识别文本

文本检测功能可执行光学字符识别（OCR），检测并提取输入视频中的文本。

文本检测适用于 Cloud Vision API 支持的所有语言。

请求对 Google Cloud Storage 中的视频执行文本检测

以下示例演示了如何对 Cloud Storage 中的文件执行文本检测。

REST 和命令行

发送视频注释请求

以下代码展示了如何向 videos:annotate 方法发送 POST 请求。本示例针对通过 Cloud SDK 为项目设置的服务帐号使用访问令牌。如需了解有关安装 Cloud SDK、使用服务帐号设置项目以及获取访问令牌的说明，请参阅 Video Intelligence API 快速入门。

在使用下面的请求数据之前，请先进行以下替换：

  • input-uri：包含要添加注释的文件的 Cloud Storage 存储分区（包括文件名）。必须以 gs:// 开头。
    例如："inputUri": "gs://cloud-videointelligence-demo/assistant.mp4",。
  • language-code：[可选]例如 “en-US”

HTTP 方法和网址：

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

请求 JSON 正文： 

{
  "inputUri": "input-uri",
  "features": ["TEXT_DETECTION"],
  "videoContext": {
    "textDetectionConfig": {
      "languageHints": ["language-code"]
    }
  }
}

如需发送您的请求，请展开以下选项之一：

您应会收到如下所示的 JSON 响应：

{
  "name": "projects/project-number/locations/location-id/operations/operation-id"
}

如果响应成功，Video Intelligence API 将返回您的操作的 name。上面的示例展示了此类响应的示例，其中 project-number 是您的项目编号，operation-id 是为请求创建的长时间运行的操作的 ID。

  • project-number：您项目的编号
  • location-id：在其中添加注释的 Cloud 区域。支持的云区域为：us-east1、us-west1、europe-west1、asia-east1。如果未指定区域，系统将根据视频文件位置确定区域。
  • operation-id：是为请求创建的长时间运行的操作的 ID，并在启动操作时在响应中提供，例如 12345...

获取注释结果

要检索操作的结果，请使用从 videos：annotate 调用返回的操作名称发出 GET 请求，如以下示例所示。

在使用下面的请求数据之前，请先进行以下替换：

  • operation-name：Video Intelligence API 返回的操作名称。操作名称采用 projects/project-number/locations/location-id/operations/operation-id 格式

HTTP 方法和网址：

GET https://videointelligence.googleapis.com/v1/operation-name

如需发送您的请求，请展开以下选项之一：

您应该收到类似以下内容的 JSON 响应：

文本检测注释以 textAnnotations 列表的形式返回。注意：仅当值为 True 时，才会返回 done 字段。操作未完成的响应中不包含该字段。

C#

public static object DetectTextGcs(string gcsUri)
{
    var client = VideoIntelligenceServiceClient.Create();
    var request = new AnnotateVideoRequest
    {
        InputUri = gcsUri,
        Features = { Feature.TextDetection },
    };

    Console.WriteLine("\nProcessing video for text detection.");
    var op = client.AnnotateVideo(request).PollUntilCompleted();

    // Retrieve the first result because only one video was processed.
    var annotationResults = op.Result.AnnotationResults[0];

    // Get only the first result.
    var textAnnotation = annotationResults.TextAnnotations[0];
    Console.WriteLine($"\nText: {textAnnotation.Text}");

    // Get the first text segment.
    var textSegment = textAnnotation.Segments[0];
    var startTime = textSegment.Segment.StartTimeOffset;
    var endTime = textSegment.Segment.EndTimeOffset;
    Console.Write(
        $"Start time: {startTime.Seconds + startTime.Nanos / 1e9 }, ");
    Console.WriteLine(
        $"End time: {endTime.Seconds + endTime.Nanos / 1e9 }");

    Console.WriteLine($"Confidence: {textSegment.Confidence}");

