import copy import io import json import os import time from collections import defaultdict # For efficient grouping from datetime import datetime from typing import Any, Dict, List, Optional, Tuple, Union import boto3 import cv2 import gradio as gr import numpy as np import pandas as pd import pymupdf from gradio import Progress from pdfminer.high_level import extract_pages from pdfminer.layout import ( LTAnno, LTTextContainer, LTTextLine, LTTextLineHorizontal, ) from pikepdf import Dictionary, Name, Pdf from PIL import Image, ImageDraw, ImageFile from presidio_analyzer import AnalyzerEngine from pymupdf import Document, Page, Rect from tqdm import tqdm from tools.aws_textract import ( analyse_page_with_textract, convert_page_question_answer_to_custom_image_recognizer_results, convert_question_answer_to_dataframe, json_to_ocrresult, load_and_convert_textract_json, ) from tools.config import ( APPLY_REDACTIONS_GRAPHICS, APPLY_REDACTIONS_IMAGES, APPLY_REDACTIONS_TEXT, AWS_ACCESS_KEY, AWS_PII_OPTION, AWS_REGION, AWS_SECRET_KEY, CHOSEN_LOCAL_OCR_MODEL, CUSTOM_BOX_COLOUR, CUSTOM_ENTITIES, DEFAULT_LANGUAGE, IMAGES_DPI, INCLUDE_OCR_VISUALISATION_IN_OUTPUT_FILES, INPUT_FOLDER, LOAD_TRUNCATED_IMAGES, MAX_DOC_PAGES, MAX_IMAGE_PIXELS, MAX_SIMULTANEOUS_FILES, MAX_TIME_VALUE, NO_REDACTION_PII_OPTION, OUTPUT_FOLDER, OVERWRITE_EXISTING_OCR_RESULTS, PAGE_BREAK_VALUE, PRIORITISE_SSO_OVER_AWS_ENV_ACCESS_KEYS, RETURN_PDF_FOR_REVIEW, RETURN_REDACTED_PDF, RUN_AWS_FUNCTIONS, SAVE_PAGE_OCR_VISUALISATIONS, SELECTABLE_TEXT_EXTRACT_OPTION, TESSERACT_TEXT_EXTRACT_OPTION, TEXTRACT_TEXT_EXTRACT_OPTION, USE_GUI_BOX_COLOURS_FOR_OUTPUTS, aws_comprehend_language_choices, textract_language_choices, ) from tools.custom_image_analyser_engine import ( CustomImageAnalyzerEngine, CustomImageRecognizerResult, OCRResult, _inference_server_page_ocr_predict, _vlm_page_ocr_predict, combine_ocr_results, recreate_page_line_level_ocr_results_with_page, run_page_text_redaction, ) from tools.file_conversion import ( convert_annotation_data_to_dataframe, convert_annotation_json_to_review_df, create_annotation_dicts_from_annotation_df, divide_coordinates_by_page_sizes, fill_missing_box_ids, fill_missing_ids, is_pdf, is_pdf_or_image, load_and_convert_ocr_results_with_words_json, prepare_image_or_pdf, process_single_page_for_image_conversion, remove_duplicate_images_with_blank_boxes, save_pdf_with_or_without_compression, word_level_ocr_output_to_dataframe, ) from tools.helper_functions import ( clean_unicode_text, get_file_name_without_type, get_textract_file_suffix, ) from tools.load_spacy_model_custom_recognisers import ( CustomWordFuzzyRecognizer, create_nlp_analyser, custom_word_list_recogniser, download_tesseract_lang_pack, load_spacy_model, nlp_analyser, score_threshold, ) from tools.secure_path_utils import ( secure_file_write, validate_folder_containment, validate_path_containment, ) # Extract numbers before 'seconds' using secure regex from tools.secure_regex_utils import safe_extract_numbers_with_seconds ImageFile.LOAD_TRUNCATED_IMAGES = LOAD_TRUNCATED_IMAGES if not MAX_IMAGE_PIXELS: Image.MAX_IMAGE_PIXELS = None else: Image.MAX_IMAGE_PIXELS = MAX_IMAGE_PIXELS image_dpi = float(IMAGES_DPI) custom_entities = CUSTOM_ENTITIES 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. """ numbers = safe_extract_numbers_with_seconds(string) # Sum up the extracted numbers sum_of_numbers = round(sum(numbers), 1) return sum_of_numbers def reverse_y_coords(df: pd.DataFrame, column: str): df[column] = df[column] df[column] = 1 - df[column].astype(float) df[column] = df[column].round(6) return df[column] def merge_page_results(data: list): merged = dict() for item in data: page = item["page"] if page not in merged: merged[page] = {"page": page, "results": {}} # Merge line-level results into the existing page merged[page]["results"].update(item.get("results", {})) return list(merged.values()) def add_page_range_suffix_to_file_path( file_path: str, page_min: int, current_loop_page: int, number_of_pages: int, page_max: int = None, ) -> str: """ Add page range suffix to file path if redaction didn't complete all pages. Args: file_path: The original file path page_min: The minimum page number to start redaction from current_loop_page: The current page being processed number_of_pages: Total number of pages in the document Returns: File path with page range suffix if partial processing, otherwise original path """ # if page_min == 0 and page_max == 0: # return file_path # If we processed all pages, don't add suffix if current_loop_page >= number_of_pages: return file_path # Calculate the page range that was actually processed start_page = page_min + 1 if page_min == 0 else page_min if current_loop_page > page_max: end_page = page_max else: end_page = (start_page + current_loop_page) - 1 if end_page < start_page: end_page = start_page # Add suffix before file extension if "." in file_path: name, ext = file_path.rsplit(".", 1) return f"{name}_{start_page}_{end_page}.