使用用戶端程式庫為影片加上註解

使用用戶端程式庫為影片加上註解

本快速入門導覽課程將介紹 Video Intelligence API。在這個快速入門導覽課程中，您將設定 Google Cloud 專案和授權，然後要求 Video Intelligence 為影片加上註解。

事前準備

1. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Roles required to select or create a project

   • Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
   • Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.
   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

2. Verify that billing is enabled for your Google Cloud project.

3. Enable the Cloud Video Intelligence API, if it is not already enabled.

   Roles required to enable APIs

   To enable APIs, you need the serviceusage.services.enable permission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.

   Enable the API

4. Install the Google Cloud CLI.

5. Configure the gcloud CLI to use your federated identity.

   For more information, see Sign in to the gcloud CLI with your federated identity.

6. To initialize the gcloud CLI, run the following command:

   gcloud init

安裝用戶端程式庫

Go

go get cloud.google.com/go/videointelligence/apiv1

Java

如果您使用 Maven，請將下列指令新增至 pom.xml 檔案。如要進一步瞭解 BOM，請參閱「Google Cloud Platform 程式庫 BOM」。

<dependencyManagement>
  <dependencies>
    <dependency>
      <groupId>com.google.cloud</groupId>
      <artifactId>libraries-bom</artifactId>
      <version>26.90.0</version>
      <type>pom</type>
      <scope>import</scope>
    </dependency>
  </dependencies>
</dependencyManagement>

<dependencies>
  <dependency>
    <groupId>com.google.cloud</groupId>
    <artifactId>google-cloud-video-intelligence</artifactId>
  </dependency>
</dependencies>

如果您使用 Gradle，請在依附元件中加入以下指令：

implementation platform('com.google.cloud:libraries-bom:26.90.0')

implementation 'com.google.cloud:google-cloud-video-intelligence'

如果您使用 sbt，請在依附元件中加入以下指令：

libraryDependencies += "com.google.cloud" % "google-cloud-video-intelligence" % "2.98.0"

如果您使用 Visual Studio Code 或 IntelliJ，可以透過下列 IDE 外掛程式，將用戶端程式庫新增至專案：

• Cloud Code for VS Code
• Cloud Code for IntelliJ

這些外掛程式會提供其他功能，例如服務帳戶的金鑰管理功能。詳情請參閱各外掛程式的說明文件。

注意：Cloud Java 用戶端程式庫目前不支援 Android。

Node.js

安裝程式庫前，請確認您已設定適當的 Node.js 開發環境。

npm install @google-cloud/video-intelligence

Python

安裝程式庫前，請確認您已設定適當的 Python 開發環境。

pip install --upgrade google-cloud-videointelligence

其他語言

C#：請按照用戶端程式庫頁面上的 C# 設定操作說明完成相關步驟，然後參閱「.NET 適用的 Video Intelligence 參考文件」。

PHP：請按照用戶端程式庫頁面上的 PHP 設定操作說明完成相關步驟，然後參閱 PHP 適用的 Video Intelligence 參考文件。

Ruby：請按照用戶端程式庫頁面上的 Ruby 設定操作說明完成相關步驟，然後參閱「Ruby 適用的 Video Intelligence 參考文件」。

設定驗證方法

1. 安裝 Google Cloud CLI，然後 使用聯合身分登入 gcloud CLI。登入後，執行下列指令來初始化 Google Cloud CLI：

   gcloud init

2. 為使用者帳戶建立本機驗證憑證：

   gcloud auth application-default login

   如果系統傳回驗證錯誤，請確認您已 使用聯合身分登入 gcloud CLI。

   登入畫面會隨即顯示。登入後，您的憑證會儲存在 ADC 使用的本機憑證檔案中。

標籤偵測

注意： VPC Service Controls 輸出限制不會對 videos:annotate 要求的 outputUri 強制執行。服務可將註解結果寫入服務周邊以外的 Cloud Storage bucket。詳情請參閱「支援的產品和限制」。

