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# 使用同步操作处理文档
Amazon Textract 可以检测和分析单页文档中以 JPEG、PNG、PDF 和 TIFF 格式提供的文本。操作是同步的，近实时返回结果。有关文档的更多信息，请参阅 [文本检测和文档分析响应对象](how-it-works-document-layout.md)。
本节介绍了如何使用 Amazon Textract 同步检测和分析单页文档中的文本。要检测和分析多页文档中的文本，或者异步检测 JPEG 和 PNG 文档，请参阅[使用异步操作处理文档](async.md).
您可以将 Amazon Textract 同步操作用于以下目的：
+ 文本检测 — 您可以使用[DetectDocumentText](API_DetectDocumentText.md)operation. 有关更多信息，请参阅 [检测文本](how-it-works-detecting.md)。
+ 文本分析 — 您可以使用[AnalyzeDocument](API_AnalyzeDocument.md)operation. 有关更多信息，请参阅 [分析文档](how-it-works-analyzing.md)。
+ 发票和收据分析 — 您可以使用 AnalyzeEsend 操作识别单页发票上检测到的文本或收据之间的财务关系。有关更多信息，请参阅 。[分析发票和收据](invoices-receipts.md)
+ 身份证件分析 — 您可以分析美国政府签发的身份证件，并提取信息以及身份证件上的常见信息类型。有关更多信息，请参阅[分析身份证件](how-it-works-identity.md).
**Topics**
+ [调用 Amazon Textract 同步操作](sync-calling.md)
+ [使用 Amazon Textract 检测文档文本](detecting-document-text.md)
+ [使用 Amazon Textract 分析文档文本](analyzing-document-text.md)
+ [使用 Amazon Textract 分析发票和收据](analyzing-document-expense.md)
+ [使用 Amazon Textract 分析身份文档](analyzing-document-identity.md)
# 调用 Amazon Textract 同步操作
Amazon Textract 操作处理存储在本地文件系统上的文档图像或存储在 Amazon S3 存储桶中的文档图像。您可以通过使用[Document](API_Document.md)输入参数。文档图像可以是 PNG、JPEG、PDF 或 TIFF 格式。同步操作的结果将立即返回，不会存储以供检索。
有关完整的示例，请参阅[使用 Amazon Textract 检测文档文本](detecting-document-text.md).
## 请求
下面介绍了请求在 Amazon Textract 中的工作方式。
### 作为图像字节传递的文档
您可以将文档图像传递给 Amazon Textract 操作，方法是将图像作为 base64 编码的字节数组传递该图像。例如，从本地文件系统加载的文档图像。如果您使用的是，代码可能无需对文档文件字节进行编码。AWS开发工具包调用 Amazon Textract API 操作。
图像字节在`Bytes`字段`Document`输入参数。以下示例显示了传递图像字节的 Amazon Textract 操作的输入 JSON`Bytes`输入参数。
```
{
"Document": {
"Bytes": "/9j/4AAQSk....."
}
}
```
**注意**
如果您使用AWS CLI，您无法将图像字节传递给 Amazon Textract 操作。相反，您必须引用存储在 Amazon S3 存储桶中的映像。
以下 Java 代码显示如何从本地文件系统加载图像和调用 Amazon Textract 操作。
```
String document="input.png";
ByteBuffer imageBytes;
try (InputStream inputStream = new FileInputStream(new File(document))) {
imageBytes = ByteBuffer.wrap(IOUtils.toByteArray(inputStream));
}
AmazonTextract client = AmazonTextractClientBuilder.defaultClient();
DetectDocumentTextRequest request = new DetectDocumentTextRequest()
.withDocument(new Document()
.withBytes(imageBytes));
DetectDocumentTextResult result = client.detectDocumentText(request);
```
### Amazon S3 存储桶中存储的文档
Amazon Textract 可以分析存储在 Amazon S3 存储桶中的文档图像。您可以使用[S3Object](API_S3Object.md)字段`Document`输入参数。以下示例显示针对处理存储在 Amazon S3 存储桶中的文档的 Amazon Textract 操作的输入 JSON。
```
{
"Document": {
"S3Object": {
"Bucket": "bucket",
"Name": "input.png"
}
}
}
```
以下示例显示如何使用存储在 Amazon S3 存储桶中的图像调用 Amazon Textract 操作。
```
String document="input.png";
String bucket="bucket";
AmazonTextract client = AmazonTextractClientBuilder.defaultClient();
DetectDocumentTextRequest request = new DetectDocumentTextRequest()
.withDocument(new Document()
.withS3Object(new S3Object()
.withName(document)
.withBucket(bucket)));
DetectDocumentTextResult result = client.detectDocumentText(request);
```
## 响应
以下示例是调用到的 JSON 响应。`DetectDocumentText`. 有关更多信息，请参阅 [检测文本](how-it-works-detecting.md)。
```
{
{
"DocumentMetadata": {
"Pages": 1
},
"Blocks": [
{
"BlockType": "PAGE",
"Geometry": {
"BoundingBox": {
"Width": 0.9995205998420715,
"Height": 1.0,
"Left": 0.0,
"Top": 0.0
},
"Polygon": [
{
"X": 0.0,
"Y": 0.0
},
{
"X": 0.9995205998420715,
"Y": 2.297314024515845E-16
},
{
"X": 0.9995205998420715,
"Y": 1.0
},
{
"X": 0.0,
"Y": 1.0
}
]
},
"Id": "ca4b9171-7109-4adb-a811-e09bbe4834dd",
"Relationships": [
{
"Type": "CHILD",
"Ids": [
"26085884-d005-4144-b4c2-4d83dc50739b",
"ee9d01bc-d91c-401d-8c0a-eec76f5f7862",
"404bb3d3-d7ab-4008-a195-5dec87a08664",
"8ae1b4ba-67c1-4486-bd20-54f461886ce9",
"47aab5ab-be2c-4c73-97c7-d0a45454e843",
"dd06bb49-6a56-4ea7-beec-a2aa09835c3c",
"8837153d-81b8-4031-a49f-83a3d81803c2",
"5dae3b74-9e95-4b62-99b7-93b88fe70648",
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"41756974-64ef-432d-b4b2-34702505975a",
"93d96d32-8b4a-4a98-9578-8b4df4f227a6",
"bc907357-63d6-43c0-ab87-80d7e76d377e",
"2d727ca7-3acb-4bb9-a564-5885c90e9325",
"f32a5989-cbfb-41e6-b0fc-ce1c77c014bd",
"e0ba06d0-dbb6-4962-8047-8cac3adfe45a",
"b6ed204d-ae01-4b75-bb91-c85d4147a37e",
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"ebc18885-48d7-45b8-90e3-d172b4357802",
"babf6360-789e-49c1-9c78-0784acc14a0c"
]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.93761444091797,
"Text": "Employment Application",
"Geometry": {
"BoundingBox": {
"Width": 0.3391372561454773,
"Height": 0.06906412541866302,
"Left": 0.29548385739326477,
"Top": 0.027493247762322426
},
"Polygon": [
{
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},
{
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"Y": 0.027493247762322426
},
{
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"Y": 0.0965573713183403
},
{
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"Y": 0.0965573713183403
}
]
},
"Id": "26085884-d005-4144-b4c2-4d83dc50739b",
"Relationships": [
{
"Type": "CHILD",
"Ids": [
"ed48dacc-d089-498f-8e93-1cee1e5f39f3",
"ac7370f3-cbb7-4cd9-a8f9-bdcb2252caaf"
]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.91246795654297,
"Text": "Application Information",
"Geometry": {
"BoundingBox": {
"Width": 0.19878505170345306,
"Height": 0.03754019737243652,
"Left": 0.03988289833068848,
"Top": 0.14050349593162537
},
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},
