Examples with InputConfig com.google.cloud.vision.v1p4beta1.InputConfig used on opensource projects

Example 6 with InputConfig

use of com.google.cloud.vision.v1p4beta1.InputConfig in project java-translate by googleapis.

the class BatchTranslateTextWithGlossary method batchTranslateTextWithGlossary.

// Batch Translate Text with a Glossary.
public static void batchTranslateTextWithGlossary(String projectId, String sourceLanguage, String targetLanguage, String inputUri, String outputUri, String glossaryId) throws IOException, ExecutionException, InterruptedException, TimeoutException {
    // the "close" method on the client to safely clean up any remaining background resources.
    try (TranslationServiceClient client = TranslationServiceClient.create()) {
        // Supported Locations: `global`, [glossary location], or [model location]
        // Glossaries must be hosted in `us-central1`
        // Custom Models must use the same location as your model. (us-central1)
        String location = "us-central1";
        LocationName parent = LocationName.of(projectId, location);
        // Configure the source of the file from a GCS bucket
        GcsSource gcsSource = GcsSource.newBuilder().setInputUri(inputUri).build();
        // Supported Mime Types: https://cloud.google.com/translate/docs/supported-formats
        InputConfig inputConfig = InputConfig.newBuilder().setGcsSource(gcsSource).setMimeType("text/plain").build();
        // Configure where to store the output in a GCS bucket
        GcsDestination gcsDestination = GcsDestination.newBuilder().setOutputUriPrefix(outputUri).build();
        OutputConfig outputConfig = OutputConfig.newBuilder().setGcsDestination(gcsDestination).build();
        // Configure the glossary used in the request
        GlossaryName glossaryName = GlossaryName.of(projectId, location, glossaryId);
        TranslateTextGlossaryConfig glossaryConfig = TranslateTextGlossaryConfig.newBuilder().setGlossary(glossaryName.toString()).build();
        // Build the request that will be sent to the API
        BatchTranslateTextRequest request = BatchTranslateTextRequest.newBuilder().setParent(parent.toString()).setSourceLanguageCode(sourceLanguage).addTargetLanguageCodes(targetLanguage).addInputConfigs(inputConfig).setOutputConfig(outputConfig).putGlossaries(targetLanguage, glossaryConfig).build();
        // Start an asynchronous request
        OperationFuture<BatchTranslateResponse, BatchTranslateMetadata> future = client.batchTranslateTextAsync(request);
        System.out.println("Waiting for operation to complete...");
        // random number between 300 - 450 (maximum allowed seconds)
        long randomNumber = ThreadLocalRandom.current().nextInt(450, 600);
        BatchTranslateResponse response = future.get(randomNumber, TimeUnit.SECONDS);
        // Display the translation for each input text provided
        System.out.printf("Total Characters: %s\n", response.getTotalCharacters());
        System.out.printf("Translated Characters: %s\n", response.getTranslatedCharacters());
    }
}

Example 7 with InputConfig

use of com.google.cloud.vision.v1p4beta1.InputConfig in project java-automl by googleapis.

the class ImportDataset method importDataset.

// Import a dataset
static void importDataset(String projectId, String datasetId, String path) throws IOException, ExecutionException, InterruptedException, TimeoutException {
    Duration totalTimeout = Duration.ofMinutes(45);
    RetrySettings retrySettings = RetrySettings.newBuilder().setTotalTimeout(totalTimeout).build();
    AutoMlSettings.Builder builder = AutoMlSettings.newBuilder();
    builder.importDataSettings().setRetrySettings(retrySettings).build();
    AutoMlSettings settings = builder.build();
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create(settings)) {
        // Get the complete path of the dataset.
        DatasetName datasetFullId = DatasetName.of(projectId, "us-central1", datasetId);
        // Get multiple Google Cloud Storage URIs to import data from
        GcsSource gcsSource = GcsSource.newBuilder().addAllInputUris(Arrays.asList(path.split(","))).build();
        // Import data from the input URI
        InputConfig inputConfig = InputConfig.newBuilder().setGcsSource(gcsSource).build();
        System.out.println("Processing import...");
        // Start the import job
        OperationFuture<Empty, OperationMetadata> operation = client.importDataAsync(datasetFullId, inputConfig);
        System.out.format("Operation name: %s%n", operation.getName());
        // If you want to wait for the operation to finish, adjust the timeout appropriately. The
        // operation will still run if you choose not to wait for it to complete. You can check the
        // status of your operation using the operation's name.
        Empty response = operation.get(45, TimeUnit.MINUTES);
        System.out.format("Dataset imported. %s%n", response);
    } catch (TimeoutException e) {
        System.out.println("The operation's polling period was not long enough.");
        System.out.println("You can use the Operation's name to get the current status.");
        System.out.println("The import job is still running and will complete as expected.");
        throw e;
    }
}

Example 8 with InputConfig

use of com.google.cloud.vision.v1p4beta1.InputConfig in project java-automl by googleapis.

the class ImportDataset method importDataset.

