辨識標誌

Video Intelligence API 可偵測、追蹤及辨識影片內容中超過 100,000 個品牌和標誌。

本頁說明如何使用 Video Intelligence API 辨識影片中的標誌。

為 Cloud Storage 中的影片加上註解

下列程式碼範例示範如何偵測 Cloud Storage 影片中的標誌。

REST

傳送處理要求

如要對本機影片檔案執行註解，請對影片檔案的內容執行 base64 編碼。在要求的 inputContent 欄位中加入 Base64 編碼內容。如要瞭解如何使用 base64 編碼影片檔案內容，請參閱「Base64 編碼」。

以下說明如何將 POST 要求傳送至 videos:annotate 方法。範例中使用的存取憑證，屬於使用 Google Cloud CLI 建立的專案服務帳戶。如需安裝 Google Cloud CLI、使用服務帳戶建立專案，以及取得存取權杖的操作說明，請參閱「Video Intelligence 快速入門導覽課程」。

使用任何要求資料之前，請先替換以下項目：

  • INPUT_URI：包含要註解檔案的 Cloud Storage bucket，包括檔案名稱。開頭必須為 gs://。
    例如：
    "inputUri": "gs://cloud-videointelligence-demo/assistant.mp4",
  • PROJECT_NUMBER： Google Cloud 專案的數字 ID

HTTP 方法和網址：

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

JSON 要求主體：

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

請展開以下其中一個選項，以傳送要求：

您應該會收到如下的 JSON 回覆：

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

如果回應成功，Video Intelligence API 會傳回作業的 name。上文顯示這類回應的範例，其中：project-number 是專案編號，operation-id 是為要求建立的長時間執行作業 ID。

  • PROJECT_NUMBER：專案編號
  • LOCATION_ID：應進行註解的 Cloud 區域。支援的雲端區域包括：us-east1、us-west1、europe-west1、asia-east1。如果沒有指定任何地區，則會依據影片檔案位置來決定地區。
  • OPERATION_ID：為要求建立的長時間作業 ID，並在您開始作業時於回應中提供，例如 12345...

取得結果

如要取得要求結果，請使用從 videos:annotate 呼叫傳回的作業名稱，傳送 GET 要求，如下列範例所示。

使用任何要求資料之前，請先替換以下項目：

  • OPERATION_NAME：Video Intelligence API 傳回的作業名稱。作業名稱的格式為 projects/PROJECT_NUMBER/locations/LOCATION_ID/operations/OPERATION_ID
  • PROJECT_NUMBER： Google Cloud 專案的數字 ID

HTTP 方法和網址：

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

請展開以下其中一個選項，以傳送要求：

您應該會收到如下的 JSON 回覆：

下載註解結果

將註解從來源複製到目標值區：(請參閱「複製檔案和物件」)

gcloud storage cp gcs_uri gs://my-bucket

注意：如果輸出 GCS URI 是由使用者提供，註解就會儲存在該 GCS URI 中。

Go

如要向 Video Intelligence 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import (
	"context"
	"fmt"
	"io"
	"time"

	video "cloud.google.com/go/videointelligence/apiv1"
	videopb "cloud.google.com/go/videointelligence/apiv1/videointelligencepb"
	"github.com/golang/protobuf/ptypes"
)

// logoDetectionGCS analyzes a video and extracts logos with their bounding boxes.
func logoDetectionGCS(w io.Writer, gcsURI string) error {
	// gcsURI := "gs://cloud-samples-data/video/googlework_tiny.mp4"

	ctx := context.Background()

	// Creates a client.
	client, err := video.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("video.NewClient: %w", err)
	}
	defer client.Close()

	ctx, cancel := context.WithTimeout(ctx, time.Second*180)
	defer cancel()

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

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

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

	// Annotations for list of logos detected, tracked and recognized in video.
	for _, annotation := range result.LogoRecognitionAnnotations {
		fmt.Fprintf(w, "Description: %q\n", annotation.Entity.GetDescription())
		// Opaque entity ID. Some IDs may be available in Google Knowledge
		// Graph Search API (https://developers.google.com/knowledge-graph/).
		if len(annotation.Entity.EntityId) > 0 {
			fmt.Fprintf(w, "\tEntity ID: %q\n", annotation.Entity.GetEntityId())
		}

		// All logo tracks where the recognized logo appears. Each track
		// corresponds to one logo instance appearing in consecutive frames.
		for _, track := range annotation.Tracks {
			// Video segment of a track.
			segment := track.GetSegment()
			start, _ := ptypes.Duration(segment.GetStartTimeOffset())
			end, _ := ptypes.Duration(segment.GetEndTimeOffset())
			fmt.Fprintf(w, "\tSegment: %v to %v\n", start, end)
			fmt.Fprintf(w, "\tConfidence: %f\n", track.GetConfidence())