{ext}" else: return f"{file_path}_{start_page}_{end_page}" def choose_and_run_redactor( file_paths: List[str], prepared_pdf_file_paths: List[str], pdf_image_file_paths: List[str], chosen_redact_entities: List[str], chosen_redact_comprehend_entities: List[str], text_extraction_method: str, in_allow_list: List[str] = list(), in_deny_list: List[str] = list(), redact_whole_page_list: List[str] = list(), latest_file_completed: int = 0, combined_out_message: List = list(), out_file_paths: List = list(), log_files_output_paths: List = list(), first_loop_state: bool = False, page_min: int = 0, page_max: int = 0, estimated_time_taken_state: float = 0.0, handwrite_signature_checkbox: List[str] = list(["Extract handwriting"]), all_request_metadata_str: str = "", annotations_all_pages: List[dict] = list(), all_page_line_level_ocr_results_df: pd.DataFrame = None, all_pages_decision_process_table: pd.DataFrame = None, pymupdf_doc=list(), 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 = list(), output_folder: str = OUTPUT_FOLDER, document_cropboxes: List = list(), page_sizes: List[dict] = list(), textract_output_found: bool = False, text_extraction_only: bool = False, duplication_file_path_outputs: list = 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: list[dict] = list(), all_page_line_level_ocr_results_with_words: list[dict] = list(), all_page_line_level_ocr_results_with_words_df: pd.DataFrame = None, chosen_local_ocr_model: str = CHOSEN_LOCAL_OCR_MODEL, language: str = DEFAULT_LANGUAGE, ocr_review_files: list = list(), prepare_images: bool = True, RETURN_REDACTED_PDF: bool = RETURN_REDACTED_PDF, RETURN_PDF_FOR_REVIEW: bool = RETURN_PDF_FOR_REVIEW, 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. - 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 empty list. Can also be entered as a string path to a CSV file, or as a single column pandas dataframe. - in_deny_list (List[List[str]], optional): A list of denied terms for redaction. Defaults to empty list. Can also be entered as a string path to a CSV file, or as a single column pandas dataframe. - redact_whole_page_list (List[List[str]], optional): A list of whole page numbers for redaction. Defaults to empty list. Can also be entered as a string path to a CSV file, or as a single column pandas dataframe. - 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 (first page). - page_max (int, optional): The maximum page number to end redaction at. Defaults to 0 (last page). - 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_page_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. - all_page_line_level_ocr_results_with_words_df (pd.Dataframe, optional): All word level text on the page with bounding boxes as a dataframe. - chosen_local_ocr_model (str): Which local model is being used for OCR on images - uses the value of CHOSEN_LOCAL_OCR_MODEL by default, choices are "tesseract", "paddle" for PaddleOCR, or "hybrid-paddle" to combine both. - language (str, optional): The language of the text in the files. Defaults to English. - language (str, optional): The language to do AWS Comprehend calls. Defaults to value of language if not provided. - ocr_review_files (list, optional): A list of OCR review files to be used for the redaction process. Defaults to an empty list. - prepare_images (bool, optional): Boolean to determine whether to load images for the PDF. - RETURN_REDACTED_PDF (bool, optional): Boolean to determine whether to return a redacted PDF at the end of the redaction process. - progress (gr.Progress, optional): A progress tracker for the redaction process. Defaults to a Progress object with track_tqdm set to True. - RETURN_PDF_FOR_REVIEW (bool, optional): Boolean to determine whether to return a review PDF at the end of the redaction process. The function returns a redacted document along with processing logs. If both RETURN_PDF_FOR_REVIEW and RETURN_REDACTED_PDF are True, the function will return