Examples with OperationMetadata com.google.cloud.documentai.v1beta2.OperationMetadata used on opensource projects

Example 1 with OperationMetadata

use of com.google.cloud.documentai.v1beta2.OperationMetadata in project spring-cloud-gcp by spring-cloud.

the class DocumentOcrTemplate method runOcrForDocument.

/**
 * Runs OCR processing for a specified {@code document} and generates OCR output files
 * under the path specified by {@code outputFilePathPrefix}.
 *
 * <p>
 * For example, if you specify an {@code outputFilePathPrefix} of
 * "gs://bucket_name/ocr_results/myDoc_", all the output files of OCR processing will be
 * saved under prefix, such as:
 *
 * <ul>
 * <li>gs://bucket_name/ocr_results/myDoc_output-1-to-5.json
 * <li>gs://bucket_name/ocr_results/myDoc_output-6-to-10.json
 * <li>gs://bucket_name/ocr_results/myDoc_output-11-to-15.json
 * </ul>
 *
 * <p>
 * Note: OCR processing operations may take several minutes to complete, so it may not be
 * advisable to block on the completion of the operation. One may use the returned
 * {@link ListenableFuture} to register callbacks or track the status of the operation.
 *
 * @param document The {@link GoogleStorageLocation} of the document to run OCR processing
 * @param outputFilePathPrefix The {@link GoogleStorageLocation} of a file, folder, or a
 *     bucket describing the path for which all output files shall be saved under
 *
 * @return A {@link ListenableFuture} allowing you to register callbacks or wait for the
 * completion of the operation.
 */
public ListenableFuture<DocumentOcrResultSet> runOcrForDocument(GoogleStorageLocation document, GoogleStorageLocation outputFilePathPrefix) {
    Assert.isTrue(document.isFile(), "Provided document location is not a valid file location: " + document);
    GcsSource gcsSource = GcsSource.newBuilder().setUri(document.uriString()).build();
    String contentType = extractContentType(document);
    InputConfig inputConfig = InputConfig.newBuilder().setMimeType(contentType).setGcsSource(gcsSource).build();
    GcsDestination gcsDestination = GcsDestination.newBuilder().setUri(outputFilePathPrefix.uriString()).build();
    OutputConfig outputConfig = OutputConfig.newBuilder().setGcsDestination(gcsDestination).setBatchSize(this.jsonOutputBatchSize).build();
    AsyncAnnotateFileRequest request = AsyncAnnotateFileRequest.newBuilder().addFeatures(DOCUMENT_OCR_FEATURE).setInputConfig(inputConfig).setOutputConfig(outputConfig).build();
    OperationFuture<AsyncBatchAnnotateFilesResponse, OperationMetadata> result = imageAnnotatorClient.asyncBatchAnnotateFilesAsync(Collections.singletonList(request));
    return extractOcrResultFuture(result);
}

Example 2 with OperationMetadata

use of com.google.cloud.documentai.v1beta2.OperationMetadata in project java-automl by googleapis.

the class BatchPredict method batchPredict.

static void batchPredict(String projectId, String modelId, String inputUri, String outputUri) throws IOException, ExecutionException, InterruptedException {
    // the "close" method on the client to safely clean up any remaining background resources.
    try (PredictionServiceClient client = PredictionServiceClient.create()) {
        // Get the full path of the model.
        ModelName name = ModelName.of(projectId, "us-central1", modelId);
        // Configure the source of the file from a GCS bucket
        GcsSource gcsSource = GcsSource.newBuilder().addInputUris(inputUri).build();
        BatchPredictInputConfig inputConfig = BatchPredictInputConfig.newBuilder().setGcsSource(gcsSource).build();
        // Configure where to store the output in a GCS bucket
        GcsDestination gcsDestination = GcsDestination.newBuilder().setOutputUriPrefix(outputUri).build();
        BatchPredictOutputConfig outputConfig = BatchPredictOutputConfig.newBuilder().setGcsDestination(gcsDestination).build();
        // Build the request that will be sent to the API
        BatchPredictRequest request = BatchPredictRequest.newBuilder().setName(name.toString()).setInputConfig(inputConfig).setOutputConfig(outputConfig).build();
        // Start an asynchronous request
        OperationFuture<BatchPredictResult, OperationMetadata> future = client.batchPredictAsync(request);
        System.out.println("Waiting for operation to complete...");
        BatchPredictResult response = future.get();
        System.out.println("Batch Prediction results saved to specified Cloud Storage bucket.");
    }
}

