Demuestra cómo usar un modelo personalizado de AutoML para la clasificación en un video
Páginas de documentación que incluyen esta muestra de código
Para ver la muestra de código usada en contexto, consulta la siguiente documentación:
Muestra de código
Java
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
/**
* 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
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,
)
)¿Qué sigue?
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