Rastreie vários objetos detectados em um vídeo armazenado no Cloud Storage.
Páginas de documentação que incluem esta amostra de código
Para visualizar o exemplo de código usado em contexto, consulte a seguinte documentação:
Amostra de código
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"
)
// 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: %v", 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: %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 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(300, 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');
// 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}`);
});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.
$features = [Feature::OBJECT_TRACKING];
$operation = $video->annotateVideo([
'inputUri' => $uri,
'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 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")A seguir
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