			// The object with timestamp and attributes per frame in the track.
			for _, timestampedObject := range track.TimestampedObjects {
				// Normalized Bounding box in a frame, where the object is
				// located.
				box := timestampedObject.GetNormalizedBoundingBox()
				fmt.Fprintf(w, "\tBounding box position:\n")
				fmt.Fprintf(w, "\t\tleft  : %f\n", box.GetLeft())
				fmt.Fprintf(w, "\t\ttop   : %f\n", box.GetTop())
				fmt.Fprintf(w, "\t\tright : %f\n", box.GetRight())
				fmt.Fprintf(w, "\t\tbottom: %f\n", box.GetBottom())

				// Optional. The attributes of the object in the bounding box.
				for _, attribute := range timestampedObject.Attributes {
					fmt.Fprintf(w, "\t\t\tName: %q\n", attribute.GetName())
					fmt.Fprintf(w, "\t\t\tConfidence: %f\n", attribute.GetConfidence())
					fmt.Fprintf(w, "\t\t\tValue: %q\n", attribute.GetValue())
				}
			}

			// Optional. Attributes in the track level.
			for _, trackAttribute := range track.Attributes {
				fmt.Fprintf(w, "\t\tName: %q\n", trackAttribute.GetName())
				fmt.Fprintf(w, "\t\tConfidence: %f\n", trackAttribute.GetConfidence())
				fmt.Fprintf(w, "\t\tValue: %q\n", trackAttribute.GetValue())
			}
		}

		// All video segments where the recognized logo appears. There might be
		// multiple instances of the same logo class appearing in one VideoSegment.
		for _, segment := range annotation.Segments {
			start, _ := ptypes.Duration(segment.GetStartTimeOffset())
			end, _ := ptypes.Duration(segment.GetEndTimeOffset())
			fmt.Fprintf(w, "\tSegment: %v to %v\n", start, end)
		}
	}

	return nil
}

Java

如要向 Video Intelligence 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。


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.DetectedAttribute;
import com.google.cloud.videointelligence.v1.Entity;
import com.google.cloud.videointelligence.v1.Feature;
import com.google.cloud.videointelligence.v1.LogoRecognitionAnnotation;
import com.google.cloud.videointelligence.v1.NormalizedBoundingBox;
import com.google.cloud.videointelligence.v1.TimestampedObject;
import com.google.cloud.videointelligence.v1.Track;
import com.google.cloud.videointelligence.v1.VideoAnnotationResults;
import com.google.cloud.videointelligence.v1.VideoIntelligenceServiceClient;
import com.google.cloud.videointelligence.v1.VideoSegment;
import com.google.protobuf.Duration;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class LogoDetectionGcs {

  public static void detectLogoGcs() throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String gcsUri = "gs://YOUR_BUCKET_ID/path/to/your/video.mp4";
    detectLogoGcs(gcsUri);
  }

  public static void detectLogoGcs(String inputUri)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (VideoIntelligenceServiceClient client = VideoIntelligenceServiceClient.create()) {
      // Create the request
      AnnotateVideoRequest request =
          AnnotateVideoRequest.newBuilder()
              .setInputUri(inputUri)
              .addFeatures(Feature.LOGO_RECOGNITION)
              .build();

      // asynchronously perform object tracking on videos
      OperationFuture<AnnotateVideoResponse, AnnotateVideoProgress> future =
          client.annotateVideoAsync(request);

      System.out.println("Waiting for operation to complete...");
      // The first result is retrieved because a single video was processed.
      AnnotateVideoResponse response = future.get(600, TimeUnit.SECONDS);
      VideoAnnotationResults annotationResult = response.getAnnotationResults(0);

      // Annotations for list of logos detected, tracked and recognized in video.
      for (LogoRecognitionAnnotation logoRecognitionAnnotation :
          annotationResult.getLogoRecognitionAnnotationsList()) {
        Entity entity = logoRecognitionAnnotation.getEntity();
        // Opaque entity ID. Some IDs may be available in
        // [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/).
        System.out.printf("Entity Id : %s\n", entity.getEntityId());
        System.out.printf("Description : %s\n", entity.getDescription());
        // All logo tracks where the recognized logo appears. Each track corresponds to one logo
        // instance appearing in consecutive frames.
        for (Track track : logoRecognitionAnnotation.getTracksList()) {

