追蹤物件

物件追蹤功能可追蹤在輸入影片中偵測到的物件。如要提出物件追蹤要求，請呼叫 annotate 方法，並在 features 欄位中指定 OBJECT_TRACKING。

對於在影片或影片片段中偵測到的實體和空間位置，物件追蹤要求會為影片加上這些實體和空間位置的適當標籤。舉例來說，如果影片中的車輛通過交通號誌，系統可能會產生「汽車」、「卡車」、「單車」、「輪胎」、「車燈」、「車窗」等標籤。每個標籤可以包含一系列定界框，每個定界框的相關聯時間片段均含有時間偏移量，指示從影片起始的持續性偏移。註解還包含了其他實體資訊，包括您可以在 Google Knowledge Graph Search API 中用來尋找實體更多資訊的實體 ID。

物件追蹤與標籤偵測

物件追蹤與標籤偵測不同。標籤偵測提供的標籤不含定界框，而物件追蹤會提供指定影片中個別物件的標籤，以及每個物件執行個體在每個時間步的定界框。

相同物件類型的多個執行個體會指派給 ObjectTrackingAnnotation 訊息的不同執行個體，而特定物件軌跡的所有出現次數都會保留在 ObjectTrackingAnnotation 的專屬執行個體中。舉例來說，如果影片中出現紅色和藍色車輛，且持續 5 秒，追蹤要求應會傳回兩個 ObjectTrackingAnnotation 執行個體。第一個執行個體會包含其中一輛車的位置資訊 (例如紅車)，第二個執行個體則會包含另一輛車的位置資訊。

要求對 Cloud Storage 中的影片進行物件追蹤

下列範例示範如何對位於 Cloud Storage 中的檔案執行物件追蹤。

REST

傳送處理要求

以下說明如何將 POST 要求傳送至 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：STORAGE_URI
    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": "STORAGE_URI",
  "features": ["OBJECT_TRACKING"]
}

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"
}

如果要求成功，Video Intelligence API 會傳回作業的 name。上例顯示這類回應的範例，其中 PROJECT_NUMBER 是專案編號，OPERATION_ID 則是為要求建立的長時間執行作業 ID。

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，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


import (
	"context"
	"fmt"
	"io"

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

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

	op, err := client.AnnotateVideo(ctx, &videopb.AnnotateVideoRequest{
		InputUri: gcsURI,
		Features: []videopb.Feature{
			videopb.Feature_OBJECT_TRACKING,
		},
	})
	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]

	for _, annotation := range result.ObjectAnnotations {
		fmt.Fprintf(w, "Description: %q\n", annotation.Entity.GetDescription())
		if len(annotation.Entity.EntityId) > 0 {
			fmt.Fprintf(w, "\tEntity ID: %q\n", annotation.Entity.GetEntityId())
		}

		segment := annotation.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", annotation.GetConfidence())

		// Here we print only the bounding box of the first frame in this segment.
		frame := annotation.GetFrames()[0]
		seconds := float32(frame.GetTimeOffset().GetSeconds())
		nanos := float32(frame.GetTimeOffset().GetNanos())
		fmt.Fprintf(w, "\tTime offset of the first frame: %fs\n", seconds+nanos/1e9)

		box := frame.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())
	}

	return nil
}

Java

/**
 * Track objects in a video.
 *
 * @param gcsUri the path to the video file to analyze.
 */
public static VideoAnnotationResults trackObjectsGcs(String gcsUri) throws Exception {
  try (VideoIntelligenceServiceClient client = VideoIntelligenceServiceClient.create()) {
    // Create the request
    AnnotateVideoRequest request =
        AnnotateVideoRequest.newBuilder()
            .setInputUri(gcsUri)
            .addFeatures(Feature.OBJECT_TRACKING)
            .setLocationId("us-east1")
            .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(450, TimeUnit.SECONDS);
    VideoAnnotationResults results = response.getAnnotationResults(0);

