El seguimiento de objetos realiza un seguimiento de varios objetos detectados en un video de entrada.
Usa videos de AutoML
Antes de comenzar
Para obtener información sobre la creación de un modelo de AutoML, consulta la Guía para principiantes de Vertex AI. Si quieres obtener instrucciones para crear tu modelo de AutoML, comienza por consultar “Crea un conjunto de datos” con la consola o la api.
Usa tu modelo de AutoML
En la siguiente muestra de código, se indica cómo usar tu modelo de AutoML para el seguimiento de objetos mediante la biblioteca cliente de transmisión.
Java
Para autenticarte en Video Intelligence, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para un entorno de desarrollo local.
import com.google.api.gax.rpc.BidiStream;
import com.google.cloud.videointelligence.v1p3beta1.ObjectTrackingAnnotation;
import com.google.cloud.videointelligence.v1p3beta1.ObjectTrackingFrame;
import com.google.cloud.videointelligence.v1p3beta1.StreamingAnnotateVideoRequest;
import com.google.cloud.videointelligence.v1p3beta1.StreamingAnnotateVideoResponse;
import com.google.cloud.videointelligence.v1p3beta1.StreamingAutomlObjectTrackingConfig;
import com.google.cloud.videointelligence.v1p3beta1.StreamingFeature;
import com.google.cloud.videointelligence.v1p3beta1.StreamingVideoAnnotationResults;
import com.google.cloud.videointelligence.v1p3beta1.StreamingVideoConfig;
import com.google.cloud.videointelligence.v1p3beta1.StreamingVideoIntelligenceServiceClient;
import com.google.protobuf.ByteString;
import io.grpc.StatusRuntimeException;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.Arrays;
class StreamingAutoMlObjectTracking {
public static void main(String[] args) throws IOException {
// TODO(developer): Replace these variables before running the sample.
String filePath = "YOUR_VIDEO_FILE";
String projectId = "YOUR_PROJECT_ID";
String modelId = "YOUR_AUTOML_OBJECT_TRACKING_MODEL_ID";
streamingAutoMlObjectTracking(filePath, projectId, modelId);
}
// Perform streaming video object tracking with an AutoML Model
static void streamingAutoMlObjectTracking(String filePath, String projectId, String modelId)
throws StatusRuntimeException, IOException {
try (StreamingVideoIntelligenceServiceClient client =
StreamingVideoIntelligenceServiceClient.create()) {
Path path = Paths.get(filePath);
byte[] data = Files.readAllBytes(path);
// Set the chunk size to 5MB (recommended less than 10MB).
int chunkSize = 5 * 1024 * 1024;
int numChunks = (int) Math.ceil((double) data.length / chunkSize);
String modelPath =
String.format("projects/%s/locations/us-central1/models/%s", projectId, modelId);
StreamingAutomlObjectTrackingConfig streamingAutomlObjectTrackingConfig =
StreamingAutomlObjectTrackingConfig.newBuilder().setModelName(modelPath).build();
StreamingVideoConfig streamingVideoConfig =
StreamingVideoConfig.newBuilder()
.setFeature(StreamingFeature.STREAMING_AUTOML_OBJECT_TRACKING)
.setAutomlObjectTrackingConfig(streamingAutomlObjectTrackingConfig)
.build();
BidiStream<StreamingAnnotateVideoRequest, StreamingAnnotateVideoResponse> call =
client.streamingAnnotateVideoCallable().call();
// The first request must **only** contain the audio configuration:
call.send(
StreamingAnnotateVideoRequest.newBuilder().setVideoConfig(streamingVideoConfig).build());
// Subsequent requests must **only** contain the audio data.
// Send the requests in chunks
for (int i = 0; i < numChunks; i++) {
call.send(
StreamingAnnotateVideoRequest.newBuilder()
.setInputContent(
ByteString.copyFrom(
Arrays.copyOfRange(data, i * chunkSize, i * chunkSize + chunkSize)))
.build());
}
// Tell the service you are done sending data
call.closeSend();
for (StreamingAnnotateVideoResponse response : call) {
StreamingVideoAnnotationResults annotationResults = response.getAnnotationResults();
for (ObjectTrackingAnnotation objectAnnotations :
annotationResults.getObjectAnnotationsList()) {
String entity = objectAnnotations.getEntity().getDescription();
float confidence = objectAnnotations.getConfidence();
long trackId = objectAnnotations.getTrackId();
System.out.format("%s: %f (ID: %d)\n", entity, confidence, trackId);
// In streaming, there is always one frame.
ObjectTrackingFrame frame = objectAnnotations.getFrames(0);
double offset =
frame.getTimeOffset().getSeconds() + frame.getTimeOffset().getNanos() / 1e9;
System.out.format("Offset: %f\n", offset);
System.out.println("Bounding Box:");
System.out.format("\tLeft: %f\n", frame.getNormalizedBoundingBox().getLeft());
System.out.format("\tTop: %f\n", frame.getNormalizedBoundingBox().getTop());
System.out.format("\tRight: %f\n", frame.getNormalizedBoundingBox().getRight());
System.out.format("\tBottom: %f\n", frame.getNormalizedBoundingBox().getBottom());
}
}
System.out.println("Video streamed successfully.");
}
}
}
Node.js
Para autenticarte en Video Intelligence, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para un entorno de desarrollo local.
