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import os
import json
import argparse
from azure.ai.formrecognizer import DocumentAnalysisClient
from azure.core.credentials import AzureKeyCredential
from utils import read_file_paths, validate_json_save_path, load_json_file
CATEGORY_MAP = {
"Title": "heading1",
"SectionHeading": "heading1",
"footnote": "footnote",
"PageHeader": "header",
"PageFooter": "footer",
"Paragraph": "paragraph",
"Subheading": "heading1",
"SectionMarks": "paragraph",
"PageNumber": "paragraph"
}
class MicrosoftInference:
def __init__(
self,
save_path,
input_formats=[".pdf", ".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".heic"]
):
"""Initialize the MicrosoftInference class
Args:
save_path (str): the json path to save the results
input_formats (list, optional): the supported file formats.
"""
KEY = os.getenv("MICROSOFT_API_KEY") or ""
ENDPOINT = os.getenv("MICROSOFT_ENDPOINT") or ""
self.document_analysis_client = DocumentAnalysisClient(
endpoint=ENDPOINT, credential=AzureKeyCredential(KEY)
)
validate_json_save_path(save_path)
self.save_path = save_path
self.processed_data = load_json_file(save_path)
self.formats = input_formats
def post_process(self, data):
processed_dict = {}
for input_key in data.keys():
output_data = data[input_key]
processed_dict[input_key] = {
"elements": []
}
id_counter = 0
for par_elem in output_data["paragraphs"]:
category = par_elem["role"]
category = CATEGORY_MAP.get(category, "paragraph")
transcription = par_elem["content"]
coord = [[pt["x"], pt["y"]] for pt in par_elem["bounding_regions"][0]["polygon"]]
xy_coord = [{"x": x, "y": y} for x, y in coord]
data_dict = {
"coordinates": xy_coord,
"category": category,
"id": id_counter,
"content": {
"text": transcription,
"html": "",
"markdown": ""
}
}
processed_dict[input_key]["elements"].append(data_dict)
id_counter += 1
html_transcription = ""
for table_elem in output_data["tables"]:
coord = [[pt["x"], pt["y"]] for pt in table_elem["bounding_regions"][0]["polygon"]]
xy_coord = [{"x": x, "y": y} for x, y in coord]
category = "table"
html_transcription += "<table>"
# Create a matrix to represent the table
table_matrix = [
["" for _ in range(table_elem["column_count"])] for _ in range(table_elem["row_count"])
]
# Fill the matrix with table data
for cell in table_elem["cells"]:
row = cell["row_index"]
col = cell["column_index"]
rowspan = cell.get("row_span", 1)
colspan = cell.get("column_span", 1)
content = cell["content"]
# Insert content into the matrix, handle rowspan and colspan
for r in range(row, row + rowspan):
for c in range(col, col + colspan):
if r == row and c == col:
table_matrix[r][c] = f"<td rowspan='{rowspan}' colspan='{colspan}'>{content}</td>"
else:
# Mark cells covered by rowspan or colspan
table_matrix[r][c] = None
# Generate HTML from the matrix
for row in table_matrix:
html_transcription += "<tr>"
for cell in row:
if cell is not None:
html_transcription += f"{cell}"
html_transcription += "</tr>"
html_transcription += "</table>"
data_dict = {
"coordinates": xy_coord,
"category": category,
"id": id_counter,
"content": {
"text": "",
"html": html_transcription,
"markdown": ""
}
}
processed_dict[input_key]["elements"].append(data_dict)
id_counter += 1
for key in self.processed_data:
processed_dict[key] = self.processed_data[key]
return processed_dict
def infer(self, file_path):
"""Infer the layout of the documents in the given file path
Args:
file_path (str): the path to the file or directory containing the documents to process
"""
paths = read_file_paths(file_path, supported_formats=self.formats)
error_files = []
result_dict = {}
for idx, filepath in enumerate(paths):
print("({}/{}) {}".format(idx+1, len(paths), filepath))
filename = filepath.name
if filename in self.processed_data.keys():
print(f"'{filename}' is already in the loaded dictionary. Skipping this sample")
continue
input_data = open(filepath, "rb")
try:
poller = self.document_analysis_client.begin_analyze_document(
"prebuilt-layout", document=input_data
)
result = poller.result()
json_result = result.to_dict()
except Exception as e:
print(e)
print("Error processing document..")
error_files.append(filepath)
continue
result_dict[filename] = json_result
result_dict = self.post_process(result_dict)
with open(self.save_path, "w") as f:
json.dump(result_dict, f)
for error_file in error_files:
print(f"Error processing file: {error_file}")
print("Finished processing all documents")
print("Results saved to: {}".format(self.save_path))
print("Number of errors: {}".format(len(error_files)))
if __name__ == "__main__":
args = argparse.ArgumentParser()
args.add_argument(
"--data_path",
type=str, default="", required=True,
help="Path containing the documents to process"
)
args.add_argument(
"--save_path",
type=str, default="", required=True,
help="Path to save the results"
)
args.add_argument(
"--input_formats",
type=list, default=[
".pdf", ".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".heic"
],
help="Supported input file formats"
)
args = args.parse_args()
microsoft_inference = MicrosoftInference(
args.save_path,
input_formats=args.input_formats
)
microsoft_inference.infer(args.data_path)
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