Detectar texto em arquivos (PDF/TIFF)

 // detectAsyncDocumentURI performs Optical Character Recognition (OCR) on a // PDF file stored in GCS. func detectAsyncDocumentURI(w io.Writer, gcsSourceURI, gcsDestinationURI string) error { 	ctx := context.Background()  	client, err := vision.NewImageAnnotatorClient(ctx) 	if err != nil { 		return err 	}  	request := &visionpb.AsyncBatchAnnotateFilesRequest{ 		Requests: []*visionpb.AsyncAnnotateFileRequest{ 			{ 				Features: []*visionpb.Feature{ 					{ 						Type: visionpb.Feature_DOCUMENT_TEXT_DETECTION, 					}, 				}, 				InputConfig: &visionpb.InputConfig{ 					GcsSource: &visionpb.GcsSource{Uri: gcsSourceURI}, 					// Supported MimeTypes are: "application/pdf" and "image/tiff". 					MimeType: "application/pdf", 				}, 				OutputConfig: &visionpb.OutputConfig{ 					GcsDestination: &visionpb.GcsDestination{Uri: gcsDestinationURI}, 					// How many pages should be grouped into each json output file. 					BatchSize: 2, 				}, 			}, 		}, 	}  	operation, err := client.AsyncBatchAnnotateFiles(ctx, request) 	if err != nil { 		return err 	}  	fmt.Fprintf(w, "Waiting for the operation to finish.")  	resp, err := operation.Wait(ctx) 	if err != nil { 		return err 	}  	fmt.Fprintf(w, "%v", resp)  	return nil } 

/**  * Performs document text OCR with PDF/TIFF as source files on Google Cloud Storage.  *  * @param gcsSourcePath The path to the remote file on Google Cloud Storage to detect document  *     text on.  * @param gcsDestinationPath The path to the remote file on Google Cloud Storage to store the  *     results on.  * @throws Exception on errors while closing the client.  */ public static void detectDocumentsGcs(String gcsSourcePath, String gcsDestinationPath)     throws Exception {    // 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 client = ImageAnnotatorClient.create()) {     List<AsyncAnnotateFileRequest> requests = new ArrayList<>();      // Set the GCS source path for the remote file.     GcsSource gcsSource = GcsSource.newBuilder().setUri(gcsSourcePath).build();      // Create the configuration with the specified MIME (Multipurpose Internet Mail Extensions)     // types     InputConfig inputConfig =         InputConfig.newBuilder()             .setMimeType(                 "application/pdf") // Supported MimeTypes: "application/pdf", "image/tiff"             .setGcsSource(gcsSource)             .build();      // Set the GCS destination path for where to save the results.     GcsDestination gcsDestination =         GcsDestination.newBuilder().setUri(gcsDestinationPath).build();      // Create the configuration for the System.output with the batch size.     // The batch size sets how many pages should be grouped into each json System.output file.     OutputConfig outputConfig =         OutputConfig.newBuilder().setBatchSize(2).setGcsDestination(gcsDestination).build();      // Select the Feature required by the vision API     Feature feature = Feature.newBuilder().setType(Feature.Type.DOCUMENT_TEXT_DETECTION).build();      // Build the OCR request     AsyncAnnotateFileRequest request =         AsyncAnnotateFileRequest.newBuilder()             .addFeatures(feature)             .setInputConfig(inputConfig)             .setOutputConfig(outputConfig)             .build();      requests.add(request);      // Perform the OCR request     OperationFuture<AsyncBatchAnnotateFilesResponse, OperationMetadata> response =         client.asyncBatchAnnotateFilesAsync(requests);      System.out.println("Waiting for the operation to finish.");      // Wait for the request to finish. (The result is not used, since the API saves the result to     // the specified location on GCS.)     List<AsyncAnnotateFileResponse> result =         response.get(180, TimeUnit.SECONDS).getResponsesList();      // Once the request has completed and the System.output has been     // written to GCS, we can list all the System.output files.     Storage storage = StorageOptions.getDefaultInstance().getService();      // Get the destination location from the gcsDestinationPath     Pattern pattern = Pattern.compile("gs://([^/]+)/(.+)");     Matcher matcher = pattern.matcher(gcsDestinationPath);      if (matcher.find()) {       String bucketName = matcher.group(1);       String prefix = matcher.group(2);        // Get the list of objects with the given prefix from the GCS bucket       Bucket bucket = storage.get(bucketName);       com.google.api.gax.paging.Page<Blob> pageList = bucket.list(BlobListOption.prefix(prefix));        Blob firstOutputFile = null;        // List objects with the given prefix.       System.out.println("Output files:");       for (Blob blob : pageList.iterateAll()) {         System.out.println(blob.getName());          // Process the first System.output file from GCS.         // Since we specified batch size = 2, the first response contains         // the first two pages of the input file.         if (firstOutputFile == null) {           firstOutputFile = blob;         }       }        // Get the contents of the file and convert the JSON contents to an AnnotateFileResponse       // object. If the Blob is small read all its content in one request       // (Note: the file is a .json file)       // Storage guide: https://cloud.google.com/storage/docs/downloading-objects       String jsonContents = new String(firstOutputFile.getContent());       Builder builder = AnnotateFileResponse.newBuilder();       JsonFormat.parser().merge(jsonContents, builder);        // Build the AnnotateFileResponse object       AnnotateFileResponse annotateFileResponse = builder.build();        // Parse through the object to get the actual response for the first page of the input file.       AnnotateImageResponse annotateImageResponse = annotateFileResponse.getResponses(0);        // Here we print the full text from the first page.       // The response contains more information:       // annotation/pages/blocks/paragraphs/words/symbols       // including confidence score and bounding boxes       System.out.format("%nText: %s%n", annotateImageResponse.getFullTextAnnotation().getText());     } else {       System.out.println("No MATCH");     }   } }

