**

1. 設定數值的 Starter 類別成員欄位至您所需的數值。

1. 在 Starter 類別函數 `main`，移除所需的函數呼叫。

### Starter 類別
<a name="streaming-started"></a>

```
//Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
//PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)

// Starter class. Use to create a StreamManager class
// and call stream processor operations.
package com.amazonaws.samples;
import com.amazonaws.samples.*;

public class Starter {

	public static void main(String[] args) {
		
		
    	String streamProcessorName="Stream Processor Name";
    	String kinesisVideoStreamArn="Kinesis Video Stream Arn";
    	String kinesisDataStreamArn="Kinesis Data Stream Arn";
    	String roleArn="Role Arn";
    	String collectionId="Collection ID";
    	Float matchThreshold=50F;

		try {
			StreamManager sm= new StreamManager(streamProcessorName,
					kinesisVideoStreamArn,
					kinesisDataStreamArn,
					roleArn,
					collectionId,
					matchThreshold);
			//sm.createStreamProcessor();
			//sm.startStreamProcessor();
			//sm.deleteStreamProcessor();
			//sm.deleteStreamProcessor();
			//sm.stopStreamProcessor();
			//sm.listStreamProcessors();
			//sm.describeStreamProcessor();
		}
		catch(Exception e){
			System.out.println(e.getMessage());
		}
	}
}
```

