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 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 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="", 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 - 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 # 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 # 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 # 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) 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 #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:# and first_loop_state==True: #print("Calling preparation function. prepare_images_flag:", prepare_images_flag) 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 = 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) # 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 # 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 textboxes.") 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 textboxes.") 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 = 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, 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 img = Image.open(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 = create_annotation_dicts_from_annotation_df(all_image_annotations_df, page_sizes) annotations_all_pages = remove_duplicate_images_with_blank_boxes(annotations_all_pages) # Save the gradio_annotation_boxes to a review csv file review_file_state = convert_annotation_json_to_review_df(annotations_all_pages, 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] 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 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) # if image or image_dimensions: # print("Dividing result by image coordinates") # image_x1, image_y1, image_x2, image_y2 = convert_pymupdf_to_image_coords(page, pymupdf_x1, pymupdf_y1, pymupdf_x2, pymupdf_y2, image, image_dimensions=image_dimensions) # img_annotation_box["xmin"] = image_x1 # img_annotation_box["ymin"] = image_y1 # img_annotation_box["xmax"] = image_x2 # img_annotation_box["ymax"] = image_y2 # else: 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) #print("image_dimensions:", image_dimensions) #print("annot:", annot) 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, 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() ### 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_pages_decision_process_table_list = [all_pages_decision_process_table] all_line_level_ocr_results_df_list = [all_line_level_ocr_results_df] # Go through each page for page_no in progress_bar: handwriting_or_signature_boxes = [] page_signature_recogniser_results = [] page_handwriting_recogniser_results = [] page_break_return = False reported_page_number = str(page_no + 1) #print("page_sizes_df for row:", page_sizes_df.loc[page_sizes_df["page"] == (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, need to check if we have page as image_path if text_extraction_method == tesseract_ocr_option: #print("image_path:", image_path) #print("print(type(image_path)):", print(type(image_path))) #if not isinstance(image_path, image_path.image_path) or not isinstance(image_path, str): raise Exception("image_path object for page", reported_page_number, "not found, cannot perform local OCR analysis.") page_word_level_ocr_results = image_analyser.perform_ocr(image_path) page_line_level_ocr_results, page_line_level_ocr_results_with_children = combine_ocr_results(page_word_level_ocr_results) # 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_children = json_to_ocrresult(text_blocks, page_width, page_height, reported_page_number) 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, page_line_level_ocr_results_with_children, 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_children, 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 print("image:", image) 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 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 # Directly append the dictionary with the required keys all_image_annotations_boxes.append({ "xmin": x0, "ymin": y0, "xmax": x1, "ymax": y1, "label": label, "color": (0, 0, 0) }) # 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} # 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/decision_process_table_with_ids.csv") # Convert to DataFrame and add to ongoing logging table line_level_ocr_results_df = pd.DataFrame([{ 'page': reported_page_number, 'text': result.text, 'left': result.left, 'top': result.top, 'width': result.width, 'height': result.height } for result in page_line_level_ocr_results]) all_line_level_ocr_results_df_list.append(line_level_ocr_results_df) 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(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) 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 # If it's an image file if is_pdf(file_path) == False: pdf_image_file_paths.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) 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 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) 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") 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 ### # 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] #print("Initial characters:", characters) 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 #analysed_bounding_boxes_df_new = fill_missing_ids(analysed_bounding_boxes_df_new) analysed_bounding_boxes_df_new.to_csv("output/analysed_bounding_boxes_df_new_with_ids.csv") 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: #print("analysed_bounding_box:", 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) #print("page_image_annotations after page", reported_page_number, "are", page_image_annotations) # 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