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}
]
},
"Id": "ee9d01bc-d91c-401d-8c0a-eec76f5f7862",
"Relationships": [
{
"Type": "CHILD",
"Ids": [
"efe3fc6d-becb-4520-80ee-49a329386aee",
"c2260852-6cfd-4a71-9fc6-62b2f9b02355"
]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.88693237304688,
"Text": "Full Name: Jane Doe",
"Geometry": {
"BoundingBox": {
"Width": 0.16733919084072113,
"Height": 0.031106337904930115,
"Left": 0.03899926319718361,
"Top": 0.21361036598682404
},
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},
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},
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}
]
},
"Id": "404bb3d3-d7ab-4008-a195-5dec87a08664",
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]
}
]
},
{
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"Confidence": 99.9206314086914,
"Text": "Phone Number: 555-0100",
"Geometry": {
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]
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]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.48902893066406,
"Text": "Home Address: 123 Any Street, Any Town. USA",
"Geometry": {
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}
]
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"Id": "47aab5ab-be2c-4c73-97c7-d0a45454e843",
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{
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]
},
{
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"Confidence": 99.89382934570312,
"Text": "Mailing Address: same as above",
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}
]
},
"Id": "dd06bb49-6a56-4ea7-beec-a2aa09835c3c",
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{
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]
}
]
},
{
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"Text": "Previous Employment History",
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]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.66949462890625,
"Text": "Start Date",
"Geometry": {
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]
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{
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{
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]
},
{
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]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.9817886352539,
"Text": "Reason for leaving",
"Geometry": {
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"Left": 0.7430596351623535,
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}
]
},
"Id": "a234f0e8-67de-46f4-a7c7-0bbe8d5159ce",
"Relationships": [
{
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"Ids": [
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]
}
]
},
{
"BlockType": "LINE",
"Confidence": 99.77413177490234,
"Text": "1/15/2009",
"Geometry": {
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}
```
# 使用 Amazon Textract 检测文档文本
要检测文档中的文本，请使用[DetectDocumentText](API_DetectDocumentText.md)操作，然后将文档文件作为输入传递。`DetectDocumentText`返回 JSON 结构，其中包含检测到的文本的行和单词、文档中文本的位置以及检测到的文本之间的关系。有关更多信息，请参阅 [检测文本](how-it-works-detecting.md)。
您可以提供输入文档作为图像字节数组 (base64 编码的图像字节) 或 Amazon S3 对象。在此过程中，您将图像文件上传到您的 S3 存储桶并指定文件名称。
**检测文档中的文本 (API)**
1. 如果您尚未执行以下操作，请：
1. 使用创建或更新 IAM 用户`AmazonTextractFullAccess`和`AmazonS3ReadOnlyAccess`权限。有关更多信息，请参阅 [第 1 步：设置 AWS 账户并创建 IAM 用户](setting-up.md#setting-up-iam)。
1. 安装和配置 AWS CLI 和 AWS 开发工具包。有关更多信息，请参阅 [第 2 步：设置AWS CLI和AWS软件开发工具包](setup-awscli-sdk.md)。
1. 将文档上传到 S3 存储桶。
有关说明，请参阅[将对象上传到 Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/UploadingObjectsintoAmazonS3.html)中的*Amazon Simple Service 用户指南*.
1. 使用以下示例调用 `DetectDocumentText` 操作。
------
#### [ Java ]
以下示例代码在检测到的文本行周围显示文档和框。
在函数`main`，替换的值`bucket`和`document`将包含您在步骤 2 中使用的 Amazon S3 存储桶名称和文档的名称。
```
//Calls DetectDocumentText.
//Loads document from S3 bucket. Displays the document and bounding boxes around detected lines/words of text.
package com.amazonaws.samples;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.util.List;
import javax.imageio.ImageIO;
import javax.swing.*;
import com.amazonaws.services.s3.AmazonS3;
import com.amazonaws.services.s3.AmazonS3ClientBuilder;
import com.amazonaws.services.s3.model.S3ObjectInputStream;
import com.amazonaws.client.builder.AwsClientBuilder.EndpointConfiguration;
import com.amazonaws.services.textract.AmazonTextract;
import com.amazonaws.services.textract.AmazonTextractClientBuilder;
import com.amazonaws.services.textract.model.Block;
import com.amazonaws.services.textract.model.BoundingBox;
import com.amazonaws.services.textract.model.DetectDocumentTextRequest;
import com.amazonaws.services.textract.model.DetectDocumentTextResult;
import com.amazonaws.services.textract.model.Document;
import com.amazonaws.services.textract.model.S3Object;
import com.amazonaws.services.textract.model.Point;
import com.amazonaws.services.textract.model.Relationship;
public class DocumentText extends JPanel {
private static final long serialVersionUID = 1L;
BufferedImage image;
DetectDocumentTextResult result;
public DocumentText(DetectDocumentTextResult documentResult, BufferedImage bufImage) throws Exception {
super();
result = documentResult; // Results of text detection.
image = bufImage; // The image containing the document.