 // Imports the Google Cloud client libraries const vision = require('@google-cloud/vision').v1;  // Creates a client const client = new vision.ImageAnnotatorClient();  /**  * TODO(developer): Uncomment the following lines before running the sample.  */ // Bucket where the file resides // const bucketName = 'my-bucket'; // Path to PDF file within bucket // const fileName = 'path/to/document.pdf'; // The folder to store the results // const outputPrefix = 'results'  const gcsSourceUri = `gs://${bucketName}/${fileName}`; const gcsDestinationUri = `gs://${bucketName}/${outputPrefix}/`;  const inputConfig = {   // Supported mime_types are: 'application/pdf' and 'image/tiff'   mimeType: 'application/pdf',   gcsSource: {     uri: gcsSourceUri,   }, }; const outputConfig = {   gcsDestination: {     uri: gcsDestinationUri,   }, }; const features = [{type: 'DOCUMENT_TEXT_DETECTION'}]; const request = {   requests: [     {       inputConfig: inputConfig,       features: features,       outputConfig: outputConfig,     },   ], };  const [operation] = await client.asyncBatchAnnotateFiles(request); const [filesResponse] = await operation.promise(); const destinationUri =   filesResponse.responses[0].outputConfig.gcsDestination.uri; console.log('Json saved to: ' + destinationUri);

def async_detect_document(gcs_source_uri, gcs_destination_uri):     """OCR with PDF/TIFF as source files on GCS"""     import json     import re     from google.cloud import vision     from google.cloud import storage      # Supported mime_types are: 'application/pdf' and 'image/tiff'     mime_type = "application/pdf"      # How many pages should be grouped into each json output file.     batch_size = 2      client = vision.ImageAnnotatorClient()      feature = vision.Feature(type_=vision.Feature.Type.DOCUMENT_TEXT_DETECTION)      gcs_source = vision.GcsSource(uri=gcs_source_uri)     input_config = vision.InputConfig(gcs_source=gcs_source, mime_type=mime_type)      gcs_destination = vision.GcsDestination(uri=gcs_destination_uri)     output_config = vision.OutputConfig(         gcs_destination=gcs_destination, batch_size=batch_size     )      async_request = vision.AsyncAnnotateFileRequest(         features=[feature], input_config=input_config, output_config=output_config     )      operation = client.async_batch_annotate_files(requests=[async_request])      print("Waiting for the operation to finish.")     operation.result(timeout=420)      # Once the request has completed and the output has been     # written to GCS, we can list all the output files.     storage_client = storage.Client()      match = re.match(r"gs://([^/]+)/(.+)", gcs_destination_uri)     bucket_name = match.group(1)     prefix = match.group(2)      bucket = storage_client.get_bucket(bucket_name)      # List objects with the given prefix, filtering out folders.     blob_list = [         blob         for blob in list(bucket.list_blobs(prefix=prefix))         if not blob.name.endswith("/")     ]     print("Output files:")     for blob in blob_list:         print(blob.name)      # Process the first output file from GCS.     # Since we specified batch_size=2, the first response contains     # the first two pages of the input file.     output = blob_list[0]      json_string = output.download_as_bytes().decode("utf-8")     response = json.loads(json_string)      # The actual response for the first page of the input file.     first_page_response = response["responses"][0]     annotation = first_page_response["fullTextAnnotation"]      # Here we print the full text from the first page.     # The response contains more information:     # annotation/pages/blocks/paragraphs/words/symbols     # including confidence scores and bounding boxes     print("Full text:\n")     print(annotation["text"])  

import ( 	"context" 	"fmt"  	vision "cloud.google.com/go/vision/apiv1" 	"google.golang.org/api/option" )  // setEndpoint changes your endpoint. func setEndpoint(endpoint string) error { 	// endpoint := "eu-vision.googleapis.com:443"  	ctx := context.Background() 	client, err := vision.NewImageAnnotatorClient(ctx, option.WithEndpoint(endpoint)) 	if err != nil { 		return fmt.Errorf("NewImageAnnotatorClient: %w", err) 	} 	defer client.Close()  	return nil } 

ImageAnnotatorSettings settings =     ImageAnnotatorSettings.newBuilder().setEndpoint("eu-vision.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. ImageAnnotatorClient client = ImageAnnotatorClient.create(settings);

// Imports the Google Cloud client library const vision = require('@google-cloud/vision');  async function setEndpoint() {   // Specifies the location of the api endpoint   const clientOptions = {apiEndpoint: 'eu-vision.googleapis.com'};    // Creates a client   const client = new vision.ImageAnnotatorClient(clientOptions);    // Performs text detection on the image file   const [result] = await client.textDetection('./resources/wakeupcat.jpg');   const labels = result.textAnnotations;   console.log('Text:');   labels.forEach(label => console.log(label.description)); } setEndpoint();

from google.cloud import vision  client_options = {"api_endpoint": "eu-vision.googleapis.com"}  client = vision.ImageAnnotatorClient(client_options=client_options)