    // Get only the first annotation for demo purposes.
    ObjectTrackingAnnotation annotation = results.getObjectAnnotations(0);
    System.out.println("Confidence: " + annotation.getConfidence());

    if (annotation.hasEntity()) {
      Entity entity = annotation.getEntity();
      System.out.println("Entity description: " + entity.getDescription());
      System.out.println("Entity id:: " + entity.getEntityId());
    }

    if (annotation.hasSegment()) {
      VideoSegment videoSegment = annotation.getSegment();
      Duration startTimeOffset = videoSegment.getStartTimeOffset();
      Duration endTimeOffset = videoSegment.getEndTimeOffset();
      // Display the segment time in seconds, 1e9 converts nanos to seconds
      System.out.println(
          String.format(
              "Segment: %.2fs to %.2fs",
              startTimeOffset.getSeconds() + startTimeOffset.getNanos() / 1e9,
              endTimeOffset.getSeconds() + endTimeOffset.getNanos() / 1e9));
    }

    // Here we print only the bounding box of the first frame in this segment.
    ObjectTrackingFrame frame = annotation.getFrames(0);
    // Display the offset time in seconds, 1e9 converts nanos to seconds
    Duration timeOffset = frame.getTimeOffset();
    System.out.println(
        String.format(
            "Time offset of the first frame: %.2fs",
            timeOffset.getSeconds() + timeOffset.getNanos() / 1e9));

    // Display the bounding box of the detected object
    NormalizedBoundingBox normalizedBoundingBox = frame.getNormalizedBoundingBox();
    System.out.println("Bounding box position:");
    System.out.println("\tleft: " + normalizedBoundingBox.getLeft());
    System.out.println("\ttop: " + normalizedBoundingBox.getTop());
    System.out.println("\tright: " + normalizedBoundingBox.getRight());
    System.out.println("\tbottom: " + normalizedBoundingBox.getBottom());
    return results;
  }
}

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」。

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

// Creates a client
const video = 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: ['OBJECT_TRACKING'],
  //recommended to use us-east1 for the best latency due to different types of processors used in this region and others
  locationId: 'us-east1',
};
// Detects objects in a video
const [operation] = await video.annotateVideo(request);
const results = await operation.promise();
console.log('Waiting for operation to complete...');
//Gets annotations for video
const annotations = results[0].annotationResults[0];
const objects = annotations.objectAnnotations;
objects.forEach(object => {
  console.log(`Entity description:  ${object.entity.description}`);
  console.log(`Entity id: ${object.entity.entityId}`);
  const time = object.segment;
  console.log(
    `Segment: ${time.startTimeOffset.seconds || 0}` +
      `.${(time.startTimeOffset.nanos / 1e6).toFixed(0)}s to ${
        time.endTimeOffset.seconds || 0
      }.` +
      `${(time.endTimeOffset.nanos / 1e6).toFixed(0)}s`
  );
  console.log(`Confidence: ${object.confidence}`);
  const frame = object.frames[0];
  const box = frame.normalizedBoundingBox;
  const timeOffset = frame.timeOffset;
  console.log(
    `Time offset for the first frame: ${timeOffset.seconds || 0}` +
      `.${(timeOffset.nanos / 1e6).toFixed(0)}s`
  );
  console.log('Bounding box position:');
  console.log(` left   :${box.left}`);
  console.log(` top    :${box.top}`);
  console.log(` right  :${box.right}`);
  console.log(` bottom :${box.bottom}`);
});

Python

"""Object tracking in a video stored on GCS."""
from google.cloud import videointelligence

video_client = videointelligence.VideoIntelligenceServiceClient()
features = [videointelligence.Feature.OBJECT_TRACKING]
operation = video_client.annotate_video(
    request={"features": features, "input_uri": gcs_uri}
)
print("\nProcessing video for object annotations.")

result = operation.result(timeout=500)
print("\nFinished processing.\n")

# The first result is retrieved because a single video was processed.
object_annotations = result.annotation_results[0].object_annotations

for object_annotation in object_annotations:
    print("Entity description: {}".format(object_annotation.entity.description))
    if object_annotation.entity.entity_id:
        print("Entity id: {}".format(object_annotation.entity.entity_id))

    print(
        "Segment: {}s to {}s".format(
            object_annotation.segment.start_time_offset.seconds
            + object_annotation.segment.start_time_offset.microseconds / 1e6,
            object_annotation.segment.end_time_offset.seconds
            + object_annotation.segment.end_time_offset.microseconds / 1e6,
        )
    )

    print("Confidence: {}".format(object_annotation.confidence))