}
// Draws the image and text bounding box.
public void paintComponent(Graphics g) {
int height = image.getHeight(this);
int width = image.getWidth(this);
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 blocks and display polygons around lines of detected text.
List blocks = result.getBlocks();
for (Block block : blocks) {
DisplayBlockInfo(block);
if ((block.getBlockType()).equals("LINE")) {
ShowPolygon(height, width, block.getGeometry().getPolygon(), g2d);
/*
ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d);
*/
} else { // its a word, so just show vertical lines.
ShowPolygonVerticals(height, width, block.getGeometry().getPolygon(), g2d);
}
}
}
// Show bounding box at supplied location.
private void ShowBoundingBox(int imageHeight, int imageWidth, BoundingBox box, Graphics2D g2d) {
float left = imageWidth * box.getLeft();
float top = imageHeight * box.getTop();
// Display bounding box.
g2d.setColor(new Color(0, 212, 0));
g2d.drawRect(Math.round(left), Math.round(top),
Math.round(imageWidth * box.getWidth()), Math.round(imageHeight * box.getHeight()));
}
// Shows polygon at supplied location
private void ShowPolygon(int imageHeight, int imageWidth, List 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.getX() * imageWidth)),
Math.round(point.getY() * imageHeight));
}
g2d.drawPolygon(polygon);
}
// Draws only the vertical lines in the supplied polygon.
private void ShowPolygonVerticals(int imageHeight, int imageWidth, List points, Graphics2D g2d) {
g2d.setColor(new Color(0, 212, 0));
Object[] parry = points.toArray();
g2d.setStroke(new BasicStroke(2));
g2d.drawLine(Math.round(((Point) parry[0]).getX() * imageWidth),
Math.round(((Point) parry[0]).getY() * imageHeight), Math.round(((Point) parry[3]).getX() * imageWidth),
Math.round(((Point) parry[3]).getY() * imageHeight));
g2d.setColor(new Color(255, 0, 0));
g2d.drawLine(Math.round(((Point) parry[1]).getX() * imageWidth),
Math.round(((Point) parry[1]).getY() * imageHeight), Math.round(((Point) parry[2]).getX() * imageWidth),
Math.round(((Point) parry[2]).getY() * imageHeight));
}
//Displays information from a block returned by text detection and text analysis
private void DisplayBlockInfo(Block block) {
System.out.println("Block Id : " + block.getId());
if (block.getText()!=null)
System.out.println(" Detected text: " + block.getText());
System.out.println(" Type: " + block.getBlockType());
if (block.getBlockType().equals("PAGE") !=true) {
System.out.println(" Confidence: " + block.getConfidence().toString());
}
if(block.getBlockType().equals("CELL"))
{
System.out.println(" Cell information:");
System.out.println(" Column: " + block.getColumnIndex());
System.out.println(" Row: " + block.getRowIndex());
System.out.println(" Column span: " + block.getColumnSpan());
System.out.println(" Row span: " + block.getRowSpan());
}
System.out.println(" Relationships");
List relationships=block.getRelationships();
if(relationships!=null) {
for (Relationship relationship : relationships) {
System.out.println(" Type: " + relationship.getType());
System.out.println(" IDs: " + relationship.getIds().toString());
}
} else {
System.out.println(" No related Blocks");
}
System.out.println(" Geometry");
System.out.println(" Bounding Box: " + block.getGeometry().getBoundingBox().toString());
System.out.println(" Polygon: " + block.getGeometry().getPolygon().toString());
List entityTypes = block.getEntityTypes();
System.out.println(" Entity Types");
if(entityTypes!=null) {
for (String entityType : entityTypes) {
System.out.println(" Entity Type: " + entityType);
}
} else {
System.out.println(" No entity type");
}
if(block.getPage()!=null)
System.out.println(" Page: " + block.getPage());
System.out.println();
}
public static void main(String arg[]) throws Exception {
// The S3 bucket and document
String document = "";
String bucket = "";
AmazonS3 s3client = AmazonS3ClientBuilder.standard()
.withEndpointConfiguration(
new EndpointConfiguration("https://s3.amazonaws.com","us-east-1"))
.build();
// Get the document from S3
com.amazonaws.services.s3.model.S3Object s3object = s3client.getObject(bucket, document);
S3ObjectInputStream inputStream = s3object.getObjectContent();
BufferedImage image = ImageIO.read(inputStream);
// Call DetectDocumentText
EndpointConfiguration endpoint = new EndpointConfiguration(
"https://textract.us-east-1.amazonaws.com", "us-east-1");
AmazonTextract client = AmazonTextractClientBuilder.standard()
.withEndpointConfiguration(endpoint).build();
DetectDocumentTextRequest request = new DetectDocumentTextRequest()
.withDocument(new Document().withS3Object(new S3Object().withName(document).withBucket(bucket)));
DetectDocumentTextResult result = client.detectDocumentText(request);
// Create frame and panel.
