Amazon Textract를 사용한 인보이스 및 영수증 분석

인보이스 또는 영수증을 분석하려면(API)

1. 아직 설정하지 않았다면 다음과 같이 하세요.

   1. 사용자에게 AmazonTextractFullAccess 및 AmazonS3ReadOnlyAccess 권한을 부여합니다. 자세한 내용은 1단계: AWS 계정 설정 및 사용자 만들기 단원을 참조하십시오.

   2. AWS CLI 및 AWS SDKs를 설치하고 구성합니다. 자세한 내용은 2단계: AWS CLI 및 AWS SDKs 설정 단원을 참조하십시오.

2. 문서가 포함된 이미지를 S3 버킷에 업로드합니다.

   이에 관한 지침은 Amazon Simple Storage Service 사용 설명서에서 Amazon S3에 객체 업로드를 참조하세요.

3. 다음 예제를 사용하여 AnalyzeExpense 작업을 호출합니다.

   AWS CLI

   이 AWS CLI 명령은 analyze-expense CLI 작업에 대한 JSON 출력을 표시합니다.

   Bucket 및의 값을 2단계에서 사용한 Amazon S3 버킷 및 문서의 이름으로 Name 바꿉니다. profile-name를 역할을 수임할 수 있는 프로파일의 이름으로 바꾸고를 코드를 실행하려는 리전region으로 바꿉니다.

   aws textract analyze-expense \
       --document '{"S3Object": {"Bucket": "bucket","Name": "object"}}' \
       --profile profile-name \
       --region region

   Python

   다음 예제 코드는 감지된 항목 주위에 문서와 상자를 표시합니다.

   함수에서 bucket 및의 값을 2단계에서 사용한 Amazon S3 버킷 및 문서의 document 이름으로 main바꿉니다. profile-name를 역할을 수임할 수 있는 프로파일의 이름으로 바꾸고를 코드를 실행하려는 리전region으로 바꿉니다.

   
                       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_expense_analysis(s3_connection, client, bucket, document):
           
       # Get the document from 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)
   
       # Analyze document
       # 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():
       session = boto3.Session(profile_name='profile-name')
       s3_connection = session.resource('s3')
       client = session.client('textract', region_name='region')
       bucket = 'bucket'
       document = 'document'
       process_expense_analysis(s3_connection, client, bucket, document)
   
   if __name__ == "__main__":
       main()
   
                     

   Java

   다음 예제 코드는 감지된 항목 주위에 문서와 상자를 표시합니다.

   함수에서 bucket 및의 값을 2단계에서 사용한 Amazon S3 버킷 및 문서 이미지의 document 이름으로 main바꿉니다.

   
                       
   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

   다음 예제 코드는 감지된 항목 주위에 문서와 상자를 표시합니다.

   함수에서 bucket 및의 값을 2단계에서 사용한 Amazon S3 버킷 및 문서의 photo 이름으로 main바꿉니다. profileName를 역할을 수임할 수 있는 프로파일의 이름으로 바꾸고를 코드를 실행하려는 리전region으로 바꿉니다.

   
                       // Import required AWS SDK clients and commands for Node.js
   import { AnalyzeExpenseCommand } from  "@aws-sdk/client-textract";
   import  { TextractClient } from "@aws-sdk/client-textract";
   import {fromIni} from '@aws-sdk/credential-providers';
   
   // Set the AWS Region.
   const REGION = "region-name"; //e.g. "us-east-1"
   const profileName = "profile-name";
   // Create SNS service object.
   const textractClient = new TextractClient({region: REGION, 
     credentials: fromIni({profile: profileName,}), 
   });
   
   const bucket = 'bucket-name'
   const photo = 'photo-name'
   
   // 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()
                     

4. 그러면 AnalyzeExpense 작업에 대한 JSON 출력이 제공됩니다.