          // Video segment of a track.
          Duration startTimeOffset = track.getSegment().getStartTimeOffset();
          System.out.printf(
              "\n\tStart Time Offset: %s.%s\n",
              startTimeOffset.getSeconds(), startTimeOffset.getNanos());
          Duration endTimeOffset = track.getSegment().getEndTimeOffset();
          System.out.printf(
              "\tEnd Time Offset: %s.%s\n", endTimeOffset.getSeconds(), endTimeOffset.getNanos());
          System.out.printf("\tConfidence: %s\n", track.getConfidence());

          // The object with timestamp and attributes per frame in the track.
          for (TimestampedObject timestampedObject : track.getTimestampedObjectsList()) {

            // Normalized Bounding box in a frame, where the object is located.
            NormalizedBoundingBox normalizedBoundingBox =
                timestampedObject.getNormalizedBoundingBox();
            System.out.printf("\n\t\tLeft: %s\n", normalizedBoundingBox.getLeft());
            System.out.printf("\t\tTop: %s\n", normalizedBoundingBox.getTop());
            System.out.printf("\t\tRight: %s\n", normalizedBoundingBox.getRight());
            System.out.printf("\t\tBottom: %s\n", normalizedBoundingBox.getBottom());

            // Optional. The attributes of the object in the bounding box.
            for (DetectedAttribute attribute : timestampedObject.getAttributesList()) {
              System.out.printf("\n\t\t\tName: %s\n", attribute.getName());
              System.out.printf("\t\t\tConfidence: %s\n", attribute.getConfidence());
              System.out.printf("\t\t\tValue: %s\n", attribute.getValue());
            }
          }

          // Optional. Attributes in the track level.
          for (DetectedAttribute trackAttribute : track.getAttributesList()) {
            System.out.printf("\n\t\tName : %s\n", trackAttribute.getName());
            System.out.printf("\t\tConfidence : %s\n", trackAttribute.getConfidence());
            System.out.printf("\t\tValue : %s\n", trackAttribute.getValue());
          }
        }

        // All video segments where the recognized logo appears. There might be multiple instances
        // of the same logo class appearing in one VideoSegment.
        for (VideoSegment segment : logoRecognitionAnnotation.getSegmentsList()) {
          System.out.printf(
              "\n\tStart Time Offset : %s.%s\n",
              segment.getStartTimeOffset().getSeconds(), segment.getStartTimeOffset().getNanos());
          System.out.printf(
              "\tEnd Time Offset : %s.%s\n",
              segment.getEndTimeOffset().getSeconds(), segment.getEndTimeOffset().getNanos());
        }
      }
    }
  }
}

Node.js

如要向 Video Intelligence 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const inputUri = 'gs://cloud-samples-data/video/googlework_short.mp4';

// Imports the Google Cloud client libraries
const Video = require('@google-cloud/video-intelligence');

// Instantiates a client
const client = new Video.VideoIntelligenceServiceClient();

// Performs asynchronous video annotation for logo recognition on a file hosted in GCS.
async function detectLogoGcs() {
  // Build the request with the input uri and logo recognition feature.
  const request = {
    inputUri: inputUri,
    features: ['LOGO_RECOGNITION'],
  };

  // Make the asynchronous request
  const [operation] = await client.annotateVideo(request);

  // Wait for the results
  const [response] = await operation.promise();

  // Get the first response, since we sent only one video.
  const annotationResult = response.annotationResults[0];
  for (const logoRecognitionAnnotation of annotationResult.logoRecognitionAnnotations) {
    const entity = logoRecognitionAnnotation.entity;
    // Opaque entity ID. Some IDs may be available in
    // [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/).
    console.log(`Entity Id: ${entity.entityId}`);
    console.log(`Description: ${entity.description}`);

    // All logo tracks where the recognized logo appears.
    // Each track corresponds to one logo instance appearing in consecutive frames.
    for (const track of logoRecognitionAnnotation.tracks) {
      console.log(
        `\n\tStart Time Offset: ${track.segment.startTimeOffset.seconds}.${track.segment.startTimeOffset.nanos}`
      );
      console.log(
        `\tEnd Time Offset: ${track.segment.endTimeOffset.seconds}.${track.segment.endTimeOffset.nanos}`
      );
      console.log(`\tConfidence: ${track.confidence}`);

