使用客户端库为视频添加注释

使用客户端库为视频添加注释

本快速入门为您介绍 Video Intelligence API。在本快速入门中， 您将设置您的 Google Cloud 项目和授权，然后 请求 Video Intelligence 为视频添加注释。

准备工作

登录您的 Google Cloud 账号。如果您是新手 Google Cloud， 请创建一个账号来评估我们的产品在 实际场景中的表现。新客户还可获享 $300 赠金，用于 运行、测试和部署工作负载。

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

Verify that billing is enabled for your Google Cloud project.

Enable the Cloud Video Intelligence API.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

Enable the API

安装 Google Cloud CLI。

如果您使用的是外部身份提供方 (IdP)，则必须先使用联合身份登录 gcloud CLI。

如需初始化 gcloud CLI，请运行以下命令：

gcloud init

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

Verify that billing is enabled for your Google Cloud project.

Enable the Cloud Video Intelligence API.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

Enable the API

安装 Google Cloud CLI。

如果您使用的是外部身份提供方 (IdP)，则必须先使用联合身份登录 gcloud CLI。

如需初始化 gcloud CLI，请运行以下命令：

gcloud init

安装客户端库

Go

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

Java

如果您使用的是 Maven，请将以下代码添加到您的 pom.xml 文件中。如需详细了解 BOM，请参阅Google Cloud Platform 库 BOM。

<dependencyManagement>
  <dependencies>
    <dependency>
      <groupId>com.google.cloud</groupId>
      <artifactId>libraries-bom</artifactId>
      <version>26.79.0</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>

如果您使用的是 Gradle， 请将以下代码添加到您的依赖项中：

implementation 'com.google.cloud:google-cloud-video-intelligence:2.87.0'

如果您使用的是 sbt，请将 以下代码添加到您的依赖项中：

libraryDependencies += "com.google.cloud" % "google-cloud-video-intelligence" % "2.87.0"

如果您使用的是 Visual Studio Code 或 IntelliJ，可以通过以下 IDE 插件将客户端库添加到您的 项目中：

• Cloud Code for VS Code
• Cloud Code for IntelliJ

上述插件还提供其他功能，例如服务账号密钥管理。如需了解详情，请参阅各个插件相应的文档。

注意 ：Cloud Java 客户端库目前不支持 Android。

Node.js

在安装库之前，请确保已经为 Node.js 开发准备好环境。

npm install @google-cloud/video-intelligence

Python

在安装库之前，请确保已经为 Python 开发准备好环境。

pip install --upgrade google-cloud-videointelligence

其他语言

C#：请按照客户端库页面上的 C# 设置说明操作，然后访问 .NET 版 Video Intelligence 参考文档。

PHP：请按照客户端库页面上的PHP 设置说明操作，然后访问PHP 版 Video Intelligence 参考文档。

Ruby：请按照客户端库页面上的Ruby 设置说明操作，然后访问Ruby 版 Video Intelligence 参考文档。

设置身份验证

1. 安装 Google Cloud CLI。 安装完成后， 初始化 Google Cloud CLI，方法是运行以下命令：

   gcloud init

   如果您使用的是外部身份提供方 (IdP)，则必须先 使用联合身份登录 gcloud CLI。

2. 如果您使用的是本地 shell，请为您的用户 账号创建本地身份验证凭证：

   gcloud auth application-default login

   如果您使用的是 Cloud Shell，则无需执行此操作。

   如果返回了身份验证错误，并且您使用的是外部身份提供方 (IdP)，请确认您已 使用联合身份登录 gcloud CLI。

   登录屏幕随即出现。在您登录后，您的凭据会存储在 ADC 使用的本地凭据文件中。

标签检测

现在，您可以使用 Video Intelligence API 请求视频或视频片段中的信息，例如标签检测。请运行以下代码以执行您的第一个视频标签检测请求：

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

在运行该示例之前，请确保已经为 Node.js 开发准备好环境。

// 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

在运行该示例之前，请确保已经为 Python 开发准备好环境。

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")

其他语言

C#：请按照客户端库页面上的 C# 设置说明操作，然后访问 .NET 版 Video Intelligence 参考文档。

PHP：请按照客户端库页面上的PHP 设置说明操作，然后访问PHP 版 Video Intelligence 参考文档。

Ruby：请按照客户端库页面上的Ruby 设置说明操作，然后访问Ruby 版 Video Intelligence 参考文档。

恭喜！您已向 Video Intelligence 发送了第一个请求。

结果怎么样？

成功了！

太棒了！ 您对本快速入门有何看法？我们可以在哪些方面做出改进？ 欢迎提供您的意见！

遇到了问题！

我们对此深表遗憾。 请告诉我们您遇到了什么问题。我们会予以修正。

清理

为避免因本页中使用的资源导致您的 Google Cloud 账号产生费用，请按照以下步骤操作。

• 使用 Google Cloud console 删除您不需要的项目。

后续步骤