JFrame frame = new JFrame("RotateImage");
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
DocumentText panel = new DocumentText(result, image);
panel.setPreferredSize(new Dimension(image.getWidth() , image.getHeight() ));
frame.setContentPane(panel);
frame.pack();
frame.setVisible(true);
}
}
```
------
#### [ AWS CLI ]
此 AWS CLI 命令显示 `detect-document-text` CLI 操作的 JSON 输出。
替换的值`Bucket`和`Name`将包含您在步骤 2 中使用的 Amazon S3 存储桶名称和文档的名称。
```
aws textract detect-document-text \
--document '{"S3Object":{"Bucket":"bucket","Name":"document"}}'
```
------
#### [ Python ]
以下示例代码显示检测到的文本行周围的文档和框。
在函数`main`，替换的值`bucket`和`document`将包含您在步骤 2 中使用的 Amazon S3 存储桶名称和文档的名称。
```
#Detects text in a document stored in an S3 bucket. Display polygon box around text and angled text
import boto3
import io
from io import BytesIO
import sys
import psutil
import time
import math
from PIL import Image, ImageDraw, ImageFont
# Displays information about a block returned by text detection and text analysis
def DisplayBlockInformation(block):
print('Id: {}'.format(block['Id']))
if 'Text' in block:
print(' Detected: ' + block['Text'])
print(' Type: ' + block['BlockType'])
if 'Confidence' in block:
print(' Confidence: ' + "{:.2f}".format(block['Confidence']) + "%")
if block['BlockType'] == 'CELL':
print(" Cell information")
print(" Column: " + str(block['ColumnIndex']))
print(" Row: " + str(block['RowIndex']))
print(" ColumnSpan: " + str(block['ColumnSpan']))
print(" RowSpan: " + str(block['RowSpan']))
if 'Relationships' in block:
print(' Relationships: {}'.format(block['Relationships']))
print(' Geometry: ')
print(' Bounding Box: {}'.format(block['Geometry']['BoundingBox']))
print(' Polygon: {}'.format(block['Geometry']['Polygon']))
if block['BlockType'] == "KEY_VALUE_SET":
print (' Entity Type: ' + block['EntityTypes'][0])
if 'Page' in block:
print('Page: ' + block['Page'])
print()
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()
stream = io.BytesIO(s3_response['Body'].read())
image=Image.open(stream)
# Detect text in the document
client = boto3.client('textract')
#process using image bytes
#image_binary = stream.getvalue()
#response = client.detect_document_text(Document={'Bytes': image_binary})
#process using S3 object
response = client.detect_document_text(
Document={'S3Object': {'Bucket': bucket, 'Name': document}})
#Get the text blocks
blocks=response['Blocks']
width, height =image.size
draw = ImageDraw.Draw(image)
print ('Detected Document Text')
# Create image showing bounding box/polygon the detected lines/text
for block in blocks:
print('Type: ' + block['BlockType'])
if block['BlockType'] != 'PAGE':
print('Detected: ' + block['Text'])
print('Confidence: ' + "{:.2f}".format(block['Confidence']) + "%")
print('Id: {}'.format(block['Id']))
if 'Relationships' in block:
print('Relationships: {}'.format(block['Relationships']))
print('Bounding Box: {}'.format(block['Geometry']['BoundingBox']))
print('Polygon: {}'.format(block['Geometry']['Polygon']))
print()
draw=ImageDraw.Draw(image)
# Draw WORD - Green - start of word, red - end of word
if block['BlockType'] == "WORD":
draw.line([(width * block['Geometry']['Polygon'][0]['X'],
height * block['Geometry']['Polygon'][0]['Y']),
(width * block['Geometry']['Polygon'][3]['X'],
height * block['Geometry']['Polygon'][3]['Y'])],fill='green',
width=2)
draw.line([(width * block['Geometry']['Polygon'][1]['X'],
height * block['Geometry']['Polygon'][1]['Y']),
(width * block['Geometry']['Polygon'][2]['X'],
height * block['Geometry']['Polygon'][2]['Y'])],
fill='red',
width=2)
# Draw box around entire LINE
if block['BlockType'] == "LINE":
points=[]
for polygon in block['Geometry']['Polygon']:
points.append((width * polygon['X'], height * polygon['Y']))
draw.polygon((points), outline='black')
# Uncomment to draw bounding box
#box=block['Geometry']['BoundingBox']
#left = width * box['Left']
#top = height * box['Top']
#draw.rectangle([left,top, left + (width * box['Width']), top +(height * box['Height'])],outline='black')
# Display the image
image.show()
# display image for 10 seconds
return len(blocks)
def main():
bucket = ''
document = ''
block_count=process_text_detection(bucket,document)
print("Blocks detected: " + str(block_count))
if __name__ == "__main__":
main()
```
------
#### [ Node.js ]
以下 Node.js 示例代码显示了检测到的文本行周围的文档和框，将结果的图像输出到运行代码的目录中。它利用`image-size`和`images`软件包。
在函数`main`，替换的值`bucket`和`document`将包含您在步骤 2 中使用的 Amazon S3 存储桶名称和文档的名称。替换的值`regionConfig`使用您的账户所在地区的名称。
```
async function main(){
// Import AWS
const AWS = require("aws-sdk")
// Use Image-Size to get
const sizeOf = require('image-size');
// Image tool to draw buffers
const images = require("images");
// Create a canvas and get the context
const { createCanvas } = require('canvas')
const canvas = createCanvas(200, 200)
const ctx = canvas.getContext('2d')
// Set variables
const bucket = 'bucket-name' // the s3 bucket name
const photo = 'image-name' // the name of file
const regionConfig = 'region'
// Set region if needed
AWS.config.update({region:regionConfig});
// Connect to Textract
const client = new AWS.Textract();
// Connect to S3 to display image
const s3 = new AWS.S3();
// Define paramaters
const params = {
Document: {
S3Object: {
Bucket: bucket,
Name: photo
},
},
}
// Function to display image
async function getImage(){
const imageData = s3.getObject(
{
Bucket: bucket,
Key: photo
}
).promise();
return imageData;
}
// get image
var imageData = await getImage()
// Get the height, width of the image
const dimensions = sizeOf(imageData.Body)
const width = dimensions.width
const height = dimensions.height
console.log(imageData.Body)
console.log(width, height)
canvas.width = width;
canvas.height = height;
try{
// Call API and log response
const res = await client.detectDocumentText(params).promise();
var image = images(imageData.Body).size(width, height)
//console.log the type of block, text, text type, and confidence
res.Blocks.forEach(block => {
console.log(`Block Type: ${block.BlockType}`),
console.log(`Text: ${block.Text}`)
console.log(`TextType: ${block.TextType}`)
console.log(`Confidence: ${block.Confidence}`)
// Draw box around detected text using polygons
ctx.strokeStyle = 'rgba(0,0,0,0.5)';
ctx.beginPath();
block.Geometry.Polygon.forEach(({X, Y}) =>
ctx.lineTo(width * X - 10, height * Y - 10)
);
ctx.closePath();
ctx.stroke();
console.log("-----")
})
// render image
var buffer = canvas.toBuffer("image/png");
image.draw(images(buffer), 10, 10)
image.save("output-image.jpg");
} catch (err){
console.error(err);}
}
main()
```
------
1. 运行示例。Python 和 Java 示例显示了文档图像。每行检测到的文本都有一个黑框。绿色垂直线是检测到的单词的开头。红色垂直线是检测到的单词的末尾。这些区域有：AWS CLI示例仅显示的 JSON 输出`DetectDocumentText`operation.
