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import time
import re
import json
import io
import os
import boto3
import copy
from tqdm import tqdm
from PIL import Image, ImageChops, ImageFile, ImageDraw
from typing import List, Dict, Tuple
import pandas as pd
from pdfminer.high_level import extract_pages
from pdfminer.layout import LTTextContainer, LTChar, LTTextLine, LTTextLineHorizontal, LTAnno
from pikepdf import Pdf, Dictionary, Name
from pymupdf import Rect, Page, Document
import gradio as gr
from gradio import Progress
from collections import defaultdict # For efficient grouping
from tools.config import OUTPUT_FOLDER, IMAGES_DPI, MAX_IMAGE_PIXELS, RUN_AWS_FUNCTIONS, AWS_ACCESS_KEY, AWS_SECRET_KEY, AWS_REGION, PAGE_BREAK_VALUE, MAX_TIME_VALUE, LOAD_TRUNCATED_IMAGES, INPUT_FOLDER
from tools.custom_image_analyser_engine import CustomImageAnalyzerEngine, OCRResult, combine_ocr_results, CustomImageRecognizerResult, run_page_text_redaction, merge_text_bounding_boxes, recreate_page_line_level_ocr_results_with_page
from tools.file_conversion import convert_annotation_json_to_review_df, redact_whole_pymupdf_page, redact_single_box, convert_pymupdf_to_image_coords, is_pdf, is_pdf_or_image, prepare_image_or_pdf, divide_coordinates_by_page_sizes, multiply_coordinates_by_page_sizes, convert_annotation_data_to_dataframe, divide_coordinates_by_page_sizes, create_annotation_dicts_from_annotation_df, remove_duplicate_images_with_blank_boxes, fill_missing_ids, fill_missing_box_ids, load_and_convert_ocr_results_with_words_json
from tools.load_spacy_model_custom_recognisers import nlp_analyser, score_threshold, custom_entities, custom_recogniser, custom_word_list_recogniser, CustomWordFuzzyRecognizer
from tools.helper_functions import get_file_name_without_type, clean_unicode_text, tesseract_ocr_option, text_ocr_option, textract_option, local_pii_detector, aws_pii_detector, no_redaction_option
from tools.aws_textract import analyse_page_with_textract, json_to_ocrresult, load_and_convert_textract_json
ImageFile.LOAD_TRUNCATED_IMAGES = LOAD_TRUNCATED_IMAGES.lower() == "true"
if not MAX_IMAGE_PIXELS: Image.MAX_IMAGE_PIXELS = None
else: Image.MAX_IMAGE_PIXELS = MAX_IMAGE_PIXELS
image_dpi = float(IMAGES_DPI)
def bounding_boxes_overlap(box1, box2):
"""Check if two bounding boxes overlap."""
return (box1[0] < box2[2] and box2[0] < box1[2] and
box1[1] < box2[3] and box2[1] < box1[3])
def sum_numbers_before_seconds(string:str):
"""Extracts numbers that precede the word 'seconds' from a string and adds them up.
Args:
string: The input string.
Returns:
The sum of all numbers before 'seconds' in the string.
"""
# Extract numbers before 'seconds' using regular expression
numbers = re.findall(r'(\d+\.\d+)?\s*seconds', string)
# Extract the numbers from the matches
numbers = [float(num.split()[0]) for num in numbers]
# Sum up the extracted numbers
sum_of_numbers = round(sum(numbers),1)
return sum_of_numbers
def choose_and_run_redactor(file_paths:List[str],
prepared_pdf_file_paths:List[str],
pdf_image_file_paths:List[str],
language:str,
chosen_redact_entities:List[str],
chosen_redact_comprehend_entities:List[str],
text_extraction_method:str,
in_allow_list:List[List[str]]=None,
custom_recogniser_word_list:List[str]=None,
redact_whole_page_list:List[str]=None,
latest_file_completed:int=0,
combined_out_message:List=[],
out_file_paths:List=[],
log_files_output_paths:List=[],
first_loop_state:bool=False,
page_min:int=0,
page_max:int=999,
estimated_time_taken_state:float=0.0,
handwrite_signature_checkbox:List[str]=["Extract handwriting", "Extract signatures"],
all_request_metadata_str:str = "",
annotations_all_pages:List[dict]=[],
all_line_level_ocr_results_df:pd.DataFrame=[],#pd.DataFrame(),
all_pages_decision_process_table:pd.DataFrame=[],#pd.DataFrame(columns=["image_path", "page", "label", "xmin", "xmax", "ymin", "ymax", "boundingBox", "text", "start","end","score"]),
pymupdf_doc=[],
current_loop_page:int=0,
page_break_return:bool=False,
pii_identification_method:str="Local",
comprehend_query_number:int=0,
max_fuzzy_spelling_mistakes_num:int=1,
match_fuzzy_whole_phrase_bool:bool=True,
aws_access_key_textbox:str='',
aws_secret_key_textbox:str='',
annotate_max_pages:int=1,
review_file_state:pd.DataFrame=[],
output_folder:str=OUTPUT_FOLDER,
document_cropboxes:List=[],
page_sizes:List[dict]=[],
textract_output_found:bool=False,
text_extraction_only:bool=False,
duplication_file_path_outputs:list=[],
review_file_path:str="",
input_folder:str=INPUT_FOLDER,
total_textract_query_number:int=0,
ocr_file_path:str="",
all_page_line_level_ocr_results = [],
all_page_line_level_ocr_results_with_words = [],
prepare_images:bool=True,
progress=gr.Progress(track_tqdm=True)):
'''
This function orchestrates the redaction process based on the specified method and parameters. It takes the following inputs:
- file_paths (List[str]): A list of paths to the files to be redacted.
- prepared_pdf_file_paths (List[str]): A list of paths to the PDF files prepared for redaction.
- pdf_image_file_paths (List[str]): A list of paths to the PDF files converted to images for redaction.
- language (str): The language of the text in the files.
- chosen_redact_entities (List[str]): A list of entity types to redact from the files using the local model (spacy) with Microsoft Presidio.
- chosen_redact_comprehend_entities (List[str]): A list of entity types to redact from files, chosen from the official list from AWS Comprehend service.
- text_extraction_method (str): The method to use to extract text from documents.
- in_allow_list (List[List[str]], optional): A list of allowed terms for redaction. Defaults to None.
- custom_recogniser_word_list (List[List[str]], optional): A list of allowed terms for redaction. Defaults to None.
- redact_whole_page_list (List[List[str]], optional): A list of allowed terms for redaction. Defaults to None.
- latest_file_completed (int, optional): The index of the last completed file. Defaults to 0.
- combined_out_message (list, optional): A list to store output messages. Defaults to an empty list.
- out_file_paths (list, optional): A list to store paths to the output files. Defaults to an empty list.
- log_files_output_paths (list, optional): A list to store paths to the log files. Defaults to an empty list.
- first_loop_state (bool, optional): A flag indicating if this is the first iteration. Defaults to False.
- page_min (int, optional): The minimum page number to start redaction from. Defaults to 0.
- page_max (int, optional): The maximum page number to end redaction at. Defaults to 999.
- estimated_time_taken_state (float, optional): The estimated time taken for the redaction process. Defaults to 0.0.
- handwrite_signature_checkbox (List[str], optional): A list of options for redacting handwriting and signatures. Defaults to ["Extract handwriting", "Extract signatures"].
- all_request_metadata_str (str, optional): A string containing all request metadata. Defaults to an empty string.
- annotations_all_pages (List[dict], optional): A list of dictionaries containing all image annotations. Defaults to an empty list.
- all_line_level_ocr_results_df (pd.DataFrame, optional): A DataFrame containing all line-level OCR results. Defaults to an empty DataFrame.
- all_pages_decision_process_table (pd.DataFrame, optional): A DataFrame containing all decision process tables. Defaults to an empty DataFrame.
- pymupdf_doc (optional): A list containing the PDF document object. Defaults to an empty list.
- current_loop_page (int, optional): The current page being processed in the loop. Defaults to 0.
- page_break_return (bool, optional): A flag indicating if the function should return after a page break. Defaults to False.
- pii_identification_method (str, optional): The method to redact personal information. Either 'Local' (spacy model), or 'AWS Comprehend' (AWS Comprehend API).
- comprehend_query_number (int, optional): A counter tracking the number of queries to AWS Comprehend.
- max_fuzzy_spelling_mistakes_num (int, optional): The maximum number of spelling mistakes allowed in a searched phrase for fuzzy matching. Can range from 0-9.
- match_fuzzy_whole_phrase_bool (bool, optional): A boolean where 'True' means that the whole phrase is fuzzy matched, and 'False' means that each word is fuzzy matched separately (excluding stop words).
- aws_access_key_textbox (str, optional): AWS access key for account with Textract and Comprehend permissions.
- aws_secret_key_textbox (str, optional): AWS secret key for account with Textract and Comprehend permissions.
- annotate_max_pages (int, optional): Maximum page value for the annotation object.
- review_file_state (pd.DataFrame, optional): Output review file dataframe.
- output_folder (str, optional): Output folder for results.
- document_cropboxes (List, optional): List of document cropboxes for the PDF.
- page_sizes (List[dict], optional): List of dictionaries of PDF page sizes in PDF or image format.
- textract_output_found (bool, optional): Boolean is true when a textract OCR output for the file has been found.
- text_extraction_only (bool, optional): Boolean to determine if function should only extract text from the document, and not redact.
- duplication_file_outputs (list, optional): List to allow for export to the duplication function page.
- review_file_path (str, optional): The latest review file path created by the app
- input_folder (str, optional): The custom input path, if provided
- total_textract_query_number (int, optional): The number of textract queries up until this point.
- ocr_file_path (str, optional): The latest ocr file path created by the app.
- all_page_line_level_ocr_results (list, optional): All line level text on the page with bounding boxes.
- all_page_line_level_ocr_results_with_words (list, optional): All word level text on the page with bounding boxes.
- prepare_images (bool, optional): Boolean to determine whether to load images for the PDF.
- progress (gr.Progress, optional): A progress tracker for the redaction process. Defaults to a Progress object with track_tqdm set to True.
The function returns a redacted document along with processing logs.