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
// const path = 'Local file to analyze, e.g. ./my-file.mp4';
// const modelId = 'AutoML model'
// const projectId = 'Your GCP Project'
const {StreamingVideoIntelligenceServiceClient} =
require('@google-cloud/video-intelligence').v1p3beta1;
const fs = require('fs');
// Instantiates a client
const client = new StreamingVideoIntelligenceServiceClient();
// Streaming configuration
const modelName = `projects/${projectId}/locations/us-central1/models/${modelId}`;
const configRequest = {
videoConfig: {
feature: 'STREAMING_AUTOML_OBJECT_TRACKING',
automlObjectTrackingConfig: {
modelName: modelName,
},
},
};
const readStream = fs.createReadStream(path, {
highWaterMark: 5 * 1024 * 1024, //chunk size set to 5MB (recommended less than 10MB)
encoding: 'base64',
});
//Load file content
// Note: Input videos must have supported video codecs. See
// https://cloud.google.com/video-intelligence/docs/streaming/streaming#supported_video_codecs
// for more details.
const chunks = [];
readStream
.on('data', chunk => {
const request = {
inputContent: chunk.toString(),
};
chunks.push(request);
})
.on('close', () => {
// configRequest should be the first in the stream of requests
stream.write(configRequest);
for (let i = 0; i < chunks.length; i++) {
stream.write(chunks[i]);
}
stream.end();
});
const stream = client.streamingAnnotateVideo().on('data', response => {
//Gets annotations for video
const annotations = response.annotationResults;
const objects = annotations.objectAnnotations;
objects.forEach(object => {
console.log(`Entity description: ${object.entity.description}`);
console.log(`Entity id: ${object.entity.entityId}`);
console.log(`Track id: ${object.trackId}`);
console.log(`Confidence: ${object.confidence}`);
console.log(
`Time offset for the frame: ${
object.frames[0].timeOffset.seconds || 0
}` + `.${(object.frames[0].timeOffset.nanos / 1e6).toFixed(0)}s`
);
//Every annotation has only one frame.
const box = object.frames[0].normalizedBoundingBox;
console.log('Bounding box position:');
console.log(`\tleft: ${box.left}`);
console.log(`\ttop: ${box.top}`);
console.log(`\tright: ${box.right}`);
console.log(`\tbottom: ${box.bottom}`);
});
});
Python
Para autenticarte en Video Intelligence, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para un entorno de desarrollo local.
import io
from google.cloud import videointelligence_v1p3beta1 as videointelligence
# path = 'path_to_file'
# project_id = 'project_id'
# model_id = 'automl_object_tracking_model_id'
client = videointelligence.StreamingVideoIntelligenceServiceClient()
model_path = "projects/{}/locations/us-central1/models/{}".format(
project_id, model_id
)
automl_config = videointelligence.StreamingAutomlObjectTrackingConfig(
model_name=model_path
)
video_config = videointelligence.StreamingVideoConfig(
feature=videointelligence.StreamingFeature.STREAMING_AUTOML_OBJECT_TRACKING,
automl_object_tracking_config=automl_config,
)
# config_request should be the first in the stream of requests.
config_request = videointelligence.StreamingAnnotateVideoRequest(
video_config=video_config
)
# Set the chunk size to 5MB (recommended less than 10MB).
chunk_size = 5 * 1024 * 1024
# Load file content.
# Note: Input videos must have supported video codecs. See
# https://cloud.google.com/video-intelligence/docs/streaming/streaming#supported_video_codecs
# for more details.
stream = []
with io.open(path, "rb") as video_file:
while True:
data = video_file.read(chunk_size)
if not data:
break
stream.append(data)
def stream_generator():
yield config_request
for chunk in stream:
yield videointelligence.StreamingAnnotateVideoRequest(input_content=chunk)
requests = stream_generator()
# streaming_annotate_video returns a generator.
# The default timeout is about 300 seconds.
# To process longer videos it should be set to
# larger than the length (in seconds) of the stream.
responses = client.streaming_annotate_video(requests, timeout=900)
# Each response corresponds to about 1 second of video.
for response in responses:
# Check for errors.
if response.error.message:
print(response.error.message)
break
object_annotations = response.annotation_results.object_annotations
# object_annotations could be empty
if not object_annotations:
continue
for annotation in object_annotations:
# Each annotation has one frame, which has a timeoffset.
frame = annotation.frames[0]
time_offset = (
frame.time_offset.seconds + frame.time_offset.microseconds / 1e6
)
description = annotation.entity.description
confidence = annotation.confidence
# track_id tracks the same object in the video.
track_id = annotation.track_id
# description is in Unicode
print("{}s".format(time_offset))
print("\tEntity description: {}".format(description))
print("\tTrack Id: {}".format(track_id))
if annotation.entity.entity_id:
print("\tEntity id: {}".format(annotation.entity.entity_id))
print("\tConfidence: {}".format(confidence))
# Every annotation has only one frame
frame = annotation.frames[0]
box = frame.normalized_bounding_box
print("\tBounding box position:")
print("\tleft : {}".format(box.left))
print("\ttop : {}".format(box.top))
print("\tright : {}".format(box.right))
print("\tbottom: {}\n".format(box.bottom))