使用 create_data_labeling_job 方法为视频创建数据标签作业。
代码示例
Java
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。
如需向 Vertex AI 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.cloud.aiplatform.v1.DataLabelingJob;
import com.google.cloud.aiplatform.v1.DatasetName;
import com.google.cloud.aiplatform.v1.JobServiceClient;
import com.google.cloud.aiplatform.v1.JobServiceSettings;
import com.google.cloud.aiplatform.v1.LocationName;
import com.google.protobuf.Value;
import com.google.protobuf.util.JsonFormat;
import com.google.type.Money;
import java.io.IOException;
import java.util.Map;
public class CreateDataLabelingJobVideoSample {
public static void main(String[] args) throws IOException {
// TODO(developer): Replace these variables before running the sample.
String project = "YOUR_PROJECT_ID";
String displayName = "YOUR_DATA_LABELING_DISPLAY_NAME";
String datasetId = "YOUR_DATASET_ID";
String instructionUri =
"gs://YOUR_GCS_SOURCE_BUCKET/path_to_your_data_labeling_source/file.pdf";
String annotationSpec = "YOUR_ANNOTATION_SPEC";
createDataLabelingJobVideo(project, displayName, datasetId, instructionUri, annotationSpec);
}
static void createDataLabelingJobVideo(
String project,
String displayName,
String datasetId,
String instructionUri,
String annotationSpec)
throws IOException {
JobServiceSettings jobServiceSettings =
JobServiceSettings.newBuilder()
.setEndpoint("us-central1-aiplatform.googleapis.com:443")
.build();
// Initialize client that will be used to send requests. This client only needs to be created
// once, and can be reused for multiple requests. After completing all of your requests, call
// the "close" method on the client to safely clean up any remaining background resources.
try (JobServiceClient jobServiceClient = JobServiceClient.create(jobServiceSettings)) {
String location = "us-central1";
LocationName locationName = LocationName.of(project, location);
String jsonString = "{\"annotation_specs\": [ " + annotationSpec + "]}";
Value.Builder annotationSpecValue = Value.newBuilder();
JsonFormat.parser().merge(jsonString, annotationSpecValue);
DatasetName datasetName = DatasetName.of(project, location, datasetId);
DataLabelingJob dataLabelingJob =
DataLabelingJob.newBuilder()
.setDisplayName(displayName)
.setLabelerCount(1)
.setInstructionUri(instructionUri)
.setInputsSchemaUri(
"gs://google-cloud-aiplatform/schema/datalabelingjob/inputs/"
+ "video_classification.yaml")
.addDatasets(datasetName.toString())
.setInputs(annotationSpecValue)
.putAnnotationLabels(
"aiplatform.googleapis.com/annotation_set_name", "my_test_saved_query")
.build();
DataLabelingJob dataLabelingJobResponse =
jobServiceClient.createDataLabelingJob(locationName, dataLabelingJob);
System.out.println("Create Data Labeling Job Video Response");
System.out.format("\tName: %s\n", dataLabelingJobResponse.getName());
System.out.format("\tDisplay Name: %s\n", dataLabelingJobResponse.getDisplayName());
System.out.format("\tDatasets: %s\n", dataLabelingJobResponse.getDatasetsList());
System.out.format("\tLabeler Count: %s\n", dataLabelingJobResponse.getLabelerCount());
System.out.format("\tInstruction Uri: %s\n", dataLabelingJobResponse.getInstructionUri());
System.out.format("\tInputs Schema Uri: %s\n", dataLabelingJobResponse.getInputsSchemaUri());
System.out.format("\tInputs: %s\n", dataLabelingJobResponse.getInputs());
System.out.format("\tState: %s\n", dataLabelingJobResponse.getState());
System.out.format("\tLabeling Progress: %s\n", dataLabelingJobResponse.getLabelingProgress());
System.out.format("\tCreate Time: %s\n", dataLabelingJobResponse.getCreateTime());
System.out.format("\tUpdate Time: %s\n", dataLabelingJobResponse.getUpdateTime());
System.out.format("\tLabels: %s\n", dataLabelingJobResponse.getLabelsMap());
System.out.format(
"\tSpecialist Pools: %s\n", dataLabelingJobResponse.getSpecialistPoolsList());
for (Map.Entry<String, String> annotationLabelMap :
dataLabelingJobResponse.getAnnotationLabelsMap().entrySet()) {
System.out.println("\tAnnotation Level");
System.out.format("\t\tkey: %s\n", annotationLabelMap.getKey());
System.out.format("\t\tvalue: %s\n", annotationLabelMap.getValue());
}
Money money = dataLabelingJobResponse.getCurrentSpend();
System.out.println("\tCurrent Spend");
System.out.format("\t\tCurrency Code: %s\n", money.getCurrencyCode());
System.out.format("\t\tUnits: %s\n", money.getUnits());
System.out.format("\t\tNanos: %s\n", money.getNanos());
}
}
}Python
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Python 设置说明执行操作。如需了解详情，请参阅 Vertex AI Python API 参考文档。
如需向 Vertex AI 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。
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
def create_data_labeling_job_video_sample(
project: str,
display_name: str,
dataset: str,
instruction_uri: str,
annotation_spec: str,
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
# The AI Platform services require regional API endpoints.
client_options = {"api_endpoint": api_endpoint}
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.JobServiceClient(client_options=client_options)
inputs_dict = {"annotation_specs": [annotation_spec]}
inputs = json_format.ParseDict(inputs_dict, Value())
data_labeling_job = {
"display_name": display_name,
# Full resource name: projects/{project_id}/locations/{location}/datasets/{dataset_id}
"datasets": [dataset],
# labeler_count must be 1, 3, or 5
"labeler_count": 1,
"instruction_uri": instruction_uri,
"inputs_schema_uri": "gs://google-cloud-aiplatform/schema/datalabelingjob/inputs/video_classification_1.0.0.yaml",
"inputs": inputs,
"annotation_labels": {
"aiplatform.googleapis.com/annotation_set_name": "my_test_saved_query"
},
}
parent = f"projects/{project}/locations/{location}"
response = client.create_data_labeling_job(
parent=parent, data_labeling_job=data_labeling_job
)
print("response:", response)
后续步骤
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