      // The object with timestamp and attributes per frame in the track.
      for (const timestampedObject of track.timestampedObjects) {
        // Normalized Bounding box in a frame, where the object is located.
        const normalizedBoundingBox = timestampedObject.normalizedBoundingBox;
        console.log(`\n\t\tLeft: ${normalizedBoundingBox.left}`);
        console.log(`\t\tTop: ${normalizedBoundingBox.top}`);
        console.log(`\t\tRight: ${normalizedBoundingBox.right}`);
        console.log(`\t\tBottom: ${normalizedBoundingBox.bottom}`);
        // Optional. The attributes of the object in the bounding box.
        for (const attribute of timestampedObject.attributes) {
          console.log(`\n\t\t\tName: ${attribute.name}`);
          console.log(`\t\t\tConfidence: ${attribute.confidence}`);
          console.log(`\t\t\tValue: ${attribute.value}`);
        }
      }

      // Optional. Attributes in the track level.
      for (const trackAttribute of track.attributes) {
        console.log(`\n\t\tName: ${trackAttribute.name}`);
        console.log(`\t\tConfidence: ${trackAttribute.confidence}`);
        console.log(`\t\tValue: ${trackAttribute.value}`);
      }
    }

    // All video segments where the recognized logo appears.
    // There might be multiple instances of the same logo class appearing in one VideoSegment.
    for (const segment of logoRecognitionAnnotation.segments) {
      console.log(
        `\n\tStart Time Offset: ${segment.startTimeOffset.seconds}.${segment.startTimeOffset.nanos}`
      );
      console.log(
        `\tEnd Time Offset: ${segment.endTimeOffset.seconds}.${segment.endTimeOffset.nanos}`
      );
    }
  }
}

detectLogoGcs();

Python

如要向 Video Intelligence 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。


from google.cloud import videointelligence


def detect_logo_gcs(input_uri="gs://YOUR_BUCKET_ID/path/to/your/file.mp4"):
    client = videointelligence.VideoIntelligenceServiceClient()

    features = [videointelligence.Feature.LOGO_RECOGNITION]

    operation = client.annotate_video(
        request={"features": features, "input_uri": input_uri}
    )

    print("Waiting for operation to complete...")
    response = operation.result()

    # Get the first response, since we sent only one video.
    annotation_result = response.annotation_results[0]

    # Annotations for list of logos detected, tracked and recognized in video.
    for logo_recognition_annotation in annotation_result.logo_recognition_annotations:
        entity = logo_recognition_annotation.entity

        # Opaque entity ID. Some IDs may be available in [Google Knowledge Graph
        # Search API](https://developers.google.com/knowledge-graph/).
        print("Entity Id : {}".format(entity.entity_id))

        print("Description : {}".format(entity.description))

        # All logo tracks where the recognized logo appears. Each track corresponds
        # to one logo instance appearing in consecutive frames.
        for track in logo_recognition_annotation.tracks:
            # Video segment of a track.
            print(
                "\n\tStart Time Offset : {}.{}".format(
                    track.segment.start_time_offset.seconds,
                    track.segment.start_time_offset.microseconds * 1000,
                )
            )
            print(
                "\tEnd Time Offset : {}.{}".format(
                    track.segment.end_time_offset.seconds,
                    track.segment.end_time_offset.microseconds * 1000,
                )
            )
            print("\tConfidence : {}".format(track.confidence))

            # The object with timestamp and attributes per frame in the track.
            for timestamped_object in track.timestamped_objects:
                # Normalized Bounding box in a frame, where the object is located.
                normalized_bounding_box = timestamped_object.normalized_bounding_box
                print("\n\t\tLeft : {}".format(normalized_bounding_box.left))
                print("\t\tTop : {}".format(normalized_bounding_box.top))
                print("\t\tRight : {}".format(normalized_bounding_box.right))
                print("\t\tBottom : {}".format(normalized_bounding_box.bottom))

                # Optional. The attributes of the object in the bounding box.
                for attribute in timestamped_object.attributes:
                    print("\n\t\t\tName : {}".format(attribute.name))
                    print("\t\t\tConfidence : {}".format(attribute.confidence))
                    print("\t\t\tValue : {}".format(attribute.value))

            # Optional. Attributes in the track level.
            for track_attribute in track.attributes:
                print("\n\t\tName : {}".format(track_attribute.name))
                print("\t\tConfidence : {}".format(track_attribute.confidence))
                print("\t\tValue : {}".format(track_attribute.value))

        # All video segments where the recognized logo appears. There might be
        # multiple instances of the same logo class appearing in one VideoSegment.
        for segment in logo_recognition_annotation.segments:
            print(
                "\n\tStart Time Offset : {}.{}".format(
                    segment.start_time_offset.seconds,
                    segment.start_time_offset.microseconds * 1000,
                )
            )
            print(
                "\tEnd Time Offset : {}.{}".format(
                    segment.end_time_offset.seconds,
                    segment.end_time_offset.microseconds * 1000,
                )
            )