both a review PDF (with annotation boxes for review) and a final redacted PDF (with text permanently removed). """ tic = time.perf_counter() out_message = "" pdf_file_name_with_ext = "" pdf_file_name_without_ext = "" page_break_return = False blank_request_metadata = list() custom_recogniser_word_list_flat = list() all_textract_request_metadata = ( all_request_metadata_str.split("\n") if all_request_metadata_str else [] ) task_textbox = "redact" selection_element_results_list_df = pd.DataFrame() form_key_value_results_list_df = pd.DataFrame() out_review_pdf_file_path = "" out_redacted_pdf_file_path = "" if not ocr_review_files: ocr_review_files = list() current_loop_page = 0 # CLI mode may provide options to enter method names in a different format if text_extraction_method == "AWS Textract": text_extraction_method = TEXTRACT_TEXT_EXTRACT_OPTION if text_extraction_method == "Local OCR": text_extraction_method = TESSERACT_TEXT_EXTRACT_OPTION print("Performing local OCR with" + chosen_local_ocr_model + " model.") if text_extraction_method == "Local text": text_extraction_method = SELECTABLE_TEXT_EXTRACT_OPTION if pii_identification_method == "None": pii_identification_method = NO_REDACTION_PII_OPTION # If output folder doesn't end with a forward slash, add one if not output_folder.endswith("/"): output_folder = output_folder + "/" # Use provided language or default language = language or DEFAULT_LANGUAGE if text_extraction_method == TEXTRACT_TEXT_EXTRACT_OPTION: if language not in textract_language_choices: out_message = f"Language '{language}' is not supported by AWS Textract. Please select a different language." raise Warning(out_message) elif pii_identification_method == AWS_PII_OPTION: if language not in aws_comprehend_language_choices: out_message = f"Language '{language}' is not supported by AWS Comprehend. Please select a different language." raise Warning(out_message) if all_page_line_level_ocr_results_with_words_df is None: all_page_line_level_ocr_results_with_words_df = pd.DataFrame() # Create copies of out_file_path objects to avoid overwriting each other on append actions out_file_paths = out_file_paths.copy() log_files_output_paths = log_files_output_paths.copy() # 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 is True: # print("First_loop_state is True") latest_file_completed = 0 current_loop_page = 0 out_file_paths = list() log_files_output_paths = list() 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 is False) & (current_loop_page == 999): # current_loop_page = 0 # total_textract_query_number = 0 # comprehend_query_number = 0 if not file_paths: raise Exception("No files to redact") if prepared_pdf_file_paths: review_out_file_paths = [prepared_pdf_file_paths[0]] else: review_out_file_paths = list() # 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 if len(file_paths_list) > MAX_SIMULTANEOUS_FILES: out_message = f"Number of files to redact is greater than {MAX_SIMULTANEOUS_FILES}. Please submit a smaller number of files." print(out_message) raise Exception(out_message) 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 [] print("Latest file completed:", latest_file_completed) # 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(combined_out_message, list): combined_out_message = "\n".join(combined_out_message) 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 from tools.secure_regex_utils import safe_remove_leading_newlines combined_out_message = safe_remove_leading_newlines(combined_out_message) end_message = "\n\nPlease review and modify the suggested redaction outputs on the 'Review redactions' tab of the app (you can find this under the introduction text at the top of the page)." if end_message not in combined_out_message: combined_out_message = combined_out_message + end_message # Only send across review file if redaction has been done if pii_identification_method != NO_REDACTION_PII_OPTION: if len(review_out_file_paths) == 1: 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 sum_numbers_before_seconds(combined_out_message) # print( # "Estimated total processing time:", # str(estimate_total_processing_time), # "seconds", # ) print(combined_out_message) gr.Info(combined_out_message) page_break_return = True return ( combined_out_message, out_file_paths, out_file_paths, latest_file_completed, log_files_output_paths, log_files_output_paths, estimated_time_taken_state, all_request_metadata_str, pymupdf_doc, annotations_all_pages, current_loop_page, page_break_return, all_page_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, all_page_line_level_ocr_results_with_words_df, review_file_state, task_textbox, ocr_review_files, ) else: # ocr_review_files will be replaced by latest file output ocr_review_files = list() # 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_TEXT_EXTRACT_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 == SELECTABLE_TEXT_EXTRACT_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, all_page_line_level_ocr_results_with_words_df, ) = prepare_image_or_pdf( file_paths_loop, text_extraction_method, all_page_line_level_ocr_results_df, all_page_line_level_ocr_results_with_words_df, 0, out_message, True, annotate_max_pages, annotations_all_pages, document_cropboxes, redact_whole_page_list, output_folder=output_folder, prepare_images=prepare_images_flag, page_sizes=page_sizes, pymupdf_doc=pymupdf_doc, input_folder=input_folder, page_min=page_min, page_max=page_max, ) 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 page_min == 0 and page_max == 0: number_of_pages_to_process = number_of_pages else: number_of_pages_to_process = (page_max - page_min) + 1 if number_of_pages_to_process > MAX_DOC_PAGES: out_message = f"Number of pages to process is greater than {MAX_DOC_PAGES}. Please submit a smaller document." print(out_message) raise Exception(out_message) # If we have reached the last page, return message and outputs if current_loop_page >= number_of_pages_to_process: print("Reached last page of document:", current_loop_page) if total_textract_query_number > number_of_pages: total_textract_query_number = number_of_pages # Reset current loop page to 0 current_loop_page = 0 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_PII_OPTION: # If only pdf currently in review outputs, add on the latest review file if len(review_out_file_paths) == 1: if review_file_path: review_out_file_paths.append(review_file_path) page_break_return = False return ( combined_out_message, out_file_paths, out_file_paths, latest_file_completed, log_files_output_paths, log_files_output_paths, estimated_time_taken_state, all_request_metadata_str, pymupdf_doc, annotations_all_pages, current_loop_page, page_break_return, all_page_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, all_page_line_level_ocr_results_with_words_df, review_file_state, task_textbox, ocr_review_files, ) ### Load/create allow list, deny list, and whole page redaction list ### Load/create allow list # If string, assume file path if isinstance(in_allow_list, str): if in_allow_list: in_allow_list = pd.read_csv(in_allow_list, header=None) # Now, should be a pandas dataframe format if isinstance(in_allow_list, pd.DataFrame): if not in_allow_list.empty: in_allow_list_flat = in_allow_list.iloc[:, 0].tolist() else: in_allow_list_flat = list() else: in_allow_list_flat = list() ### Load/create deny list # If string, assume file path if isinstance(in_deny_list, str): if in_deny_list: in_deny_list = pd.read_csv(in_deny_list, header=None) if isinstance(in_deny_list, pd.DataFrame): if not in_deny_list.empty: custom_recogniser_word_list_flat = in_deny_list.iloc[:, 0].tolist() else: custom_recogniser_word_list_flat = list() # 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 ) else: custom_recogniser_word_list_flat = list() ### Load/create whole page redaction list # If string, assume file path if isinstance(redact_whole_page_list, str): if redact_whole_page_list: redact_whole_page_list = pd.read_csv(redact_whole_page_list, header=None) 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 = list() else: redact_whole_page_list_flat = list() ### Load/create PII identification method # Try to connect to AWS services directly only if RUN_AWS_FUNCTIONS environmental variable is True, otherwise an environment variable or direct textbox input is needed. if pii_identification_method == AWS_PII_OPTION: if RUN_AWS_FUNCTIONS and PRIORITISE_SSO_OVER_AWS_ENV_ACCESS_KEYS: print("Connecting to Comprehend via existing SSO connection") comprehend_client = boto3.client("comprehend", region_name=AWS_REGION) elif 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: 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_TEXT_EXTRACT_OPTION: if RUN_AWS_FUNCTIONS and PRIORITISE_SSO_OVER_AWS_ENV_ACCESS_KEYS: print("Connecting to Textract via existing SSO connection") textract_client = boto3.client("textract", region_name=AWS_REGION) elif 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: 