'''
tic = time.perf_counter()
out_message = ""
pdf_file_name_with_ext = ""
pdf_file_name_without_ext = ""
blank_request_metadata = []
all_textract_request_metadata = all_request_metadata_str.split('\n') if all_request_metadata_str else []
review_out_file_paths = [prepared_pdf_file_paths[0]]
# Ensure all_pages_decision_process_table is in correct format for downstream processes
if isinstance(all_pages_decision_process_table,list):
if not all_pages_decision_process_table: all_pages_decision_process_table = pd.DataFrame(columns=["image_path", "page", "label", "xmin", "xmax", "ymin", "ymax", "boundingBox", "text", "start","end","score", "id"])
elif isinstance(all_pages_decision_process_table, pd.DataFrame):
if all_pages_decision_process_table.empty:
all_pages_decision_process_table = pd.DataFrame(columns=["image_path", "page", "label", "xmin", "xmax", "ymin", "ymax", "boundingBox", "text", "start","end","score", "id"])
# If this is the first time around, set variables to 0/blank
if first_loop_state==True:
#print("First_loop_state is True")
latest_file_completed = 0
current_loop_page = 0
out_file_paths = []
estimate_total_processing_time = 0
estimated_time_taken_state = 0
comprehend_query_number = 0
total_textract_query_number = 0
elif current_loop_page == 0:
comprehend_query_number = 0
total_textract_query_number = 0
# If not the first time around, and the current page loop has been set to a huge number (been through all pages), reset current page to 0
elif (first_loop_state == False) & (current_loop_page == 999):
current_loop_page = 0
total_textract_query_number = 0
comprehend_query_number = 0
# Choose the correct file to prepare
if isinstance(file_paths, str): file_paths_list = [os.path.abspath(file_paths)]
elif isinstance(file_paths, dict):
file_paths = file_paths["name"]
file_paths_list = [os.path.abspath(file_paths)]
else: file_paths_list = file_paths
valid_extensions = {".pdf", ".jpg", ".jpeg", ".png"}
# Filter only files with valid extensions. Currently only allowing one file to be redacted at a time
# Filter the file_paths_list to include only files with valid extensions
filtered_files = [file for file in file_paths_list if os.path.splitext(file)[1].lower() in valid_extensions]
# Check if any files were found and assign to file_paths_list
file_paths_list = filtered_files if filtered_files else []
# If latest_file_completed is used, get the specific file
if not isinstance(file_paths, (str, dict)): file_paths_loop = [file_paths_list[int(latest_file_completed)]] if len(file_paths_list) > latest_file_completed else []
else: file_paths_loop = file_paths_list
latest_file_completed = int(latest_file_completed)
if isinstance(file_paths,str): number_of_files = 1
else: number_of_files = len(file_paths_list)
# If we have already redacted the last file, return the input out_message and file list to the relevant outputs
if latest_file_completed >= number_of_files:
print("Completed last file")
progress(0.95, "Completed last file, performing final checks")
current_loop_page = 0
if isinstance(out_message, list) and out_message:
combined_out_message = combined_out_message + '\n'.join(out_message)
elif out_message:
combined_out_message = combined_out_message + '\n' + out_message
combined_out_message = re.sub(r'^\n+', '', combined_out_message).strip()
# Only send across review file if redaction has been done
if pii_identification_method != no_redaction_option:
if len(review_out_file_paths) == 1:
#review_file_path = [x for x in out_file_paths if "review_file" in x]
if review_file_path: review_out_file_paths.append(review_file_path)
if not isinstance(pymupdf_doc, list):
number_of_pages = pymupdf_doc.page_count
if total_textract_query_number > number_of_pages:
total_textract_query_number = number_of_pages
estimate_total_processing_time = sum_numbers_before_seconds(combined_out_message)
print("Estimated total processing time:", str(estimate_total_processing_time))
return combined_out_message, out_file_paths, out_file_paths, gr.Number(value=latest_file_completed, label="Number of documents redacted", interactive=False, visible=False), log_files_output_paths, log_files_output_paths, estimated_time_taken_state, all_request_metadata_str, pymupdf_doc, annotations_all_pages, gr.Number(value=current_loop_page,precision=0, interactive=False, label = "Last redacted page in document", visible=False), gr.Checkbox(value = True, label="Page break reached", visible=False), all_line_level_ocr_results_df, all_pages_decision_process_table, comprehend_query_number, review_out_file_paths, annotate_max_pages, annotate_max_pages, prepared_pdf_file_paths, pdf_image_file_paths, review_file_state, page_sizes, duplication_file_path_outputs, duplication_file_path_outputs, review_file_path, total_textract_query_number, ocr_file_path, all_page_line_level_ocr_results, all_page_line_level_ocr_results_with_words
#if first_loop_state == False:
# Prepare documents and images as required if they don't already exist
prepare_images_flag = None # Determines whether to call prepare_image_or_pdf
if textract_output_found and text_extraction_method == textract_option:
print("Existing Textract outputs found, not preparing images or documents.")
prepare_images_flag = False
#return # No need to call `prepare_image_or_pdf`, exit early
elif text_extraction_method == text_ocr_option:
print("Running text extraction analysis, not preparing images.")
prepare_images_flag = False
elif prepare_images and not pdf_image_file_paths:
print("Prepared PDF images not found, loading from file")
prepare_images_flag = True
elif not prepare_images:
print("Not loading images for file")
prepare_images_flag = False
else:
print("Loading images for file")
prepare_images_flag = True
# Call prepare_image_or_pdf only if needed
if prepare_images_flag is not None:
out_message, prepared_pdf_file_paths, pdf_image_file_paths, annotate_max_pages, annotate_max_pages_bottom, pymupdf_doc, annotations_all_pages, review_file_state, document_cropboxes, page_sizes, textract_output_found, all_img_details_state, placeholder_ocr_results_df, local_ocr_output_found_checkbox = prepare_image_or_pdf(
file_paths_loop, text_extraction_method, 0, out_message, True,
annotate_max_pages, annotations_all_pages, document_cropboxes, redact_whole_page_list,
output_folder, prepare_images=prepare_images_flag, page_sizes=page_sizes, input_folder=input_folder
)
page_sizes_df = pd.DataFrame(page_sizes)
if page_sizes_df.empty:
page_sizes_df=pd.DataFrame(columns=["page", "image_path", "image_width", "image_height", "mediabox_width", "mediabox_height", "cropbox_width", "cropbox_height", "original_cropbox"])
page_sizes_df[["page"]] = page_sizes_df[["page"]].apply(pd.to_numeric, errors="coerce")
page_sizes = page_sizes_df.to_dict(orient="records")
number_of_pages = pymupdf_doc.page_count
# If we have reached the last page, return message and outputs
if current_loop_page >= number_of_pages:
print("Reached last page of document:", current_loop_page)
if total_textract_query_number > number_of_pages:
total_textract_query_number = number_of_pages
# Set to a very high number so as not to mix up with subsequent file processing by the user
current_loop_page = 999
if out_message:
combined_out_message = combined_out_message + "\n" + out_message
# Only send across review file if redaction has been done
if pii_identification_method != no_redaction_option:
# If only pdf currently in review outputs, add on the latest review file
if len(review_out_file_paths) == 1:
#review_file_path = [x for x in out_file_paths if "review_file" in x]
if review_file_path: review_out_file_paths.append(review_file_path)
return combined_out_message, out_file_paths, out_file_paths, gr.Number(value=latest_file_completed, label="Number of documents redacted", interactive=False, visible=False), log_files_output_paths, log_files_output_paths, estimated_time_taken_state, all_request_metadata_str, pymupdf_doc, annotations_all_pages, gr.Number(value=current_loop_page,precision=0, interactive=False, label = "Last redacted page in document", visible=False), gr.Checkbox(value = False, label="Page break reached", visible=False), all_line_level_ocr_results_df, all_pages_decision_process_table, comprehend_query_number, review_out_file_paths, annotate_max_pages, annotate_max_pages, prepared_pdf_file_paths, pdf_image_file_paths, review_file_state, page_sizes, duplication_file_path_outputs, duplication_file_path_outputs, review_file_path, total_textract_query_number, ocr_file_path, all_page_line_level_ocr_results, all_page_line_level_ocr_results_with_words
# Load/create allow list
# If string, assume file path
if isinstance(in_allow_list, str): in_allow_list = pd.read_csv(in_allow_list)
# Now, should be a pandas dataframe format
if not in_allow_list.empty:
in_allow_list_flat = in_allow_list.iloc[:,0].tolist()
else:
in_allow_list_flat = []
# If string, assume file path
if isinstance(custom_recogniser_word_list, str):
custom_recogniser_word_list = pd.read_csv(custom_recogniser_word_list)
if isinstance(custom_recogniser_word_list, pd.DataFrame):
if not custom_recogniser_word_list.empty:
custom_recogniser_word_list_flat = custom_recogniser_word_list.iloc[:, 0].tolist()
else:
custom_recogniser_word_list_flat = []
# Sort the strings in order from the longest string to the shortest
custom_recogniser_word_list_flat = sorted(custom_recogniser_word_list_flat, key=len, reverse=True)
# If string, assume file path
if isinstance(redact_whole_page_list, str):
redact_whole_page_list = pd.read_csv(redact_whole_page_list)
if isinstance(redact_whole_page_list, pd.DataFrame):
if not redact_whole_page_list.empty:
try:
redact_whole_page_list_flat = redact_whole_page_list.iloc[:,0].astype(int).tolist()
except Exception as e:
print("Could not convert whole page redaction data to number list due to:", e)
redact_whole_page_list_flat = redact_whole_page_list.iloc[:,0].tolist()
else:
redact_whole_page_list_flat = []
# Try to connect to AWS services directly only if RUN_AWS_FUNCTIONS environmental variable is 1, otherwise an environment variable or direct textbox input is needed.
if pii_identification_method == aws_pii_detector:
if aws_access_key_textbox and aws_secret_key_textbox:
print("Connecting to Comprehend using AWS access key and secret keys from user input.")
comprehend_client = boto3.client('comprehend',
aws_access_key_id=aws_access_key_textbox,
aws_secret_access_key=aws_secret_key_textbox, region_name=AWS_REGION)
elif RUN_AWS_FUNCTIONS == "1":
print("Connecting to Comprehend via existing SSO connection")
comprehend_client = boto3.client('comprehend', region_name=AWS_REGION)
elif AWS_ACCESS_KEY and AWS_SECRET_KEY:
print("Getting Comprehend credentials from environment variables")
comprehend_client = boto3.client('comprehend',
aws_access_key_id=AWS_ACCESS_KEY,
aws_secret_access_key=AWS_SECRET_KEY, region_name=AWS_REGION)
else:
comprehend_client = ""
out_message = "Cannot connect to AWS Comprehend service. Please provide access keys under Textract settings on the Redaction settings tab, or choose another PII identification method."
print(out_message)
raise Exception(out_message)
else:
comprehend_client = ""
# Try to connect to AWS Textract Client if using that text extraction method
if text_extraction_method == textract_option:
if aws_access_key_textbox and aws_secret_key_textbox:
print("Connecting to Textract using AWS access key and secret keys from user input.")
textract_client = boto3.client('textract',
aws_access_key_id=aws_access_key_textbox,
aws_secret_access_key=aws_secret_key_textbox, region_name=AWS_REGION)
elif RUN_AWS_FUNCTIONS == "1":
print("Connecting to Textract via existing SSO connection")
textract_client = boto3.client('textract', region_name=AWS_REGION)
elif AWS_ACCESS_KEY and AWS_SECRET_KEY:
print("Getting Textract credentials from environment variables.")
textract_client = boto3.client('textract',
aws_access_key_id=AWS_ACCESS_KEY,
aws_secret_access_key=AWS_SECRET_KEY, region_name=AWS_REGION)
elif textract_output_found==True:
print("Existing Textract data found for file, no need to connect to AWS Textract")
textract_client = boto3.client('textract', region_name=AWS_REGION)
else:
textract_client = ""
out_message = "Cannot connect to AWS Textract service."
print(out_message)
raise Exception(out_message)
else:
textract_client = ""
# Check if output_folder exists, create it if it doesn't
if not os.path.exists(output_folder): os.makedirs(output_folder)
progress(0.5, desc="Extracting text and redacting document")
all_pages_decision_process_table = pd.DataFrame(columns=["image_path", "page", "label", "xmin", "xmax", "ymin", "ymax", "boundingBox", "text", "start","end","score", "id"])
all_line_level_ocr_results_df = pd.DataFrame()
# Run through file loop, redact each file at a time
for file in file_paths_loop:
# Get a string file path
if isinstance(file, str): file_path = file
else: file_path = file.name
if file_path:
pdf_file_name_without_ext = get_file_name_without_type(file_path)
pdf_file_name_with_ext = os.path.basename(file_path)
is_a_pdf = is_pdf(file_path) == True
if is_a_pdf == False and text_extraction_method == text_ocr_option:
# If user has not submitted a pdf, assume it's an image
print("File is not a PDF, assuming that image analysis needs to be used.")
text_extraction_method = tesseract_ocr_option
else:
out_message = "No file selected"
print(out_message)
raise Exception(out_message)
# Output file paths names
orig_pdf_file_path = output_folder + pdf_file_name_with_ext
review_file_path = orig_pdf_file_path + '_review_file.csv'
# Remove any existing review_file paths from the review file outputs
if text_extraction_method == tesseract_ocr_option or text_extraction_method == textract_option:
#Analyse and redact image-based pdf or image
if is_pdf_or_image(file_path) == False:
out_message = "Please upload a PDF file or image file (JPG, PNG) for image analysis."
