Recognizing text

Text Detection performs Optical Character Recognition (OCR). It detects and extracts text within an input video.

Text detection is available for all of the languages supported by the Cloud Vision API.

Request Text Detection for a Video on Google Cloud Storage

The following samples demonstrate text detection on a file located in Google Cloud Storage.

Protocol

The following shows how to send a POST request to the videos:annotate method. The example uses the access token for a service account set up for the project using the Google Cloud Platform Cloud SDK. For instructions on installing the Cloud SDK, setting up a project with a service account, and obtaining an access token, see the Video Intelligence API Quickstart.

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     --data "{
      'inputUri': 'gs://cloud-videointelligence-demo/assistant.mp4',
      'features': ['TEXT_DETECTION'],
    }" "https://videointelligence.googleapis.com/v1/videos:annotate"

If the request is successful, the Cloud Video Intelligence API returns the name for your operation. The following shows an example of such a response, where project-name is the name of your project and operation-id is the ID of the long running operation created for the request.

{
  "name": "projects/project-name/locations/us-west1/operations/operation-id"
}

To retrieve the result of the operation, make a GET request, using the operation name returned from the call to videos:annotate, as shown in the following example.

curl -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
  https://videointelligence.googleapis.com/v1/operation-name

Note: The done field is only returned when its value is True. It is not included in responses for which the operation has not completed.

Text detection annotations are returned as a textAnnotations list.

"textAnnotations": [
  {
    "text": "Hair Salon",
    "segments": [
      {
        "segment": {
          "startTimeOffset": "0.833333s",
          "endTimeOffset": "2.291666s"
        },
        "confidence": 0.99438506,
        "frames": [
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.7015625,
                  "y": 0.59583336
                },
                {
                  "x": 0.7984375,
                  "y": 0.59583336
                },
                {
                  "x": 0.7984375,
                  "y": 0.64166665
                },
                {
                  "x": 0.7015625,
                  "y": 0.64166665
                }
              ]
            },
            "timeOffset": "0.833333s"
          },
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.70234376,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6333333
                },
                {
                  "x": 0.70234376,
                  "y": 0.6333333
                }
              ]
            },
            "timeOffset": "1.041666s"
          },
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.70234376,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6333333
                },
                {
                  "x": 0.70234376,
                  "y": 0.6333333
                }
              ]
            },
            "timeOffset": "1.250s"
          },
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.70234376,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6319444
                },
                {
                  "x": 0.70234376,
                  "y": 0.6319444
                }
              ]
            },
            "timeOffset": "1.458333s"
          },
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.70234376,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6333333
                },
                {
                  "x": 0.70234376,
                  "y": 0.6333333
                }
              ]
            },
            "timeOffset": "1.666666s"
          },
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.70234376,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6333333
                },
                {
                  "x": 0.70234376,
                  "y": 0.6333333
                }
              ]
            },
            "timeOffset": "1.875s"
          },
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.70234376,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6333333
                },
                {
                  "x": 0.70234376,
                  "y": 0.6333333
                }
              ]
            },
            "timeOffset": "2.083333s"
          },
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.70234376,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6333333
                },
                {
                  "x": 0.70234376,
                  "y": 0.6333333
                }
              ]
            },
            "timeOffset": "2.291666s"
          }
        ]
      }
    ]
  },
  {
    "text": "\"Sure, give me one second.\"",
    "segments": [
      {
        "segment": {
          "startTimeOffset": "10.625s",
          "endTimeOffset": "13.333333s"
        },
        "confidence": 0.98716676,
        "frames": [
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.60859376,
                  "y": 0.59583336
                },
                {
                  "x": 0.8952959,
                  "y": 0.5903528
                },
                {
                  "x": 0.89560676,
                  "y": 0.6417387
                },
                {
                  "x": 0.60890454,
                  "y": 0.64721924
                }
              ]
            },
            "timeOffset": "10.625s"
          },
  ...

    ]
  }

C#

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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

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import (
	"fmt"
	"io"
	"log"

	"context"

	"github.com/golang/protobuf/ptypes"

	video "cloud.google.com/go/videointelligence/apiv1"
	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 {
		log.Fatalf("Failed to create client: %v", err)
	}

	op, err := client.AnnotateVideo(ctx, &videopb.AnnotateVideoRequest{
		InputUri: gcsURI,
		Features: []videopb.Feature{
			videopb.Feature_TEXT_DETECTION,
		},
	})
	if err != nil {
		log.Fatalf("Failed to start annotation job: %v", err)
	}

	resp, err := op.Wait(ctx)
	if err != nil {
		log.Fatalf("Failed to annotate: %v", err)
	}

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

	for _, annotation := range result.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

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/**
 * 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(600, 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

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// Imports the Google Cloud Video Intelligence library
const Video = require('@google-cloud/video-intelligence').v1p2beta1;
// 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;
    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}`);
    segment.frames.forEach(frame => {
      const timeOffset = frame.timeOffset;
      if (timeOffset.seconds === undefined) {
        timeOffset.seconds = 0;
      }
      if (timeOffset.nanos === undefined) {
        timeOffset.nanos = 0;
      }
      console.log(
        `Time offset for the frame: ${timeOffset.seconds}` +
          `.${(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}`);
      });
    });
  });
});