    // Show the result for the first frame in this segment.
    var frame = textSegment.Frames[0];
    var timeOffset = frame.TimeOffset;
    Console.Write("Time offset for the first frame: ");
    Console.WriteLine(timeOffset.Seconds + timeOffset.Nanos * 1e9);
    Console.WriteLine("Rotated Bounding Box Vertices:");
    foreach (var vertex in frame.RotatedBoundingBox.Vertices)
    {
        Console.WriteLine(
            $"\tVertex x: {vertex.X}, Vertex.y: {vertex.Y}");
    }
    return 0;
}

Go


import (
	"context"
	"fmt"
	"io"

	video "cloud.google.com/go/videointelligence/apiv1"
	"github.com/golang/protobuf/ptypes"
	videopb "google.golang.org/genproto/googleapis/cloud/videointelligence/v1"
)

// textDetectionGCS analyzes a video and extracts the text from the video's audio.
func textDetectionGCS(w io.Writer, gcsURI string) error {
	// gcsURI := "gs://python-docs-samples-tests/video/googlework_short.mp4"

	ctx := context.Background()

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

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

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

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

	for _, annotation := range result.TextAnnotations {
		fmt.Fprintf(w, "Text: %q\n", annotation.GetText())

		// Get the first text segment.
		segment := annotation.GetSegments()[0]
		start, _ := ptypes.Duration(segment.GetSegment().GetStartTimeOffset())
		end, _ := ptypes.Duration(segment.GetSegment().GetEndTimeOffset())
		fmt.Fprintf(w, "\tSegment: %v to %v\n", start, end)

		fmt.Fprintf(w, "\tConfidence: %f\n", segment.GetConfidence())

		// Show the result for the first frame in this segment.
		frame := segment.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)

		fmt.Fprintf(w, "\tRotated bounding box vertices:\n")
		for _, vertex := range frame.GetRotatedBoundingBox().GetVertices() {
			fmt.Fprintf(w, "\t\tVertex x=%f, y=%f\n", vertex.GetX(), vertex.GetY())
		}
	}

	return nil
}

Java

/**
 * Detect Text in a video.
 *
 * @param gcsUri the path to the video file to analyze.
 */
public static VideoAnnotationResults detectTextGcs(String gcsUri) throws Exception {
  try (VideoIntelligenceServiceClient client = VideoIntelligenceServiceClient.create()) {
    // Create the request
    AnnotateVideoRequest request =
        AnnotateVideoRequest.newBuilder()
            .setInputUri(gcsUri)
            .addFeatures(Feature.TEXT_DETECTION)
            .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 results = response.getAnnotationResults(0);

    // Get only the first annotation for demo purposes.
    TextAnnotation annotation = results.getTextAnnotations(0);
    System.out.println("Text: " + annotation.getText());

    // Get the first text segment.
    TextSegment textSegment = annotation.getSegments(0);
    System.out.println("Confidence: " + textSegment.getConfidence());
    // For the text segment display it's time offset
    VideoSegment videoSegment = textSegment.getSegment();
    Duration startTimeOffset = videoSegment.getStartTimeOffset();
    Duration endTimeOffset = videoSegment.getEndTimeOffset();
    // Display the offset times in seconds, 1e9 is part of the formula to convert nanos to seconds
    System.out.println(
        String.format(
            "Start time: %.2f", startTimeOffset.getSeconds() + startTimeOffset.getNanos() / 1e9));
    System.out.println(
        String.format(
            "End time: %.2f", endTimeOffset.getSeconds() + endTimeOffset.getNanos() / 1e9));

    // Show the first result for the first frame in the segment.
    TextFrame textFrame = textSegment.getFrames(0);
    Duration timeOffset = textFrame.getTimeOffset();
    System.out.println(
        String.format(
            "Time offset for the first frame: %.2f",
            timeOffset.getSeconds() + timeOffset.getNanos() / 1e9));

    // Display the rotated bounding box for where the text is on the frame.
    System.out.println("Rotated Bounding Box Vertices:");
    List<NormalizedVertex> vertices = textFrame.getRotatedBoundingBox().getVerticesList();
    for (NormalizedVertex normalizedVertex : vertices) {
      System.out.println(
          String.format(
              "\tVertex.x: %.2f, Vertex.y: %.2f",
              normalizedVertex.getX(), normalizedVertex.getY()));
    }
    return results;
  }
}