// Import a dataset
static void importDataset(String projectId, String datasetId, String path) throws IOException, ExecutionException, InterruptedException, TimeoutException {
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
        // Get the complete path of the dataset.
        DatasetName datasetFullId = DatasetName.of(projectId, "us-central1", datasetId);
        // Get multiple Google Cloud Storage URIs to import data from
        GcsSource gcsSource = GcsSource.newBuilder().addAllInputUris(Arrays.asList(path.split(","))).build();
        // Import data from the input URI
        InputConfig inputConfig = InputConfig.newBuilder().setGcsSource(gcsSource).build();
        System.out.println("Processing import...");
        // Start the import job
        OperationFuture<Empty, OperationMetadata> operation = client.importDataAsync(datasetFullId, inputConfig);
        System.out.format("Operation name: %s%n", operation.getName());
        // If you want to wait for the operation to finish, adjust the timeout appropriately. The
        // operation will still run if you choose not to wait for it to complete. You can check the
        // status of your operation using the operation's name.
        Empty response = operation.get(45, TimeUnit.MINUTES);
        System.out.format("Dataset imported. %s%n", response);
    } catch (TimeoutException e) {
        System.out.println("The operation's polling period was not long enough.");
        System.out.println("You can use the Operation's name to get the current status.");
        System.out.println("The import job is still running and will complete as expected.");
        throw e;
    }
}

Example 9 with InputConfig

use of com.google.cloud.vision.v1p4beta1.InputConfig in project spring-cloud-gcp by GoogleCloudPlatform.

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 10 with InputConfig

use of com.google.cloud.vision.v1p4beta1.InputConfig in project java-vision by googleapis.

the class BatchAnnotateFilesGcs method batchAnnotateFilesGcs.

public static void batchAnnotateFilesGcs(String gcsUri) throws IOException {
    // the "close" method on the client to safely clean up any remaining background resources.
    try (ImageAnnotatorClient imageAnnotatorClient = ImageAnnotatorClient.create()) {
        // You can send multiple files to be annotated, this sample demonstrates how to do this with
        // one file. If you want to use multiple files, you have to create a `AnnotateImageRequest`
        // object for each file that you want annotated.
        // First specify where the vision api can find the image
        GcsSource gcsSource = GcsSource.newBuilder().setUri(gcsUri).build();
        // Specify the input config with the file's uri and its type.
        // Supported mime_type: application/pdf, image/tiff, image/gif
        // https://cloud.google.com/vision/docs/reference/rpc/google.cloud.vision.v1#inputconfig
        InputConfig inputConfig = InputConfig.newBuilder().setMimeType("application/pdf").setGcsSource(gcsSource).build();
        // Set the type of annotation you want to perform on the file
        // https://cloud.google.com/vision/docs/reference/rpc/google.cloud.vision.v1#google.cloud.vision.v1.Feature.Type
        Feature feature = Feature.newBuilder().setType(Feature.Type.DOCUMENT_TEXT_DETECTION).build();
        // Build the request object for that one file. Note: for additional file you have to create
        // additional `AnnotateFileRequest` objects and store them in a list to be used below.
        // Since we are sending a file of type `application/pdf`, we can use the `pages` field to
        // specify which pages to process. The service can process up to 5 pages per document file.
        // https://cloud.google.com/vision/docs/reference/rpc/google.cloud.vision.v1#google.cloud.vision.v1.AnnotateFileRequest
        AnnotateFileRequest fileRequest = AnnotateFileRequest.newBuilder().setInputConfig(inputConfig).addFeatures(feature).addPages(// Process the first page
        1).addPages(// Process the second page
        2).addPages(// Process the last page
        -1).build();
        // Add each `AnnotateFileRequest` object to the batch request.
        BatchAnnotateFilesRequest request = BatchAnnotateFilesRequest.newBuilder().addRequests(fileRequest).build();
        // Make the synchronous batch request.
        BatchAnnotateFilesResponse response = imageAnnotatorClient.batchAnnotateFiles(request);
        // sample.
        for (AnnotateImageResponse imageResponse : response.getResponsesList().get(0).getResponsesList()) {
            System.out.format("Full text: %s%n", imageResponse.getFullTextAnnotation().getText());
            for (Page page : imageResponse.getFullTextAnnotation().getPagesList()) {
                for (Block block : page.getBlocksList()) {
                    System.out.format("%nBlock confidence: %s%n", block.getConfidence());
                    for (Paragraph par : block.getParagraphsList()) {
                        System.out.format("\tParagraph confidence: %s%n", par.getConfidence());
                        for (Word word : par.getWordsList()) {
                            System.out.format("\t\tWord confidence: %s%n", word.getConfidence());
                            for (Symbol symbol : word.getSymbolsList()) {
                                System.out.format("\t\t\tSymbol: %s, (confidence: %s)%n", symbol.getText(), symbol.getConfidence());
                            }
                        }
                    }
                }
            }
        }
    }
}