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」。

為本機影片加上註解

下列程式碼範例示範如何偵測本機影片檔中的標誌。

REST

傳送影片註解要求

如要對本機影片檔案執行註解，請務必對影片檔案的內容執行 base64 編碼。 在要求的 inputContent 欄位中加入 Base64 編碼的內容。 如要瞭解如何對影片檔案內容進行 base64 編碼，請參閱「Base64 編碼」。

以下說明如何將 POST 要求傳送至 videos:annotate 方法。其中使用的存取權杖，屬於透過 Google Cloud CLI 建立的專案服務帳戶。如需安裝 Google Cloud CLI、使用服務帳戶建立專案，以及取得存取權杖的操作說明，請參閱 Video Intelligence API 快速入門導覽課程。

使用任何要求資料之前，請先替換以下項目：

  • 「inputContent」：BASE64_ENCODED_CONTENT
    例如：
    "UklGRg41AwBBVkkgTElTVAwBAABoZHJsYXZpaDgAAAA1ggAAxPMBAAAAAAAQCAA..."
  • LANGUAGE_CODE：[選用] 查看支援的語言
  • PROJECT_NUMBER： Google Cloud 專案的數字 ID

HTTP 方法和網址：

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

JSON 要求主體：

{
  "inputContent": "BASE64_ENCODED_CONTENT",
  "features": ["LOGO_RECOGNITION"],
  "videoContext": {
  }
}

請展開以下其中一個選項，以傳送要求：

您應該會收到如下的 JSON 回覆：

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

如果回應成功，Video Intelligence API 會傳回作業的 name。上文顯示這類回應的範例，其中 project-number 是專案名稱，而 operation-id 是為要求建立的長時間執行作業 ID。

  • OPERATION_ID：啟動作業時在回應中提供，例如 12345...

取得註解結果

如要擷取作業結果，請使用對 videos:annotate 的呼叫傳回的作業名稱，發出 GET 要求，如下列範例所示。

使用任何要求資料之前，請先替換以下項目：

  • PROJECT_NUMBER： Google Cloud 專案的數字 ID

HTTP 方法和網址：

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

請展開以下其中一個選項，以傳送要求：

您應該會收到如下的 JSON 回覆：

文字偵測註解會以 textAnnotations 清單傳回。 注意：只有在 done 欄位的值為 True 時，系統才會傳回這個欄位。 如果作業未完成，則回應不會含有這個欄位。

Go

如要向 Video Intelligence 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import (
	"context"
	"fmt"
	"io"
	"os"
	"time"

	video "cloud.google.com/go/videointelligence/apiv1"
	videopb "cloud.google.com/go/videointelligence/apiv1/videointelligencepb"
	"github.com/golang/protobuf/ptypes"
)

// logoDetection analyzes a video and extracts logos with their bounding boxes.
func logoDetection(w io.Writer, filename string) error {
	// filename := "../testdata/googlework_short.mp4"

	ctx := context.Background()

	// Creates a client.
	client, err := video.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("video.NewClient: %w", err)
	}
	defer client.Close()

	ctx, cancel := context.WithTimeout(ctx, time.Second*180)
	defer cancel()

	fileBytes, err := os.ReadFile(filename)
	if err != nil {
		return fmt.Errorf("os.ReadFile: %w", err)
	}

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

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

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

	// Annotations for list of logos detected, tracked and recognized in video.
	for _, annotation := range result.LogoRecognitionAnnotations {
		fmt.Fprintf(w, "Description: %q\n", annotation.Entity.GetDescription())
		// Opaque entity ID. Some IDs may be available in Google Knowledge
		// Graph Search API (https://developers.google.com/knowledge-graph/).
		if len(annotation.Entity.EntityId) > 0 {
			fmt.Fprintf(w, "\tEntity ID: %q\n", annotation.Entity.GetEntityId())
		}

		// All logo tracks where the recognized logo appears. Each track
		// corresponds to one logo instance appearing in consecutive frames.
		for _, track := range annotation.Tracks {
			// Video segment of a track.
			segment := track.GetSegment()
			start, _ := ptypes.Duration(segment.GetStartTimeOffset())
			end, _ := ptypes.Duration(segment.GetEndTimeOffset())
			fmt.Fprintf(w, "\tSegment: %v to %v\n", start, end)
			fmt.Fprintf(w, "\tConfidence: %f\n", track.GetConfidence())

