O rastreamento de objetos acompanha vários objetos detectados em um vídeo de entrada.
Como usar vídeo do AutoML
Antes de começar
Para saber mais sobre como criar um modelo do AutoML, confira o Guia para iniciantes do Vertex AI. Para ver instruções sobre como criar o modelo do AutoML, comece com "Como criar um conjunto de dados" usando o console ou a api.
Usar o modelo AutoML
O exemplo de código a seguir demonstra como usar o modelo do AutoML para o rastreamento de objetos com a biblioteca de cliente de streaming.
Java
Para autenticar na Video Intelligence, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento 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 autenticar na Video Intelligence, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento 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 autenticar na Video Intelligence, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento 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))