# 使用 Amazon Textract 分析文档文本
要分析文档中的文本，请使用[AnalyzeDocument](API_AnalyzeDocument.md)操作，然后将文档文件作为输入传递。`AnalyzeDocument`返回一个 JSON 结构，其中包含分析的文本。有关更多信息，请参阅 [分析文档](how-it-works-analyzing.md)。
您可以提供输入文档作为图像字节数组 (base64 编码的图像字节) 或 Amazon S3 对象。在此过程中，您将图像文件上传到您的 S3 存储桶并指定文件名称。
**分析文档中的文本 (API)**
1. 如果您尚未执行以下操作，请：
1. 使用创建或更新 IAM 用户`AmazonTextractFullAccess`和`AmazonS3ReadOnlyAccess`权限。有关更多信息，请参阅 [第 1 步：设置 AWS 账户并创建 IAM 用户](setting-up.md#setting-up-iam)。
1. 安装和配置 AWS CLI 和 AWS 开发工具包。有关更多信息，请参阅 [第 2 步：设置AWS CLI和AWS软件开发工具包](setup-awscli-sdk.md)。
1. 将包含文档的图像上传到您的 S3 存储桶。
有关说明，请参阅[将对象上传到 Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/UploadingObjectsintoAmazonS3.html)中的*Amazon Simple Service 用户指南*.
1. 使用以下示例调用 `AnalyzeDocument` 操作。
------
#### [ Java ]
以下示例代码显示检测到的项目周围的文档和框。
在函数`main`，替换的值`bucket`和`document`将包含您在步骤 2 中使用的 Amazon S3 存储桶名称和文档图像的名称。
```
//Loads document from S3 bucket. Displays the document and polygon around detected lines of text.
package com.amazonaws.samples;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.util.List;
import javax.imageio.ImageIO;
import javax.swing.*;
import com.amazonaws.services.s3.AmazonS3;
import com.amazonaws.services.s3.AmazonS3ClientBuilder;
import com.amazonaws.services.s3.model.S3ObjectInputStream;
import com.amazonaws.services.textract.AmazonTextract;
import com.amazonaws.services.textract.AmazonTextractClientBuilder;
import com.amazonaws.services.textract.model.AnalyzeDocumentRequest;
import com.amazonaws.services.textract.model.AnalyzeDocumentResult;
import com.amazonaws.services.textract.model.Block;
import com.amazonaws.services.textract.model.BoundingBox;
import com.amazonaws.services.textract.model.Document;
import com.amazonaws.services.textract.model.S3Object;
import com.amazonaws.services.textract.model.Point;
import com.amazonaws.services.textract.model.Relationship;
import com.amazonaws.client.builder.AwsClientBuilder.EndpointConfiguration;
public class AnalyzeDocument extends JPanel {
private static final long serialVersionUID = 1L;
BufferedImage image;
AnalyzeDocumentResult result;
public AnalyzeDocument(AnalyzeDocumentResult documentResult, BufferedImage bufImage) throws Exception {
super();
result = documentResult; // Results of text detection.
image = bufImage; // The image containing the document.
}
// Draws the image and text bounding box.
public void paintComponent(Graphics g) {
int height = image.getHeight(this);
int width = image.getWidth(this);
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 blocks and display bounding boxes around everything.
List blocks = result.getBlocks();
for (Block block : blocks) {
DisplayBlockInfo(block);
switch(block.getBlockType()) {
case "KEY_VALUE_SET":
if (block.getEntityTypes().contains("KEY")){
ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(255,0,0));
}
else { //VALUE
ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(0,255,0));
}
break;
case "TABLE":
ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(0,0,255));
break;
case "CELL":
ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(255,255,0));
break;
case "SELECTION_ELEMENT":
if (block.getSelectionStatus().equals("SELECTED"))
ShowSelectedElement(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(0,0,255));
break;
default:
//PAGE, LINE & WORD
//ShowBoundingBox(height, width, block.getGeometry().getBoundingBox(), g2d, new Color(200,200,0));
}
}
// uncomment to show polygon around all blocks
//ShowPolygon(height,width,block.getGeometry().getPolygon(),g2d);
}
// Show bounding box at supplied location.
private void ShowBoundingBox(int imageHeight, int imageWidth, BoundingBox box, Graphics2D g2d, Color color) {
float left = imageWidth * box.getLeft();
float top = imageHeight * box.getTop();
// Display bounding box.
g2d.setColor(color);
g2d.drawRect(Math.round(left), Math.round(top),
Math.round(imageWidth * box.getWidth()), Math.round(imageHeight * box.getHeight()));
}
private void ShowSelectedElement(int imageHeight, int imageWidth, BoundingBox box, Graphics2D g2d, Color color) {
float left = imageWidth * box.getLeft();
float top = imageHeight * box.getTop();
// Display bounding box.