raise Exception(out_message)
print("Redacting file " + pdf_file_name_with_ext + " as an image-based file")
pymupdf_doc, all_pages_decision_process_table, out_file_paths, new_textract_request_metadata, annotations_all_pages, current_loop_page, page_break_return, all_line_level_ocr_results_df, comprehend_query_number, all_page_line_level_ocr_results, all_page_line_level_ocr_results_with_words = redact_image_pdf(file_path,
pdf_image_file_paths,
language,
chosen_redact_entities,
chosen_redact_comprehend_entities,
in_allow_list_flat,
page_min,
page_max,
text_extraction_method,
handwrite_signature_checkbox,
blank_request_metadata,
current_loop_page,
page_break_return,
annotations_all_pages,
all_line_level_ocr_results_df,
all_pages_decision_process_table,
pymupdf_doc,
pii_identification_method,
comprehend_query_number,
comprehend_client,
textract_client,
custom_recogniser_word_list_flat,
redact_whole_page_list_flat,
max_fuzzy_spelling_mistakes_num,
match_fuzzy_whole_phrase_bool,
page_sizes_df,
text_extraction_only,
all_page_line_level_ocr_results,
all_page_line_level_ocr_results_with_words,
log_files_output_paths=log_files_output_paths,
output_folder=output_folder)
# Save Textract request metadata (if exists)
if new_textract_request_metadata and isinstance(new_textract_request_metadata, list):
all_textract_request_metadata.extend(new_textract_request_metadata)
elif text_extraction_method == text_ocr_option:
if is_pdf(file_path) == False:
out_message = "Please upload a PDF file for text analysis. If you have an image, select 'Image analysis'."
raise Exception(out_message)
# Analyse text-based pdf
print('Redacting file as text-based PDF')
pymupdf_doc, all_pages_decision_process_table, all_line_level_ocr_results_df, annotations_all_pages, current_loop_page, page_break_return, comprehend_query_number = redact_text_pdf(
file_path,
language,
chosen_redact_entities,
chosen_redact_comprehend_entities,
in_allow_list_flat,
page_min,
page_max,
current_loop_page,
page_break_return,
annotations_all_pages,
all_line_level_ocr_results_df,
all_pages_decision_process_table,
pymupdf_doc,
pii_identification_method,
comprehend_query_number,
comprehend_client,
custom_recogniser_word_list_flat,
redact_whole_page_list_flat,
max_fuzzy_spelling_mistakes_num,
match_fuzzy_whole_phrase_bool,
page_sizes_df,
document_cropboxes,
text_extraction_only)
else:
out_message = "No redaction method selected"
print(out_message)
raise Exception(out_message)
# If at last page, save to file
if current_loop_page >= number_of_pages:
print("Current page loop:", current_loop_page, "is the last page.")
latest_file_completed += 1
current_loop_page = 999
if latest_file_completed != len(file_paths_list):
print("Completed file number:", str(latest_file_completed), "there are more files to do")
progress(0.9, "Saving redacted PDF file")
# Save redacted file
if pii_identification_method != no_redaction_option:
if is_pdf(file_path) == False:
out_redacted_pdf_file_path = output_folder + pdf_file_name_without_ext + "_redacted.png"
# pymupdf_doc is an image list in this case
if isinstance(pymupdf_doc[-1], str):
img = Image.open(pymupdf_doc[-1])
# Otherwise could be an image object
else:
img = pymupdf_doc[-1]
img.save(out_redacted_pdf_file_path, "PNG" ,resolution=image_dpi)
#
else:
out_redacted_pdf_file_path = output_folder + pdf_file_name_without_ext + "_redacted.pdf"
print("Saving redacted PDF file:", out_redacted_pdf_file_path)
pymupdf_doc.save(out_redacted_pdf_file_path, garbage=4, deflate=True, clean=True)
out_file_paths.append(out_redacted_pdf_file_path)
if not all_line_level_ocr_results_df.empty:
all_line_level_ocr_results_df = all_line_level_ocr_results_df[["page", "text", "left", "top", "width", "height"]]
else: all_line_level_ocr_results_df = pd.DataFrame(columns=["page", "text", "left", "top", "width", "height"])
ocr_file_path = orig_pdf_file_path + "_ocr_output.csv"
all_line_level_ocr_results_df.sort_values(["page", "top", "left"], inplace=True)
all_line_level_ocr_results_df.to_csv(ocr_file_path, index = None, encoding="utf-8")
out_file_paths.append(ocr_file_path)
duplication_file_path_outputs.append(ocr_file_path)
# Convert the gradio annotation boxes to relative coordinates
# Convert annotations_all_pages to a consistent relative coordinate format output
page_sizes = page_sizes_df.to_dict(orient="records")
all_image_annotations_df = convert_annotation_data_to_dataframe(annotations_all_pages)
all_image_annotations_df = divide_coordinates_by_page_sizes(all_image_annotations_df, page_sizes_df, xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax")
annotations_all_pages_divide = create_annotation_dicts_from_annotation_df(all_image_annotations_df, page_sizes)
annotations_all_pages_divide = remove_duplicate_images_with_blank_boxes(annotations_all_pages_divide)
# Save the gradio_annotation_boxes to a review csv file
review_file_state = convert_annotation_json_to_review_df(annotations_all_pages_divide, all_pages_decision_process_table, page_sizes=page_sizes)
# Don't need page sizes in outputs
review_file_state.drop(["image_width", "image_height", "mediabox_width", "mediabox_height", "cropbox_width", "cropbox_height"], axis=1, inplace=True, errors="ignore")
review_file_state.to_csv(review_file_path, index=None)
if pii_identification_method != no_redaction_option:
out_file_paths.append(review_file_path)
# Make a combined message for the file
if isinstance(out_message, list) and out_message:
combined_out_message = combined_out_message + '\n'.join(out_message) # Ensure out_message is a list of strings
elif out_message:
combined_out_message = combined_out_message + '\n' + out_message
toc = time.perf_counter()
time_taken = toc - tic
estimated_time_taken_state += time_taken
out_time_message = f" Redacted in {estimated_time_taken_state:0.1f} seconds."
combined_out_message = combined_out_message + " " + out_time_message # Ensure this is a single string
estimate_total_processing_time = sum_numbers_before_seconds(combined_out_message)
else:
toc = time.perf_counter()
time_taken = toc - tic
estimated_time_taken_state += time_taken
# If textract requests made, write to logging file. Alos record number of Textract requests
if all_textract_request_metadata and isinstance(all_textract_request_metadata, list):
all_request_metadata_str = '\n'.join(all_textract_request_metadata).strip()
all_textract_request_metadata_file_path = output_folder + pdf_file_name_without_ext + "_textract_metadata.txt"
with open(all_textract_request_metadata_file_path, "w") as f:
f.write(all_request_metadata_str)
# Add the request metadata to the log outputs if not there already
if all_textract_request_metadata_file_path not in log_files_output_paths:
log_files_output_paths.append(all_textract_request_metadata_file_path)
new_textract_query_numbers = len(all_textract_request_metadata)
total_textract_query_number += new_textract_query_numbers
# Ensure no duplicated output files
log_files_output_paths = sorted(list(set(log_files_output_paths)))
out_file_paths = sorted(list(set(out_file_paths)))
# Output file paths
if not review_file_path: review_out_file_paths = [prepared_pdf_file_paths[-1]]
else: review_out_file_paths = [prepared_pdf_file_paths[-1], review_file_path]
if total_textract_query_number > number_of_pages:
total_textract_query_number = number_of_pages
return combined_out_message, out_file_paths, out_file_paths, gr.Number(value=latest_file_completed, label="Number of documents redacted", interactive=False, visible=False), log_files_output_paths, log_files_output_paths, estimated_time_taken_state, all_request_metadata_str, pymupdf_doc, annotations_all_pages_divide, gr.Number(value=current_loop_page, precision=0, interactive=False, label = "Last redacted page in document", visible=False), gr.Checkbox(value = True, label="Page break reached", visible=False), all_line_level_ocr_results_df, all_pages_decision_process_table, comprehend_query_number, review_out_file_paths, annotate_max_pages, annotate_max_pages, prepared_pdf_file_paths, pdf_image_file_paths, review_file_state, page_sizes, duplication_file_path_outputs, duplication_file_path_outputs, review_file_path, total_textract_query_number, ocr_file_path, all_page_line_level_ocr_results, all_page_line_level_ocr_results_with_words
def convert_pikepdf_coords_to_pymupdf(pymupdf_page:Page, pikepdf_bbox, type="pikepdf_annot"):
'''
Convert annotations from pikepdf to pymupdf format, handling the mediabox larger than rect.
'''
# Use cropbox if available, otherwise use mediabox
reference_box = pymupdf_page.rect
mediabox = pymupdf_page.mediabox
reference_box_height = reference_box.height
reference_box_width = reference_box.width
# Convert PyMuPDF coordinates back to PDF coordinates (bottom-left origin)
media_height = mediabox.height
media_width = mediabox.width
media_reference_y_diff = media_height - reference_box_height
media_reference_x_diff = media_width - reference_box_width
y_diff_ratio = media_reference_y_diff / reference_box_height
x_diff_ratio = media_reference_x_diff / reference_box_width
# Extract the annotation rectangle field
if type=="pikepdf_annot":
rect_field = pikepdf_bbox["/Rect"]
else:
rect_field = pikepdf_bbox
rect_coordinates = [float(coord) for coord in rect_field] # Convert to floats
# Unpack coordinates
x1, y1, x2, y2 = rect_coordinates
new_x1 = x1 - (media_reference_x_diff * x_diff_ratio)
new_y1 = media_height - y2 - (media_reference_y_diff * y_diff_ratio)
new_x2 = x2 - (media_reference_x_diff * x_diff_ratio)
new_y2 = media_height - y1 - (media_reference_y_diff * y_diff_ratio)
return new_x1, new_y1, new_x2, new_y2
def convert_pikepdf_to_image_coords(pymupdf_page, annot, image:Image, type="pikepdf_annot"):
'''
Convert annotations from pikepdf coordinates to image coordinates.
'''
# Get the dimensions of the page in points with pymupdf
rect_height = pymupdf_page.rect.height
rect_width = pymupdf_page.rect.width
# Get the dimensions of the image
image_page_width, image_page_height = image.size
# Calculate scaling factors between pymupdf and PIL image
scale_width = image_page_width / rect_width
scale_height = image_page_height / rect_height
# Extract the /Rect field
if type=="pikepdf_annot":
rect_field = annot["/Rect"]
else:
rect_field = annot
# Convert the extracted /Rect field to a list of floats
rect_coordinates = [float(coord) for coord in rect_field]
# Convert the Y-coordinates (flip using the image height)
x1, y1, x2, y2 = rect_coordinates
x1_image = x1 * scale_width
new_y1_image = image_page_height - (y2 * scale_height) # Flip Y0 (since it starts from bottom)
x2_image = x2 * scale_width
new_y2_image = image_page_height - (y1 * scale_height) # Flip Y1
return x1_image, new_y1_image, x2_image, new_y2_image
def convert_pikepdf_decision_output_to_image_coords(pymupdf_page:Document, pikepdf_decision_ouput_data:List[dict], image:Image):
if isinstance(image, str):
image_path = image
image = Image.open(image_path)
# Loop through each item in the data
for item in pikepdf_decision_ouput_data:
# Extract the bounding box
bounding_box = item['boundingBox']
# Create a pikepdf_bbox dictionary to match the expected input
pikepdf_bbox = {"/Rect": bounding_box}
# Call the conversion function
new_x1, new_y1, new_x2, new_y2 = convert_pikepdf_to_image_coords(pymupdf_page, pikepdf_bbox, image, type="pikepdf_annot")
# Update the original object with the new bounding box values
item['boundingBox'] = [new_x1, new_y1, new_x2, new_y2]
return pikepdf_decision_ouput_data
def convert_image_coords_to_pymupdf(pymupdf_page:Document, annot:dict, image:Image, type:str="image_recognizer"):
'''
Converts an image with redaction coordinates from a CustomImageRecognizerResult or pikepdf object with image coordinates to pymupdf coordinates.