    # Here we print only the bounding box of the first frame in the segment
    frame = object_annotation.frames[0]
    box = frame.normalized_bounding_box
    print(
        "Time offset of the first frame: {}s".format(
            frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
        )
    )
    print("Bounding box position:")
    print("\tleft  : {}".format(box.left))
    print("\ttop   : {}".format(box.top))
    print("\tright : {}".format(box.right))
    print("\tbottom: {}".format(box.bottom))
    print("\n")

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」。

要求對本機檔案中的影片進行物件追蹤

下列範例示範如何對本機儲存的檔案進行物件追蹤。

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。其中使用的存取憑證，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 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..."
  • 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": ["OBJECT_TRACKING"]
}

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：

如果要求成功，Video Intelligence API 會傳回作業的 name。以下是這類回應的範例，其中 PROJECT_NUMBER 是專案編號，OPERATION_ID 則是為要求建立的長時間執行作業 ID。

取得結果

如要取得要求結果，您必須使用從 videos:annotate 呼叫傳回的作業名稱，傳送 GET，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：

Go


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

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

// objectTracking analyzes a video and extracts entities with their bounding boxes.
func objectTracking(w io.Writer, filename string) error {
	// filename := "../testdata/cat.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()

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

	op, err := client.AnnotateVideo(ctx, &videopb.AnnotateVideoRequest{
		InputContent: fileBytes,
		Features: []videopb.Feature{
			videopb.Feature_OBJECT_TRACKING,
		},
	})
	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]

	for _, annotation := range result.ObjectAnnotations {
		fmt.Fprintf(w, "Description: %q\n", annotation.Entity.GetDescription())
		if len(annotation.Entity.EntityId) > 0 {
			fmt.Fprintf(w, "\tEntity ID: %q\n", annotation.Entity.GetEntityId())
		}

		segment := annotation.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", annotation.GetConfidence())

		// Here we print only the bounding box of the first frame in this segment.
		frame := annotation.GetFrames()[0]
		seconds := float32(frame.GetTimeOffset().GetSeconds())
		nanos := float32(frame.GetTimeOffset().GetNanos())
		fmt.Fprintf(w, "\tTime offset of the first frame: %fs\n", seconds+nanos/1e9)

		box := frame.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())
	}

	return nil
}

Java

/**
 * Track objects in a video.
 *
 * @param filePath the path to the video file to analyze.
 */
public static VideoAnnotationResults trackObjects(String filePath) throws Exception {
  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.OBJECT_TRACKING)
            .setLocationId("us-east1")
            .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(450, TimeUnit.SECONDS);
    VideoAnnotationResults results = response.getAnnotationResults(0);

    // Get only the first annotation for demo purposes.
    ObjectTrackingAnnotation annotation = results.getObjectAnnotations(0);
    System.out.println("Confidence: " + annotation.getConfidence());

    if (annotation.hasEntity()) {
      Entity entity = annotation.getEntity();
      System.out.println("Entity description: " + entity.getDescription());
      System.out.println("Entity id:: " + entity.getEntityId());
    }

    if (annotation.hasSegment()) {
      VideoSegment videoSegment = annotation.getSegment();
      Duration startTimeOffset = videoSegment.getStartTimeOffset();
      Duration endTimeOffset = videoSegment.getEndTimeOffset();
      // Display the segment time in seconds, 1e9 converts nanos to seconds
      System.out.println(
          String.format(
              "Segment: %.2fs to %.2fs",
              startTimeOffset.getSeconds() + startTimeOffset.getNanos() / 1e9,
              endTimeOffset.getSeconds() + endTimeOffset.getNanos() / 1e9));
    }

