Haz un seguimiento de varios objetos detectados en un video almacenado en Cloud Storage.
Páginas de documentación que incluyen esta muestra de código
Para ver la muestra de código usada en contexto, consulta la siguiente documentación:
Muestra 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")¿Qué sigue?
Para buscar y filtrar muestras de código para otros productos de Google Cloud, consulta el navegador de muestra de Google Cloud.