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Análisis de facturas y recibos con Amazon Textract
Para analizar documentos de factura y recibo, utiliza la API AnalyzeExpense y pasa un archivo de documento como entrada.AnalyzeExpense
es una operación sincrónica que devuelve una estructura JSON que contiene el texto analizado. Para obtener más información, consulte Análisis de facturas y recibos.
Para analizar facturas y recibos de forma asíncrona, utiliceStartExpenseAnalysis
para empezar a procesar un archivo de documento de entrada y utilizarGetExpenseAnalysis
para obtener los resultados.
Puede proporcionar un documento de entrada como matriz de bytes de imagen (bytes de imagen con codificación en base64) o como objeto Amazon S3. En este procedimiento cargará un archivo de imagen en su bucket de S3; y especificará el nombre de archivo.
Para analizar una factura o un recibo (API)
Si aún no lo ha hecho:
Crear o actualizar un usuario de IAM con
AmazonTextractFullAccess
yAmazonS3ReadOnlyAccess
permisos. Para obtener más información, consulte Paso 1: Configuración de una cuenta de AWS y creación de un usuario de IAM.Instale y configure la AWS CLI y los AWS SDK. Para obtener más información, consulte Paso 2: Configurar laAWS CLIyAWSSDK de.
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Cargue una imagen que contenga un documento en su bucket de S3.
Para obtener instrucciones, consulteCarga de objetos en Amazon S3en laAmazon Simple Storage Service Manual del usuario.
Utilice los siguientes ejemplos para llamar a la operación
AnalyzeExpense
.- CLI
aws textract analyze-expense --document '{"S3Object": {"Bucket": "
bucket name
","Name": "object name
"}}'- Python
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import boto3 import io from PIL import Image, ImageDraw def draw_bounding_box(key, val, width, height, draw): # If a key is Geometry, draw the bounding box info in it if "Geometry" in key: # Draw bounding box information box = val["BoundingBox"] left = width * box['Left'] top = height * box['Top'] draw.rectangle([left, top, left + (width * box['Width']), top + (height * box['Height'])], outline='black') # Takes a field as an argument and prints out the detected labels and values def print_labels_and_values(field): # Only if labels are detected and returned if "LabelDetection" in field: print("Summary Label Detection - Confidence: {}".format( str(field.get("LabelDetection")["Confidence"])) + ", " + "Summary Values: {}".format(str(field.get("LabelDetection")["Text"]))) print(field.get("LabelDetection")["Geometry"]) else: print("Label Detection - No labels returned.") if "ValueDetection" in field: print("Summary Value Detection - Confidence: {}".format( str(field.get("ValueDetection")["Confidence"])) + ", " + "Summary Values: {}".format(str(field.get("ValueDetection")["Text"]))) print(field.get("ValueDetection")["Geometry"]) else: print("Value Detection - No values returned") def process_text_detection(bucket, document): # Get the document from S3 s3_connection = boto3.resource('s3') s3_object = s3_connection.Object(bucket, document) s3_response = s3_object.get() # opening binary stream using an in-memory bytes buffer stream = io.BytesIO(s3_response['Body'].read()) # loading stream into image image = Image.open(stream) # Detect text in the document client = boto3.client('textract', region_name="us-east-1") # process using S3 object response = client.analyze_expense( Document={'S3Object': {'Bucket': bucket, 'Name': document}}) # Set width and height to display image and draw bounding boxes # Create drawing object width, height = image.size draw = ImageDraw.Draw(image) for expense_doc in response["ExpenseDocuments"]: for line_item_group in expense_doc["LineItemGroups"]: for line_items in line_item_group["LineItems"]: for expense_fields in line_items["LineItemExpenseFields"]: print_labels_and_values(expense_fields) print() print("Summary:") for summary_field in expense_doc["SummaryFields"]: print_labels_and_values(summary_field) print() #For draw bounding boxes for line_item_group in expense_doc["LineItemGroups"]: for line_items in line_item_group["LineItems"]: for expense_fields in line_items["LineItemExpenseFields"]: for key, val in expense_fields["ValueDetection"].items(): if "Geometry" in key: draw_bounding_box(key, val, width, height, draw) for label in expense_doc["SummaryFields"]: if "LabelDetection" in label: for key, val in label["LabelDetection"].items(): draw_bounding_box(key, val, width, height, draw) # Display the image image.show() def main(): bucket = 'Bucket-Name' document = 'Document-Name' process_text_detection(bucket, document) if __name__ == "__main__": main()