Node.js

// 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: ['TEXT_DETECTION'],
};
// Detects text 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 textAnnotations = results[0].annotationResults[0].textAnnotations;
textAnnotations.forEach(textAnnotation => {
  console.log(`Text ${textAnnotation.text} occurs at:`);
  textAnnotation.segments.forEach(segment => {
    const time = segment.segment;
    console.log(
      ` Start: ${time.startTimeOffset.seconds || 0}.${(
        time.startTimeOffset.nanos / 1e6
      ).toFixed(0)}s`
    );
    console.log(
      ` End: ${time.endTimeOffset.seconds || 0}.${(
        time.endTimeOffset.nanos / 1e6
      ).toFixed(0)}s`
    );
    console.log(` Confidence: ${segment.confidence}`);
    segment.frames.forEach(frame => {
      const timeOffset = frame.timeOffset;
      console.log(
        `Time offset for the frame: ${timeOffset.seconds || 0}` +
          `.${(timeOffset.nanos / 1e6).toFixed(0)}s`
      );
      console.log('Rotated Bounding Box Vertices:');
      frame.rotatedBoundingBox.vertices.forEach(vertex => {
        console.log(`Vertex.x:${vertex.x}, Vertex.y:${vertex.y}`);
      });
    });
  });
});

PHP

use Google\Cloud\VideoIntelligence\V1\VideoIntelligenceServiceClient;
use Google\Cloud\VideoIntelligence\V1\Feature;

/** Uncomment and populate these variables in your code */
// $uri = 'The cloud storage object to analyze (gs://your-bucket-name/your-object-name)';
// $options = [];

# Instantiate a client.
$video = new VideoIntelligenceServiceClient();

# Execute a request.
$operation = $video->annotateVideo([
    'inputUri' => $uri,
    'features' => [Feature::TEXT_DETECTION]
]);

# Wait for the request to complete.
$operation->pollUntilComplete($options);

# Print the results.
if ($operation->operationSucceeded()) {
    $results = $operation->getResult()->getAnnotationResults()[0];

    # Process video/segment level label annotations
    foreach ($results->getTextAnnotations() as $text) {
        printf('Video text description: %s' . PHP_EOL, $text->getText());
        foreach ($text->getSegments() as $segment) {
            $start = $segment->getSegment()->getStartTimeOffset();
            $end = $segment->getSegment()->getEndTimeOffset();
            printf('  Segment: %ss to %ss' . PHP_EOL,
                $start->getSeconds() + $start->getNanos()/1000000000.0,
                $end->getSeconds() + $end->getNanos()/1000000000.0);
            printf('  Confidence: %f' . PHP_EOL, $segment->getConfidence());
        }
    }
    print(PHP_EOL);
} else {
    print_r($operation->getError());
}

Python

"""Detect text in a video stored on GCS."""
from google.cloud import videointelligence

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

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

print("\nProcessing video for text detection.")
result = operation.result(timeout=600)

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

for text_annotation in annotation_result.text_annotations:
    print("\nText: {}".format(text_annotation.text))

    # Get the first text segment
    text_segment = text_annotation.segments[0]
    start_time = text_segment.segment.start_time_offset
    end_time = text_segment.segment.end_time_offset
    print(
        "start_time: {}, end_time: {}".format(
            start_time.seconds + start_time.microseconds * 1e-6,
            end_time.seconds + end_time.microseconds * 1e-6,
        )
    )

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

    # Show the result for the first frame in this segment.
    frame = text_segment.frames[0]
    time_offset = frame.time_offset
    print(
        "Time offset for the first frame: {}".format(
            time_offset.seconds + time_offset.microseconds * 1e-6
        )
    )
    print("Rotated Bounding Box Vertices:")
    for vertex in frame.rotated_bounding_box.vertices:
        print("\tVertex.x: {}, Vertex.y: {}".format(vertex.x, vertex.y))

Ruby

# path = "Path to a video file on Google Cloud Storage: gs://bucket/video.mp4"

require "google/cloud/video_intelligence"

video = Google::Cloud::VideoIntelligence.video_intelligence_service

# Register a callback during the method call
operation = video.annotate_video features: [:TEXT_DETECTION], input_uri: path

puts "Processing video for text detection:"
operation.wait_until_done!