'''
rect_height = pymupdf_page.rect.height
rect_width = pymupdf_page.rect.width
image_page_width, image_page_height = image.size
# Calculate scaling factors between PIL image and pymupdf
scale_width = rect_width / image_page_width
scale_height = rect_height / image_page_height
# Calculate scaled coordinates
if type == "image_recognizer":
x1 = (annot.left * scale_width)# + page_x_adjust
new_y1 = (annot.top * scale_height)# - page_y_adjust # Flip Y0 (since it starts from bottom)
x2 = ((annot.left + annot.width) * scale_width)# + page_x_adjust # Calculate x1
new_y2 = ((annot.top + annot.height) * scale_height)# - page_y_adjust # Calculate y1 correctly
# Else assume it is a pikepdf derived object
else:
rect_field = annot["/Rect"]
rect_coordinates = [float(coord) for coord in rect_field] # Convert to floats
# Unpack coordinates
x1, y1, x2, y2 = rect_coordinates
x1 = (x1* scale_width)# + page_x_adjust
new_y1 = ((y2 + (y1 - y2))* scale_height)# - page_y_adjust # Calculate y1 correctly
x2 = ((x1 + (x2 - x1)) * scale_width)# + page_x_adjust # Calculate x1
new_y2 = (y2 * scale_height)# - page_y_adjust # Flip Y0 (since it starts from bottom)
return x1, new_y1, x2, new_y2
def convert_gradio_image_annotator_object_coords_to_pymupdf(pymupdf_page:Page, annot:dict, image:Image, image_dimensions:dict=None):
'''
Converts an image with redaction coordinates from a gradio annotation component to pymupdf coordinates.
'''
rect_height = pymupdf_page.rect.height
rect_width = pymupdf_page.rect.width
if image_dimensions:
image_page_width = image_dimensions['image_width']
image_page_height = image_dimensions['image_height']
elif image:
image_page_width, image_page_height = image.size
# Calculate scaling factors between PIL image and pymupdf
scale_width = rect_width / image_page_width
scale_height = rect_height / image_page_height
# Calculate scaled coordinates
x1 = (annot["xmin"] * scale_width)# + page_x_adjust
new_y1 = (annot["ymin"] * scale_height)# - page_y_adjust # Flip Y0 (since it starts from bottom)
x2 = ((annot["xmax"]) * scale_width)# + page_x_adjust # Calculate x1
new_y2 = ((annot["ymax"]) * scale_height)# - page_y_adjust # Calculate y1 correctly
return x1, new_y1, x2, new_y2
def move_page_info(file_path: str) -> str:
# Split the string at '.png'
base, extension = file_path.rsplit('.pdf', 1)
# Extract the page info
page_info = base.split('page ')[1].split(' of')[0] # Get the page number
new_base = base.replace(f'page {page_info} of ', '') # Remove the page info from the original position
# Construct the new file path
new_file_path = f"{new_base}_page_{page_info}.png"
return new_file_path
def prepare_custom_image_recogniser_result_annotation_box(page:Page, annot:dict, image:Image, page_sizes_df:pd.DataFrame):
'''
Prepare an image annotation box and coordinates based on a CustomImageRecogniserResult, PyMuPDF page, and PIL Image.
'''
img_annotation_box = {}
# For efficient lookup, set 'page' as index if it's not already
if 'page' in page_sizes_df.columns:
page_sizes_df = page_sizes_df.set_index('page')
# PyMuPDF page numbers are 0-based, DataFrame index assumed 1-based
page_num_one_based = page.number + 1
pymupdf_x1, pymupdf_y1, pymupdf_x2, pymupdf_y2 = 0, 0, 0, 0 # Initialize defaults
if image:
pymupdf_x1, pymupdf_y1, pymupdf_x2, pymupdf_y2 = convert_image_coords_to_pymupdf(page, annot, image)
else:
# --- Calculate coordinates when no image is present ---
# Assumes annot coords are normalized relative to MediaBox (top-left origin)
try:
# 1. Get MediaBox dimensions from the DataFrame
page_info = page_sizes_df.loc[page_num_one_based]
mb_width = page_info['mediabox_width']
mb_height = page_info['mediabox_height']
x_offset = page_info['cropbox_x_offset']
y_offset = page_info['cropbox_y_offset_from_top']
# Check for invalid dimensions
if mb_width <= 0 or mb_height <= 0:
print(f"Warning: Invalid MediaBox dimensions ({mb_width}x{mb_height}) for page {page_num_one_based}. Setting coords to 0.")
else:
pymupdf_x1 = annot.left - x_offset
pymupdf_x2 = annot.left + annot.width - x_offset
pymupdf_y1 = annot.top - y_offset
pymupdf_y2 = annot.top + annot.height - y_offset
except KeyError:
print(f"Warning: Page number {page_num_one_based} not found in page_sizes_df. Cannot get MediaBox dimensions. Setting coords to 0.")
except AttributeError as e:
print(f"Error accessing attributes ('left', 'top', etc.) on 'annot' object for page {page_num_one_based}: {e}")
except Exception as e:
print(f"Error during coordinate calculation for page {page_num_one_based}: {e}")
rect = Rect(pymupdf_x1, pymupdf_y1, pymupdf_x2, pymupdf_y2) # Create the PyMuPDF Rect
# Now creating image annotation object
image_x1 = annot.left
image_x2 = annot.left + annot.width
image_y1 = annot.top
image_y2 = annot.top + annot.height
# Create image annotation boxes
img_annotation_box["xmin"] = image_x1
img_annotation_box["ymin"] = image_y1
img_annotation_box["xmax"] = image_x2 # annot.left + annot.width
img_annotation_box["ymax"] = image_y2 # annot.top + annot.height
img_annotation_box["color"] = (0,0,0)
try:
img_annotation_box["label"] = str(annot.entity_type)
except:
img_annotation_box["label"] = "Redaction"
if hasattr(annot, 'text') and annot.text:
img_annotation_box["text"] = str(annot.text)
else:
img_annotation_box["text"] = ""
# Assign an id
img_annotation_box = fill_missing_box_ids(img_annotation_box)
return img_annotation_box, rect
def convert_pikepdf_annotations_to_result_annotation_box(page:Page, annot:dict, image:Image=None, convert_pikepdf_to_pymupdf_coords:bool=True, page_sizes_df:pd.DataFrame=pd.DataFrame(), image_dimensions:dict={}):
'''
Convert redaction objects with pikepdf coordinates to annotation boxes for PyMuPDF that can then be redacted from the document. First 1. converts pikepdf to pymupdf coordinates, then 2. converts pymupdf coordinates to image coordinates if page is an image.
'''
img_annotation_box = {}
page_no = page.number
if convert_pikepdf_to_pymupdf_coords == True:
pymupdf_x1, pymupdf_y1, pymupdf_x2, pymupdf_y2 = convert_pikepdf_coords_to_pymupdf(page, annot)
else:
pymupdf_x1, pymupdf_y1, pymupdf_x2, pymupdf_y2 = convert_image_coords_to_pymupdf(page, annot, image, type="pikepdf_image_coords")
rect = Rect(pymupdf_x1, pymupdf_y1, pymupdf_x2, pymupdf_y2)
convert_df = pd.DataFrame({
"page": [page_no],
"xmin": [pymupdf_x1],
"ymin": [pymupdf_y1],
"xmax": [pymupdf_x2],
"ymax": [pymupdf_y2]
})
converted_df = convert_df #divide_coordinates_by_page_sizes(convert_df, page_sizes_df, xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax")
img_annotation_box["xmin"] = converted_df["xmin"].max()
img_annotation_box["ymin"] = converted_df["ymin"].max()
img_annotation_box["xmax"] = converted_df["xmax"].max()
img_annotation_box["ymax"] = converted_df["ymax"].max()
img_annotation_box["color"] = (0, 0, 0)
if isinstance(annot, Dictionary):
img_annotation_box["label"] = str(annot["/T"])
if hasattr(annot, 'Contents'):
img_annotation_box["text"] = str(annot.Contents)
else:
img_annotation_box["text"] = ""
else:
img_annotation_box["label"] = "REDACTION"
img_annotation_box["text"] = ""
return img_annotation_box, rect
def redact_page_with_pymupdf(page:Page, page_annotations:dict, image:Image=None, custom_colours:bool=False, redact_whole_page:bool=False, convert_pikepdf_to_pymupdf_coords:bool=True, original_cropbox:List[Rect]=[], page_sizes_df:pd.DataFrame=pd.DataFrame()):
rect_height = page.rect.height
rect_width = page.rect.width
mediabox_height = page.mediabox.height
mediabox_width = page.mediabox.width
page_no = page.number
page_num_reported = page_no + 1
page_sizes_df[["page"]] = page_sizes_df[["page"]].apply(pd.to_numeric, errors="coerce")
# Check if image dimensions for page exist in page_sizes_df
image_dimensions = {}
if not image and 'image_width' in page_sizes_df.columns:
page_sizes_df[['image_width']] = page_sizes_df[['image_width']].apply(pd.to_numeric, errors="coerce")
page_sizes_df[['image_height']] = page_sizes_df[['image_height']].apply(pd.to_numeric, errors="coerce")
image_dimensions['image_width'] = page_sizes_df.loc[page_sizes_df['page']==page_num_reported, "image_width"].max()
image_dimensions['image_height'] = page_sizes_df.loc[page_sizes_df['page']==page_num_reported, "image_height"].max()
if pd.isna(image_dimensions['image_width']):
image_dimensions = {}
out_annotation_boxes = {}
all_image_annotation_boxes = []
if isinstance(image, Image.Image):
image_path = move_page_info(str(page))
image.save(image_path)
elif isinstance(image, str):
if os.path.exists(image):
image_path = image
image = Image.open(image_path)
elif 'image_path' in page_sizes_df.columns:
try:
image_path = page_sizes_df.loc[page_sizes_df["page"]==(page_no+1), "image_path"].iloc[0]
except IndexError:
image_path = ""
image=None
else:
image_path = ""
image=None
else:
#print("image is not an Image object or string")
image_path = ""
image=None
# Check if this is an object used in the Gradio Annotation component
if isinstance (page_annotations, dict):
page_annotations = page_annotations["boxes"]
for annot in page_annotations:
# Check if an Image recogniser result, or a Gradio annotation object
if (isinstance(annot, CustomImageRecognizerResult)) | isinstance(annot, dict):
img_annotation_box = {}
# Should already be in correct format if img_annotator_box is an input
if isinstance(annot, dict):
annot = fill_missing_box_ids(annot)
img_annotation_box = annot
box_coordinates = (img_annotation_box['xmin'], img_annotation_box['ymin'], img_annotation_box['xmax'], img_annotation_box['ymax'])
# Check if all coordinates are equal to or less than 1
are_coordinates_relative = all(coord <= 1 for coord in box_coordinates)
if are_coordinates_relative == True:
# Check if coordinates are relative, if so then multiply by mediabox size
pymupdf_x1 = img_annotation_box['xmin'] * mediabox_width
pymupdf_y1 = img_annotation_box['ymin'] * mediabox_height
pymupdf_x2 = img_annotation_box['xmax'] * mediabox_width
pymupdf_y2 = img_annotation_box['ymax'] * mediabox_height
elif image_dimensions or image:
pymupdf_x1, pymupdf_y1, pymupdf_x2, pymupdf_y2 = convert_gradio_image_annotator_object_coords_to_pymupdf(page, img_annotation_box, image, image_dimensions)
else:
print("Could not convert image annotator coordinates in redact_page_with_pymupdf")
print("img_annotation_box", img_annotation_box)
pymupdf_x1 = img_annotation_box['xmin']
pymupdf_y1 = img_annotation_box['ymin']
pymupdf_x2 = img_annotation_box['xmax']
pymupdf_y2 = img_annotation_box['ymax']
if hasattr(annot, 'text') and annot.text:
img_annotation_box["text"] = str(annot.text)
else:
img_annotation_box["text"] = ""
rect = Rect(pymupdf_x1, pymupdf_y1, pymupdf_x2, pymupdf_y2) # Create the PyMuPDF Rect
# Else should be CustomImageRecognizerResult
elif isinstance(annot, CustomImageRecognizerResult):
#print("annot is a CustomImageRecognizerResult")
img_annotation_box, rect = prepare_custom_image_recogniser_result_annotation_box(page, annot, image, page_sizes_df)
# Else it should be a pikepdf annotation object
else:
if not image: convert_pikepdf_to_pymupdf_coords = True
else: convert_pikepdf_to_pymupdf_coords = False
