对象跟踪功能可跟踪在输入视频中检测到的多个对象。
使用 AutoML 视频
准备工作
如需了解创建 AutoML 模型的背景信息，请查看 Vertex AI 新手指南。如需了解如何创建 AutoML 模型，请先使用控制台或 api“创建数据集”。
使用您的 AutoML 模型
以下代码示例演示了如何使用 AutoML 模型，通过流式客户端库跟踪对象。
Java
如需向 Video Intelligence 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
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
如需向 Video Intelligence 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
/**
* 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
如需向 Video Intelligence 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
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))