与 AWS SDK或AnalyzeDocument一起使用 CLI - AWS SDK代码示例

AWS 文档 AWS SDK示例 GitHub 存储库中还有更多SDK示例。

本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。

与 AWS SDK或AnalyzeDocument一起使用 CLI

以下代码示例演示如何使用 AnalyzeDocument。

CLI
AWS CLI

分析文档中的文本

以下 analyze-document 示例演示如何分析文档中的文本。

Linux/macOS：

aws textract analyze-document \
    --document '{"S3Object":{"Bucket":"bucket","Name":"document"}}' \
    --feature-types '["TABLES","FORMS"]'

Windows:

aws textract analyze-document \
    --document "{\"S3Object\":{\"Bucket\":\"bucket\",\"Name\":\"document\"}}" \
    --feature-types "[\"TABLES\",\"FORMS\"]" \
    --region region-name

输出：

{
    "Blocks": [
        {
            "Geometry": {
                "BoundingBox": {
                    "Width": 1.0,
                    "Top": 0.0,
                    "Left": 0.0,
                    "Height": 1.0
                },
                "Polygon": [
                    {
                        "Y": 0.0,
                        "X": 0.0
                    },
                    {
                        "Y": 0.0,
                        "X": 1.0
                    },
                    {
                        "Y": 1.0,
                        "X": 1.0
                    },
                    {
                        "Y": 1.0,
                        "X": 0.0
                    }
                ]
            },
            "Relationships": [
                {
                    "Type": "CHILD",
                    "Ids": [
                        "87586964-d50d-43e2-ace5-8a890657b9a0",
                        "a1e72126-21d9-44f4-a8d6-5c385f9002ba",
                        "e889d012-8a6b-4d2e-b7cd-7a8b327d876a"
                    ]
                }
            ],
            "BlockType": "PAGE",
            "Id": "c2227f12-b25d-4e1f-baea-1ee180d926b2"
        }
    ],
    "DocumentMetadata": {
        "Pages": 1
    }
}

有关更多信息，请参阅《Amazon Textract 开发人员指南》中的“使用 Amazon Textract 分析文档文本”

Java
SDK适用于 Java 2.x
注意

还有更多相关信息 GitHub。查找完整示例，学习如何在 AWS 代码示例存储库中进行设置和运行。

import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.textract.TextractClient;
import software.amazon.awssdk.services.textract.model.AnalyzeDocumentRequest;
import software.amazon.awssdk.services.textract.model.Document;
import software.amazon.awssdk.services.textract.model.FeatureType;
import software.amazon.awssdk.services.textract.model.AnalyzeDocumentResponse;
import software.amazon.awssdk.services.textract.model.Block;
import software.amazon.awssdk.services.textract.model.TextractException;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.InputStream;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;

/**
 * Before running this Java V2 code example, set up your development
 * environment, including your credentials.
 *
 * For more information, see the following documentation topic:
 *
 * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
 */
public class AnalyzeDocument {
    public static void main(String[] args) {
        final String usage = """

                Usage:
                    <sourceDoc>\s

                Where:
                    sourceDoc - The path where the document is located (must be an image, for example, C:/AWS/book.png).\s
                """;

        if (args.length != 1) {
            System.out.println(usage);
            System.exit(1);
        }

        String sourceDoc = args[0];
        Region region = Region.US_EAST_2;
        TextractClient textractClient = TextractClient.builder()
                .region(region)
                .build();

        analyzeDoc(textractClient, sourceDoc);
        textractClient.close();
    }

    public static void analyzeDoc(TextractClient textractClient, String sourceDoc) {
        try {
            InputStream sourceStream = new FileInputStream(new File(sourceDoc));
            SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream);

            // Get the input Document object as bytes
            Document myDoc = Document.builder()
                    .bytes(sourceBytes)
                    .build();

            List<FeatureType> featureTypes = new ArrayList<FeatureType>();
            featureTypes.add(FeatureType.FORMS);
            featureTypes.add(FeatureType.TABLES);

            AnalyzeDocumentRequest analyzeDocumentRequest = AnalyzeDocumentRequest.builder()
                    .featureTypes(featureTypes)
                    .document(myDoc)
                    .build();

            AnalyzeDocumentResponse analyzeDocument = textractClient.analyzeDocument(analyzeDocumentRequest);
            List<Block> docInfo = analyzeDocument.blocks();
            Iterator<Block> blockIterator = docInfo.iterator();

            while (blockIterator.hasNext()) {
                Block block = blockIterator.next();
                System.out.println("The block type is " + block.blockType().toString());
            }

        } catch (TextractException | FileNotFoundException e) {

            System.err.println(e.getMessage());
            System.exit(1);
        }
    }
}

  • 有关API详细信息，请参阅 “AWS SDK for Java 2.x API参考 AnalyzeDocument” 中的。

Python
SDK适用于 Python (Boto3)
注意

还有更多相关信息 GitHub。查找完整示例，学习如何在 AWS 代码示例存储库中进行设置和运行。

class TextractWrapper:
    """Encapsulates Textract functions."""

    def __init__(self, textract_client, s3_resource, sqs_resource):
        """
        :param textract_client: A Boto3 Textract client.
        :param s3_resource: A Boto3 Amazon S3 resource.
        :param sqs_resource: A Boto3 Amazon SQS resource.
        """
        self.textract_client = textract_client
        self.s3_resource = s3_resource
        self.sqs_resource = sqs_resource


    def analyze_file(
        self, feature_types, *, document_file_name=None, document_bytes=None
    ):
        """
        Detects text and additional elements, such as forms or tables, in a local image
        file or from in-memory byte data.
        The image must be in PNG or JPG format.