Python

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"""Detect text in a video stored on GCS."""
from google.cloud import videointelligence_v1p2beta1 as videointelligence

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

operation = video_client.annotate_video(
    input_uri=input_uri,
    features=features)

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]

# Get only the first result
text_annotation = annotation_result.text_annotations[0]
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.nanos * 1e-9,
    end_time.seconds + end_time.nanos * 1e-9))

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.nanos * 1e-9))
print('Rotated Bounding Box Vertices:')
for vertex in frame.rotated_bounding_box.vertices:
    print('\tVertex.x: {}, Vertex.y: {}'.format(vertex.x, vertex.y))

Ruby

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

require "google/cloud/video_intelligence"

video = Google::Cloud::VideoIntelligence.new

# Register a callback during the method call
operation = video.annotate_video input_uri: path, features: [:TEXT_DETECTION] do |operation|
  raise operation.results.message? if operation.error?
  puts "Finished Processing."

  text_annotations = operation.results.annotation_results.first.text_annotations

  text_annotations.each do |text_annotation|
    puts "Text: #{text_annotation.text}"

    # Print information about the first segment of the text.
    text_segment = text_annotation.segments.first
    start_time_offset = text_segment.segment.start_time_offset
    end_time_offset = text_segment.segment.end_time_offset
    start_time = (start_time_offset.seconds +
                   start_time_offset.nanos / 1e9)
    end_time =   (end_time_offset.seconds +
                   end_time_offset.nanos / 1e9)
    puts "start_time: #{start_time}, end_time: #{end_time}"

    puts "Confidence: #{text_segment.confidence}"

    # Print information about the first frame of the segment.
    frame = text_segment.frames.first
    time_offset = (frame.time_offset.seconds +
                    frame.time_offset.nanos / 1e9)
    puts "Time offset for the first frame: #{time_offset}"

    puts "Rotated bounding box vertices:"
    frame.rotated_bounding_box.vertices.each do |vertex|
      puts "\tVertex.x: #{vertex.x}, Vertex.y: #{vertex.y}"
    end
  end
end

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

Request Text Detection for Video from a Local File

The following samples demonstrate text detection on a file stored locally.

Protocol

To perform annotation on a local video file, base64-encode the contents of the video file. Include the base64-encoded contents in the inputContent field of the request. For information on how to base64-encode the contents of a video file, see Base64 Encoding.

The following shows how to send a POST request to the videos:annotate method. The example uses the access token for a service account set up for the project using the Google Cloud Platform Cloud SDK. For instructions on installing the Cloud SDK, setting up a project with a service account, and obtaining an access token, see the Video Intelligence API Quickstart.

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     --data "{
      'inputContent': 'UklGRg41AwBBVkkgTElTVAwBAABoZHJsYXZpaDgAAAA1ggAAxPMBAAAAAAAQCAA...',
      'features': ['TEXT_DETECTION'],
    }" "https://videointelligence.googleapis.com/v1/videos:annotate"

If the request is successful, the Cloud Video Intelligence API returns the name for your operation. The following shows an example of such a response, where project-name is the name of your project and operation-id is the ID of the long running operation created for the request.

{
  "name": "projects/project-name/locations/us-west1/operations/operation-id"
}

To retrieve the result of the operation, make a GET request, using the operation name returned from the call to videos:annotate, as shown in the following example.

curl -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
  https://videointelligence.googleapis.com/v1/operation-name

Note: The done field is only returned when its value is True. It is not included in responses for which the operation has not completed.

Text detection annotations are returned as a textAnnotations list.

