Menganalisis video untuk label

Video Intelligence API dapat mengidentifikasi entitas yang ditampilkan dalam rekaman video menggunakan fitur LABEL_DETECTION. Fitur ini mengidentifikasi objek, lokasi, aktivitas, spesies hewan, produk, dan lainnya.

Analisis dapat dibagi menjadi beberapa bagian sebagai berikut:

  • Tingkat frame:
    Entitas diidentifikasi dan diberi label dalam setiap frame (dengan sampling satu frame per detik).
  • Tingkat pengambilan gambar:
    Pengambilan gambar otomatis terdeteksi dalam setiap segmen (atau video). Entitas kemudian diidentifikasi dan diberi label dalam setiap pengambilan gambar.
  • Tingkat segmen:
    Segmen video yang dipilih pengguna dapat ditentukan untuk analisis dengan menetapkan selisih waktu awal dan akhir untuk tujuan anotasi (lihat VideoSegment). Entitas kemudian diidentifikasi dan diberi label dalam setiap segmen. Jika tidak ada segmen yang ditentukan, seluruh video akan diperlakukan sebagai satu segmen.

Menganotasi file lokal

Berikut adalah contoh cara melakukan analisis video untuk label pada file lokal.

Mencari informasi yang lebih mendalam? Lihat tutorial Python mendetail kami.

REST

Mengirim permintaan proses

Berikut ini cara mengirim permintaan POST ke metode videos:annotate. Anda dapat mengonfigurasi LabelDetectionMode ke anotasi tingkat pengambilan gambar dan/atau tingkat frame. Sebaiknya gunakan SHOT_AND_FRAME_MODE. Contoh ini menggunakan token akses untuk akun layanan yang disiapkan untuk project menggunakan Google Cloud CLI. Untuk mengetahui petunjuk cara menginstal Google Cloud CLI, menyiapkan project dengan akun layanan, dan mendapatkan token akses, lihat panduan memulai Video Intelligence.

Sebelum menggunakan salah satu data permintaan, lakukan penggantian berikut:

Metode HTTP dan URL: 

POST https://videointelligence.googleapis.com/v1/videos:annotate

Meminta isi JSON: 

{
  "inputContent": "BASE64_ENCODED_CONTENT",
  "features": ["LABEL_DETECTION"],
}

Untuk mengirim permintaan Anda, perluas salah satu opsi berikut:

Anda akan melihat respons JSON seperti berikut:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/operations/OPERATION_ID"
}

Jika permintaan berhasil, Video Intelligence akan menampilkan nama operasi Anda.

Mendapatkan hasil

Untuk mendapatkan hasil permintaan, Anda harus mengirim permintaan GET ke resource projects.locations.operations. Berikut ini cara mengirim permintaan tersebut.

Sebelum menggunakan salah satu data permintaan, lakukan penggantian berikut:

  • OPERATION_NAME: nama operasi seperti yang ditampilkan oleh Video Intelligence API. Nama operasi memiliki format projects/PROJECT_NUMBER/locations/LOCATION_ID/operations/OPERATION_ID
  • PROJECT_NUMBER: ID numerik untuk project Google Cloud Anda

Metode HTTP dan URL: 

GET https://videointelligence.googleapis.com/v1/OPERATION_NAME

Untuk mengirim permintaan, perluas salah satu opsi berikut:

Anda akan menerima respons JSON yang mirip dengan yang berikut ini:

Go


func label(w io.Writer, file string) error {
	ctx := context.Background()
	client, err := video.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("video.NewClient: %w", err)
	}
	defer client.Close()

	fileBytes, err := os.ReadFile(file)
	if err != nil {
		return err
	}

	op, err := client.AnnotateVideo(ctx, &videopb.AnnotateVideoRequest{
		Features: []videopb.Feature{
			videopb.Feature_LABEL_DETECTION,
		},
		InputContent: fileBytes,
	})
	if err != nil {
		return fmt.Errorf("AnnotateVideo: %w", err)
	}

	resp, err := op.Wait(ctx)
	if err != nil {
		return fmt.Errorf("Wait: %w", err)
	}

	printLabels := func(labels []*videopb.LabelAnnotation) {
		for _, label := range labels {
			fmt.Fprintf(w, "\tDescription: %s\n", label.Entity.Description)
			for _, category := range label.CategoryEntities {
				fmt.Fprintf(w, "\t\tCategory: %s\n", category.Description)
			}
			for _, segment := range label.Segments {
				start, _ := ptypes.Duration(segment.Segment.StartTimeOffset)
				end, _ := ptypes.Duration(segment.Segment.EndTimeOffset)
				fmt.Fprintf(w, "\t\tSegment: %s to %s\n", start, end)
			}
		}
	}

