Mengekspor Tabel ke File CSV

Konfigurasikan lingkungan Anda. Untuk informasi selengkapnya, lihat Prasyarat.

Simpan kode contoh berikut ke file bernama textract_python_table_parser.py. Dalam fungsinyaget_table_csv_results, ganti profile-name dengan nama profil yang dapat mengambil peran dan region dengan wilayah tempat Anda ingin menjalankan kode.

import webbrowser, os
import json
import boto3
import io
from io import BytesIO
import sys
from pprint import pprint


def get_rows_columns_map(table_result, blocks_map):
    rows = {}
    scores = []
    for relationship in table_result['Relationships']:
        if relationship['Type'] == 'CHILD':
            for child_id in relationship['Ids']:
                cell = blocks_map[child_id]
                if cell['BlockType'] == 'CELL':
                    row_index = cell['RowIndex']
                    col_index = cell['ColumnIndex']
                    if row_index not in rows:
                        # create new row
                        rows[row_index] = {}
                    
                    # get confidence score
                    scores.append(str(cell['Confidence']))
                        
                    # get the text value
                    rows[row_index][col_index] = get_text(cell, blocks_map)
    return rows, scores


def get_text(result, blocks_map):
    text = ''
    if 'Relationships' in result:
        for relationship in result['Relationships']:
            if relationship['Type'] == 'CHILD':
                for child_id in relationship['Ids']:
                    word = blocks_map[child_id]
                    if word['BlockType'] == 'WORD':
                        if "," in word['Text'] and word['Text'].replace(",", "").isnumeric():
                            text += '"' + word['Text'] + '"' + ' '
                        else:
                            text += word['Text'] + ' '
                    if word['BlockType'] == 'SELECTION_ELEMENT':
                        if word['SelectionStatus'] =='SELECTED':
                            text +=  'X '
    return text


def get_table_csv_results(file_name):

    with open(file_name, 'rb') as file:
        img_test = file.read()
        bytes_test = bytearray(img_test)
        print('Image loaded', file_name)

    # process using image bytes
    # get the results
    session = boto3.Session(profile_name='profile-name')
    client = session.client('textract', region_name='region')
    response = client.analyze_document(Document={'Bytes': bytes_test}, FeatureTypes=['TABLES'])

    # Get the text blocks
    blocks=response['Blocks']
    pprint(blocks)

    blocks_map = {}
    table_blocks = []
    for block in blocks:
        blocks_map[block['Id']] = block
        if block['BlockType'] == "TABLE":
            table_blocks.append(block)

    if len(table_blocks) <= 0:
        return "<b> NO Table FOUND </b>"

    csv = ''
    for index, table in enumerate(table_blocks):
        csv += generate_table_csv(table, blocks_map, index +1)
        csv += '\n\n'

    return csv

def generate_table_csv(table_result, blocks_map, table_index):
    rows, scores = get_rows_columns_map(table_result, blocks_map)

    table_id = 'Table_' + str(table_index)
    
    # get cells.
    csv = 'Table: {0}\n\n'.format(table_id)

    for row_index, cols in rows.items():
        for col_index, text in cols.items():
            col_indices = len(cols.items())
            csv += '{}'.format(text) + ","
        csv += '\n'
        
    csv += '\n\n Confidence Scores % (Table Cell) \n'
    cols_count = 0
    for score in scores:
        cols_count += 1
        csv += score + ","
        if cols_count == col_indices:
            csv += '\n'
            cols_count = 0

    csv += '\n\n\n'
    return csv

def main(file_name):
    table_csv = get_table_csv_results(file_name)

    output_file = 'output.csv'

    # replace content
    with open(output_file, "wt") as fout:
        fout.write(table_csv)

    # show the results
    print('CSV OUTPUT FILE: ', output_file)


if __name__ == "__main__":
    file_name = sys.argv[1]
    main(file_name)

Pada prompt perintah, masukkan perintah berikut. Ganti file dengan nama file gambar dokumen yang ingin Anda analisis.

python textract_python_table_parser.py file

Konfigurasikan lingkungan Anda. Untuk informasi selengkapnya, lihat Prasyarat.