"textAnnotations": [
  {
    "text": "Hair Salon",
    "segments": [
      {
        "segment": {
          "startTimeOffset": "0.833333s",
          "endTimeOffset": "2.291666s"
        },
        "confidence": 0.99438506,
        "frames": [
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.7015625,
                  "y": 0.59583336
                },
                {
                  "x": 0.7984375,
                  "y": 0.59583336
                },
                {
                  "x": 0.7984375,
                  "y": 0.64166665
                },
                {
                  "x": 0.7015625,
                  "y": 0.64166665
                }
              ]
            },
            "timeOffset": "0.833333s"
          },
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.70234376,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6333333
                },
                {
                  "x": 0.70234376,
                  "y": 0.6333333
                }
              ]
            },
            "timeOffset": "1.041666s"
          },
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.70234376,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6333333
                },
                {
                  "x": 0.70234376,
                  "y": 0.6333333
                }
              ]
            },
            "timeOffset": "1.250s"
          },
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.70234376,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6319444
                },
                {
                  "x": 0.70234376,
                  "y": 0.6319444
                }
              ]
            },
            "timeOffset": "1.458333s"
          },
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.70234376,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6333333
                },
                {
                  "x": 0.70234376,
                  "y": 0.6333333
                }
              ]
            },
            "timeOffset": "1.666666s"
          },
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.70234376,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6333333
                },
                {
                  "x": 0.70234376,
                  "y": 0.6333333
                }
              ]
            },
            "timeOffset": "1.875s"
          },
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.70234376,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6333333
                },
                {
                  "x": 0.70234376,
                  "y": 0.6333333
                }
              ]
            },
            "timeOffset": "2.083333s"
          },
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.70234376,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6
                },
                {
                  "x": 0.7992188,
                  "y": 0.6333333
                },
                {
                  "x": 0.70234376,
                  "y": 0.6333333
                }
              ]
            },
            "timeOffset": "2.291666s"
          }
        ]
      }
    ]
  },
  {
    "text": "\"Sure, give me one second.\"",
    "segments": [
      {
        "segment": {
          "startTimeOffset": "10.625s",
          "endTimeOffset": "13.333333s"
        },
        "confidence": 0.98716676,
        "frames": [
          {
            "rotatedBoundingBox": {
              "vertices": [
                {
                  "x": 0.60859376,
                  "y": 0.59583336
                },
                {
                  "x": 0.8952959,
                  "y": 0.5903528
                },
                {
                  "x": 0.89560676,
                  "y": 0.6417387
                },
                {
                  "x": 0.60890454,
                  "y": 0.64721924
                }
              ]
            },
            "timeOffset": "10.625s"
          },
  ...

    ]
  }

C#

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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

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import (
	"fmt"
	"io"
	"io/ioutil"
	"log"

	"context"

	"github.com/golang/protobuf/ptypes"

	video "cloud.google.com/go/videointelligence/apiv1"
	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 := "resources/googlework_short.mp4"

	ctx := context.Background()

	// Creates a client.
	client, err := video.NewClient(ctx)
	if err != nil {
		log.Fatalf("Failed to create client: %v", err)
	}

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

	op, err := client.AnnotateVideo(ctx, &videopb.AnnotateVideoRequest{
		InputContent: fileBytes,
		Features: []videopb.Feature{
			videopb.Feature_TEXT_DETECTION,
		},
	})
	if err != nil {
		log.Fatalf("Failed to start annotation job: %v", err)
	}

	resp, err := op.Wait(ctx)
	if err != nil {
		log.Fatalf("Failed to annotate: %v", err)
	}

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

	for _, annotation := range result.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

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/**
 * 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(600, 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

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// Imports the Google Cloud Video Intelligence library + Node's fs library
const Video = require('@google-cloud/video-intelligence').v1p2beta1;
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: ${time.startTimeOffset.seconds}` +
        `.${(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}`);
    segment.frames.forEach(frame => {
      const timeOffset = frame.timeOffset;
      if (timeOffset.seconds === undefined) {
        timeOffset.seconds = 0;
      }
      if (timeOffset.nanos === undefined) {
        timeOffset.nanos = 0;
      }
      console.log(
        `Time offset for the frame: ${timeOffset.seconds}` +
          `.${(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}`);
      });
    });
  });
});

Python

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"""Detect text in a local video."""
from google.cloud import videointelligence_v1p2beta1 as videointelligence

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

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

operation = video_client.annotate_video(
    input_content=input_content,  # the bytes of the video file
    features=features,
    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]

# Get only the first result
text_annotation = annotation_result.text_annotations[0]
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.nanos * 1e-9,
    end_time.seconds + end_time.nanos * 1e-9))

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.nanos * 1e-9))
print('Rotated Bounding Box Vertices:')
for vertex in frame.rotated_bounding_box.vertices:
    print('\tVertex.x: {}, Vertex.y: {}'.format(vertex.x, vertex.y))

Ruby

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

require "google/cloud/video_intelligence"

video = Google::Cloud::VideoIntelligence.new

video_contents = File.binread path

# Register a callback during the method call
operation = video.annotate_video input_content: video_contents, features: [:TEXT_DETECTION] do |operation|
  raise operation.results.message? if operation.error?
  puts "Finished Processing."

  text_annotations = operation.results.annotation_results.first.text_annotations

  text_annotations.each do |text_annotation|
    puts "Text: #{text_annotation.text}"

    # Print information about the first segment of the text.
    text_segment = text_annotation.segments.first
    start_time_offset = text_segment.segment.start_time_offset
    end_time_offset = text_segment.segment.end_time_offset
    start_time = (start_time_offset.seconds +
                   start_time_offset.nanos / 1e9)
    end_time =   (end_time_offset.seconds +
                   end_time_offset.nanos / 1e9)
    puts "start_time: #{start_time}, end_time: #{end_time}"

    puts "Confidence: #{text_segment.confidence}"

    # Print information about the first frame of the segment.
    frame = text_segment.frames.first
    time_offset = (frame.time_offset.seconds +
                    frame.time_offset.nanos / 1e9)
    puts "Time offset for the first frame: #{time_offset}"

    puts "Rotated bounding box vertices:"
    frame.rotated_bounding_box.vertices.each do |vertex|
      puts "\tVertex.x: #{vertex.x}, Vertex.y: #{vertex.y}"
    end
  end
end

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