	// A single video was processed. Get the first result.
	result := resp.AnnotationResults[0]

	fmt.Fprintln(w, "SegmentLabelAnnotations:")
	printLabels(result.SegmentLabelAnnotations)
	fmt.Fprintln(w, "ShotLabelAnnotations:")
	printLabels(result.ShotLabelAnnotations)
	fmt.Fprintln(w, "FrameLabelAnnotations:")
	printLabels(result.FrameLabelAnnotations)

	return nil
}

Java

// Instantiate a com.google.cloud.videointelligence.v1.VideoIntelligenceServiceClient
try (VideoIntelligenceServiceClient client = VideoIntelligenceServiceClient.create()) {
  // Read file and encode into Base64
  Path path = Paths.get(filePath);
  byte[] data = Files.readAllBytes(path);

  AnnotateVideoRequest request =
      AnnotateVideoRequest.newBuilder()
          .setInputContent(ByteString.copyFrom(data))
          .addFeatures(Feature.LABEL_DETECTION)
          .build();
  // Create an operation that will contain the response when the operation completes.
  OperationFuture<AnnotateVideoResponse, AnnotateVideoProgress> response =
      client.annotateVideoAsync(request);

  System.out.println("Waiting for operation to complete...");
  for (VideoAnnotationResults results : response.get().getAnnotationResultsList()) {
    // process video / segment level label annotations
    System.out.println("Locations: ");
    for (LabelAnnotation labelAnnotation : results.getSegmentLabelAnnotationsList()) {
      System.out.println("Video label: " + labelAnnotation.getEntity().getDescription());
      // categories
      for (Entity categoryEntity : labelAnnotation.getCategoryEntitiesList()) {
        System.out.println("Video label category: " + categoryEntity.getDescription());
      }
      // segments
      for (LabelSegment segment : labelAnnotation.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:%.2f\n", startTime, endTime);
        System.out.println("Confidence: " + segment.getConfidence());
      }
    }

    // process shot label annotations
    for (LabelAnnotation labelAnnotation : results.getShotLabelAnnotationsList()) {
      System.out.println("Shot label: " + labelAnnotation.getEntity().getDescription());
      // categories
      for (Entity categoryEntity : labelAnnotation.getCategoryEntitiesList()) {
        System.out.println("Shot label category: " + categoryEntity.getDescription());
      }
      // segments
      for (LabelSegment segment : labelAnnotation.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:%.2f\n", startTime, endTime);
        System.out.println("Confidence: " + segment.getConfidence());
      }
    }

    // process frame label annotations
    for (LabelAnnotation labelAnnotation : results.getFrameLabelAnnotationsList()) {
      System.out.println("Frame label: " + labelAnnotation.getEntity().getDescription());
      // categories
      for (Entity categoryEntity : labelAnnotation.getCategoryEntitiesList()) {
        System.out.println("Frame label category: " + categoryEntity.getDescription());
      }
      // segments
      for (LabelSegment segment : labelAnnotation.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:%.2f\n", startTime, endTime);
        System.out.println("Confidence: " + segment.getConfidence());
      }
    }
  }
}

Node.js

// Imports the Google Cloud Video Intelligence library + Node's fs library
const video = require('@google-cloud/video-intelligence').v1;
const fs = require('fs');
const util = require('util');

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

/**
 * TODO(developer): Uncomment the following line before running the sample.
 */
// const path = 'Local file to analyze, e.g. ./my-file.mp4';

// Reads a local video file and converts it to base64
const readFile = util.promisify(fs.readFile);
const file = await readFile(path);
const inputContent = file.toString('base64');

// Constructs request
const request = {
  inputContent: inputContent,
  features: ['LABEL_DETECTION'],
};

// Detects labels in a video
const [operation] = await client.annotateVideo(request);
console.log('Waiting for operation to complete...');
const [operationResult] = await operation.promise();
// Gets annotations for video
const annotations = operationResult.annotationResults[0];

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

Python

Untuk informasi selengkapnya tentang cara menginstal dan menggunakan Library Klien Video Intelligence API untuk Python, lihat Library Klien Video Intelligence API. 
"""Detect labels given a file path."""
video_client = videointelligence.VideoIntelligenceServiceClient()
features = [videointelligence.Feature.LABEL_DETECTION]

with io.open(path, "rb") as movie:
    input_content = movie.read()

operation = video_client.annotate_video(
    request={"features": features, "input_content": input_content}
)
print("\nProcessing video for label annotations:")

result = operation.result(timeout=90)
print("\nFinished processing.")