img_annotation_box, rect = convert_pikepdf_annotations_to_result_annotation_box(page, annot, image, convert_pikepdf_to_pymupdf_coords, page_sizes_df, image_dimensions=image_dimensions)
img_annotation_box = fill_missing_box_ids(img_annotation_box)
all_image_annotation_boxes.append(img_annotation_box)
# Redact the annotations from the document
redact_single_box(page, rect, img_annotation_box, custom_colours)
# If whole page is to be redacted, do that here
if redact_whole_page == True:
whole_page_img_annotation_box = redact_whole_pymupdf_page(rect_height, rect_width, image, page, custom_colours, border = 5, image_dimensions=image_dimensions)
all_image_annotation_boxes.append(whole_page_img_annotation_box)
out_annotation_boxes = {
"image": image_path, #Image.open(image_path), #image_path,
"boxes": all_image_annotation_boxes
}
page.apply_redactions(images=0, graphics=0)
page.set_cropbox(original_cropbox) # Set CropBox to original size
page.clean_contents()
return page, out_annotation_boxes
###
# IMAGE-BASED OCR PDF TEXT DETECTION/REDACTION WITH TESSERACT OR AWS TEXTRACT
###
def merge_img_bboxes(bboxes, combined_results: Dict, page_signature_recogniser_results=[], page_handwriting_recogniser_results=[], handwrite_signature_checkbox: List[str]=["Extract handwriting", "Extract signatures"], horizontal_threshold:int=50, vertical_threshold:int=12):
all_bboxes = []
merged_bboxes = []
grouped_bboxes = defaultdict(list)
# Deep copy original bounding boxes to retain them
original_bboxes = copy.deepcopy(bboxes)
# Process signature and handwriting results
if page_signature_recogniser_results or page_handwriting_recogniser_results:
if "Extract handwriting" in handwrite_signature_checkbox:
merged_bboxes.extend(copy.deepcopy(page_handwriting_recogniser_results))
if "Extract signatures" in handwrite_signature_checkbox:
merged_bboxes.extend(copy.deepcopy(page_signature_recogniser_results))
# Reconstruct bounding boxes for substrings of interest
reconstructed_bboxes = []
for bbox in bboxes:
bbox_box = (bbox.left, bbox.top, bbox.left + bbox.width, bbox.top + bbox.height)
for line_text, line_info in combined_results.items():
line_box = line_info['bounding_box']
if bounding_boxes_overlap(bbox_box, line_box):
if bbox.text in line_text:
start_char = line_text.index(bbox.text)
end_char = start_char + len(bbox.text)
relevant_words = []
current_char = 0
for word in line_info['words']:
word_end = current_char + len(word['text'])
if current_char <= start_char < word_end or current_char < end_char <= word_end or (start_char <= current_char and word_end <= end_char):
relevant_words.append(word)
if word_end >= end_char:
break
current_char = word_end
if not word['text'].endswith(' '):
current_char += 1 # +1 for space if the word doesn't already end with a space
if relevant_words:
left = min(word['bounding_box'][0] for word in relevant_words)
top = min(word['bounding_box'][1] for word in relevant_words)
right = max(word['bounding_box'][2] for word in relevant_words)
bottom = max(word['bounding_box'][3] for word in relevant_words)
combined_text = " ".join(word['text'] for word in relevant_words)
reconstructed_bbox = CustomImageRecognizerResult(
bbox.entity_type,
bbox.start,
bbox.end,
bbox.score,
left,
top,
right - left, # width
bottom - top, # height,
combined_text
)
#reconstructed_bboxes.append(bbox) # Add original bbox
reconstructed_bboxes.append(reconstructed_bbox) # Add merged bbox
break
else:
reconstructed_bboxes.append(bbox)
# Group reconstructed bboxes by approximate vertical proximity
for box in reconstructed_bboxes:
grouped_bboxes[round(box.top / vertical_threshold)].append(box)
# Merge within each group
for _, group in grouped_bboxes.items():
group.sort(key=lambda box: box.left)
merged_box = group[0]
for next_box in group[1:]:
if next_box.left - (merged_box.left + merged_box.width) <= horizontal_threshold:
if next_box.text != merged_box.text:
new_text = merged_box.text + " " + next_box.text
else:
new_text = merged_box.text
if merged_box.entity_type != next_box.entity_type:
new_entity_type = merged_box.entity_type + " - " + next_box.entity_type
else:
new_entity_type = merged_box.entity_type
new_left = min(merged_box.left, next_box.left)
new_top = min(merged_box.top, next_box.top)
new_width = max(merged_box.left + merged_box.width, next_box.left + next_box.width) - new_left
new_height = max(merged_box.top + merged_box.height, next_box.top + next_box.height) - new_top
merged_box = CustomImageRecognizerResult(
new_entity_type, merged_box.start, merged_box.end, merged_box.score, new_left, new_top, new_width, new_height, new_text
)
else:
merged_bboxes.append(merged_box)
merged_box = next_box
merged_bboxes.append(merged_box)
all_bboxes.extend(original_bboxes)
all_bboxes.extend(merged_bboxes)
# Return the unique original and merged bounding boxes
unique_bboxes = list({(bbox.left, bbox.top, bbox.width, bbox.height): bbox for bbox in all_bboxes}.values())
return unique_bboxes
def redact_image_pdf(file_path:str,
pdf_image_file_paths:List[str],
language:str,
chosen_redact_entities:List[str],
chosen_redact_comprehend_entities:List[str],
allow_list:List[str]=None,
page_min:int=0,
page_max:int=999,
text_extraction_method:str=tesseract_ocr_option,
handwrite_signature_checkbox:List[str]=["Extract handwriting", "Extract signatures"],
textract_request_metadata:list=[],
current_loop_page:int=0,
page_break_return:bool=False,
annotations_all_pages:List=[],
all_line_level_ocr_results_df:pd.DataFrame = pd.DataFrame(),
all_pages_decision_process_table:pd.DataFrame = pd.DataFrame(columns=["image_path", "page", "label", "xmin", "xmax", "ymin", "ymax", "boundingBox", "text", "start","end","score", "id"]),
pymupdf_doc:Document = [],
pii_identification_method:str="Local",
comprehend_query_number:int=0,
comprehend_client:str="",
textract_client:str="",
custom_recogniser_word_list:List[str]=[],
redact_whole_page_list:List[str]=[],
max_fuzzy_spelling_mistakes_num:int=1,
match_fuzzy_whole_phrase_bool:bool=True,
page_sizes_df:pd.DataFrame=pd.DataFrame(),
text_extraction_only:bool=False,
all_page_line_level_ocr_results = [],
all_page_line_level_ocr_results_with_words = [],
page_break_val:int=int(PAGE_BREAK_VALUE),
log_files_output_paths:List=[],
max_time:int=int(MAX_TIME_VALUE),
output_folder:str=OUTPUT_FOLDER,
progress=Progress(track_tqdm=True)):
'''
This function redacts sensitive information from a PDF document. It takes the following parameters:
- file_path (str): The path to the PDF file to be redacted.
- pdf_image_file_paths (List[str]): A list of paths to the PDF file pages converted to images.
- language (str): The language of the text in the PDF.
- chosen_redact_entities (List[str]): A list of entity types to redact from the PDF.
- chosen_redact_comprehend_entities (List[str]): A list of entity types to redact from the list allowed by the AWS Comprehend service.
- allow_list (List[str], optional): A list of entity types to allow in the PDF. Defaults to None.
- page_min (int, optional): The minimum page number to start redaction from. Defaults to 0.
- page_max (int, optional): The maximum page number to end redaction at. Defaults to 999.
- text_extraction_method (str, optional): The type of analysis to perform on the PDF. Defaults to tesseract_ocr_option.
- handwrite_signature_checkbox (List[str], optional): A list of options for redacting handwriting and signatures. Defaults to ["Extract handwriting", "Extract signatures"].
- textract_request_metadata (list, optional): Metadata related to the redaction request. Defaults to an empty string.
- page_break_return (bool, optional): Indicates if the function should return after a page break. Defaults to False.
- annotations_all_pages (List, optional): List of annotations on all pages that is used by the gradio_image_annotation object.
- all_line_level_ocr_results_df (pd.DataFrame, optional): All line level OCR results for the document as a Pandas dataframe,
- all_pages_decision_process_table (pd.DataFrame, optional): All redaction decisions for document as a Pandas dataframe.
- pymupdf_doc (Document, optional): The document as a PyMupdf object.
- pii_identification_method (str, optional): The method to redact personal information. Either 'Local' (spacy model), or 'AWS Comprehend' (AWS Comprehend API).
- comprehend_query_number (int, optional): A counter tracking the number of queries to AWS Comprehend.
- comprehend_client (optional): A connection to the AWS Comprehend service via the boto3 package.
- textract_client (optional): A connection to the AWS Textract service via the boto3 package.
- custom_recogniser_word_list (optional): A list of custom words that the user has chosen specifically to redact.
- redact_whole_page_list (optional, List[str]): A list of pages to fully redact.
- max_fuzzy_spelling_mistakes_num (int, optional): The maximum number of spelling mistakes allowed in a searched phrase for fuzzy matching. Can range from 0-9.
- match_fuzzy_whole_phrase_bool (bool, optional): A boolean where 'True' means that the whole phrase is fuzzy matched, and 'False' means that each word is fuzzy matched separately (excluding stop words).
- page_sizes_df (pd.DataFrame, optional): A pandas dataframe of PDF page sizes in PDF or image format.
- text_extraction_only (bool, optional): Should the function only extract text, or also do redaction.
- page_break_val (int, optional): The value at which to trigger a page break. Defaults to 3.
- log_files_output_paths (List, optional): List of file paths used for saving redaction process logging results.
- max_time (int, optional): The maximum amount of time (s) that the function should be running before it breaks. To avoid timeout errors with some APIs.
- output_folder (str, optional): The folder for file outputs.
- progress (Progress, optional): A progress tracker for the redaction process. Defaults to a Progress object with track_tqdm set to True.
The function returns a redacted PDF document along with processing output objects.
'''
tic = time.perf_counter()
file_name = get_file_name_without_type(file_path)
comprehend_query_number_new = 0
# Update custom word list analyser object with any new words that have been added to the custom deny list
if custom_recogniser_word_list:
nlp_analyser.registry.remove_recognizer("CUSTOM")
new_custom_recogniser = custom_word_list_recogniser(custom_recogniser_word_list)
nlp_analyser.registry.add_recognizer(new_custom_recogniser)
nlp_analyser.registry.remove_recognizer("CustomWordFuzzyRecognizer")
new_custom_fuzzy_recogniser = CustomWordFuzzyRecognizer(supported_entities=["CUSTOM_FUZZY"], custom_list=custom_recogniser_word_list, spelling_mistakes_max=max_fuzzy_spelling_mistakes_num, search_whole_phrase=match_fuzzy_whole_phrase_bool)
nlp_analyser.registry.add_recognizer(new_custom_fuzzy_recogniser)
image_analyser = CustomImageAnalyzerEngine(nlp_analyser)
if pii_identification_method == "AWS Comprehend" and comprehend_client == "":
out_message = "Connection to AWS Comprehend service unsuccessful."
print(out_message)
raise Exception(out_message)
if text_extraction_method == textract_option and textract_client == "":
out_message_warning = "Connection to AWS Textract service unsuccessful. Redaction will only continue if local AWS Textract results can be found."