raise operation.results.message? if operation.error?
puts "Finished Processing."

text_annotations = operation.results.annotation_results.first.text_annotations
print_text_annotations text_annotations

请求对本地文件中的视频执行文本检测

以下示例展示如何对本地存储的文件执行文本检测。

REST 和命令行

发送视频注释请求

要对本地视频文件执行注释，请务必对视频文件的内容进行 base64 编码。在请求的 inputContent 字段中添加 base64 编码的内容。如需了解如何对视频文件的内容进行 base64 编码，请参阅 Base64 编码。

以下代码展示了如何向 videos:annotate 方法发送 POST 请求。本示例针对通过 Cloud SDK 为项目设置的服务帐号使用访问令牌。如需了解有关安装 Cloud SDK、使用服务帐号设置项目以及获取访问令牌的说明，请参阅 Video Intelligence API 快速入门。

在使用下面的请求数据之前，请先进行以下替换：

  • "inputContent": base-64-encoded-content
    例如：
    "UklGRg41AwBBVkkgTElTVAwBAABoZHJsYXZpaDgAAAA1ggAAxPMBAAAAAAAQCAA..."
  • language-code：[可选]例如 “en-US”

HTTP 方法和网址：

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

请求 JSON 正文： 

{
  "inputContent": "base-64-encoded-content",
  "features": ["TEXT_DETECTION"],
  "videoContext": {
    "textDetectionConfig": {
      "languageHints": ["language-code"]
    }
  }
}

如需发送您的请求，请展开以下选项之一：

您应会收到如下所示的 JSON 响应：

{
  "name": "projects/project-number/locations/location-id/operations/operation-id"
}

如果响应成功，Video Intelligence API 将返回您的操作的 name。上面的示例展示了此类响应的示例，其中 project-number 是您的项目名称，operation-id 是为请求创建的长时间运行的操作的 ID。

  • operation-id：并在启动操作时在响应中提供，例如 12345...

获取注释结果

要检索操作的结果，请使用从 videos：annotate 调用返回的操作名称发出 GET 请求，如以下示例所示。

HTTP 方法和网址：

GET https://videointelligence.googleapis.com/v1/operation-name

如需发送您的请求，请展开以下选项之一：

您应该收到类似以下内容的 JSON 响应：

文本检测注释以 textAnnotations 列表的形式返回。注意：仅当值为 True 时，才会返回 done 字段。操作未完成的响应中不包含该字段。

C#

public static object DetectText(string filePath)
{
    var client = VideoIntelligenceServiceClient.Create();
    var request = new AnnotateVideoRequest
    {
        InputContent = Google.Protobuf.ByteString.CopyFrom(File.ReadAllBytes(filePath)),
        Features = { Feature.TextDetection },
    };

    Console.WriteLine("\nProcessing video for text detection.");
    var op = client.AnnotateVideo(request).PollUntilCompleted();

    // Retrieve the first result because only one video was processed.
    var annotationResults = op.Result.AnnotationResults[0];

    // Get only the first result.
    var textAnnotation = annotationResults.TextAnnotations[0];
    Console.WriteLine($"\nText: {textAnnotation.Text}");

    // Get the first text segment.
    var textSegment = textAnnotation.Segments[0];
    var startTime = textSegment.Segment.StartTimeOffset;
    var endTime = textSegment.Segment.EndTimeOffset;
    Console.Write(
        $"Start time: {startTime.Seconds + startTime.Nanos / 1e9 }, ");
    Console.WriteLine(
        $"End time: {endTime.Seconds + endTime.Nanos / 1e9 }");

    Console.WriteLine($"Confidence: {textSegment.Confidence}");

    // Show the result for the first frame in this segment.
    var frame = textSegment.Frames[0];
    var timeOffset = frame.TimeOffset;
    Console.Write("Time offset for the first frame: ");
    Console.WriteLine(timeOffset.Seconds + timeOffset.Nanos * 1e9);
    Console.WriteLine("Rotated Bounding Box Vertices:");
    foreach (var vertex in frame.RotatedBoundingBox.Vertices)
    {
        Console.WriteLine(
            $"\tVertex x: {vertex.X}, Vertex.y: {vertex.Y}");
    }
    return 0;
}