			// The object with timestamp and attributes per frame in the track.
			for _, timestampedObject := range track.TimestampedObjects {
				// Normalized Bounding box in a frame, where the object is
				// located.
				box := timestampedObject.GetNormalizedBoundingBox()
				fmt.Fprintf(w, "\tBounding box position:\n")
				fmt.Fprintf(w, "\t\tleft  : %f\n", box.GetLeft())
				fmt.Fprintf(w, "\t\ttop   : %f\n", box.GetTop())
				fmt.Fprintf(w, "\t\tright : %f\n", box.GetRight())
				fmt.Fprintf(w, "\t\tbottom: %f\n", box.GetBottom())

				// Optional. The attributes of the object in the bounding box.
				for _, attribute := range timestampedObject.Attributes {
					fmt.Fprintf(w, "\t\t\tName: %q\n", attribute.GetName())
					fmt.Fprintf(w, "\t\t\tConfidence: %f\n", attribute.GetConfidence())
					fmt.Fprintf(w, "\t\t\tValue: %q\n", attribute.GetValue())
				}
			}

			// Optional. Attributes in the track level.
			for _, trackAttribute := range track.Attributes {
				fmt.Fprintf(w, "\t\tName: %q\n", trackAttribute.GetName())
				fmt.Fprintf(w, "\t\tConfidence: %f\n", trackAttribute.GetConfidence())
				fmt.Fprintf(w, "\t\tValue: %q\n", trackAttribute.GetValue())
			}
		}

		// All video segments where the recognized logo appears. There might be
		// multiple instances of the same logo class appearing in one VideoSegment.
		for _, segment := range annotation.Segments {
			start, _ := ptypes.Duration(segment.GetStartTimeOffset())
			end, _ := ptypes.Duration(segment.GetEndTimeOffset())
			fmt.Fprintf(w, "\tSegment: %v to %v\n", start, end)
		}
	}

	return nil
}

Java

如要向 Video Intelligence 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。


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.DetectedAttribute;
import com.google.cloud.videointelligence.v1.Entity;
import com.google.cloud.videointelligence.v1.Feature;
import com.google.cloud.videointelligence.v1.LogoRecognitionAnnotation;
import com.google.cloud.videointelligence.v1.NormalizedBoundingBox;
import com.google.cloud.videointelligence.v1.TimestampedObject;
import com.google.cloud.videointelligence.v1.Track;
import com.google.cloud.videointelligence.v1.VideoAnnotationResults;
import com.google.cloud.videointelligence.v1.VideoIntelligenceServiceClient;
import com.google.cloud.videointelligence.v1.VideoSegment;
import com.google.protobuf.ByteString;
import com.google.protobuf.Duration;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class LogoDetection {

  public static void detectLogo() throws Exception {
    // TODO(developer): Replace these variables before running the sample.
    String localFilePath = "path/to/your/video.mp4";
    detectLogo(localFilePath);
  }

  public static void detectLogo(String filePath)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (VideoIntelligenceServiceClient client = VideoIntelligenceServiceClient.create()) {
      // Read file
      Path path = Paths.get(filePath);
      byte[] data = Files.readAllBytes(path);
      // Create the request
      AnnotateVideoRequest request =
          AnnotateVideoRequest.newBuilder()
              .setInputContent(ByteString.copyFrom(data))
              .addFeatures(Feature.LOGO_RECOGNITION)
              .build();

      // asynchronously perform object tracking on videos
      OperationFuture<AnnotateVideoResponse, AnnotateVideoProgress> future =
          client.annotateVideoAsync(request);

      System.out.println("Waiting for operation to complete...");
      // The first result is retrieved because a single video was processed.
      AnnotateVideoResponse response = future.get(300, TimeUnit.SECONDS);
      VideoAnnotationResults annotationResult = response.getAnnotationResults(0);

      // Annotations for list of logos detected, tracked and recognized in video.
      for (LogoRecognitionAnnotation logoRecognitionAnnotation :
          annotationResult.getLogoRecognitionAnnotationsList()) {
        Entity entity = logoRecognitionAnnotation.getEntity();
        // Opaque entity ID. Some IDs may be available in
        // [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/).
        System.out.printf("Entity Id : %s\n", entity.getEntityId());
        System.out.printf("Description : %s\n", entity.getDescription());
        // All logo tracks where the recognized logo appears. Each track corresponds to one logo
        // instance appearing in consecutive frames.
        for (Track track : logoRecognitionAnnotation.getTracksList()) {

          // Video segment of a track.
          Duration startTimeOffset = track.getSegment().getStartTimeOffset();
          System.out.printf(
              "\n\tStart Time Offset: %s.%s\n",
              startTimeOffset.getSeconds(), startTimeOffset.getNanos());
          Duration endTimeOffset = track.getSegment().getEndTimeOffset();
          System.out.printf(
              "\tEnd Time Offset: %s.%s\n", endTimeOffset.getSeconds(), endTimeOffset.getNanos());
          System.out.printf("\tConfidence: %s\n", track.getConfidence());