g2d.setColor(color);
g2d.fillRect(Math.round(left), Math.round(top),
Math.round(imageWidth * box.getWidth()), Math.round(imageHeight * box.getHeight()));
}
// Shows polygon at supplied location
private void ShowPolygon(int imageHeight, int imageWidth, List 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.getX() * imageWidth)),
Math.round(point.getY() * imageHeight));
}
g2d.drawPolygon(polygon);
}
//Displays information from a block returned by text detection and text analysis
private void DisplayBlockInfo(Block block) {
System.out.println("Block Id : " + block.getId());
if (block.getText()!=null)
System.out.println(" Detected text: " + block.getText());
System.out.println(" Type: " + block.getBlockType());
if (block.getBlockType().equals("PAGE") !=true) {
System.out.println(" Confidence: " + block.getConfidence().toString());
}
if(block.getBlockType().equals("CELL"))
{
System.out.println(" Cell information:");
System.out.println(" Column: " + block.getColumnIndex());
System.out.println(" Row: " + block.getRowIndex());
System.out.println(" Column span: " + block.getColumnSpan());
System.out.println(" Row span: " + block.getRowSpan());
}
System.out.println(" Relationships");
List relationships=block.getRelationships();
if(relationships!=null) {
for (Relationship relationship : relationships) {
System.out.println(" Type: " + relationship.getType());
System.out.println(" IDs: " + relationship.getIds().toString());
}
} else {
System.out.println(" No related Blocks");
}
System.out.println(" Geometry");
System.out.println(" Bounding Box: " + block.getGeometry().getBoundingBox().toString());
System.out.println(" Polygon: " + block.getGeometry().getPolygon().toString());
List entityTypes = block.getEntityTypes();
System.out.println(" Entity Types");
if(entityTypes!=null) {
for (String entityType : entityTypes) {
System.out.println(" Entity Type: " + entityType);
}
} else {
System.out.println(" No entity type");
}
if(block.getBlockType().equals("SELECTION_ELEMENT")) {
System.out.print(" Selection element detected: ");
if (block.getSelectionStatus().equals("SELECTED")){
System.out.println("Selected");
}else {
System.out.println(" Not selected");
}
}
if(block.getPage()!=null)
System.out.println(" Page: " + block.getPage());
System.out.println();
}
public static void main(String arg[]) throws Exception {
// The S3 bucket and document
String document = "";
String bucket = "";
AmazonS3 s3client = AmazonS3ClientBuilder.standard()
.withEndpointConfiguration(
new EndpointConfiguration("https://s3.amazonaws.com","us-east-1"))
.build();
// Get the document from S3
com.amazonaws.services.s3.model.S3Object s3object = s3client.getObject(bucket, document);
S3ObjectInputStream inputStream = s3object.getObjectContent();
BufferedImage image = ImageIO.read(inputStream);
// Call AnalyzeDocument
EndpointConfiguration endpoint = new EndpointConfiguration(
"https://textract.us-east-1.amazonaws.com", "us-east-1");
AmazonTextract client = AmazonTextractClientBuilder.standard()
.withEndpointConfiguration(endpoint).build();
AnalyzeDocumentRequest request = new AnalyzeDocumentRequest()
.withFeatureTypes("TABLES","FORMS")
.withDocument(new Document().
withS3Object(new S3Object().withName(document).withBucket(bucket)));
AnalyzeDocumentResult result = client.analyzeDocument(request);
// Create frame and panel.
JFrame frame = new JFrame("RotateImage");
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
AnalyzeDocument panel = new AnalyzeDocument(result, image);
panel.setPreferredSize(new Dimension(image.getWidth(), image.getHeight()));
frame.setContentPane(panel);
frame.pack();
frame.setVisible(true);
}
}
```
------
#### [ AWS CLI ]
此 AWS CLI 命令显示 `detect-document-text` CLI 操作的 JSON 输出。
替换的值`Bucket`和`Name`将包含您在步骤 2 中使用的 Amazon S3 存储桶名称和文档的名称。
```
aws textract analyze-document \
--document '{"S3Object":{"Bucket":"bucket","Name":"document"}}' \
--feature-types '["TABLES","FORMS"]'
```
------
#### [ Python ]
以下示例代码显示检测到的项目周围的文档和框。
在函数`main`，替换的值`bucket`和`document`将包含您在步骤 2 中使用的 Amazon S3 存储桶名称和文档的名称。
```
#Analyzes text in a document stored in an S3 bucket. Display polygon box around text and angled text
import boto3
import io
from io import BytesIO
import sys
import math
from PIL import Image, ImageDraw, ImageFont
def ShowBoundingBox(draw,box,width,height,boxColor):
left = width * box['Left']
top = height * box['Top']
draw.rectangle([left,top, left + (width * box['Width']), top +(height * box['Height'])],outline=boxColor)
def ShowSelectedElement(draw,box,width,height,boxColor):
left = width * box['Left']
top = height * box['Top']
draw.rectangle([left,top, left + (width * box['Width']), top +(height * box['Height'])],fill=boxColor)
# Displays information about a block returned by text detection and text analysis
def DisplayBlockInformation(block):
print('Id: {}'.format(block['Id']))
if 'Text' in block:
print(' Detected: ' + block['Text'])
print(' Type: ' + block['BlockType'])
if 'Confidence' in block:
print(' Confidence: ' + "{:.2f}".format(block['Confidence']) + "%")
if block['BlockType'] == 'CELL':
print(" Cell information")
print(" Column:" + str(block['ColumnIndex']))
print(" Row:" + str(block['RowIndex']))
print(" Column Span:" + str(block['ColumnSpan']))
print(" RowSpan:" + str(block['ColumnSpan']))
if 'Relationships' in block:
print(' Relationships: {}'.format(block['Relationships']))
print(' Geometry: ')
print(' Bounding Box: {}'.format(block['Geometry']['BoundingBox']))
print(' Polygon: {}'.format(block['Geometry']['Polygon']))
if block['BlockType'] == "KEY_VALUE_SET":
print (' Entity Type: ' + block['EntityTypes'][0])
if block['BlockType'] == 'SELECTION_ELEMENT':
print(' Selection element detected: ', end='')
if block['SelectionStatus'] =='SELECTED':
print('Selected')
else:
print('Not selected')
if 'Page' in block:
print('Page: ' + block['Page'])
print()
def process_text_analysis(bucket, document):
#Get the document from S3
s3_connection = boto3.resource('s3')
s3_object = s3_connection.Object(bucket,document)
s3_response = s3_object.get()
stream = io.BytesIO(s3_response['Body'].read())
image=Image.open(stream)
# Analyze the document
client = boto3.client('textract')
image_binary = stream.getvalue()
response = client.analyze_document(Document={'Bytes': image_binary},