Example 3 with OperationMetadata

use of com.google.cloud.documentai.v1beta2.OperationMetadata in project java-automl by googleapis.

the class VisionObjectDetectionCreateModel method createModel.

// Create a model
static void createModel(String projectId, String datasetId, String displayName) throws IOException, ExecutionException, InterruptedException {
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
        // A resource that represents Google Cloud Platform location.
        LocationName projectLocation = LocationName.of(projectId, "us-central1");
        // Set model metadata.
        ImageObjectDetectionModelMetadata metadata = ImageObjectDetectionModelMetadata.newBuilder().build();
        Model model = Model.newBuilder().setDisplayName(displayName).setDatasetId(datasetId).setImageObjectDetectionModelMetadata(metadata).build();
        // Create a model with the model metadata in the region.
        OperationFuture<Model, OperationMetadata> future = client.createModelAsync(projectLocation, model);
        // OperationFuture.get() will block until the model is created, which may take several hours.
        // You can use OperationFuture.getInitialFuture to get a future representing the initial
        // response to the request, which contains information while the operation is in progress.
        System.out.format("Training operation name: %s\n", future.getInitialFuture().get().getName());
        System.out.println("Training started...");
    }
}

Example 4 with OperationMetadata

use of com.google.cloud.documentai.v1beta2.OperationMetadata in project java-automl by googleapis.

the class ModelApi method createModel.

// [START automl_vision_create_model]
/**
 * Demonstrates using the AutoML client to create a model.
 *
 * @param projectId the Id of the project.
 * @param computeRegion the Region name.
 * @param dataSetId the Id of the dataset to which model is created.
 * @param modelName the Name of the model.
 * @param trainBudget the Budget for training the model.
 */
static void createModel(String projectId, String computeRegion, String dataSetId, String modelName, String trainBudget) {
    // Instantiates a client
    try (AutoMlClient client = AutoMlClient.create()) {
        // A resource that represents Google Cloud Platform location.
        LocationName projectLocation = LocationName.of(projectId, computeRegion);
        // Set model metadata.
        ImageClassificationModelMetadata imageClassificationModelMetadata = Long.valueOf(trainBudget) == 0 ? ImageClassificationModelMetadata.newBuilder().build() : ImageClassificationModelMetadata.newBuilder().setTrainBudget(Long.valueOf(trainBudget)).build();
        // Set model name and model metadata for the image dataset.
        Model myModel = Model.newBuilder().setDisplayName(modelName).setDatasetId(dataSetId).setImageClassificationModelMetadata(imageClassificationModelMetadata).build();
        // Create a model with the model metadata in the region.
        OperationFuture<Model, OperationMetadata> response = client.createModelAsync(projectLocation, myModel);
        System.out.println(String.format("Training operation name: %s", response.getInitialFuture().get().getName()));
        System.out.println("Training started...");
    } catch (IOException | ExecutionException | InterruptedException e) {
        e.printStackTrace();
    }
}

Example 5 with OperationMetadata

use of com.google.cloud.documentai.v1beta2.OperationMetadata in project java-automl by googleapis.

the class DeployModel method deployModel.

// Deploy a model for prediction
static void deployModel(String projectId, String modelId) throws IOException, ExecutionException, InterruptedException {
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
        // Get the full path of the model.
        ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);
        DeployModelRequest request = DeployModelRequest.newBuilder().setName(modelFullId.toString()).build();
        OperationFuture<Empty, OperationMetadata> future = client.deployModelAsync(request);
        future.get();
        System.out.println("Model deployment finished");
    }
}