Membuat anotasi video menggunakan library klien

Membuat anotasi video menggunakan library klien

Panduan memulai ini memperkenalkan Video Intelligence API kepada Anda. Dalam panduan memulai cepat ini, Anda akan menyiapkan project dan otorisasi Google Cloud , lalu membuat permintaan agar Video Intelligence menganotasi video.

Sebelum memulai

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

Menginstal library klien

Go

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

Java

Jika Anda menggunakan Maven, tambahkan kode berikut ke file pom.xml Anda: Untuk informasi lebih lanjut tentang BOM, lihat BOM Library Google Cloud Platform.

<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>

Jika menggunakan Gradle, tambahkan kode berikut ke dependensi Anda:

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

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

Jika Anda menggunakan sbt, tambahkan kode berikut ke dependensi Anda:

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

Jika menggunakan Visual Studio Code atau IntelliJ, Anda dapat menambahkan library klien ke project menggunakan plugin IDE berikut:

• Cloud Code for VS Code
• Cloud Code for IntelliJ

Plugin menyediakan fungsi tambahan, seperti pengelolaan kunci untuk akun layanan. Lihat dokumentasi setiap plugin untuk mengetahui detailnya.

Catatan: Library klien Cloud Java saat ini tidak mendukung Android.

Node.js

Sebelum menginstal library, pastikan Anda telah menyiapkan lingkungan untuk pengembangan Node.js.

npm install @google-cloud/video-intelligence

Python

Sebelum menginstal library, pastikan Anda telah menyiapkan lingkungan untuk pengembangan Python.

pip install --upgrade google-cloud-videointelligence

Bahasa tambahan

C#: Ikuti petunjuk penyiapan C# di halaman library klien, lalu buka dokumentasi referensi Video Intelligence untuk .NET.

PHP: Ikuti Petunjuk penyiapan PHP di halaman library klien, lalu buka dokumentasi referensi Video Intelligence untuk PHP.

Ruby: Ikuti petunjuk penyiapan Ruby di halaman library klien, lalu lihat dokumentasi referensi Video Intelligence untuk Ruby.

Menyiapkan autentikasi

1. Instal Google Cloud CLI, lalu login ke gcloud CLI dengan identitas gabungan Anda. Setelah login, inisialisasi Google Cloud CLI dengan menjalankan perintah berikut:

   gcloud init

2. Buat kredensial autentikasi lokal untuk akun pengguna Anda:

   gcloud auth application-default login

   Jika error autentikasi ditampilkan, pastikan Anda telah login ke gcloud CLI dengan identitas gabungan Anda.

   Layar login akan muncul. Setelah Anda login, kredensial Anda akan disimpan dalam file kredensial lokal yang digunakan oleh ADC.

Deteksi label

Perhatian: Pembatasan egress Kontrol Layanan VPC tidak diterapkan pada outputUri permintaan videos:annotate. Layanan ini dapat menulis hasil anotasi ke bucket Cloud Storage di luar perimeter layanan Anda. Untuk mengetahui detailnya, lihat Produk yang didukung dan batasan.

Sekarang Anda dapat menggunakan Video Intelligence API untuk meminta informasi dari video atau segmen video, seperti deteksi label. Jalankan kode berikut untuk melakukan permintaan deteksi label video pertama Anda:

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

Sebelum menjalankan contoh, pastikan Anda telah menyiapkan lingkungan untuk pengembangan Node.js.

// 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

Sebelum menjalankan contoh, pastikan Anda telah menyiapkan lingkungan untuk pengembangan Python.

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")

Bahasa tambahan

C#: Ikuti petunjuk penyiapan C# di halaman library klien, lalu buka dokumentasi referensi Video Intelligence untuk .NET.

PHP: Ikuti Petunjuk penyiapan PHP di halaman library klien, lalu buka dokumentasi referensi Video Intelligence untuk PHP.

Ruby: Ikuti Petunjuk penyiapan Ruby di halaman library klien, lalu buka Dokumentasi referensi Video Intelligence untuk Ruby.

Selamat! Anda telah mengirimkan permintaan pertama ke Video Intelligence API.

Bagaimana hasilnya?

Berhasil!

Bagus! Apa yang Anda sukai dari panduan memulai ini? Hal apa saja yang bisa kami tingkatkan? Beri tahu kami.

Saya mengalami kebuntuan.

Mohon maaf atas ketidaknyamanan ini. Beri tahu kami masalah yang Anda alami. Kami akan memperbaikinya.

Pembersihan

Agar akun Google Cloud Anda tidak dikenai biaya untuk resource yang digunakan pada halaman ini, ikuti langkah-langkah berikut.

• Gunakan Google Cloud console untuk menghapus project jika Anda tidak membutuhkannya.

Langkah berikutnya