FeatureTypes=["TABLES", "FORMS"])
### Alternatively, process using S3 object ###
#response = client.analyze_document(
# Document={'S3Object': {'Bucket': bucket, 'Name': document}},
# FeatureTypes=["TABLES", "FORMS"])
### To use a local file ###
# with open("pathToFile", 'rb') as img_file:
### To display image using PIL ###
# image = Image.open()
### Read bytes ###
# img_bytes = img_file.read()
# response = client.analyze_document(Document={'Bytes': img_bytes}, FeatureTypes=["TABLES", "FORMS"])
#Get the text blocks
blocks=response['Blocks']
width, height =image.size
draw = ImageDraw.Draw(image)
print ('Detected Document Text')
# Create image showing bounding box/polygon the detected lines/text
for block in blocks:
DisplayBlockInformation(block)
draw=ImageDraw.Draw(image)
if block['BlockType'] == "KEY_VALUE_SET":
if block['EntityTypes'][0] == "KEY":
ShowBoundingBox(draw, block['Geometry']['BoundingBox'],width,height,'red')
else:
ShowBoundingBox(draw, block['Geometry']['BoundingBox'],width,height,'green')
if block['BlockType'] == 'TABLE':
ShowBoundingBox(draw, block['Geometry']['BoundingBox'],width,height, 'blue')
if block['BlockType'] == 'CELL':
ShowBoundingBox(draw, block['Geometry']['BoundingBox'],width,height, 'yellow')
if block['BlockType'] == 'SELECTION_ELEMENT':
if block['SelectionStatus'] =='SELECTED':
ShowSelectedElement(draw, block['Geometry']['BoundingBox'],width,height, 'blue')
#uncomment to draw polygon for all Blocks
#points=[]
#for polygon in block['Geometry']['Polygon']:
# points.append((width * polygon['X'], height * polygon['Y']))
#draw.polygon((points), outline='blue')
# Display the image
image.show()
return len(blocks)
def main():
bucket = ''
document = ''
block_count=process_text_analysis(bucket,document)
print("Blocks detected: " + str(block_count))
if __name__ == "__main__":
main()
```
------
#### [ Node.js ]
以下示例代码显示检测到的项目周围的文档和框。
在下面的代码中，替换`bucket`和`photo`将包含您在步骤 2 中使用的 Amazon S3 存储桶名称和文档的名称。替换的值`region`使用与您的账户关联的区域。
```
// Import required AWS SDK clients and commands for Node.js
import { AnalyzeDocumentCommand } 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 = 'buckets'
const photo = 'photo'
// Set params
const params = {
Document: {
S3Object: {
Bucket: bucket,
Name: photo
},
},
FeatureTypes: ['TABLES', 'FORMS'],
}
const displayBlockInfo = async (response) => {
try {
response.Blocks.forEach(block => {
console.log(`ID: ${block.Id}`)
console.log(`Block Type: ${block.BlockType}`)
if ("Text" in block && block.Text !== undefined){
console.log(`Text: ${block.Text}`)
}
else{}
if ("Confidence" in block && block.Confidence !== undefined){
console.log(`Confidence: ${block.Confidence}`)
}
else{}
if (block.BlockType == 'CELL'){
console.log("Cell info:")
console.log(` Column Index - ${block.ColumnIndex}`)
console.log(` Row - ${block.RowIndex}`)
console.log(` Column Span - ${block.ColumnSpan}`)
console.log(` Row Span - ${block.RowSpan}`)
}
if ("Relationships" in block && block.Relationships !== undefined){
console.log(block.Relationships)
console.log("Geometry:")
console.log(` Bounding Box - ${JSON.stringify(block.Geometry.BoundingBox)}`)
console.log(` Polygon - ${JSON.stringify(block.Geometry.Polygon)}`)
}
console.log("-----")
});
} catch (err) {
console.log("Error", err);
}
}
const analyze_document_text = async () => {
try {
const analyzeDoc = new AnalyzeDocumentCommand(params);
const response = await textractClient.send(analyzeDoc);
//console.log(response)
displayBlockInfo(response)
return response; // For unit tests.
} catch (err) {
console.log("Error", err);
}
}
analyze_document_text()
```
------
1. 运行示例。Python 和 Java 示例使用以下彩色边界框显示文档图像：
+ 红色 — KEY Block 对象
+ 绿色 — VALUE Block 对象
+ 蓝色 — TABLE Block 对象
+ 黄色 — CELL Block 对象
选定的选择元素用蓝色填充。
这些区域有：AWS CLI示例仅显示的 JSON 输出`AnalyzeDocument`operation.
# 使用 Amazon Textract 分析发票和收据
要分析发票和收据单据，您可以使用 AnalyzeEsend API，然后将文档文件作为输入进行传递。`AnalyzeExpense`是一种同步操作，它返回包含分析过的文本的 JSON 结构。有关更多信息，请参阅 [分析发票和收据](invoices-receipts.md)。
要异步分析发票和收据，请使用`StartExpenseAnalysis`开始处理输入文档文件并使用`GetExpenseAnalysis`以获得结果。
您可以提供输入文档作为图像字节数组 (base64 编码的图像字节) 或 Amazon S3 对象。在此过程中，您将图像文件上传到您的 S3 存储桶并指定文件名称。
**分析发票或收据 (API)**
1. 如果您尚未执行以下操作，请：
1. 使用创建或更新 IAM 用户`AmazonTextractFullAccess`和`AmazonS3ReadOnlyAccess`权限。有关更多信息，请参阅 [第 1 步：设置 AWS 账户并创建 IAM 用户](setting-up.md#setting-up-iam)。
1. 安装和配置 AWS CLI 和 AWS 开发工具包。有关更多信息，请参阅 [第 2 步：设置AWS CLI和AWS软件开发工具包](setup-awscli-sdk.md)。
1. 将包含文档的图像上传到您的 S3 存储桶。
有关说明，请参阅[将对象上传到 Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/UploadingObjectsintoAmazonS3.html)中的*Amazon Simple Service 用户指南*.
1. 使用以下示例调用 `AnalyzeExpense` 操作。
------
#### [ CLI ]
```
aws textract analyze-expense --document '{"S3Object": {"Bucket": "bucket name","Name":
"object name"}}'
```
------
#### [ Python ]
```
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 ]
```
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 expenseDocuments = result.expenseDocuments();
for (ExpenseDocument expenseDocument : expenseDocuments) {
if (expenseDocument.hasSummaryFields()) {
DisplayAnalyzeExpenseSummaryInfo(expenseDocument);
List 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 lineitemgroups = expenseDocument.lineItemGroups();
for (LineItemGroup lineitemgroup : lineitemgroups) {
if (lineitemgroup.hasLineItems()) {
List lineItems = lineitemgroup.lineItems();
for (LineItemFields lineitemfield : lineItems) {
if (lineitemfield.hasLineItemExpenseFields()) {
List 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 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 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 lineitemgroups = expensedocument.lineItemGroups();
for (LineItemGroup lineitemgroup : lineitemgroups) {
System.out.println(" Expense LineItemGroupsIndexID :" + lineitemgroup.lineItemGroupIndex());
if (lineitemgroup.hasLineItems()) {
List lineItems = lineitemgroup.lineItems();
for (LineItemFields lineitemfield : lineItems) {
if (lineitemfield.hasLineItemExpenseFields()) {
List 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> 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> 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 ]
```
// 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()
```
------
1. 这将为您提供的 JSON 输出`AnalyzeExpense`operation.