- Java
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package com.amazonaws.samples; import java.awt.*; import java.awt.image.BufferedImage; import java.io.ByteArrayInputStream; import java.io.IOException; import java.util.List; import java.util.concurrent.CompletableFuture; import javax.imageio.ImageIO; import javax.swing.*; import software.amazon.awssdk.auth.credentials.AwsBasicCredentials; import software.amazon.awssdk.auth.credentials.StaticCredentialsProvider; import software.amazon.awssdk.core.ResponseBytes; import software.amazon.awssdk.core.async.AsyncResponseTransformer; import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.s3.*; import software.amazon.awssdk.services.s3.model.GetObjectRequest; import software.amazon.awssdk.services.s3.model.GetObjectResponse; import software.amazon.awssdk.services.textract.TextractClient; import software.amazon.awssdk.services.textract.model.AnalyzeExpenseRequest; import software.amazon.awssdk.services.textract.model.AnalyzeExpenseResponse; import software.amazon.awssdk.services.textract.model.BoundingBox; import software.amazon.awssdk.services.textract.model.Document; import software.amazon.awssdk.services.textract.model.ExpenseDocument; import software.amazon.awssdk.services.textract.model.ExpenseField; import software.amazon.awssdk.services.textract.model.LineItemFields; import software.amazon.awssdk.services.textract.model.LineItemGroup; import software.amazon.awssdk.services.textract.model.S3Object; import software.amazon.awssdk.services.textract.model.Point; /** * * Demo code to parse Textract AnalyzeExpense API * */ public class TextractAnalyzeExpenseSample extends JPanel { private static final long serialVersionUID = 1L; BufferedImage image; static AnalyzeExpenseResponse result; public TextractAnalyzeExpenseSample(AnalyzeExpenseResponse documentResult, BufferedImage bufImage) throws Exception { super(); result = documentResult; // Results of analyzeexpense summaryfields and lineitemgroups detection. image = bufImage; // The image containing the document. } // Draws the image and text bounding box. public void paintComponent(Graphics g) { Graphics2D g2d = (Graphics2D) g; // Create a Java2D version of g. // Draw the image. g2d.drawImage(image, 0, 0, image.getWidth(this), image.getHeight(this), this); // Iterate through summaryfields and lineitemgroups and display boundedboxes around lines of detected label and value. List<ExpenseDocument> expenseDocuments = result.expenseDocuments(); for (ExpenseDocument expenseDocument : expenseDocuments) { if (expenseDocument.hasSummaryFields()) { DisplayAnalyzeExpenseSummaryInfo(expenseDocument); List<ExpenseField> summaryfields = expenseDocument.summaryFields(); for (ExpenseField summaryfield : summaryfields) { if (summaryfield.valueDetection() != null) { ShowBoundingBox(image.getHeight(this), image.getWidth(this), summaryfield.valueDetection().geometry().boundingBox(), g2d, new Color(0, 0, 0)); } if (summaryfield.labelDetection() != null) { ShowBoundingBox(image.getHeight(this), image.getWidth(this), summaryfield.labelDetection().geometry().boundingBox(), g2d, new Color(0, 0, 0)); } } } if (expenseDocument.hasLineItemGroups()) { DisplayAnalyzeExpenseLineItemGroupsInfo(expenseDocument); List<LineItemGroup> lineitemgroups = expenseDocument.lineItemGroups(); for (LineItemGroup lineitemgroup : lineitemgroups) { if (lineitemgroup.hasLineItems()) { List<LineItemFields> lineItems = lineitemgroup.lineItems(); for (LineItemFields lineitemfield : lineItems) { if (lineitemfield.hasLineItemExpenseFields()) { List<ExpenseField> expensefields = lineitemfield.lineItemExpenseFields(); for (ExpenseField expensefield : expensefields) { if (expensefield.valueDetection() != null) { ShowBoundingBox(image.getHeight(this), image.getWidth(this), expensefield.valueDetection().geometry().boundingBox(), g2d, new Color(0, 0, 0)); } if (expensefield.labelDetection() != null) { ShowBoundingBox(image.getHeight(this), image.getWidth(this), expensefield.labelDetection().geometry().boundingBox(), g2d, new Color(0, 0, 0)); } } } } } } } } } // Show bounding box at supplied location. private void ShowBoundingBox(float imageHeight, float imageWidth, BoundingBox box, Graphics2D g2d, Color color) { float left = imageWidth * box.left(); float top = imageHeight * box.top(); // Display bounding box. g2d.setColor(color); g2d.drawRect(Math.round(left), Math.round(top), Math.round(imageWidth * box.width()), Math.round(imageHeight * box.height())); } private void ShowSelectedElement(float imageHeight, float imageWidth, BoundingBox box, Graphics2D g2d, Color color) { float left = (float) imageWidth * (float) box.left(); float top = (float) imageHeight * (float) box.top(); System.out.println(left); System.out.println(top); // Display bounding box. g2d.setColor(color); g2d.fillRect(Math.round(left), Math.round(top), Math.round(imageWidth * box.width()), Math.round(imageHeight * box.height())); } // Shows polygon at supplied location private void ShowPolygon(int imageHeight, int imageWidth, List<Point> points, Graphics2D g2d) { g2d.setColor(new Color(0, 0, 0)); Polygon polygon = new Polygon(); // Construct polygon and display for (Point point : points) { polygon.addPoint((Math.round(point.x() * imageWidth)), Math.round(point.y() * imageHeight)); } g2d.drawPolygon(polygon); } private void DisplayAnalyzeExpenseSummaryInfo(ExpenseDocument expensedocument) { System.out.println(" ExpenseId : " + expensedocument.expenseIndex()); System.out.println(" Expense Summary information:"); if (expensedocument.hasSummaryFields()) { List<ExpenseField> summaryfields = expensedocument.summaryFields(); for (ExpenseField summaryfield : summaryfields) { System.out.println(" Page: " + summaryfield.pageNumber()); if (summaryfield.type() != null) { System.out.println(" Expense Summary Field Type:" + summaryfield.type().text()); } if (summaryfield.labelDetection() != null) { System.out.println(" Expense Summary Field Label:" + summaryfield.labelDetection().text()); System.out.println(" Geometry"); System.out.println(" Bounding Box: " + summaryfield.labelDetection().geometry().boundingBox().toString()); System.out.println( " Polygon: " + summaryfield.labelDetection().geometry().polygon().toString()); } if (summaryfield.valueDetection() != null) { System.out.println(" Expense Summary Field Value:" + summaryfield.valueDetection().text()); System.out.println(" Geometry"); System.out.println(" Bounding Box: " + summaryfield.valueDetection().geometry().boundingBox().toString()); System.out.println( " Polygon: " + summaryfield.valueDetection().geometry().polygon().toString()); } } } } private void DisplayAnalyzeExpenseLineItemGroupsInfo(ExpenseDocument expensedocument) { System.out.println(" ExpenseId : " + expensedocument.expenseIndex()); System.out.println(" Expense LineItemGroups information:"); if (expensedocument.hasLineItemGroups()) { List<LineItemGroup> lineitemgroups = expensedocument.lineItemGroups(); for (LineItemGroup lineitemgroup : lineitemgroups) { System.out.println(" Expense LineItemGroupsIndexID :" + lineitemgroup.lineItemGroupIndex()); if (lineitemgroup.hasLineItems()) { List<LineItemFields> lineItems = lineitemgroup.lineItems(); for (LineItemFields lineitemfield : lineItems) { if (lineitemfield.hasLineItemExpenseFields()) { List<ExpenseField> expensefields = lineitemfield.lineItemExpenseFields(); for (ExpenseField expensefield : expensefields) { if (expensefield.type() != null) { System.out.println(" Expense LineItem Field Type:" + expensefield.type().text()); } if (expensefield.valueDetection() != null) { System.out.println( " Expense Summary Field Value:" + expensefield.valueDetection().text()); System.out.println(" Geometry"); System.out.println(" Bounding