Membuat anotasi video menggunakan library klien

Membuat anotasi video menggunakan library klien

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

Sebelum memulai

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

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

   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

3. Make sure that billing is enabled for your Google Cloud project.

4. Enable the Cloud Video Intelligence API.

   Enable the API

5. Install the Google Cloud CLI.

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

   gcloud init

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

   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

8. Make sure that billing is enabled for your Google Cloud project.

9. Enable the Cloud Video Intelligence API.

   Enable the API

10. Install the Google Cloud CLI.

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

    gcloud init

Menginstal library klien

Go

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

Java

Node.js

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

npm install --save @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 kunjungi Dokumentasi referensi Video Intelligence untuk .NET.

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

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

Menyiapkan autentikasi

1. Install the Google Cloud CLI, then initialize it by running the following command:

   gcloud init

2. If you're using a local shell, then create local authentication credentials for your user account:

   gcloud auth application-default login

   You don't need to do this if you're using Cloud Shell.

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

Deteksi label

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 kunjungi Dokumentasi referensi Video Intelligence untuk .NET.

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

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

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

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Pembersihan

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

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

Langkah selanjutnya