現在您可以使用 Video Intelligence API，要求影片或影片片段的資訊，例如標籤偵測。執行下列程式碼，發出第一項影片標籤偵測要求：

Go

// Sample video_quickstart uses the Google Cloud Video Intelligence API to label a video.
package main

import (
	"context"
	"fmt"
	"log"

	"github.com/golang/protobuf/ptypes"

	video "cloud.google.com/go/videointelligence/apiv1"
	videopb "cloud.google.com/go/videointelligence/apiv1/videointelligencepb"
)

func main() {
	ctx := context.Background()

	// Creates a client.
	client, err := video.NewClient(ctx)
	if err != nil {
		log.Fatalf("Failed to create client: %v", err)
	}
	defer client.Close()

	op, err := client.AnnotateVideo(ctx, &videopb.AnnotateVideoRequest{
		InputUri: "gs://cloud-samples-data/video/cat.mp4",
		Features: []videopb.Feature{
			videopb.Feature_LABEL_DETECTION,
		},
	})
	if err != nil {
		log.Fatalf("Failed to start annotation job: %v", err)
	}

	resp, err := op.Wait(ctx)
	if err != nil {
		log.Fatalf("Failed to annotate: %v", err)
	}

	// Only one video was processed, so get the first result.
	result := resp.GetAnnotationResults()[0]

	for _, annotation := range result.SegmentLabelAnnotations {
		fmt.Printf("Description: %s\n", annotation.Entity.Description)

		for _, category := range annotation.CategoryEntities {
			fmt.Printf("\tCategory: %s\n", category.Description)
		}

		for _, segment := range annotation.Segments {
			start, _ := ptypes.Duration(segment.Segment.StartTimeOffset)
			end, _ := ptypes.Duration(segment.Segment.EndTimeOffset)
			fmt.Printf("\tSegment: %s to %s\n", start, end)
			fmt.Printf("\tConfidence: %v\n", segment.Confidence)
		}
	}
}

Java

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.videointelligence.v1.AnnotateVideoProgress;
import com.google.cloud.videointelligence.v1.AnnotateVideoRequest;
import com.google.cloud.videointelligence.v1.AnnotateVideoResponse;
import com.google.cloud.videointelligence.v1.Entity;
import com.google.cloud.videointelligence.v1.Feature;
import com.google.cloud.videointelligence.v1.LabelAnnotation;
import com.google.cloud.videointelligence.v1.LabelSegment;
import com.google.cloud.videointelligence.v1.VideoAnnotationResults;
import com.google.cloud.videointelligence.v1.VideoIntelligenceServiceClient;
import java.util.List;

public class QuickstartSample {

  /** Demonstrates using the video intelligence client to detect labels in a video file. */
  public static void main(String[] args) throws Exception {
    // Instantiate a video intelligence client
    try (VideoIntelligenceServiceClient client = VideoIntelligenceServiceClient.create()) {
      // The Google Cloud Storage path to the video to annotate.
      String gcsUri = "gs://cloud-samples-data/video/cat.mp4";

      // Create an operation that will contain the response when the operation completes.
      AnnotateVideoRequest request =
          AnnotateVideoRequest.newBuilder()
              .setInputUri(gcsUri)
              .addFeatures(Feature.LABEL_DETECTION)
              .build();

      OperationFuture<AnnotateVideoResponse, AnnotateVideoProgress> response =
          client.annotateVideoAsync(request);

      System.out.println("Waiting for operation to complete...");

      List<VideoAnnotationResults> results = response.get().getAnnotationResultsList();
      if (results.isEmpty()) {
        System.out.println("No labels detected in " + gcsUri);
        return;
      }
      for (VideoAnnotationResults result : results) {
        System.out.println("Labels:");
        // get video segment label annotations
        for (LabelAnnotation annotation : result.getSegmentLabelAnnotationsList()) {
          System.out.println(
              "Video label description : " + annotation.getEntity().getDescription());
          // categories
          for (Entity categoryEntity : annotation.getCategoryEntitiesList()) {
            System.out.println("Label Category description : " + categoryEntity.getDescription());
          }
          // segments
          for (LabelSegment segment : annotation.getSegmentsList()) {
            double startTime =
                segment.getSegment().getStartTimeOffset().getSeconds()
                    + segment.getSegment().getStartTimeOffset().getNanos() / 1e9;
            double endTime =
                segment.getSegment().getEndTimeOffset().getSeconds()
                    + segment.getSegment().getEndTimeOffset().getNanos() / 1e9;
            System.out.printf("Segment location : %.3f:%.3f\n", startTime, endTime);
            System.out.println("Confidence : " + segment.getConfidence());
          }
        }
      }
    }
  }
}