Go


import (
	"context"
	"fmt"
	"io"
	"io/ioutil"

	video "cloud.google.com/go/videointelligence/apiv1"
	"github.com/golang/protobuf/ptypes"
	videopb "google.golang.org/genproto/googleapis/cloud/videointelligence/v1"
)

// textDetection analyzes a video and extracts the text from the video's audio.
func textDetection(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: %v", err)
	}

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

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

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

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

	for _, annotation := range result.TextAnnotations {
		fmt.Fprintf(w, "Text: %q\n", annotation.GetText())

		// Get the first text segment.
		segment := annotation.GetSegments()[0]
		start, _ := ptypes.Duration(segment.GetSegment().GetStartTimeOffset())
		end, _ := ptypes.Duration(segment.GetSegment().GetEndTimeOffset())
		fmt.Fprintf(w, "\tSegment: %v to %v\n", start, end)

		fmt.Fprintf(w, "\tConfidence: %f\n", segment.GetConfidence())

		// Show the result for the first frame in this segment.
		frame := segment.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)

		fmt.Fprintf(w, "\tRotated bounding box vertices:\n")
		for _, vertex := range frame.GetRotatedBoundingBox().GetVertices() {
			fmt.Fprintf(w, "\t\tVertex x=%f, y=%f\n", vertex.GetX(), vertex.GetY())
		}
	}

	return nil
}

Java

/**
 * Detect text in a video.
 *
 * @param filePath the path to the video file to analyze.
 */
public static VideoAnnotationResults detectText(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.TEXT_DETECTION)
            .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 results = response.getAnnotationResults(0);

    // Get only the first annotation for demo purposes.
    TextAnnotation annotation = results.getTextAnnotations(0);
    System.out.println("Text: " + annotation.getText());

    // Get the first text segment.
    TextSegment textSegment = annotation.getSegments(0);
    System.out.println("Confidence: " + textSegment.getConfidence());
    // For the text segment display it's time offset
    VideoSegment videoSegment = textSegment.getSegment();
    Duration startTimeOffset = videoSegment.getStartTimeOffset();
    Duration endTimeOffset = videoSegment.getEndTimeOffset();
    // Display the offset times in seconds, 1e9 is part of the formula to convert nanos to seconds
    System.out.println(
        String.format(
            "Start time: %.2f", startTimeOffset.getSeconds() + startTimeOffset.getNanos() / 1e9));
    System.out.println(
        String.format(
            "End time: %.2f", endTimeOffset.getSeconds() + endTimeOffset.getNanos() / 1e9));

    // Show the first result for the first frame in the segment.
    TextFrame textFrame = textSegment.getFrames(0);
    Duration timeOffset = textFrame.getTimeOffset();
    System.out.println(
        String.format(
            "Time offset for the first frame: %.2f",
            timeOffset.getSeconds() + timeOffset.getNanos() / 1e9));

    // Display the rotated bounding box for where the text is on the frame.
    System.out.println("Rotated Bounding Box Vertices:");
    List<NormalizedVertex> vertices = textFrame.getRotatedBoundingBox().getVerticesList();
    for (NormalizedVertex normalizedVertex : vertices) {
      System.out.println(
          String.format(
              "\tVertex.x: %.2f, Vertex.y: %.2f",
              normalizedVertex.getX(), normalizedVertex.getY()));
    }
    return results;
  }
}