# Process video/segment level label annotations
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")

# Process shot level label annotations
shot_labels = result.annotation_results[0].shot_label_annotations
for i, shot_label in enumerate(shot_labels):
    print("Shot label description: {}".format(shot_label.entity.description))
    for category_entity in shot_label.category_entities:
        print(
            "\tLabel category description: {}".format(category_entity.description)
        )

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

# Process frame level label annotations
frame_labels = result.annotation_results[0].frame_label_annotations
for i, frame_label in enumerate(frame_labels):
    print("Frame label description: {}".format(frame_label.entity.description))
    for category_entity in frame_label.category_entities:
        print(
            "\tLabel category description: {}".format(category_entity.description)
        )

    # Each frame_label_annotation has many frames,
    # here we print information only about the first frame.
    frame = frame_label.frames[0]
    time_offset = frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
    print("\tFirst frame time offset: {}s".format(time_offset))
    print("\tFirst frame confidence: {}".format(frame.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.

Menganotasi file di Cloud Storage

Berikut adalah contoh cara melakukan analisis video untuk label pada file yang terletak di Cloud Storage.

REST

Untuk informasi selengkapnya tentang cara menginstal dan menggunakan Library Klien Video Intelligence API untuk Python, lihat Library Klien Video Intelligence API.

Mengirim permintaan proses

Berikut ini cara mengirim permintaan POST ke metode annotate. Contoh ini menggunakan token akses untuk akun layanan yang disiapkan untuk project menggunakan Google Cloud CLI. Untuk mengetahui petunjuk cara menginstal Google Cloud CLI, menyiapkan project dengan akun layanan, dan mendapatkan token akses, lihat panduan memulai Video Intelligence.

Sebelum menggunakan salah satu data permintaan, lakukan penggantian berikut:

  • INPUT_URI: bucket Cloud Storage yang berisi file yang ingin Anda anotasikan, termasuk nama file. Harus diawali dengan gs://.
  • PROJECT_NUMBER: ID numerik untuk project Google Cloud Anda

Metode HTTP dan URL: 

POST https://videointelligence.googleapis.com/v1/videos:annotate

Meminta isi JSON: 

{
  "inputUri": "INPUT_URI",
  "features": ["LABEL_DETECTION"],
}

Untuk mengirim permintaan Anda, perluas salah satu opsi berikut:

Anda akan melihat respons JSON seperti berikut:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/operations/OPERATION_ID"
}

Jika permintaan berhasil, Video Intelligence akan menampilkan nama operasi Anda.

Mendapatkan hasil

Untuk mendapatkan hasil permintaan, Anda harus mengirim permintaan GET ke resource projects.locations.operations. Berikut ini cara mengirim permintaan tersebut.

Sebelum menggunakan salah satu data permintaan, lakukan penggantian berikut:

  • OPERATION_NAME: nama operasi seperti yang ditampilkan oleh Video Intelligence API. Nama operasi memiliki format projects/PROJECT_NUMBER/locations/LOCATION_ID/operations/OPERATION_ID
  • PROJECT_NUMBER: ID numerik untuk project Google Cloud Anda

Metode HTTP dan URL: 

GET https://videointelligence.googleapis.com/v1/OPERATION_NAME

Untuk mengirim permintaan, perluas salah satu opsi berikut:

Anda akan menerima respons JSON yang mirip dengan yang berikut ini:

Mendownload hasil anotasi

Salin anotasi dari sumber ke bucket tujuan: (lihat Menyalin file dan objek)

gcloud storage cp gcs_uri gs://my-bucket

Catatan: Jika uri gcs output disediakan oleh pengguna, anotasi akan disimpan di uri gcs tersebut.

Go


func labelURI(w io.Writer, file string) error {
	ctx := context.Background()
	client, err := video.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("video.NewClient: %w", err)
	}
	defer client.Close()

	op, err := client.AnnotateVideo(ctx, &videopb.AnnotateVideoRequest{
		Features: []videopb.Feature{
			videopb.Feature_LABEL_DETECTION,
		},
		InputUri: file,
	})
	if err != nil {
		return fmt.Errorf("AnnotateVideo: %w", err)
	}

	resp, err := op.Wait(ctx)
	if err != nil {
		return fmt.Errorf("Wait: %w", err)
	}

	printLabels := func(labels []*videopb.LabelAnnotation) {
		for _, label := range labels {
			fmt.Fprintf(w, "\tDescription: %s\n", label.Entity.Description)
			for _, category := range label.CategoryEntities {
				fmt.Fprintf(w, "\t\tCategory: %s\n", category.Description)
			}
			for _, segment := range label.Segments {
				start, _ := ptypes.Duration(segment.Segment.StartTimeOffset)
				end, _ := ptypes.Duration(segment.Segment.EndTimeOffset)
				fmt.Fprintf(w, "\t\tSegment: %s to %s\n", start, end)
			}
		}
	}