Box: " + expensefield.valueDetection().geometry().boundingBox().toString()); System.out.println(" Polygon: " + expensefield.valueDetection().geometry().polygon().toString()); } if (expensefield.labelDetection() != null) { System.out.println( " Expense LineItem Field Label:" + expensefield.labelDetection().text()); System.out.println(" Geometry"); System.out.println(" Bounding Box: " + expensefield.labelDetection().geometry().boundingBox().toString()); System.out.println(" Polygon: " + expensefield.labelDetection().geometry().polygon().toString()); } } } } } } } } public static void main(String arg[]) throws Exception { // Creates a default async client with credentials and AWS Region loaded from // the // environment S3AsyncClient client = S3AsyncClient.builder().region(Region.US_EAST_1).build(); System.out.println("Creating the S3 Client"); // Start the call to Amazon S3, not blocking to wait for the result CompletableFuture<ResponseBytes<GetObjectResponse>> responseFuture = client.getObject( GetObjectRequest.builder().bucket("textractanalyzeexpense").key("input/sample-receipt.jpg").build(), AsyncResponseTransformer.toBytes()); System.out.println("Successfully read the object"); // When future is complete (either successfully or in error), handle the // response CompletableFuture<ResponseBytes<GetObjectResponse>> operationCompleteFuture = responseFuture .whenComplete((getObjectResponse, exception) -> { if (getObjectResponse != null) { // At this point, the file my-file.out has been created with the data // from S3; let's just print the object version // Convert this into Async call and remove the below block from here and put it // outside TextractClient textractclient = TextractClient.builder().region(Region.US_EAST_1).build(); AnalyzeExpenseRequest request = AnalyzeExpenseRequest.builder() .document( Document.builder().s3Object(S3Object.builder().name("YOURObjectName") .bucket("YOURBucket").build()).build()) .build(); AnalyzeExpenseResponse result = textractclient.analyzeExpense(request); System.out.print(result.toString()); ByteArrayInputStream bais = new ByteArrayInputStream(getObjectResponse.asByteArray()); try { BufferedImage image = ImageIO.read(bais); System.out.println("Successfully read the image"); JFrame frame = new JFrame("Expense Image"); frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); TextractAnalyzeExpense panel = new TextractAnalyzeExpense(result, image); panel.setPreferredSize(new Dimension(image.getWidth(), image.getHeight())); frame.setContentPane(panel); frame.pack(); frame.setVisible(true); } catch (IOException e) { throw new RuntimeException(e); } catch (Exception e) { // TODO Auto-generated catch block e.printStackTrace(); } } else { // Handle the error exception.printStackTrace(); } }); // We could do other work while waiting for the AWS call to complete in // the background, but we'll just wait for "whenComplete" to finish instead operationCompleteFuture.join(); } }
- Node.Js
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// Import required AWS SDK clients and commands for Node.js import { AnalyzeExpenseCommand } from "@aws-sdk/client-textract"; import { TextractClient } from "@aws-sdk/client-textract"; // Set the AWS Region. const REGION = "region"; //e.g. "us-east-1" // Create SNS service object. const textractClient = new TextractClient({ region: REGION }); const bucket = 'bucket' const photo = 'photo' // Set params const params = { Document: { S3Object: { Bucket: bucket, Name: photo }, }, } const process_text_detection = async () => { try { const aExpense = new AnalyzeExpenseCommand(params); const response = await textractClient.send(aExpense); //console.log(response) response.ExpenseDocuments.forEach(doc => { doc.LineItemGroups.forEach(items => { items.LineItems.forEach(fields => { fields.LineItemExpenseFields.forEach(expenseFields =>{ console.log(expenseFields) }) } )} ) } ) return response; // For unit tests. } catch (err) { console.log("Error", err); } } process_text_detection()
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Esto le proporcionará la salida de JSON para la
AnalyzeExpense
.