Node.js

執行範例前，請確認已設定適當的 Node.js 開發環境。

// Imports the Google Cloud Video Intelligence library
const videoIntelligence = require('@google-cloud/video-intelligence');

// Creates a client
const client = new videoIntelligence.VideoIntelligenceServiceClient();

// The GCS uri of the video to analyze
const gcsUri = 'gs://cloud-samples-data/video/cat.mp4';

// Construct request
const request = {
  inputUri: gcsUri,
  features: ['LABEL_DETECTION'],
};

// Execute request
const [operation] = await client.annotateVideo(request);

console.log(
  'Waiting for operation to complete... (this may take a few minutes)'
);

const [operationResult] = await operation.promise();

// Gets annotations for video
const annotations = operationResult.annotationResults[0];

// Gets labels for video from its annotations
const labels = annotations.segmentLabelAnnotations;
labels.forEach(label => {
  console.log(`Label ${label.entity.description} occurs at:`);
  label.segments.forEach(segment => {
    segment = segment.segment;
    console.log(
      `\tStart: ${segment.startTimeOffset.seconds}` +
        `.${(segment.startTimeOffset.nanos / 1e6).toFixed(0)}s`
    );
    console.log(
      `\tEnd: ${segment.endTimeOffset.seconds}.` +
        `${(segment.endTimeOffset.nanos / 1e6).toFixed(0)}s`
    );
  });
});

Python

執行範例前，請確認您已設定適當的 Python 開發環境。

from google.cloud import videointelligence

video_client = videointelligence.VideoIntelligenceServiceClient()
features = [videointelligence.Feature.LABEL_DETECTION]
operation = video_client.annotate_video(
    request={
        "features": features,
        "input_uri": "gs://cloud-samples-data/video/cat.mp4",
    }
)
print("\nProcessing video for label annotations:")

result = operation.result(timeout=180)
print("\nFinished processing.")

# first result is retrieved because a single video was processed
segment_labels = result.annotation_results[0].segment_label_annotations
for i, segment_label in enumerate(segment_labels):
    print("Video label description: {}".format(segment_label.entity.description))
    for category_entity in segment_label.category_entities:
        print(
            "\tLabel category description: {}".format(category_entity.description)
        )

    for i, segment in enumerate(segment_label.segments):
        start_time = (
            segment.segment.start_time_offset.seconds
            + segment.segment.start_time_offset.microseconds / 1e6
        )
        end_time = (
            segment.segment.end_time_offset.seconds
            + segment.segment.end_time_offset.microseconds / 1e6
        )
        positions = "{}s to {}s".format(start_time, end_time)
        confidence = segment.confidence
        print("\tSegment {}: {}".format(i, positions))
        print("\tConfidence: {}".format(confidence))
    print("\n")

其他語言

C#：請按照用戶端程式庫頁面上的 C# 設定操作說明完成相關步驟，然後參閱 .NET 適用的 Video Intelligence 參考文件。

PHP： 請按照用戶端程式庫頁面上的 PHP 設定操作說明完成相關步驟，然後參閱「PHP 適用的 Video Intelligence 參考文件」。

Ruby：請按照用戶端程式庫頁面上的 Ruby 設定操作說明完成相關步驟，然後參閱「Ruby 適用的 Video Intelligence 參考文件」。

恭喜！您已將第一個要求傳送至 Video Intelligence API。

還順利嗎？

可以正常運作！

太好了！ 您對這個快速入門導覽課程有什麼看法？我們有哪些地方需要改善？歡迎提供您的意見！

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• 使用 Google Cloud console 刪除不需要的專案。

後續步驟