# 使用 Amazon Textract 分析身份文档
要分析身份证件，您可以使用 AnalyZEID API，然后将文档文件作为输入进行传递。`AnalyzeID`返回一个 JSON 结构，其中包含分析的文本。有关更多信息，请参阅 [分析身份证件](how-it-works-identity.md)。
您可以提供输入文档作为图像字节数组 (base64 编码的图像字节) 或 Amazon S3 对象。在此过程中，您将图像文件上传到您的 S3 存储桶并指定文件名称。
**分析身份证明文件 (API)**
1. 如果您尚未执行以下操作，请：
1. 使用创建或更新 IAM 用户`AmazonTextractFullAccess`和`AmazonS3ReadOnlyAccess`权限。有关更多信息，请参阅 [第 1 步：设置 AWS 账户并创建 IAM 用户](setting-up.md#setting-up-iam)。
1. 安装和配置 AWS CLI 和 AWS 开发工具包。有关更多信息，请参阅 [第 2 步：设置AWS CLI和AWS软件开发工具包](setup-awscli-sdk.md)。
1. 将包含文档的图像上传到您的 S3 存储桶。
有关说明，请参阅[将对象上传到 Amazon S3](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/UploadingObjectsintoAmazonS3.html)中的*Amazon Simple Service 用户指南*.
1. 使用以下示例调用 `AnalyzeID` 操作。
------
#### [ CLI ]
以下示例从 S3 存储桶获取输入文件并运行`AnalyzeID`对它进行操作。在下面的代码中，替换*桶*将您的 S3 存储桶的名称用于*文件*将包含存储桶中的文件的名称以及的值*领域*有的名字`region`与您的账户关联。
```
aws textract analyze-id --document-pages '[{"S3Object":{"Bucket":"bucket","Name":"name"}}]' --region region
```
您还可以通过向输入中添加另一个 S3 对象，使用驾驶执照的正面和背面调用 API。
```
aws textract analyze-id --document-pages '[{"S3Object":{"Bucket":"bucket","Name":"name front"}}, {"S3Object":{"Bucket":"bucket","Name":"name back"}}]' --region us-east-1
```
如果您在 Windows 设备上访问 CLI，请使用双引号而不是单引号，并用反斜杠（即\$1）转义内部双引号，以解决可能遇到的任何解析器错误。有关示例，请参阅下面的内容：
```
aws textract analyze-id --document-pages "[{\"S3Object\":{\"Bucket\":\"bucket\",\"Name\":\"name\"}}]" --region region
```
------
#### [ Python ]
以下示例从 S3 存储桶获取输入文件并运行`AnalyzeID`在其上操作，返回检测到的键值对。在下面的代码中，替换*bucket\$1name*将您的 S3 存储桶的名称用于*file\$1name*将包含存储桶中的文件的名称以及的值*领域*有的名字`region`与您的账户关联。
```
import boto3
bucket_name = "bucket-name"
file_name = "file-name"
region = "region-name"
def analyze_id(region, bucket_name, file_name):
textract_client = boto3.client('textract', region_name=region)
response = textract_client.analyze_id(DocumentPages=[{"S3Object":{"Bucket":bucket_name,"Name":file_name}}])
for doc_fields in response['IdentityDocuments']:
for id_field in doc_fields['IdentityDocumentFields']:
for key, val in id_field.items():
if "Type" in str(key):
print("Type: " + str(val['Text']))
for key, val in id_field.items():
if "ValueDetection" in str(key):
print("Value Detection: " + str(val['Text']))
print()
analyze_id(region, bucket_name, file_name)
```
------
#### [ Java ]
以下示例从 S3 存储桶获取输入文件并运行`AnalyzeID`对其进行操作，返回检测到的数据。在函数 main 中，将以下值`s3bucket`和`sourceDoc`将包含您在步骤 2 中使用的 Amazon S3 存储桶名称和文档图像的名称。
```
/*
Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
SPDX-License-Identifier: Apache-2.0
*/
package com.amazonaws.samples;
import com.amazonaws.regions.Regions;
import com.amazonaws.services.textract.AmazonTextractClient;
import com.amazonaws.services.textract.AmazonTextractClientBuilder;
import com.amazonaws.services.textract.model.*;
import java.util.ArrayList;
import java.util.List;
public class AnalyzeIdentityDocument {
public static void main(String[] args) {
final String USAGE = "\n" +
"Usage:\n" +
" \n\n" +
"Where:\n" +
" s3bucket - the Amazon S3 bucket where the document is located. \n" +
" sourceDoc - the name of the document. \n";
if (args.length != 1) {
System.out.println(USAGE);
System.exit(1);
}
String s3bucket = "bucket-name"; //args[0];
String sourceDoc = "sourcedoc-name"; //args[1];
AmazonTextractClient textractClient = (AmazonTextractClient) AmazonTextractClientBuilder.standard()
.withRegion(Regions.US_EAST_1)
.build();
getDocDetails(textractClient, s3bucket, sourceDoc);
}
public static void getDocDetails(AmazonTextractClient textractClient, String s3bucket, String sourceDoc ) {
try {
S3Object s3 = new S3Object();
s3.setBucket(s3bucket);
s3.setName(sourceDoc);
com.amazonaws.services.textract.model.Document myDoc = new com.amazonaws.services.textract.model.Document();
myDoc.setS3Object(s3);
List list1 = new ArrayList();
list1.add(myDoc);
AnalyzeIDRequest idRequest = new AnalyzeIDRequest();
idRequest.setDocumentPages(list1);
AnalyzeIDResult result = textractClient.analyzeID(idRequest);
List docs = result.getIdentityDocuments();
for (IdentityDocument doc: docs) {
ListidFields = doc.getIdentityDocumentFields();
for (IdentityDocumentField field: idFields) {
System.out.println("Field type is "+ field.getType().getText());
System.out.println("Field value is "+ field.getValueDetection().getText());
}
}
} catch (Exception e) {
e.printStackTrace();
}
}
}
```
------
1. 这将为您提供的 JSON 输出`AnalyzeID`operation.