Pastikan bahwa Anda telah mengikuti instruksi yang diberikan di seeMengkonfigurasi Amazon Ttract untuk Operasi Asinkron. Proses yang didokumentasikan pada halaman tersebut memungkinkan Anda mengirim dan menerima pesan tentang status penyelesaian pekerjaan asinkron.

Dalam contoh kode berikut, ganti nilai roleArn dengan Arn yang ditetapkan ke peran yang Anda buat di Langkah 2. Ganti nilai bucket dengan nama bucket S3 yang berisi dokumen Anda. Ganti nilai document dengan nama dokumen di bucket S3 Anda. Ganti nilai region_name dengan nama wilayah bucket Anda.

Simpan kode contoh berikut ke file bernama start_doc_analysis_for_table_extraction.py. .

import boto3
import time

class DocumentProcessor:

    jobId = ''
    region_name = ''

    roleArn = ''
    bucket = ''
    document = ''

    sqsQueueUrl = ''
    snsTopicArn = ''
    processType = ''

    def __init__(self, role, bucket, document, region):
        self.roleArn = role
        self.bucket = bucket
        self.document = document
        self.region_name = region

        self.textract = boto3.client('textract', region_name=self.region_name)
        self.sqs = boto3.client('sqs')
        self.sns = boto3.client('sns')

    def ProcessDocument(self):

        jobFound = False

        response = self.textract.start_document_analysis(DocumentLocation={'S3Object': {'Bucket': self.bucket, 'Name': self.document}},
                FeatureTypes=["TABLES", "FORMS"], NotificationChannel={'RoleArn': self.roleArn, 'SNSTopicArn': self.snsTopicArn})
        print('Processing type: Analysis')

        print('Start Job Id: ' + response['JobId'])

        print('Done!')

    def CreateTopicandQueue(self):

        millis = str(int(round(time.time() * 1000)))

        # Create SNS topic
        snsTopicName = "AmazonTextractTopic" + millis

        topicResponse = self.sns.create_topic(Name=snsTopicName)
        self.snsTopicArn = topicResponse['TopicArn']

        # create SQS queue
        sqsQueueName = "AmazonTextractQueue" + millis
        self.sqs.create_queue(QueueName=sqsQueueName)
        self.sqsQueueUrl = self.sqs.get_queue_url(QueueName=sqsQueueName)['QueueUrl']

        attribs = self.sqs.get_queue_attributes(QueueUrl=self.sqsQueueUrl,
                                                AttributeNames=['QueueArn'])['Attributes']

        sqsQueueArn = attribs['QueueArn']

        # Subscribe SQS queue to SNS topic
        self.sns.subscribe(TopicArn=self.snsTopicArn, Protocol='sqs', Endpoint=sqsQueueArn)

        # Authorize SNS to write SQS queue
        policy = """{{
      "Version":"2012-10-17",
      "Statement":[
        {{
          "Sid":"MyPolicy",
          "Effect":"Allow",
          "Principal" : {{"AWS" : "*"}},
          "Action":"SQS:SendMessage",
          "Resource": "{}",
          "Condition":{{
            "ArnEquals":{{
              "aws:SourceArn": "{}"
            }}
          }}
        }}
      ]
    }}""".format(sqsQueueArn, self.snsTopicArn)

        response = self.sqs.set_queue_attributes(
            QueueUrl=self.sqsQueueUrl,
            Attributes={
                'Policy': policy
            })

def main():
    roleArn = 'role-arn'
    bucket = 'bucket-name'
    document = 'document-name'
    region_name = 'region-name'

    analyzer = DocumentProcessor(roleArn, bucket, document, region_name)
    analyzer.CreateTopicandQueue()
    analyzer.ProcessDocument()

if __name__ == "__main__":
    main()

Jalankan kode tersebut. Kode akan mencetak file JobId. Salin ini JobId ke bawah.

