Pelacakan objek melacak beberapa objek yang terdeteksi dalam video input.
Gunakan model standar
Contoh kode berikut menunjukkan cara melakukan pelacakan objek menggunakan library klien streaming.
Java
Untuk mengautentikasi ke Video Intelligence, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.
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.StreamingFeature;
import com.google.cloud.videointelligence.v1p3beta1.StreamingLabelDetectionConfig;
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;
import java.util.concurrent.TimeoutException;
class StreamingObjectTracking {
// Perform streaming video object tracking
static void streamingObjectTracking(String filePath)
throws IOException, TimeoutException, StatusRuntimeException {
// String filePath = "path_to_your_video_file";
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);
StreamingLabelDetectionConfig labelConfig =
StreamingLabelDetectionConfig.newBuilder().setStationaryCamera(false).build();
StreamingVideoConfig streamingVideoConfig =
StreamingVideoConfig.newBuilder()
.setFeature(StreamingFeature.STREAMING_OBJECT_TRACKING)
.setLabelDetectionConfig(labelConfig)
.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());
}
}
}
}
}Node.js
Untuk mengautentikasi ke Video Intelligence, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.
/**
* TODO(developer): Uncomment these variables before running the sample.
*/
// const path = 'Local file to analyze, e.g. ./my-file.mp4';
const {StreamingVideoIntelligenceServiceClient} =
require('@google-cloud/video-intelligence').v1p3beta1;
const fs = require('fs');
// Instantiates a client
const client = new StreamingVideoIntelligenceServiceClient();
// Streaming configuration
const configRequest = {
videoConfig: {
feature: 'STREAMING_OBJECT_TRACKING',
},
};
const readStream = fs.createReadStream(path, {
highWaterMark: 5 * 1024 * 1024, //chunk size set to 5MB (recommended less than 10MB)
encoding: 'base64',
});
//Load file content
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 options = {timeout: 120000};
// Create a job using a long-running operation
const stream = client.streamingAnnotateVideo(options).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(` left :${box.left}`);
console.log(` top :${box.top}`);
console.log(` right :${box.right}`);
console.log(` bottom:${box.bottom}`);
});
});Python
Untuk mengautentikasi ke Video Intelligence, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.
from google.cloud import videointelligence_v1p3beta1 as videointelligence
# path = 'path_to_file'
client = videointelligence.StreamingVideoIntelligenceServiceClient()
# Set streaming config.
config = videointelligence.StreamingVideoConfig(
feature=(videointelligence.StreamingFeature.STREAMING_OBJECT_TRACKING)
)
# config_request should be the first in the stream of requests.
config_request = videointelligence.StreamingAnnotateVideoRequest(
video_config=config
)
# Set the chunk size to 5MB (recommended less than 10MB).
chunk_size = 5 * 1024 * 1024
# Load file content.
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))