Node.js

// Imports the Google Cloud Video Intelligence library + Node's fs 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: ['TEXT_DETECTION'],
};
// Detects text 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 textAnnotations = results[0].annotationResults[0].textAnnotations;
textAnnotations.forEach(textAnnotation => {
  console.log(`Text ${textAnnotation.text} occurs at:`);
  textAnnotation.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: ${time.startTimeOffset.seconds || 0}` +
        `.${(time.startTimeOffset.nanos / 1e6).toFixed(0)}s`
    );
    console.log(
      `\tEnd: ${time.endTimeOffset.seconds || 0}.` +
        `${(time.endTimeOffset.nanos / 1e6).toFixed(0)}s`
    );
    console.log(`\tConfidence: ${segment.confidence}`);
    segment.frames.forEach(frame => {
      const timeOffset = frame.timeOffset;
      console.log(
        `Time offset for the frame: ${timeOffset.seconds || 0}` +
          `.${(timeOffset.nanos / 1e6).toFixed(0)}s`
      );
      console.log('Rotated Bounding Box Vertices:');
      frame.rotatedBoundingBox.vertices.forEach(vertex => {
        console.log(`Vertex.x:${vertex.x}, Vertex.y:${vertex.y}`);
      });
    });
  });
});

PHP

use Google\Cloud\VideoIntelligence\V1\VideoIntelligenceServiceClient;
use Google\Cloud\VideoIntelligence\V1\Feature;

/** Uncomment and populate these variables in your code */
// $path = 'File path to a video file to analyze';
// $options = [];

# Instantiate a client.
$video = new VideoIntelligenceServiceClient();

# Read the local video file
$inputContent = file_get_contents($path);

# Execute a request.
$operation = $video->annotateVideo([
    'inputContent' => $inputContent,
    'features' => [Feature::TEXT_DETECTION]
]);

# Wait for the request to complete.
$operation->pollUntilComplete($options);

# Print the results.
if ($operation->operationSucceeded()) {
    $results = $operation->getResult()->getAnnotationResults()[0];

    # Process video/segment level label annotations
    foreach ($results->getTextAnnotations() as $text) {
        printf('Video text description: %s' . PHP_EOL, $text->getText());
        foreach ($text->getSegments() as $segment) {
            $start = $segment->getSegment()->getStartTimeOffset();
            $end = $segment->getSegment()->getEndTimeOffset();
            printf('  Segment: %ss to %ss' . PHP_EOL,
                $start->getSeconds() + $start->getNanos()/1000000000.0,
                $end->getSeconds() + $end->getNanos()/1000000000.0);
            printf('  Confidence: %f' . PHP_EOL, $segment->getConfidence());
        }
    }
    print(PHP_EOL);
} else {
    print_r($operation->getError());
}

Python

"""Detect text in a local video."""
from google.cloud import videointelligence

video_client = videointelligence.VideoIntelligenceServiceClient()
features = [videointelligence.Feature.TEXT_DETECTION]
video_context = videointelligence.VideoContext()

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

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

print("\nProcessing video for text detection.")
result = operation.result(timeout=300)

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

for text_annotation in annotation_result.text_annotations:
    print("\nText: {}".format(text_annotation.text))

    # Get the first text segment
    text_segment = text_annotation.segments[0]
    start_time = text_segment.segment.start_time_offset
    end_time = text_segment.segment.end_time_offset
    print(
        "start_time: {}, end_time: {}".format(
            start_time.seconds + start_time.microseconds * 1e-6,
            end_time.seconds + end_time.microseconds * 1e-6,
        )
    )

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

    # Show the result for the first frame in this segment.
    frame = text_segment.frames[0]
    time_offset = frame.time_offset
    print(
        "Time offset for the first frame: {}".format(
            time_offset.seconds + time_offset.microseconds * 1e-6
        )
    )
    print("Rotated Bounding Box Vertices:")
    for vertex in frame.rotated_bounding_box.vertices:
        print("\tVertex.x: {}, Vertex.y: {}".format(vertex.x, vertex.y))

Ruby

# "Path to a local video file: path/to/file.mp4"

require "google/cloud/video_intelligence"

video = Google::Cloud::VideoIntelligence.video_intelligence_service

video_contents = File.binread path

# Register a callback during the method call
operation = video.annotate_video features: [:TEXT_DETECTION], input_content: video_contents

puts "Processing video for text detection:"
operation.wait_until_done!

raise operation.results.message? if operation.error?
puts "Finished Processing."

text_annotations = operation.results.annotation_results.first.text_annotations
print_text_annotations text_annotations