          // The object with timestamp and attributes per frame in the track.
          for (TimestampedObject timestampedObject : track.getTimestampedObjectsList()) {

            // Normalized Bounding box in a frame, where the object is located.
            NormalizedBoundingBox normalizedBoundingBox =
                timestampedObject.getNormalizedBoundingBox();
            System.out.printf("\n\t\tLeft: %s\n", normalizedBoundingBox.getLeft());
            System.out.printf("\t\tTop: %s\n", normalizedBoundingBox.getTop());
            System.out.printf("\t\tRight: %s\n", normalizedBoundingBox.getRight());
            System.out.printf("\t\tBottom: %s\n", normalizedBoundingBox.getBottom());

            // Optional. The attributes of the object in the bounding box.
            for (DetectedAttribute attribute : timestampedObject.getAttributesList()) {
              System.out.printf("\n\t\t\tName: %s\n", attribute.getName());
              System.out.printf("\t\t\tConfidence: %s\n", attribute.getConfidence());
              System.out.printf("\t\t\tValue: %s\n", attribute.getValue());
            }
          }

          // Optional. Attributes in the track level.
          for (DetectedAttribute trackAttribute : track.getAttributesList()) {
            System.out.printf("\n\t\tName : %s\n", trackAttribute.getName());
            System.out.printf("\t\tConfidence : %s\n", trackAttribute.getConfidence());
            System.out.printf("\t\tValue : %s\n", trackAttribute.getValue());
          }
        }

        // All video segments where the recognized logo appears. There might be multiple instances
        // of the same logo class appearing in one VideoSegment.
        for (VideoSegment segment : logoRecognitionAnnotation.getSegmentsList()) {
          System.out.printf(
              "\n\tStart Time Offset : %s.%s\n",
              segment.getStartTimeOffset().getSeconds(), segment.getStartTimeOffset().getNanos());
          System.out.printf(
              "\tEnd Time Offset : %s.%s\n",
              segment.getEndTimeOffset().getSeconds(), segment.getEndTimeOffset().getNanos());
        }
      }
    }
  }
}

Node.js

如要向 Video Intelligence 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const localFilePath = 'path/to/your/video.mp4'

// Imports the Google Cloud client libraries
const Video = require('@google-cloud/video-intelligence');
const fs = require('fs');

// Instantiates a client
const client = new Video.VideoIntelligenceServiceClient();

// Performs asynchronous video annotation for logo recognition on a file.
async function detectLogo() {
  const inputContent = fs.readFileSync(localFilePath).toString('base64');

  // Build the request with the input content and logo recognition feature.
  const request = {
    inputContent: inputContent,
    features: ['LOGO_RECOGNITION'],
  };

  // Make the asynchronous request
  const [operation] = await client.annotateVideo(request);

  // Wait for the results
  const [response] = await operation.promise();

  // Get the first response, since we sent only one video.
  const annotationResult = response.annotationResults[0];
  for (const logoRecognitionAnnotation of annotationResult.logoRecognitionAnnotations) {
    const entity = logoRecognitionAnnotation.entity;
    // Opaque entity ID. Some IDs may be available in
    // [Google Knowledge Graph Search API](https://developers.google.com/knowledge-graph/).
    console.log(`Entity Id: ${entity.entityId}`);
    console.log(`Description: ${entity.description}`);

    // All logo tracks where the recognized logo appears.
    // Each track corresponds to one logo instance appearing in consecutive frames.
    for (const track of logoRecognitionAnnotation.tracks) {
      console.log(
        `\n\tStart Time Offset: ${track.segment.startTimeOffset.seconds}.${track.segment.startTimeOffset.nanos}`
      );
      console.log(
        `\tEnd Time Offset: ${track.segment.endTimeOffset.seconds}.${track.segment.endTimeOffset.nanos}`
      );
      console.log(`\tConfidence: ${track.confidence}`);

      // The object with timestamp and attributes per frame in the track.
      for (const timestampedObject of track.timestampedObjects) {
        // Normalized Bounding box in a frame, where the object is located.
        const normalizedBoundingBox = timestampedObject.normalizedBoundingBox;
        console.log(`\n\t\tLeft: ${normalizedBoundingBox.left}`);
        console.log(`\t\tTop: ${normalizedBoundingBox.top}`);
        console.log(`\t\tRight: ${normalizedBoundingBox.right}`);
        console.log(`\t\tBottom: ${normalizedBoundingBox.bottom}`);
        // Optional. The attributes of the object in the bounding box.
        for (const attribute of timestampedObject.attributes) {
          console.log(`\n\t\t\tName: ${attribute.name}`);
          console.log(`\t\t\tConfidence: ${attribute.confidence}`);
          console.log(`\t\t\tValue: ${attribute.value}`);
        }
      }