### StreamManager 類別
<a name="streaming-manager"></a>

```
//Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
//PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)

// Stream manager class. Provides methods for calling
// Stream Processor operations.
package com.amazonaws.samples;

import com.amazonaws.services.rekognition.AmazonRekognition;
import com.amazonaws.services.rekognition.AmazonRekognitionClientBuilder;
import com.amazonaws.services.rekognition.model.CreateStreamProcessorRequest;
import com.amazonaws.services.rekognition.model.CreateStreamProcessorResult;
import com.amazonaws.services.rekognition.model.DeleteStreamProcessorRequest;
import com.amazonaws.services.rekognition.model.DeleteStreamProcessorResult;
import com.amazonaws.services.rekognition.model.DescribeStreamProcessorRequest;
import com.amazonaws.services.rekognition.model.DescribeStreamProcessorResult;
import com.amazonaws.services.rekognition.model.FaceSearchSettings;
import com.amazonaws.services.rekognition.model.KinesisDataStream;
import com.amazonaws.services.rekognition.model.KinesisVideoStream;
import com.amazonaws.services.rekognition.model.ListStreamProcessorsRequest;
import com.amazonaws.services.rekognition.model.ListStreamProcessorsResult;
import com.amazonaws.services.rekognition.model.StartStreamProcessorRequest;
import com.amazonaws.services.rekognition.model.StartStreamProcessorResult;
import com.amazonaws.services.rekognition.model.StopStreamProcessorRequest;
import com.amazonaws.services.rekognition.model.StopStreamProcessorResult;
import com.amazonaws.services.rekognition.model.StreamProcessor;
import com.amazonaws.services.rekognition.model.StreamProcessorInput;
import com.amazonaws.services.rekognition.model.StreamProcessorOutput;
import com.amazonaws.services.rekognition.model.StreamProcessorSettings;

public class StreamManager {

    private String streamProcessorName;
    private String kinesisVideoStreamArn;
    private String kinesisDataStreamArn;
    private String roleArn;
    private String collectionId;
    private float matchThreshold;

    private AmazonRekognition rekognitionClient;
    

    public StreamManager(String spName,
    		String kvStreamArn,
    		String kdStreamArn,
    		String iamRoleArn,
    		String collId,
    		Float threshold){
    	streamProcessorName=spName;
    	kinesisVideoStreamArn=kvStreamArn;
    	kinesisDataStreamArn=kdStreamArn;
    	roleArn=iamRoleArn;
    	collectionId=collId;
    	matchThreshold=threshold;
    	rekognitionClient=AmazonRekognitionClientBuilder.defaultClient();
    	
    }
    
    public void createStreamProcessor() {
    	//Setup input parameters
        KinesisVideoStream kinesisVideoStream = new KinesisVideoStream().withArn(kinesisVideoStreamArn);
        StreamProcessorInput streamProcessorInput =
                new StreamProcessorInput().withKinesisVideoStream(kinesisVideoStream);
        KinesisDataStream kinesisDataStream = new KinesisDataStream().withArn(kinesisDataStreamArn);
        StreamProcessorOutput streamProcessorOutput =
                new StreamProcessorOutput().withKinesisDataStream(kinesisDataStream);
        FaceSearchSettings faceSearchSettings =
                new FaceSearchSettings().withCollectionId(collectionId).withFaceMatchThreshold(matchThreshold);
        StreamProcessorSettings streamProcessorSettings =
                new StreamProcessorSettings().withFaceSearch(faceSearchSettings);

        //Create the stream processor
        CreateStreamProcessorResult createStreamProcessorResult = rekognitionClient.createStreamProcessor(
                new CreateStreamProcessorRequest().withInput(streamProcessorInput).withOutput(streamProcessorOutput)
                        .withSettings(streamProcessorSettings).withRoleArn(roleArn).withName(streamProcessorName));

        //Display result
        System.out.println("Stream Processor " + streamProcessorName + " created.");
        System.out.println("StreamProcessorArn - " + createStreamProcessorResult.getStreamProcessorArn());
    }

    public void startStreamProcessor() {
        StartStreamProcessorResult startStreamProcessorResult =
                rekognitionClient.startStreamProcessor(new StartStreamProcessorRequest().withName(streamProcessorName));
        System.out.println("Stream Processor " + streamProcessorName + " started.");
    }

    public void stopStreamProcessor() {
        StopStreamProcessorResult stopStreamProcessorResult =
                rekognitionClient.stopStreamProcessor(new StopStreamProcessorRequest().withName(streamProcessorName));
        System.out.println("Stream Processor " + streamProcessorName + " stopped.");
    }

    public void deleteStreamProcessor() {
        DeleteStreamProcessorResult deleteStreamProcessorResult = rekognitionClient
                .deleteStreamProcessor(new DeleteStreamProcessorRequest().withName(streamProcessorName));
        System.out.println("Stream Processor " + streamProcessorName + " deleted.");
    }

    public void describeStreamProcessor() {
        DescribeStreamProcessorResult describeStreamProcessorResult = rekognitionClient
                .describeStreamProcessor(new DescribeStreamProcessorRequest().withName(streamProcessorName));

        //Display various stream processor attributes.
        System.out.println("Arn - " + describeStreamProcessorResult.getStreamProcessorArn());
        System.out.println("Input kinesisVideo stream - "
                + describeStreamProcessorResult.getInput().getKinesisVideoStream().getArn());
        System.out.println("Output kinesisData stream - "
                + describeStreamProcessorResult.getOutput().getKinesisDataStream().getArn());
        System.out.println("RoleArn - " + describeStreamProcessorResult.getRoleArn());
        System.out.println(
                "CollectionId - " + describeStreamProcessorResult.getSettings().getFaceSearch().getCollectionId());
        System.out.println("Status - " + describeStreamProcessorResult.getStatus());
        System.out.println("Status message - " + describeStreamProcessorResult.getStatusMessage());
        System.out.println("Creation timestamp - " + describeStreamProcessorResult.getCreationTimestamp());
        System.out.println("Last update timestamp - " + describeStreamProcessorResult.getLastUpdateTimestamp());
    }

    public void listStreamProcessors() {
        ListStreamProcessorsResult listStreamProcessorsResult =
                rekognitionClient.listStreamProcessors(new ListStreamProcessorsRequest().withMaxResults(100));

        //List all stream processors (and state) returned from Rekognition
        for (StreamProcessor streamProcessor : listStreamProcessorsResult.getStreamProcessors()) {
            System.out.println("StreamProcessor name - " + streamProcessor.getName());
            System.out.println("Status - " + streamProcessor.getStatus());
        }
    }
}
```