    // Here we print only the bounding box of the first frame in this segment.
    ObjectTrackingFrame frame = annotation.getFrames(0);
    // Display the offset time in seconds, 1e9 converts nanos to seconds
    Duration timeOffset = frame.getTimeOffset();
    System.out.println(
        String.format(
            "Time offset of the first frame: %.2fs",
            timeOffset.getSeconds() + timeOffset.getNanos() / 1e9));

    // Display the bounding box of the detected object
    NormalizedBoundingBox normalizedBoundingBox = frame.getNormalizedBoundingBox();
    System.out.println("Bounding box position:");
    System.out.println("\tleft: " + normalizedBoundingBox.getLeft());
    System.out.println("\ttop: " + normalizedBoundingBox.getTop());
    System.out.println("\tright: " + normalizedBoundingBox.getRight());
    System.out.println("\tbottom: " + normalizedBoundingBox.getBottom());
    return results;
  }
}

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」。

// Imports the Google Cloud Video Intelligence library
const Video = require('@google-cloud/video-intelligence');
const fs = require('fs');
const util = require('util');
// Creates a client
const video = 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 file = await util.promisify(fs.readFile)(path);
const inputContent = file.toString('base64');

const request = {
  inputContent: inputContent,
  features: ['OBJECT_TRACKING'],
  //recommended to use us-east1 for the best latency due to different types of processors used in this region and others
  locationId: 'us-east1',
};
// Detects objects in a video
const [operation] = await video.annotateVideo(request);
const results = await operation.promise();
console.log('Waiting for operation to complete...');
//Gets annotations for video
const annotations = results[0].annotationResults[0];
const objects = annotations.objectAnnotations;
objects.forEach(object => {
  console.log(`Entity description:  ${object.entity.description}`);
  console.log(`Entity id: ${object.entity.entityId}`);
  const time = object.segment;
  console.log(
    `Segment: ${time.startTimeOffset.seconds || 0}` +
      `.${(time.startTimeOffset.nanos / 1e6).toFixed(0)}s to ${
        time.endTimeOffset.seconds || 0
      }.` +
      `${(time.endTimeOffset.nanos / 1e6).toFixed(0)}s`
  );
  console.log(`Confidence: ${object.confidence}`);
  const frame = object.frames[0];
  const box = frame.normalizedBoundingBox;
  const timeOffset = frame.timeOffset;
  console.log(
    `Time offset for the first frame: ${timeOffset.seconds || 0}` +
      `.${(timeOffset.nanos / 1e6).toFixed(0)}s`
  );
  console.log('Bounding box position:');
  console.log(` left   :${box.left}`);
  console.log(` top    :${box.top}`);
  console.log(` right  :${box.right}`);
  console.log(` bottom :${box.bottom}`);
});

Python

"""Object tracking in a local video."""
from google.cloud import videointelligence

video_client = videointelligence.VideoIntelligenceServiceClient()
features = [videointelligence.Feature.OBJECT_TRACKING]

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

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

result = operation.result(timeout=500)
print("\nFinished processing.\n")

# The first result is retrieved because a single video was processed.
object_annotations = result.annotation_results[0].object_annotations

# Get only the first annotation for demo purposes.
object_annotation = object_annotations[0]
print("Entity description: {}".format(object_annotation.entity.description))
if object_annotation.entity.entity_id:
    print("Entity id: {}".format(object_annotation.entity.entity_id))

print(
    "Segment: {}s to {}s".format(
        object_annotation.segment.start_time_offset.seconds
        + object_annotation.segment.start_time_offset.microseconds / 1e6,
        object_annotation.segment.end_time_offset.seconds
        + object_annotation.segment.end_time_offset.microseconds / 1e6,
    )
)

print("Confidence: {}".format(object_annotation.confidence))

# Here we print only the bounding box of the first frame in this segment
frame = object_annotation.frames[0]
box = frame.normalized_bounding_box
print(
    "Time offset of the first frame: {}s".format(
        frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
    )
)
print("Bounding box position:")
print("\tleft  : {}".format(box.left))
print("\ttop   : {}".format(box.top))
print("\tright : {}".format(box.right))
print("\tbottom: {}".format(box.bottom))
print("\n")

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」。