	// A single video was processed. Get the first result.
	result := resp.AnnotationResults[0]

	fmt.Fprintln(w, "SegmentLabelAnnotations:")
	printLabels(result.SegmentLabelAnnotations)
	fmt.Fprintln(w, "ShotLabelAnnotations:")
	printLabels(result.ShotLabelAnnotations)
	fmt.Fprintln(w, "FrameLabelAnnotations:")
	printLabels(result.FrameLabelAnnotations)

	return nil
}

Java

// Instantiate a com.google.cloud.videointelligence.v1.VideoIntelligenceServiceClient
try (VideoIntelligenceServiceClient client = VideoIntelligenceServiceClient.create()) {
  // Provide path to file hosted on GCS as "gs://bucket-name/..."
  AnnotateVideoRequest request =
      AnnotateVideoRequest.newBuilder()
          .setInputUri(gcsUri)
          .addFeatures(Feature.LABEL_DETECTION)
          .build();
  // Create an operation that will contain the response when the operation completes.
  OperationFuture<AnnotateVideoResponse, AnnotateVideoProgress> response =
      client.annotateVideoAsync(request);

  System.out.println("Waiting for operation to complete...");
  for (VideoAnnotationResults results : response.get().getAnnotationResultsList()) {
    // process video / segment level label annotations
    System.out.println("Locations: ");
    for (LabelAnnotation labelAnnotation : results.getSegmentLabelAnnotationsList()) {
      System.out.println("Video label: " + labelAnnotation.getEntity().getDescription());
      // categories
      for (Entity categoryEntity : labelAnnotation.getCategoryEntitiesList()) {
        System.out.println("Video label category: " + categoryEntity.getDescription());
      }
      // segments
      for (LabelSegment segment : labelAnnotation.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());
      }
    }

    // process shot label annotations
    for (LabelAnnotation labelAnnotation : results.getShotLabelAnnotationsList()) {
      System.out.println("Shot label: " + labelAnnotation.getEntity().getDescription());
      // categories
      for (Entity categoryEntity : labelAnnotation.getCategoryEntitiesList()) {
        System.out.println("Shot label category: " + categoryEntity.getDescription());
      }
      // segments
      for (LabelSegment segment : labelAnnotation.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());
      }
    }

    // process frame label annotations
    for (LabelAnnotation labelAnnotation : results.getFrameLabelAnnotationsList()) {
      System.out.println("Frame label: " + labelAnnotation.getEntity().getDescription());
      // categories
      for (Entity categoryEntity : labelAnnotation.getCategoryEntitiesList()) {
        System.out.println("Frame label category: " + categoryEntity.getDescription());
      }
      // segments
      for (LabelSegment segment : labelAnnotation.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:%.2f\n", startTime, endTime);
        System.out.println("Confidence: " + segment.getConfidence());
      }
    }
  }
}

Node.js

// Imports the Google Cloud Video Intelligence library
const video = require('@google-cloud/video-intelligence').v1;

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

/**
 * TODO(developer): Uncomment the following line before running the sample.
 */
// const gcsUri = 'GCS URI of the video to analyze, e.g. gs://my-bucket/my-video.mp4';

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

// Detects labels in a video
const [operation] = await client.annotateVideo(request);
console.log('Waiting for operation to complete...');
const [operationResult] = await operation.promise();

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

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

Python

"""Detects labels given a GCS path."""
video_client = videointelligence.VideoIntelligenceServiceClient()
features = [videointelligence.Feature.LABEL_DETECTION]

mode = videointelligence.LabelDetectionMode.SHOT_AND_FRAME_MODE
config = videointelligence.LabelDetectionConfig(label_detection_mode=mode)
context = videointelligence.VideoContext(label_detection_config=config)

operation = video_client.annotate_video(
    request={"features": features, "input_uri": path, "video_context": context}
)
print("\nProcessing video for label annotations:")

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

# Process video/segment level label annotations
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")

# Process shot level label annotations
shot_labels = result.annotation_results[0].shot_label_annotations
for i, shot_label in enumerate(shot_labels):
    print("Shot label description: {}".format(shot_label.entity.description))
    for category_entity in shot_label.category_entities:
        print(
            "\tLabel category description: {}".format(category_entity.description)
        )

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

# Process frame level label annotations
frame_labels = result.annotation_results[0].frame_label_annotations
for i, frame_label in enumerate(frame_labels):
    print("Frame label description: {}".format(frame_label.entity.description))
    for category_entity in frame_label.category_entities:
        print(
            "\tLabel category description: {}".format(category_entity.description)
        )

    # Each frame_label_annotation has many frames,
    # here we print information only about the first frame.
    frame = frame_label.frames[0]
    time_offset = frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
    print("\tFirst frame time offset: {}s".format(time_offset))
    print("\tFirst frame confidence: {}".format(frame.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.