print(out_message_warning)
#raise Exception(out_message)
number_of_pages = pymupdf_doc.page_count
print("Number of pages:", str(number_of_pages))
# Check that page_min and page_max are within expected ranges
if page_max > number_of_pages or page_max == 0: page_max = number_of_pages
if page_min <= 0: page_min = 0
else: page_min = page_min - 1
print("Page range:", str(page_min + 1), "to", str(page_max))
# If running Textract, check if file already exists. If it does, load in existing data
if text_extraction_method == textract_option:
textract_json_file_path = output_folder + file_name + "_textract.json"
textract_data, is_missing, log_files_output_paths = load_and_convert_textract_json(textract_json_file_path, log_files_output_paths, page_sizes_df)
original_textract_data = textract_data.copy()
print("Successfully loaded in Textract analysis results from file")
# If running local OCR option, check if file already exists. If it does, load in existing data
if text_extraction_method == tesseract_ocr_option:
all_page_line_level_ocr_results_with_words_json_file_path = output_folder + file_name + "_ocr_results_with_words.json"
all_page_line_level_ocr_results_with_words, is_missing, log_files_output_paths = load_and_convert_ocr_results_with_words_json(all_page_line_level_ocr_results_with_words_json_file_path, log_files_output_paths, page_sizes_df)
original_all_page_line_level_ocr_results_with_words = all_page_line_level_ocr_results_with_words.copy()
print("Loaded in local OCR analysis results from file")
###
if current_loop_page == 0: page_loop_start = 0
else: page_loop_start = current_loop_page
progress_bar = tqdm(range(page_loop_start, number_of_pages), unit="pages remaining", desc="Redacting pages")
all_line_level_ocr_results_df_list = [all_line_level_ocr_results_df]
all_pages_decision_process_table_list = [all_pages_decision_process_table]
# Go through each page
for page_no in progress_bar:
handwriting_or_signature_boxes = []
page_signature_recogniser_results = []
page_handwriting_recogniser_results = []
page_line_level_ocr_results_with_words = []
page_break_return = False
reported_page_number = str(page_no + 1)
# Try to find image location
try:
image_path = page_sizes_df.loc[page_sizes_df["page"] == (page_no + 1), "image_path"].iloc[0]
except Exception as e:
print("Could not find image_path in page_sizes_df due to:", e)
image_path = pdf_image_file_paths[page_no]
page_image_annotations = {"image": image_path, "boxes": []}
pymupdf_page = pymupdf_doc.load_page(page_no)
if page_no >= page_min and page_no < page_max:
# Need image size to convert OCR outputs to the correct sizes
if isinstance(image_path, str):
if os.path.exists(image_path):
image = Image.open(image_path)
page_width, page_height = image.size
else:
#print("Image path does not exist, using mediabox coordinates as page sizes")
image = None
page_width = pymupdf_page.mediabox.width
page_height = pymupdf_page.mediabox.height
elif not isinstance(image_path, Image.Image):
print(f"Unexpected image_path type: {type(image_path)}, using page mediabox coordinates as page sizes") # Ensure image_path is valid
image = None
page_width = pymupdf_page.mediabox.width
page_height = pymupdf_page.mediabox.height
try:
if not page_sizes_df.empty:
original_cropbox = page_sizes_df.loc[page_sizes_df["page"]==(page_no+1), "original_cropbox"].iloc[0]
except IndexError:
print("Can't find original cropbox details for page, using current PyMuPDF page cropbox")
original_cropbox = pymupdf_page.cropbox.irect
# Possibility to use different languages
if language == 'en': ocr_lang = 'eng'
else: ocr_lang = language
# Step 1: Perform OCR. Either with Tesseract, or with AWS Textract
# If using Tesseract
if text_extraction_method == tesseract_ocr_option:
if all_page_line_level_ocr_results_with_words:
# Find the first dict where 'page' matches
matching_page = next(
(item for item in all_page_line_level_ocr_results_with_words if int(item.get('page', -1)) == int(reported_page_number)),
None
)
page_line_level_ocr_results_with_words = matching_page if matching_page else []
else: page_line_level_ocr_results_with_words = []
if page_line_level_ocr_results_with_words:
print("Found OCR results for page in existing OCR with words object")
page_line_level_ocr_results = recreate_page_line_level_ocr_results_with_page(page_line_level_ocr_results_with_words)
else:
page_word_level_ocr_results = image_analyser.perform_ocr(image_path)
page_line_level_ocr_results, page_line_level_ocr_results_with_words = combine_ocr_results(page_word_level_ocr_results, page=reported_page_number)
all_page_line_level_ocr_results_with_words.append(page_line_level_ocr_results_with_words)
# Check if page exists in existing textract data. If not, send to service to analyse
if text_extraction_method == textract_option:
text_blocks = []
if not textract_data:
try:
# Convert the image_path to bytes using an in-memory buffer
image_buffer = io.BytesIO()
image.save(image_buffer, format='PNG') # Save as PNG, or adjust format if needed
pdf_page_as_bytes = image_buffer.getvalue()
text_blocks, new_textract_request_metadata = analyse_page_with_textract(pdf_page_as_bytes, reported_page_number, textract_client, handwrite_signature_checkbox) # Analyse page with Textract
if textract_json_file_path not in log_files_output_paths:
log_files_output_paths.append(textract_json_file_path)
textract_data = {"pages":[text_blocks]}
except Exception as e:
print("Textract extraction for page", reported_page_number, "failed due to:", e)
textract_data = {"pages":[]}
new_textract_request_metadata = "Failed Textract API call"
textract_request_metadata.append(new_textract_request_metadata)
else:
# Check if the current reported_page_number exists in the loaded JSON
page_exists = any(page['page_no'] == reported_page_number for page in textract_data.get("pages", []))
if not page_exists: # If the page does not exist, analyze again
print(f"Page number {reported_page_number} not found in existing Textract data. Analysing.")
try:
# Convert the image_path to bytes using an in-memory buffer
image_buffer = io.BytesIO()
image.save(image_buffer, format='PNG') # Save as PNG, or adjust format if needed
pdf_page_as_bytes = image_buffer.getvalue()
text_blocks, new_textract_request_metadata = analyse_page_with_textract(pdf_page_as_bytes, reported_page_number, textract_client, handwrite_signature_checkbox) # Analyse page with Textract
# Check if "pages" key exists, if not, initialise it as an empty list
if "pages" not in textract_data: textract_data["pages"] = []
# Append the new page data
textract_data["pages"].append(text_blocks)
except Exception as e:
out_message = "Textract extraction for page " + reported_page_number + " failed due to:" + str(e)
print(out_message)
text_blocks = []
new_textract_request_metadata = "Failed Textract API call"
# Check if "pages" key exists, if not, initialise it as an empty list
if "pages" not in textract_data: textract_data["pages"] = []
raise Exception(out_message)
textract_request_metadata.append(new_textract_request_metadata)
else:
# If the page exists, retrieve the data
text_blocks = next(page['data'] for page in textract_data["pages"] if page['page_no'] == reported_page_number)
page_line_level_ocr_results, handwriting_or_signature_boxes, page_signature_recogniser_results, page_handwriting_recogniser_results, page_line_level_ocr_results_with_words = json_to_ocrresult(text_blocks, page_width, page_height, reported_page_number)
# Convert to DataFrame and add to ongoing logging table
line_level_ocr_results_df = pd.DataFrame([{
'page': page_line_level_ocr_results['page'],
'text': result.text,
'left': result.left,
'top': result.top,
'width': result.width,
'height': result.height
} for result in page_line_level_ocr_results['results']])
all_line_level_ocr_results_df_list.append(line_level_ocr_results_df)
if pii_identification_method != no_redaction_option:
# Step 2: Analyse text and identify PII
if chosen_redact_entities or chosen_redact_comprehend_entities:
page_redaction_bounding_boxes, comprehend_query_number_new = image_analyser.analyze_text(
page_line_level_ocr_results['results'],
page_line_level_ocr_results_with_words['results'],
chosen_redact_comprehend_entities = chosen_redact_comprehend_entities,
pii_identification_method = pii_identification_method,
comprehend_client=comprehend_client,
language=language,
entities=chosen_redact_entities,
allow_list=allow_list,
score_threshold=score_threshold
)
comprehend_query_number = comprehend_query_number + comprehend_query_number_new
else: page_redaction_bounding_boxes = []
# Merge redaction bounding boxes that are close together
page_merged_redaction_bboxes = merge_img_bboxes(page_redaction_bounding_boxes, page_line_level_ocr_results_with_words['results'], page_signature_recogniser_results, page_handwriting_recogniser_results, handwrite_signature_checkbox)
else: page_merged_redaction_bboxes = []
# 3. Draw the merged boxes
## Apply annotations to pdf with pymupdf
if is_pdf(file_path) == True:
if redact_whole_page_list:
int_reported_page_number = int(reported_page_number)
if int_reported_page_number in redact_whole_page_list: redact_whole_page = True
else: redact_whole_page = False
else: redact_whole_page = False
pymupdf_page, page_image_annotations = redact_page_with_pymupdf(pymupdf_page, page_merged_redaction_bboxes, image_path, redact_whole_page=redact_whole_page, original_cropbox=original_cropbox, page_sizes_df=page_sizes_df)
# If an image_path file, draw onto the image_path
elif is_pdf(file_path) == False:
if isinstance(image_path, str):
if os.path.exists(image_path):
image = Image.open(image_path)
elif isinstance(image_path, Image.Image):
image = image_path
else:
# Assume image_path is an image
image = image_path
fill = (0, 0, 0) # Fill colour for redactions
draw = ImageDraw.Draw(image)
all_image_annotations_boxes = []
for box in page_merged_redaction_bboxes:
try:
x0 = box.left
y0 = box.top
x1 = x0 + box.width
y1 = y0 + box.height
label = box.entity_type # Attempt to get the label
text = box.text
except AttributeError as e:
print(f"Error accessing box attributes: {e}")
label = "Redaction" # Default label if there's an error
# Check if coordinates are valid numbers
if any(v is None for v in [x0, y0, x1, y1]):
print(f"Invalid coordinates for box: {box}")
continue # Skip this box if coordinates are invalid
img_annotation_box = {
"xmin": x0,
"ymin": y0,
"xmax": x1,
"ymax": y1,
"label": label,
"color": (0, 0, 0),
"text": text
}
img_annotation_box = fill_missing_box_ids(img_annotation_box)
# Directly append the dictionary with the required keys
all_image_annotations_boxes.append(img_annotation_box)
# Draw the rectangle
try:
draw.rectangle([x0, y0, x1, y1], fill=fill)
except Exception as e:
print(f"Error drawing rectangle: {e}")
page_image_annotations = {"image": file_path, "boxes": all_image_annotations_boxes}
print("page_image_annotations at box drawing:", page_image_annotations)
redacted_image = image.copy()
#redacted_image.save("test_out_image.png")
# Convert decision process to table
decision_process_table = pd.DataFrame([{
'text': result.text,
'xmin': result.left,
'ymin': result.top,
'xmax': result.left + result.width,
'ymax': result.top + result.height,
'label': result.entity_type,
'start': result.start,
'end': result.end,
'score': result.score,
'page': reported_page_number
} for result in page_merged_redaction_bboxes])
all_pages_decision_process_table_list.append(decision_process_table)
decision_process_table = fill_missing_ids(decision_process_table)
decision_process_table.to_csv(output_folder + "decision_process_table_with_ids.csv")
toc = time.perf_counter()
time_taken = toc - tic
# Break if time taken is greater than max_time seconds
if time_taken > max_time:
print("Processing for", max_time, "seconds, breaking loop.")
