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# 将表导出到 CSV 文件
这些 Python 示例演示了如何将表从文档图像导出为以逗号分隔的值 (CSV) 文件。
同步文档分析的示例从调用中收集表格信息[AnalyzeDocument](API_AnalyzeDocument.md). 异步文档分析的示例调用[StartDocumentAnalysis](API_StartDocumentAnalysis.md)然后从中检索结果[GetDocumentAnalysis](API_GetDocumentAnalysis.md)如同`Block`对象。
表格信息返回为[Block](API_Block.md)来自调用的对象[AnalyzeDocument](API_AnalyzeDocument.md). 有关更多信息，请参阅 [表](how-it-works-tables.md)。这些区域有：`Block`对象存储在用于将表数据导出到 CSV 文件的地图结构中。
------
#### [ Synchronous ]
在此示例中，您将使用以下函数：
+ `get_table_csv_results`— 调用[AnalyzeDocument](API_AnalyzeDocument.md)，然后构建文档中检测到的表格的映射。创建所有检测到的表格的 CSV 表示形式。
+ `generate_table_csv`— 为单个表生成 CSV 文件。
+ `get_rows_columns_map`— 从地图中获取行和列。
+ `get_text`— 从单元格中获取文本。
**将表导出到 CSV 文件**
1. 配置您的环境。有关更多信息，请参阅 [先决条件](examples-blocks.md#examples-prerequisites)。
1. 将以下示例代码保存到名为的文件中*textract\$1python\$1table\$1parser.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 " NO Table FOUND "
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)
```
1. 在命令提示符处，输入以下命令：Replace`file`将显示您要分析的文档图像文件的名称。
```
python textract_python_table_parser.py file
```
运行示例时，CSV 输出将保存在名为的文件中`output.csv`.
------
#### [ Asynchronous ]
在此示例中，您将使用两个不同的脚本。第一个脚本启动了异步分析文档的过程`StartDocumentAnalysis`并获取`Block`返回的信息`GetDocumentAnalysis`. 第二个脚本采用返回的`Block`每个页面的信息，将数据格式化为表格，然后将表格保存到 CSV 文件中。
**将表导出到 CSV 文件**
1. 配置您的环境。有关更多信息，请参阅 [先决条件](examples-blocks.md#examples-prerequisites)。
1. 确保你已按照见上的说明进行操作[为异步操作配置 Amazon Textract](api-async-roles.md). 该页面上记录的过程使您能够发送和接收有关异步作业完成状态的消息。
1. 在以下代码示例中，替换`roleArn`将 Arn 分配给您在步骤 2 中创建的角色。替换的值`bucket`将包含您的文档的 S3 存储桶的名称。替换的值`document`将包含您的 S3 存储桶中的文档的名称。替换的值`region_name`将包含您的存储桶所在区域的名称。
将以下示例代码保存到名为的文件中*start\$1doc\$1analysis\$1for\$1table\$1extraction.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()
```
1. 运行该代码。该代码将打印一个 JobId。向下复制这个 JobId。
1. 等待您的作业完成处理，完成后，将以下代码复制到名为的文件*get\$1doc\$1analysis\$1for\$1table\$1extraction.py*. 替换的值`jobId`用你之前复制的 Job ID。替换的值`region_name`使用与 Textract 角色关联的区域的名称。替换的值`file_name`使用您要为输出 CSV 指定的名称。
```
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 " NO Table FOUND "
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)
```
1. 运行该代码。
获得结果后，请务必删除关联的 SNS 和 SQS 资源，否则可能会为它们产生费用。
------