동영상 분류는 객체, 위치, 활동, 동물 종, 제품 등을 식별합니다.
AutoML 동영상 사용
시작하기 전에
AutoML 모델 생성에 대한 배경 정보는 Vertex AI 초보자 가이드를 참조하세요. AutoML 모델을 만드는 방법에 대한 자세한 내용은 Vertex AI 문서의 'ML 모델 개발 및 사용'에서 동영상 데이터를 참조하세요.
AutoML 모델 사용
다음 코드 샘플은 스트리밍 클라이언트 라이브러리를 사용하는 동영상 분류를 위해 AutoML 모델을 사용하는 방법을 보여줍니다.
Java
Video Intelligence에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
import com.google.api.gax.rpc.BidiStream;
import com.google.cloud.videointelligence.v1p3beta1.LabelAnnotation;
import com.google.cloud.videointelligence.v1p3beta1.LabelFrame;
import com.google.cloud.videointelligence.v1p3beta1.StreamingAnnotateVideoRequest;
import com.google.cloud.videointelligence.v1p3beta1.StreamingAnnotateVideoResponse;
import com.google.cloud.videointelligence.v1p3beta1.StreamingAutomlClassificationConfig;
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;
import java.util.concurrent.TimeoutException;
class StreamingAutoMlClassification {
// Perform streaming video classification with an AutoML Model
static void streamingAutoMlClassification(String filePath, String projectId, String modelId)
throws TimeoutException, StatusRuntimeException, IOException {
// String filePath = "path_to_your_video_file";
// String projectId = "YOUR_GCP_PROJECT_ID";
// String modelId = "YOUR_AUTO_ML_CLASSIFICATION_MODEL_ID";
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);
System.out.println(modelPath);
StreamingAutomlClassificationConfig streamingAutomlClassificationConfig =
StreamingAutomlClassificationConfig.newBuilder().setModelName(modelPath).build();
StreamingVideoConfig streamingVideoConfig =
StreamingVideoConfig.newBuilder()
.setFeature(StreamingFeature.STREAMING_AUTOML_CLASSIFICATION)
.setAutomlClassificationConfig(streamingAutomlClassificationConfig)
.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) {
if (response.hasError()) {
System.out.println(response.getError().getMessage());
break;
}
StreamingVideoAnnotationResults annotationResults = response.getAnnotationResults();
for (LabelAnnotation annotation : annotationResults.getLabelAnnotationsList()) {
String entity = annotation.getEntity().getDescription();
// There is only one frame per annotation
LabelFrame labelFrame = annotation.getFrames(0);
double offset =
labelFrame.getTimeOffset().getSeconds() + labelFrame.getTimeOffset().getNanos() / 1e9;
float confidence = labelFrame.getConfidence();
System.out.format("At %fs segment: %s (%f)\n", offset, entity, confidence);
}
}
System.out.println("Video streamed successfully.");
}
}
}Node.js
Video Intelligence에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
/**
* 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 modelPath = `projects/${projectId}/locations/us-central1/models/${modelId}`;
const configRequest = {
videoConfig: {
feature: 'STREAMING_AUTOML_CLASSIFICATION',
automlClassificationConfig: {
modelName: modelPath,
},
},
};
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 labels = annotations.labelAnnotations;
labels.forEach(label => {
console.log(
`Label ${label.entity.description} occurs at: ${
label.frames[0].timeOffset.seconds || 0
}` + `.${(label.frames[0].timeOffset.nanos / 1e6).toFixed(0)}s`
);
console.log(` Confidence: ${label.frames[0].confidence}`);
});
})
.on('error', response => {
console.error(response);
});Python
Video Intelligence에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
import io
from google.cloud import videointelligence_v1p3beta1 as videointelligence
# path = 'path_to_file'
# project_id = 'gcp_project_id'
# model_id = 'automl_classification_model_id'
client = videointelligence.StreamingVideoIntelligenceServiceClient()
model_path = "projects/{}/locations/us-central1/models/{}".format(
project_id, model_id
)
# Here we use classification as an example.
automl_config = videointelligence.StreamingAutomlClassificationConfig(
model_name=model_path
)
video_config = videointelligence.StreamingVideoConfig(
feature=videointelligence.StreamingFeature.STREAMING_AUTOML_CLASSIFICATION,
automl_classification_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=600)
for response in responses:
# Check for errors.
if response.error.message:
print(response.error.message)
break
for label in response.annotation_results.label_annotations:
for frame in label.frames:
print(
"At {:3d}s segment, {:5.1%} {}".format(
frame.time_offset.seconds,
frame.confidence,
label.entity.entity_id,
)
)