Tunggu pekerjaan Anda selesai pemrosesan, dan setelah selesai, salin kode berikut ke file bernama get_doc_analysis_for_table_extraction.py. Ganti nilai jobId dengan Job ID yang Anda salin sebelumnya. Ganti nilai region_name dengan nama wilayah yang terkait dengan peran Textract Anda. Ganti nilai file_name dengan nama yang ingin Anda berikan output CSV.

import boto3
from pprint import pprint

jobId = ''
region_name = ''
file_name = ''

textract = boto3.client('textract', region_name=region_name)

# Display information about a block
def DisplayBlockInfo(block):
    print("Block Id: " + block['Id'])
    print("Type: " + block['BlockType'])
    if 'EntityTypes' in block:
        print('EntityTypes: {}'.format(block['EntityTypes']))

    if 'Text' in block:
        print("Text: " + block['Text'])

    if block['BlockType'] != 'PAGE':
        print("Confidence: " + "{:.2f}".format(block['Confidence']) + "%")

def GetResults(jobId, file_name):
    maxResults = 1000
    paginationToken = None
    finished = False

    while finished == False:

        response = None

        if paginationToken == None:
            response = textract.get_document_analysis(JobId=jobId, MaxResults=maxResults)
        else:
            response = textract.get_document_analysis(JobId=jobId, MaxResults=maxResults,
                                                           NextToken=paginationToken)

        blocks = response['Blocks']
        table_csv = get_table_csv_results(blocks)
        output_file = file_name + ".csv"
        # replace content
        with open(output_file, "at") as fout:
            fout.write(table_csv)
        # show the results
        print('Detected Document Text')
        print('Pages: {}'.format(response['DocumentMetadata']['Pages']))
        print('OUTPUT TO CSV FILE: ', output_file)

        # Display block information
        for block in blocks:
            DisplayBlockInfo(block)
            print()
            print()

        if 'NextToken' in response:
            paginationToken = response['NextToken']
        else:
            finished = True


def get_rows_columns_map(table_result, blocks_map):
    rows = {}
    for relationship in table_result['Relationships']:
        if relationship['Type'] == 'CHILD':
            for child_id in relationship['Ids']:
                try:
                    cell = blocks_map[child_id]
                    if cell['BlockType'] == 'CELL':
                        row_index = cell['RowIndex']
                        col_index = cell['ColumnIndex']
                        if row_index not in rows:
                            # create new row
                            rows[row_index] = {}

                        # get the text value
                        rows[row_index][col_index] = get_text(cell, blocks_map)
                except KeyError:
                    print("Error extracting Table data - {}:".format(KeyError))
                    pass
    return rows


def get_text(result, blocks_map):
    text = ''
    if 'Relationships' in result:
        for relationship in result['Relationships']:
            if relationship['Type'] == 'CHILD':
                for child_id in relationship['Ids']:
                    try:
                        word = blocks_map[child_id]
                        if word['BlockType'] == 'WORD':
                            text += word['Text'] + ' '
                        if word['BlockType'] == 'SELECTION_ELEMENT':
                            if word['SelectionStatus'] == 'SELECTED':
                                text += 'X '
                    except KeyError:
                        print("Error extracting Table data - {}:".format(KeyError))

    return text


def get_table_csv_results(blocks):

    pprint(blocks)

    blocks_map = {}
    table_blocks = []
    for block in blocks:
        blocks_map[block['Id']] = block
        if block['BlockType'] == "TABLE":
            table_blocks.append(block)

    if len(table_blocks) <= 0:
        return "<b> NO Table FOUND </b>"

    csv = ''
    for index, table in enumerate(table_blocks):
        csv += generate_table_csv(table, blocks_map, index + 1)
        csv += '\n\n'
        # In order to generate separate CSV file for every table, uncomment code below
        #inner_csv = ''
        #inner_csv += generate_table_csv(table, blocks_map, index + 1)
        #inner_csv += '\n\n'
        #output_file = file_name + "___" + str(index) + ".csv"
        # replace content
        #with open(output_file, "at") as fout:
        #    fout.write(inner_csv)

    return csv


def generate_table_csv(table_result, blocks_map, table_index):
    rows = get_rows_columns_map(table_result, blocks_map)

    table_id = 'Table_' + str(table_index)

    # get cells.
    csv = 'Table: {0}\n\n'.format(table_id)

    for row_index, cols in rows.items():

        for col_index, text in cols.items():
            csv += '{}'.format(text) + ","
        csv += '\n'

    csv += '\n\n\n'
    return csv

response_blocks = GetResults(jobId, file_name)

Jalankan kode tersebut.

Setelah Anda mendapatkan hasil, pastikan untuk menghapus sumber daya SNS dan SQS terkait, atau Anda mungkin akan dikenakan biaya untuk mereka.