page_break_return = True
progress.close(_tqdm=progress_bar)
tqdm._instances.clear()
if is_pdf(file_path) == False:
pdf_image_file_paths.append(redacted_image) # .append(image_path)
pymupdf_doc = pdf_image_file_paths
# Check if the image_path already exists in annotations_all_pages
existing_index = next((index for index, ann in enumerate(annotations_all_pages) if ann["image"] == page_image_annotations["image"]), None)
if existing_index is not None:
# Replace the existing annotation
annotations_all_pages[existing_index] = page_image_annotations
else:
# Append new annotation if it doesn't exist
annotations_all_pages.append(page_image_annotations)
if text_extraction_method == textract_option:
if original_textract_data != textract_data:
# Write the updated existing textract data back to the JSON file
with open(textract_json_file_path, 'w') as json_file:
json.dump(textract_data, json_file, separators=(",", ":")) # indent=4 makes the JSON file pretty-printed
if textract_json_file_path not in log_files_output_paths:
log_files_output_paths.append(textract_json_file_path)
if text_extraction_method == tesseract_ocr_option:
if original_all_page_line_level_ocr_results_with_words != all_page_line_level_ocr_results_with_words:
# Write the updated existing textract data back to the JSON file
with open(all_page_line_level_ocr_results_with_words_json_file_path, 'w') as json_file:
json.dump(all_page_line_level_ocr_results_with_words, json_file, separators=(",", ":")) # indent=4 makes the JSON file pretty-printed
if all_page_line_level_ocr_results_with_words_json_file_path not in log_files_output_paths:
log_files_output_paths.append(all_page_line_level_ocr_results_with_words_json_file_path)
all_pages_decision_process_table = pd.concat(all_pages_decision_process_table_list)
all_line_level_ocr_results_df = pd.concat(all_line_level_ocr_results_df_list)
current_loop_page += 1
return pymupdf_doc, all_pages_decision_process_table, log_files_output_paths, textract_request_metadata, annotations_all_pages, current_loop_page, page_break_return, all_line_level_ocr_results_df, comprehend_query_number, all_page_line_level_ocr_results, all_page_line_level_ocr_results_with_words
# If it's an image file
if is_pdf(file_path) == False:
pdf_image_file_paths.append(redacted_image)#.append(image_path)
pymupdf_doc = pdf_image_file_paths
# Check if the image_path already exists in annotations_all_pages
existing_index = next((index for index, ann in enumerate(annotations_all_pages) if ann["image"] == page_image_annotations["image"]), None)
if existing_index is not None:
# Replace the existing annotation
annotations_all_pages[existing_index] = page_image_annotations
else:
# Append new annotation if it doesn't exist
annotations_all_pages.append(page_image_annotations)
current_loop_page += 1
# Break if new page is a multiple of chosen page_break_val
if current_loop_page % page_break_val == 0:
page_break_return = True
progress.close(_tqdm=progress_bar)
tqdm._instances.clear()
if text_extraction_method == textract_option:
# Write the updated existing textract data back to the JSON file
if original_textract_data != textract_data:
with open(textract_json_file_path, 'w') as json_file:
json.dump(textract_data, json_file, separators=(",", ":")) # indent=4 makes the JSON file pretty-printed
if textract_json_file_path not in log_files_output_paths:
log_files_output_paths.append(textract_json_file_path)
if text_extraction_method == tesseract_ocr_option:
if original_all_page_line_level_ocr_results_with_words != all_page_line_level_ocr_results_with_words:
# Write the updated existing textract data back to the JSON file
with open(all_page_line_level_ocr_results_with_words_json_file_path, 'w') as json_file:
json.dump(all_page_line_level_ocr_results_with_words, json_file, separators=(",", ":")) # indent=4 makes the JSON file pretty-printed
if all_page_line_level_ocr_results_with_words_json_file_path not in log_files_output_paths:
log_files_output_paths.append(all_page_line_level_ocr_results_with_words_json_file_path)
all_pages_decision_process_table = pd.concat(all_pages_decision_process_table_list)
all_line_level_ocr_results_df = pd.concat(all_line_level_ocr_results_df_list)
return pymupdf_doc, all_pages_decision_process_table, log_files_output_paths, textract_request_metadata, annotations_all_pages, current_loop_page, page_break_return, all_line_level_ocr_results_df, comprehend_query_number, all_page_line_level_ocr_results, all_page_line_level_ocr_results_with_words
if text_extraction_method == textract_option:
# Write the updated existing textract data back to the JSON file
if original_textract_data != textract_data:
with open(textract_json_file_path, 'w') as json_file:
json.dump(textract_data, json_file, separators=(",", ":")) # indent=4 makes the JSON file pretty-printed
if textract_json_file_path not in log_files_output_paths:
log_files_output_paths.append(textract_json_file_path)
if text_extraction_method == tesseract_ocr_option:
if original_all_page_line_level_ocr_results_with_words != all_page_line_level_ocr_results_with_words:
# Write the updated existing textract data back to the JSON file
with open(all_page_line_level_ocr_results_with_words_json_file_path, 'w') as json_file:
json.dump(all_page_line_level_ocr_results_with_words, json_file, separators=(",", ":")) # indent=4 makes the JSON file pretty-printed
if all_page_line_level_ocr_results_with_words_json_file_path not in log_files_output_paths:
log_files_output_paths.append(all_page_line_level_ocr_results_with_words_json_file_path)
all_pages_decision_process_table = pd.concat(all_pages_decision_process_table_list)
all_line_level_ocr_results_df = pd.concat(all_line_level_ocr_results_df_list)
# Convert decision table and ocr results to relative coordinates
all_pages_decision_process_table = divide_coordinates_by_page_sizes(all_pages_decision_process_table, page_sizes_df, xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax")
all_line_level_ocr_results_df = divide_coordinates_by_page_sizes(all_line_level_ocr_results_df, page_sizes_df, xmin="left", xmax="width", ymin="top", ymax="height")
return pymupdf_doc, all_pages_decision_process_table, log_files_output_paths, textract_request_metadata, annotations_all_pages, current_loop_page, page_break_return, all_line_level_ocr_results_df, comprehend_query_number, all_page_line_level_ocr_results, all_page_line_level_ocr_results_with_words
###
# PIKEPDF TEXT DETECTION/REDACTION
###
def get_text_container_characters(text_container:LTTextContainer):
if isinstance(text_container, LTTextContainer):
characters = [char
for line in text_container
if isinstance(line, LTTextLine) or isinstance(line, LTTextLineHorizontal)
for char in line]
return characters
return []
def create_text_bounding_boxes_from_characters(char_objects:List[LTChar]) -> Tuple[List[OCRResult], List[LTChar]]:
'''
Create an OCRResult object based on a list of pdfminer LTChar objects.
'''
line_level_results_out = []
line_level_characters_out = []
#all_line_level_characters_out = []
character_objects_out = [] # New list to store character objects
# character_text_objects_out = []
# Initialize variables
full_text = ""
added_text = ""
overall_bbox = [float('inf'), float('inf'), float('-inf'), float('-inf')] # [x0, y0, x1, y1]
word_bboxes = []
# Iterate through the character objects
current_word = ""
current_word_bbox = [float('inf'), float('inf'), float('-inf'), float('-inf')] # [x0, y0, x1, y1]
for char in char_objects:
character_objects_out.append(char) # Collect character objects
if not isinstance(char, LTAnno):
character_text = char.get_text()
# character_text_objects_out.append(character_text)
if isinstance(char, LTAnno):
added_text = char.get_text()
# Handle double quotes
#added_text = added_text.replace('"', '\\"') # Escape double quotes
# Handle space separately by finalizing the word
full_text += added_text # Adds space or newline
if current_word: # Only finalize if there is a current word
word_bboxes.append((current_word, current_word_bbox))
current_word = ""
current_word_bbox = [float('inf'), float('inf'), float('-inf'), float('-inf')] # Reset for next word
# Check for line break (assuming a new line is indicated by a specific character)
if '\n' in added_text:
# Finalize the current line
if current_word:
word_bboxes.append((current_word, current_word_bbox))
# Create an OCRResult for the current line
line_level_results_out.append(OCRResult(full_text.strip(), round(overall_bbox[0], 2), round(overall_bbox[1], 2), round(overall_bbox[2] - overall_bbox[0], 2), round(overall_bbox[3] - overall_bbox[1], 2)))
line_level_characters_out.append(character_objects_out)
# Reset for the next line
character_objects_out = []
full_text = ""
overall_bbox = [float('inf'), float('inf'), float('-inf'), float('-inf')]
current_word = ""
current_word_bbox = [float('inf'), float('inf'), float('-inf'), float('-inf')]
continue
# Concatenate text for LTChar
#full_text += char.get_text()
#added_text = re.sub(r'[^\x00-\x7F]+', ' ', char.get_text())
added_text = char.get_text()
if re.search(r'[^\x00-\x7F]', added_text): # Matches any non-ASCII character
#added_text.encode('latin1', errors='replace').decode('utf-8')
added_text = clean_unicode_text(added_text)
full_text += added_text # Adds space or newline, removing
# Update overall bounding box
x0, y0, x1, y1 = char.bbox
overall_bbox[0] = min(overall_bbox[0], x0) # x0
overall_bbox[1] = min(overall_bbox[1], y0) # y0
overall_bbox[2] = max(overall_bbox[2], x1) # x1
overall_bbox[3] = max(overall_bbox[3], y1) # y1
# Update current word
#current_word += char.get_text()
current_word += added_text
# Update current word bounding box
current_word_bbox[0] = min(current_word_bbox[0], x0) # x0
current_word_bbox[1] = min(current_word_bbox[1], y0) # y0
current_word_bbox[2] = max(current_word_bbox[2], x1) # x1
current_word_bbox[3] = max(current_word_bbox[3], y1) # y1
# Finalize the last word if any
if current_word:
word_bboxes.append((current_word, current_word_bbox))
if full_text:
if re.search(r'[^\x00-\x7F]', full_text): # Matches any non-ASCII character
# Convert special characters to a human-readable format
full_text = clean_unicode_text(full_text)
full_text = full_text.strip()
line_level_results_out.append(OCRResult(full_text.strip(), round(overall_bbox[0],2), round(overall_bbox[1], 2), round(overall_bbox[2]-overall_bbox[0],2), round(overall_bbox[3]-overall_bbox[1],2)))
#line_level_characters_out = character_objects_out
return line_level_results_out, line_level_characters_out # Return both results and character objects
def create_text_redaction_process_results(analyser_results, analysed_bounding_boxes, page_num):
decision_process_table = pd.DataFrame()
if len(analyser_results) > 0:
# Create summary df of annotations to be made
analysed_bounding_boxes_df_new = pd.DataFrame(analysed_bounding_boxes)
# Remove brackets and split the string into four separate columns
# Split the boundingBox list into four separate columns
analysed_bounding_boxes_df_new[['xmin', 'ymin', 'xmax', 'ymax']] = analysed_bounding_boxes_df_new['boundingBox'].apply(pd.Series)
# Convert the new columns to integers (if needed)
#analysed_bounding_boxes_df_new.loc[:, ['xmin', 'ymin', 'xmax', 'ymax']] = (analysed_bounding_boxes_df_new[['xmin', 'ymin', 'xmax', 'ymax']].astype(float) / 5).round() * 5
analysed_bounding_boxes_df_text = analysed_bounding_boxes_df_new['result'].astype(str).str.split(",",expand=True).replace(".*: ", "", regex=True)
analysed_bounding_boxes_df_text.columns = ["label", "start", "end", "score"]
analysed_bounding_boxes_df_new = pd.concat([analysed_bounding_boxes_df_new, analysed_bounding_boxes_df_text], axis = 1)
analysed_bounding_boxes_df_new['page'] = page_num + 1
decision_process_table = pd.concat([decision_process_table, analysed_bounding_boxes_df_new], axis = 0).drop('result', axis=1)
return decision_process_table
def create_pikepdf_annotations_for_bounding_boxes(analysed_bounding_boxes):
pikepdf_redaction_annotations_on_page = []
for analysed_bounding_box in analysed_bounding_boxes:
bounding_box = analysed_bounding_box["boundingBox"]
annotation = Dictionary(
Type=Name.Annot,
Subtype=Name.Square, #Name.Highlight,
QuadPoints=[bounding_box[0], bounding_box[3], bounding_box[2], bounding_box[3],
bounding_box[0], bounding_box[1], bounding_box[2], bounding_box[1]],
Rect=[bounding_box[0], bounding_box[1], bounding_box[2], bounding_box[3]],
C=[0, 0, 0],
IC=[0, 0, 0],
CA=1, # Transparency
T=analysed_bounding_box["result"].entity_type,
Contents=analysed_bounding_box["text"],
BS=Dictionary(
W=0, # Border width: 1 point
S=Name.S # Border style: solid
)
)
pikepdf_redaction_annotations_on_page.append(annotation)
return pikepdf_redaction_annotations_on_page
def redact_text_pdf(
filename: str, # Path to the PDF file to be redacted
language: str, # Language of the PDF content
chosen_redact_entities: List[str], # List of entities to be redacted
chosen_redact_comprehend_entities: List[str],
allow_list: List[str] = None, # Optional list of allowed entities
page_min: int = 0, # Minimum page number to start redaction
page_max: int = 999, # Maximum page number to end redaction
current_loop_page: int = 0, # Current page being processed in the loop
page_break_return: bool = False, # Flag to indicate if a page break should be returned
annotations_all_pages: List[dict] = [], # List of annotations across all pages
all_line_level_ocr_results_df: pd.DataFrame = pd.DataFrame(), # DataFrame for OCR results
all_pages_decision_process_table:pd.DataFrame = pd.DataFrame(columns=["image_path", "page", "label", "xmin", "xmax", "ymin", "ymax", "text", "id"]), # DataFrame for decision process table
pymupdf_doc: List = [], # List of PyMuPDF documents
pii_identification_method: str = "Local",
comprehend_query_number:int = 0,
comprehend_client="",
custom_recogniser_word_list:List[str]=[],
redact_whole_page_list:List[str]=[],
max_fuzzy_spelling_mistakes_num:int=1,
match_fuzzy_whole_phrase_bool:bool=True,
page_sizes_df:pd.DataFrame=pd.DataFrame(),
original_cropboxes:List[dict]=[],
text_extraction_only:bool=False,
page_break_val: int = int(PAGE_BREAK_VALUE), # Value for page break
max_time: int = int(MAX_TIME_VALUE),
progress: Progress = Progress(track_tqdm=True) # Progress tracking object
):
'''
Redact chosen entities from a PDF that is made up of multiple pages that are not images.
