Mit dem Objekt-Tracking können in einem Eingabevideo mehrere Objekte erkannt werden.
Standardmodell verwenden
Im folgenden Codebeispiel wird veranschaulicht, wie Objekt-Tracking mithilfe der Streaming-Clientbibliothek durchgeführt wird.
Java
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
/**
* 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
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(u"\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))