        :param feature_types: The types of additional document features to detect.
        :param document_file_name: The name of a document image file.
        :param document_bytes: In-memory byte data of a document image.
        :return: The response from Amazon Textract, including a list of blocks
                 that describe elements detected in the image.
        """
        if document_file_name is not None:
            with open(document_file_name, "rb") as document_file:
                document_bytes = document_file.read()
        try:
            response = self.textract_client.analyze_document(
                Document={"Bytes": document_bytes}, FeatureTypes=feature_types
            )
            logger.info("Detected %s blocks.", len(response["Blocks"]))
        except ClientError:
            logger.exception("Couldn't detect text.")
            raise
        else:
            return response

  • 有关API详细信息，请参阅AnalyzeDocument中的 AWS SDKPython (Boto3) API 参考。

SAP ABAP
SDK对于 SAP ABAP
注意

还有更多相关信息 GitHub。查找完整示例，学习如何在 AWS 代码示例存储库中进行设置和运行。


    "Detects text and additional elements, such as forms or tables,"
    "in a local image file or from in-memory byte data."
    "The image must be in PNG or JPG format."


    "Create ABAP objects for feature type."
    "Add TABLES to return information about the tables."
    "Add FORMS to return detected form data."
    "To perform both types of analysis, add TABLES and FORMS to FeatureTypes."

    DATA(lt_featuretypes) = VALUE /aws1/cl_texfeaturetypes_w=>tt_featuretypes(
      ( NEW /aws1/cl_texfeaturetypes_w( iv_value = 'FORMS' ) )
      ( NEW /aws1/cl_texfeaturetypes_w( iv_value = 'TABLES' ) ) ).

    "Create an ABAP object for the Amazon Simple Storage Service (Amazon S3) object."
    DATA(lo_s3object) = NEW /aws1/cl_texs3object( iv_bucket = iv_s3bucket
      iv_name   = iv_s3object ).

    "Create an ABAP object for the document."
    DATA(lo_document) = NEW /aws1/cl_texdocument( io_s3object = lo_s3object ).

    "Analyze document stored in Amazon S3."
    TRY.
        oo_result = lo_tex->analyzedocument(      "oo_result is returned for testing purposes."
          io_document        = lo_document
          it_featuretypes    = lt_featuretypes ).
        LOOP AT oo_result->get_blocks( ) INTO DATA(lo_block).
          IF lo_block->get_text( ) = 'INGREDIENTS: POWDERED SUGAR* (CANE SUGAR,'.
            MESSAGE 'Found text in the doc: ' && lo_block->get_text( ) TYPE 'I'.
          ENDIF.
        ENDLOOP.
        MESSAGE 'Analyze document completed.' TYPE 'I'.
      CATCH /aws1/cx_texaccessdeniedex.
        MESSAGE 'You do not have permission to perform this action.' TYPE 'E'.
      CATCH /aws1/cx_texbaddocumentex.
        MESSAGE 'Amazon Textract is not able to read the document.' TYPE 'E'.
      CATCH /aws1/cx_texdocumenttoolargeex.
        MESSAGE 'The document is too large.' TYPE 'E'.
      CATCH /aws1/cx_texhlquotaexceededex.
        MESSAGE 'Human loop quota exceeded.' TYPE 'E'.
      CATCH /aws1/cx_texinternalservererr.
        MESSAGE 'Internal server error.' TYPE 'E'.
      CATCH /aws1/cx_texinvalidparameterex.
        MESSAGE 'Request has non-valid parameters.' TYPE 'E'.

      CATCH /aws1/cx_texinvalids3objectex.
        MESSAGE 'Amazon S3 object is not valid.' TYPE 'E'.
      CATCH /aws1/cx_texprovthruputexcdex.
        MESSAGE 'Provisioned throughput exceeded limit.' TYPE 'E'.
      CATCH /aws1/cx_texthrottlingex.
        MESSAGE 'The request processing exceeded the limit.' TYPE 'E'.
      CATCH /aws1/cx_texunsupporteddocex.
        MESSAGE 'The document is not supported.' TYPE 'E'.
    ENDTRY.

  • 有关API详细信息，请参阅AnalyzeDocument中的AWS SDK以供SAPABAPAPI参考。