Rastrear objetos em um arquivo de vídeo local

Rastrear vários objetos detectados em um arquivo de vídeo armazenado localmente.

Páginas de documentação que incluem esta amostra de código

Para visualizar o exemplo de código usado em contexto, consulte a seguinte documentação:

Exemplo de código

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"
)

// 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: %v", err)
	}
	defer client.Close()

	fileBytes, err := ioutil.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: %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.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

// 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}`);
});

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.
$features = [Feature::OBJECT_TRACKING];
$operation = $video->annotateVideo([
    'inputContent' => $inputContent,
    'features' => $features,
]);

# 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
    $objectEntity = $results->getObjectAnnotations()[0];

    printf('Video object entity: %s' . PHP_EOL, $objectEntity->getEntity()->getEntityId());
    printf('Video object description: %s' . PHP_EOL, $objectEntity->getEntity()->getDescription());

    $start = $objectEntity->getSegment()->getStartTimeOffset();
    $end = $objectEntity->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, $objectEntity->getConfidence());

    foreach ($objectEntity->getFrames() as $objectEntityFrame) {
        $offset = $objectEntityFrame->getTimeOffset();
        $boundingBox = $objectEntityFrame->getNormalizedBoundingBox();
        printf('  Time offset: %ss' . PHP_EOL,
            $offset->getSeconds() + $offset->getNanos() / 1000000000.0);
        printf('  Bounding box position:' . PHP_EOL);
        printf('   Left: %s', $boundingBox->getLeft());
        printf('   Top: %s', $boundingBox->getTop());
        printf('   Right: %s', $boundingBox->getRight());
        printf('   Bottom: %s', $boundingBox->getBottom());
    }
    print(PHP_EOL);
} else {
    print_r($operation->getError());
}

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")

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