Anotar um lote de arquivos no Cloud Storage

Anotar um lote de arquivos no Cloud Storage enquanto estiver on-line.

Mais informações

Para ver a documentação detalhada que inclui este exemplo de código, consulte:

Exemplo de código

Java

Antes de testar esta amostra, siga as instruções de configuração do Java no Guia de início rápido do Vision: como usar bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API VisionJava.

Para autenticar no Vision, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local. 

import com.google.cloud.vision.v1.AnnotateFileRequest;
import com.google.cloud.vision.v1.AnnotateImageResponse;
import com.google.cloud.vision.v1.BatchAnnotateFilesRequest;
import com.google.cloud.vision.v1.BatchAnnotateFilesResponse;
import com.google.cloud.vision.v1.Block;
import com.google.cloud.vision.v1.Feature;
import com.google.cloud.vision.v1.GcsSource;
import com.google.cloud.vision.v1.ImageAnnotatorClient;
import com.google.cloud.vision.v1.InputConfig;
import com.google.cloud.vision.v1.Page;
import com.google.cloud.vision.v1.Paragraph;
import com.google.cloud.vision.v1.Symbol;
import com.google.cloud.vision.v1.Word;
import java.io.IOException;

public class BatchAnnotateFilesGcs {

  public static void batchAnnotateFilesGcs() throws IOException {
    String gcsUri = "gs://cloud-samples-data/vision/document_understanding/kafka.pdf";
    batchAnnotateFilesGcs(gcsUri);
  }

  public static void batchAnnotateFilesGcs(String gcsUri) throws IOException {
    // 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 (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(1) // Process the first page
              .addPages(2) // Process the second page
              .addPages(-1) // Process the last page
              .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);

      // Process the results, just get the first result, since only one file was sent in this
      // 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());
                }
              }
            }
          }
        }
      }
    }
  }
}

Node.js

Antes de testar esta amostra, siga as instruções de configuração do Node.js no Guia de início rápido do Vision: como usar bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API VisionNode.js.

Para autenticar no Vision, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local. 

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const gcsSourceUri = 'gs://cloud-samples-data/vision/document_understanding/kafka.pdf';

// Imports the Google Cloud client libraries
const {ImageAnnotatorClient} = require('@google-cloud/vision').v1;

// Instantiates a client
const client = new ImageAnnotatorClient();

// 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 request object for each file that you want annotated.
async function batchAnnotateFiles() {
  // First 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
  const inputConfig = {
    mimeType: 'application/pdf',
    gcsSource: {
      uri: gcsSourceUri,
    },
  };

  // 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
  const features = [{type: 'DOCUMENT_TEXT_DETECTION'}];

  // Build the request object for that one file. Note: for additional files you have to create
  // additional file request 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
  const fileRequest = {
    inputConfig: inputConfig,
    features: features,
    // Annotate the first two pages and the last one (max 5 pages)
    // First page starts at 1, and not 0. Last page is -1.
    pages: [1, 2, -1],
  };

  // Add each `AnnotateFileRequest` object to the batch request.
  const request = {
    requests: [fileRequest],
  };

  // Make the synchronous batch request.
  const [result] = await client.batchAnnotateFiles(request);

  // Process the results, just get the first result, since only one file was sent in this
  // sample.
  const responses = result.responses[0].responses;

  for (const response of responses) {
    console.log(`Full text: ${response.fullTextAnnotation.text}`);
    for (const page of response.fullTextAnnotation.pages) {
      for (const block of page.blocks) {
        console.log(`Block confidence: ${block.confidence}`);
        for (const paragraph of block.paragraphs) {
          console.log(` Paragraph confidence: ${paragraph.confidence}`);
          for (const word of paragraph.words) {
            const symbol_texts = word.symbols.map(symbol => symbol.text);
            const word_text = symbol_texts.join('');
            console.log(
              `  Word text: ${word_text} (confidence: ${word.confidence})`
            );
            for (const symbol of word.symbols) {
              console.log(
                `   Symbol: ${symbol.text} (confidence: ${symbol.confidence})`
              );
            }
          }
        }
      }
    }
  }
}

batchAnnotateFiles();

Python

Antes de testar esta amostra, siga as instruções de configuração do Python no Guia de início rápido do Vision: como usar bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API VisionPython.

Para autenticar no Vision, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local. 


from google.cloud import vision_v1


def sample_batch_annotate_files(
    storage_uri="gs://cloud-samples-data/vision/document_understanding/kafka.pdf",
):
    """Perform batch file annotation."""
    mime_type = "application/pdf"

    client = vision_v1.ImageAnnotatorClient()

    gcs_source = {"uri": storage_uri}
    input_config = {"gcs_source": gcs_source, "mime_type": mime_type}
    features = [{"type_": vision_v1.Feature.Type.DOCUMENT_TEXT_DETECTION}]

    # The service can process up to 5 pages per document file.
    # Here we specify the first, second, and last page of the document to be
    # processed.
    pages = [1, 2, -1]
    requests = [{"input_config": input_config, "features": features, "pages": pages}]

    response = client.batch_annotate_files(requests=requests)
    for image_response in response.responses[0].responses:
        print(f"Full text: {image_response.full_text_annotation.text}")
        for page in image_response.full_text_annotation.pages:
            for block in page.blocks:
                print(f"\nBlock confidence: {block.confidence}")
                for par in block.paragraphs:
                    print(f"\tParagraph confidence: {par.confidence}")
                    for word in par.words:
                        print(f"\t\tWord confidence: {word.confidence}")
                        for symbol in word.symbols:
                            print(
                                "\t\t\tSymbol: {}, (confidence: {})".format(
                                    symbol.text, symbol.confidence
                                )
                            )

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