Input Variables:
- filename: Path to the PDF file to be redacted
- language: Language of the PDF content
- chosen_redact_entities: List of entities to be redacted
- chosen_redact_comprehend_entities: List of entities to be redacted for AWS Comprehend
- allow_list: Optional list of allowed entities
- page_min: Minimum page number to start redaction
- page_max: Maximum page number to end redaction
- text_extraction_method: Type of analysis to perform
- current_loop_page: Current page being processed in the loop
- page_break_return: Flag to indicate if a page break should be returned
- annotations_all_pages: List of annotations across all pages
- all_line_level_ocr_results_df: DataFrame for OCR results
- all_pages_decision_process_table: DataFrame for decision process table
- pymupdf_doc: List of PyMuPDF documents
- pii_identification_method (str, optional): The method to redact personal information. Either 'Local' (spacy model), or 'AWS Comprehend' (AWS Comprehend API).
- comprehend_query_number (int, optional): A counter tracking the number of queries to AWS Comprehend.
- comprehend_client (optional): A connection to the AWS Comprehend service via the boto3 package.
- custom_recogniser_word_list (optional, List[str]): A list of custom words that the user has chosen specifically to redact.
- redact_whole_page_list (optional, List[str]): A list of pages to fully redact.
- max_fuzzy_spelling_mistakes_num (int, optional): The maximum number of spelling mistakes allowed in a searched phrase for fuzzy matching. Can range from 0-9.
- match_fuzzy_whole_phrase_bool (bool, optional): A boolean where 'True' means that the whole phrase is fuzzy matched, and 'False' means that each word is fuzzy matched separately (excluding stop words).
- page_sizes_df (pd.DataFrame, optional): A pandas dataframe containing page size information.
- original_cropboxes (List[dict], optional): A list of dictionaries containing pymupdf cropbox information.
- text_extraction_only (bool, optional): Should the function only extract text, or also do redaction.
- page_break_val: Value for page break
- max_time (int, optional): The maximum amount of time (s) that the function should be running before it breaks. To avoid timeout errors with some APIs.
- progress: Progress tracking object
'''
tic = time.perf_counter()
if isinstance(all_line_level_ocr_results_df, pd.DataFrame):
all_line_level_ocr_results_df_list = [all_line_level_ocr_results_df]
if isinstance(all_pages_decision_process_table, pd.DataFrame):
# Convert decision outputs to list of dataframes:
all_pages_decision_process_table_list = [all_pages_decision_process_table]
if pii_identification_method == "AWS Comprehend" and comprehend_client == "":
out_message = "Connection to AWS Comprehend service not found."
raise Exception(out_message)
# Update custom word list analyser object with any new words that have been added to the custom deny list
if custom_recogniser_word_list:
nlp_analyser.registry.remove_recognizer("CUSTOM")
new_custom_recogniser = custom_word_list_recogniser(custom_recogniser_word_list)
nlp_analyser.registry.add_recognizer(new_custom_recogniser)
nlp_analyser.registry.remove_recognizer("CustomWordFuzzyRecognizer")
new_custom_fuzzy_recogniser = CustomWordFuzzyRecognizer(supported_entities=["CUSTOM_FUZZY"], custom_list=custom_recogniser_word_list, spelling_mistakes_max=max_fuzzy_spelling_mistakes_num, search_whole_phrase=match_fuzzy_whole_phrase_bool)
nlp_analyser.registry.add_recognizer(new_custom_fuzzy_recogniser)
# Open with Pikepdf to get text lines
pikepdf_pdf = Pdf.open(filename)
number_of_pages = len(pikepdf_pdf.pages)
# Check that page_min and page_max are within expected ranges
if page_max > number_of_pages or page_max == 0:
page_max = number_of_pages
if page_min <= 0: page_min = 0
else: page_min = page_min - 1
print("Page range is",str(page_min + 1), "to", str(page_max))
# Run through each page in document to 1. Extract text and then 2. Create redaction boxes
progress_bar = tqdm(range(current_loop_page, number_of_pages), unit="pages remaining", desc="Redacting pages")
for page_no in progress_bar:
reported_page_number = str(page_no + 1)
# Create annotations for every page, even if blank.
# Try to find image path location
try:
image_path = page_sizes_df.loc[page_sizes_df["page"] == int(reported_page_number), "image_path"].iloc[0]
except Exception as e:
print("Image path not found:", e)
image_path = ''
page_image_annotations = {"image": image_path, "boxes": []} # image
pymupdf_page = pymupdf_doc.load_page(page_no)
pymupdf_page.set_cropbox(pymupdf_page.mediabox) # Set CropBox to MediaBox
if page_min <= page_no < page_max:
# Go page by page
for page_layout in extract_pages(filename, page_numbers = [page_no], maxpages=1):
all_page_line_text_extraction_characters = []
all_page_line_level_text_extraction_results_list = []
page_analyser_results = []
page_redaction_bounding_boxes = []
characters = []
pikepdf_redaction_annotations_on_page = []
page_decision_process_table = pd.DataFrame()
page_text_ocr_outputs = pd.DataFrame()
for n, text_container in enumerate(page_layout):
characters = []
if isinstance(text_container, LTTextContainer) or isinstance(text_container, LTAnno):
characters = get_text_container_characters(text_container)
# Create dataframe for all the text on the page
line_level_text_results_list, line_characters = create_text_bounding_boxes_from_characters(characters)
### Create page_text_ocr_outputs (OCR format outputs)
if line_level_text_results_list:
# Convert to DataFrame and add to ongoing logging table
line_level_text_results_df = pd.DataFrame([{
'page': page_no + 1,
'text': (result.text).strip(),
'left': result.left,
'top': result.top,
'width': result.width,
'height': result.height
} for result in line_level_text_results_list])
page_text_ocr_outputs = pd.concat([page_text_ocr_outputs, line_level_text_results_df])
all_page_line_level_text_extraction_results_list.extend(line_level_text_results_list)
all_page_line_text_extraction_characters.extend(line_characters)
### REDACTION
if pii_identification_method != no_redaction_option:
if chosen_redact_entities or chosen_redact_comprehend_entities:
page_redaction_bounding_boxes = run_page_text_redaction(
language,
chosen_redact_entities,
chosen_redact_comprehend_entities,
all_page_line_level_text_extraction_results_list,
all_page_line_text_extraction_characters,
page_analyser_results,
page_redaction_bounding_boxes,
comprehend_client,
allow_list,
pii_identification_method,
nlp_analyser,
score_threshold,
custom_entities,
comprehend_query_number
)
# Annotate redactions on page
pikepdf_redaction_annotations_on_page = create_pikepdf_annotations_for_bounding_boxes(page_redaction_bounding_boxes)
else: pikepdf_redaction_annotations_on_page = []
# Make pymupdf page redactions
if redact_whole_page_list:
int_reported_page_number = int(reported_page_number)
if int_reported_page_number in redact_whole_page_list: redact_whole_page = True
else: redact_whole_page = False
else: redact_whole_page = False
pymupdf_page, page_image_annotations = redact_page_with_pymupdf(pymupdf_page, pikepdf_redaction_annotations_on_page, image_path, redact_whole_page=redact_whole_page, convert_pikepdf_to_pymupdf_coords=True, original_cropbox=original_cropboxes[page_no], page_sizes_df=page_sizes_df)
# Create decision process table
page_decision_process_table = create_text_redaction_process_results(page_analyser_results, page_redaction_bounding_boxes, current_loop_page)
if not page_decision_process_table.empty:
all_pages_decision_process_table_list.append(page_decision_process_table)
# Else, user chose not to run redaction
else:
pass
#print("Not redacting page:", page_no)
# Join extracted text outputs for all lines together
if not page_text_ocr_outputs.empty:
page_text_ocr_outputs = page_text_ocr_outputs.sort_values(["top", "left"], ascending=[False, False]).reset_index(drop=True)
page_text_ocr_outputs = page_text_ocr_outputs.loc[:, ["page", "text", "left", "top", "width", "height"]]
all_line_level_ocr_results_df_list.append(page_text_ocr_outputs)
toc = time.perf_counter()
time_taken = toc - tic
# Break if time taken is greater than max_time seconds
if time_taken > max_time:
print("Processing for", max_time, "seconds, breaking.")
page_break_return = True
progress.close(_tqdm=progress_bar)
tqdm._instances.clear()
# Check if the image already exists in annotations_all_pages
existing_index = next((index for index, ann in enumerate(annotations_all_pages) if ann["image"] == page_image_annotations["image"]), None)
if existing_index is not None:
# Replace the existing annotation
annotations_all_pages[existing_index] = page_image_annotations
else:
# Append new annotation if it doesn't exist
annotations_all_pages.append(page_image_annotations)
# Write logs
all_pages_decision_process_table = pd.concat(all_pages_decision_process_table_list)
all_line_level_ocr_results_df = pd.concat(all_line_level_ocr_results_df_list)
current_loop_page += 1
return pymupdf_doc, all_pages_decision_process_table, all_line_level_ocr_results_df, annotations_all_pages, current_loop_page, page_break_return, comprehend_query_number
# Check if the image already exists in annotations_all_pages
existing_index = next((index for index, ann in enumerate(annotations_all_pages) if ann["image"] == page_image_annotations["image"]), None)
if existing_index is not None:
# Replace the existing annotation
annotations_all_pages[existing_index] = page_image_annotations
else:
# Append new annotation if it doesn't exist
annotations_all_pages.append(page_image_annotations)
current_loop_page += 1
# Break if new page is a multiple of page_break_val
if current_loop_page % page_break_val == 0:
page_break_return = True
progress.close(_tqdm=progress_bar)
# Write logs
all_pages_decision_process_table = pd.concat(all_pages_decision_process_table_list)
return pymupdf_doc, all_pages_decision_process_table, all_line_level_ocr_results_df, annotations_all_pages, current_loop_page, page_break_return, comprehend_query_number
# Write all page outputs
all_pages_decision_process_table = pd.concat(all_pages_decision_process_table_list)
all_line_level_ocr_results_df = pd.concat(all_line_level_ocr_results_df_list)
# Convert decision table to relative coordinates
all_pages_decision_process_table = divide_coordinates_by_page_sizes(all_pages_decision_process_table, page_sizes_df, xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax")
# Coordinates need to be reversed for ymin and ymax to match with image annotator objects downstream
all_pages_decision_process_table['ymin'] = 1 - all_pages_decision_process_table['ymin']
all_pages_decision_process_table['ymax'] = 1 - all_pages_decision_process_table['ymax']
# Convert decision table to relative coordinates
all_line_level_ocr_results_df = divide_coordinates_by_page_sizes(all_line_level_ocr_results_df, page_sizes_df, xmin="left", xmax="width", ymin="top", ymax="height")
# Coordinates need to be reversed for ymin and ymax to match with image annotator objects downstream
all_line_level_ocr_results_df['top'] = all_line_level_ocr_results_df['top'].astype(float)
all_line_level_ocr_results_df['top'] = 1 - all_line_level_ocr_results_df['top']
return pymupdf_doc, all_pages_decision_process_table, all_line_level_ocr_results_df, annotations_all_pages, current_loop_page, page_break_return, comprehend_query_number |