Mengekspor Tabel ke File CSV

Contoh Python ini menunjukkan cara untuk mengekspor tabel dari gambar dokumen ke file nilai yang dipisahkan koma (CSV).

Contoh untuk analisis dokumen sinkron mengumpulkan informasi tabel dari panggilan keAnalyzeDocument. Contoh untuk analisis dokumen asinkron membuat panggilan keStartDocumentAnalysisdan kemudian mengambil hasil dariGetDocumentAnalysissebagaiBlockbenda.

Informasi tabel dikembalikan sebagaiBlockobjek dari panggilan keAnalyzeDocument. Untuk informasi selengkapnya, lihat Tabel. ParameterBlockobjek disimpan dalam struktur peta yang digunakan untuk mengekspor data tabel ke dalam file CSV.

Synchronous

Dalam contoh ini, Anda akan menggunakan fungsi:

get_table_csv_results— PanggilanAnalyzeDocument, dan membangun peta tabel yang terdeteksi dalam dokumen. Menciptakan representasi CSV dari semua tabel terdeteksi.
generate_table_csv— Menghasilkan file CSV untuk tabel individu.
get_rows_columns_map— Mendapat baris dan kolom dari peta.
get_textMendapat teks dari sel.

Mengekspor tabel ke dalam file CSV

Konfigurasikan lingkungan Anda. Untuk informasi selengkapnya, lihat Prasyarat.

Simpan kode contoh berikut ini ke sebuah file dengan namatextract_python_table_parser.py.


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 = {}
    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 the text value
                    rows[row_index][col_index] = get_text(cell, blocks_map)
    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']:
                    word = blocks_map[child_id]
                    if word['BlockType'] == 'WORD':
                        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
    client = boto3.client('textract')

    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 = 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

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)

Di prompt perintah, masukkan perintah berikut. Gantifiledengan nama file gambar dokumen yang ingin Anda analisis.
```
python textract_python_table_parser.py file
```

Ketika Anda menjalankan contoh, output CSV disimpan dalam file bernamaoutput.csv.

Asynchronous

Dalam contoh ini, Anda akan menggunakan make menggunakan dua script yang berbeda. Script pertama memulai proses asynchronoulsy menganalisis dokumen denganStartDocumentAnalysisdan mendapatBlockinformasi yang dikembalikan olehGetDocumentAnalysis. Skrip kedua mengambil kembaliBlockinformasi untuk setiap halaman, memformat data sebagai tabel, dan menyimpan tabel ke file CSV.

Mengekspor tabel ke dalam file CSV

Konfigurasikan lingkungan Anda. Untuk informasi selengkapnya, lihat Prasyarat.
Pastikan bahwa Anda telah mengikuti instruksi yang diberikan di lihatMengkonfigurasi Amazon Textract untuk Operasi Asynchronous. Proses yang didokumentasikan pada halaman tersebut memungkinkan Anda mengirim dan menerima pesan tentang status penyelesaian pekerjaan asinkron.

Pada contoh kode berikut, ganti nilairoleArndengan Arn yang ditetapkan untuk peran yang Anda buat di Langkah 2. Ganti nilaibucketdengan nama bucket S3 yang mengandung dokumen Anda. Ganti nilaidocumentdengan nama dokumen di bucket S3. Ganti nilairegion_nameDengan nama wilayah bucket Anda.

Simpan kode contoh berikut ini ke sebuah file dengan namastart_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 JobId. Salin JobId ini ke bawah.

Tunggu sampai pekerjaan Anda selesai diproses, dan setelah selesai, salin kode berikut ke file bernamaget_doc_analysis_for_table_extraction.py. Ganti nilaijobIddengan ID Job yang Anda salin sebelumnya. Ganti nilairegion_namedengan nama wilayah yang terkait dengan peran Textract Anda. Ganti nilaifile_namedengan nama yang ingin Anda berikan output CSV.


import boto3
from pprint import pprint

jobId = 'job-id'
region_name = 'region-name'
file_name = "output-file-name.csv"

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
        # 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'

    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 memperoleh hasil, pastikan untuk menghapus sumber daya SNS dan SQS terkait, atau Anda dapat memperoleh biaya untuk mereka.

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