      // Optional. Attributes in the track level.
      for (const trackAttribute of track.attributes) {
        console.log(`\n\t\tName: ${trackAttribute.name}`);
        console.log(`\t\tConfidence: ${trackAttribute.confidence}`);
        console.log(`\t\tValue: ${trackAttribute.value}`);
      }
    }

    // All video segments where the recognized logo appears.
    // There might be multiple instances of the same logo class appearing in one VideoSegment.
    for (const segment of logoRecognitionAnnotation.segments) {
      console.log(
        `\n\tStart Time Offset: ${segment.startTimeOffset.seconds}.${segment.startTimeOffset.nanos}`
      );
      console.log(
        `\tEnd Time Offset: ${segment.endTimeOffset.seconds}.${segment.endTimeOffset.nanos}`
      );
    }
  }
}

detectLogo();

Python

如要向 Video Intelligence 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證機制」。

import io

from google.cloud import videointelligence


def detect_logo(local_file_path="path/to/your/video.mp4"):
    """Performs asynchronous video annotation for logo recognition on a local file."""

    client = videointelligence.VideoIntelligenceServiceClient()

    with io.open(local_file_path, "rb") as f:
        input_content = f.read()
    features = [videointelligence.Feature.LOGO_RECOGNITION]

    operation = client.annotate_video(
        request={"features": features, "input_content": input_content}
    )

    print("Waiting for operation to complete...")
    response = operation.result()

    # Get the first response, since we sent only one video.
    annotation_result = response.annotation_results[0]

    # Annotations for list of logos detected, tracked and recognized in video.
    for logo_recognition_annotation in annotation_result.logo_recognition_annotations:
        entity = logo_recognition_annotation.entity

        # Opaque entity ID. Some IDs may be available in [Google Knowledge Graph
        # Search API](https://developers.google.com/knowledge-graph/).
        print("Entity Id : {}".format(entity.entity_id))

        print("Description : {}".format(entity.description))

        # All logo tracks where the recognized logo appears. Each track corresponds
        # to one logo instance appearing in consecutive frames.
        for track in logo_recognition_annotation.tracks:
            # Video segment of a track.
            print(
                "\n\tStart Time Offset : {}.{}".format(
                    track.segment.start_time_offset.seconds,
                    track.segment.start_time_offset.microseconds * 1000,
                )
            )
            print(
                "\tEnd Time Offset : {}.{}".format(
                    track.segment.end_time_offset.seconds,
                    track.segment.end_time_offset.microseconds * 1000,
                )
            )
            print("\tConfidence : {}".format(track.confidence))

            # The object with timestamp and attributes per frame in the track.
            for timestamped_object in track.timestamped_objects:
                # Normalized Bounding box in a frame, where the object is located.
                normalized_bounding_box = timestamped_object.normalized_bounding_box
                print("\n\t\tLeft : {}".format(normalized_bounding_box.left))
                print("\t\tTop : {}".format(normalized_bounding_box.top))
                print("\t\tRight : {}".format(normalized_bounding_box.right))
                print("\t\tBottom : {}".format(normalized_bounding_box.bottom))

                # Optional. The attributes of the object in the bounding box.
                for attribute in timestamped_object.attributes:
                    print("\n\t\t\tName : {}".format(attribute.name))
                    print("\t\t\tConfidence : {}".format(attribute.confidence))
                    print("\t\t\tValue : {}".format(attribute.value))

            # Optional. Attributes in the track level.
            for track_attribute in track.attributes:
                print("\n\t\tName : {}".format(track_attribute.name))
                print("\t\tConfidence : {}".format(track_attribute.confidence))
                print("\t\tValue : {}".format(track_attribute.value))

        # All video segments where the recognized logo appears. There might be
        # multiple instances of the same logo class appearing in one VideoSegment.
        for segment in logo_recognition_annotation.segments:
            print(
                "\n\tStart Time Offset : {}.{}".format(
                    segment.start_time_offset.seconds,
                    segment.start_time_offset.microseconds * 1000,
                )
            )
            print(
                "\tEnd Time Offset : {}.{}".format(
                    segment.end_time_offset.seconds,
                    segment.end_time_offset.microseconds * 1000,
                )
            )

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」。