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 is 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 = "" ### Language check - check if selected language packs exist try: if ( text_extraction_method == TESSERACT_TEXT_EXTRACT_OPTION and chosen_local_ocr_model == "tesseract" ): if language != "en": progress( 0.1, desc=f"Downloading Tesseract language pack for {language}" ) download_tesseract_lang_pack(language) if language != "en": progress(0.1, desc=f"Loading SpaCy model for {language}") load_spacy_model(language) except Exception as e: print(f"Error downloading language packs for {language}: {e}") raise Exception(f"Error downloading language packs for {language}: {e}") # 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_page_line_level_ocr_results_df = pd.DataFrame( columns=["page", "text", "left", "top", "width", "height", "line", "conf"] ) # 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) is True if ( is_a_pdf is False and text_extraction_method == SELECTABLE_TEXT_EXTRACT_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_TEXT_EXTRACT_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_without_ext # Load in all_ocr_results_with_words if it exists as a file path and doesn't exist already if text_extraction_method == SELECTABLE_TEXT_EXTRACT_OPTION: file_ending = "local_text" elif text_extraction_method == TESSERACT_TEXT_EXTRACT_OPTION: file_ending = "local_ocr" elif text_extraction_method == TEXTRACT_TEXT_EXTRACT_OPTION: file_ending = "textract" else: print( "No valid text extraction method found. Defaulting to local text extraction." ) text_extraction_method = SELECTABLE_TEXT_EXTRACT_OPTION file_ending = "local_text" all_page_line_level_ocr_results_with_words_json_file_path = ( output_folder + pdf_file_name_without_ext + "_ocr_results_with_words_" + file_ending + ".json" ) if not all_page_line_level_ocr_results_with_words: if ( not OVERWRITE_EXISTING_OCR_RESULTS and local_ocr_output_found_checkbox is True and os.path.exists( all_page_line_level_ocr_results_with_words_json_file_path ) ): ( 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() # Remove any existing review_file paths from the review file outputs if ( text_extraction_method == TESSERACT_TEXT_EXTRACT_OPTION or text_extraction_method == TEXTRACT_TEXT_EXTRACT_OPTION ): # Analyse and redact image-based pdf or image if is_pdf_or_image(file_path) is 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, log_files_output_paths, new_textract_request_metadata, annotations_all_pages, current_loop_page, page_break_return, all_page_line_level_ocr_results_df, comprehend_query_number, all_page_line_level_ocr_results, all_page_line_level_ocr_results_with_words, selection_element_results_list_df, form_key_value_results_list_df, out_file_paths, ) = 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_page_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, textract_output_found, all_page_line_level_ocr_results, all_page_line_level_ocr_results_with_words, chosen_local_ocr_model, log_files_output_paths=log_files_output_paths, out_file_paths=out_file_paths, nlp_analyser=nlp_analyser, output_folder=output_folder, input_folder=input_folder, ) # This line creates a copy of out_file_paths to break potential links with log_files_output_paths out_file_paths = out_file_paths.copy() # 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 == SELECTABLE_TEXT_EXTRACT_OPTION: if is_pdf(file_path) is 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_page_line_level_ocr_results_df, annotations_all_pages, current_loop_page, page_break_return, comprehend_query_number, all_page_line_level_ocr_results_with_words, ) = 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_page_line_level_ocr_results_df, all_pages_decision_process_table, pymupdf_doc, all_page_line_level_ocr_results_with_words, 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, output_folder=output_folder, input_folder=input_folder, ) else: out_message = "No redaction method selected" print(out_message) raise Exception(out_message) # If at last page, save to file - CHANGED - now will return outputs regardless of page progress. # if current_loop_page >= number_of_pages_to_process: print( "Current page number", (page_min + current_loop_page), "is the last page processed.", ) 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", ) # Save redacted file if pii_identification_method != NO_REDACTION_PII_OPTION: if RETURN_REDACTED_PDF is True: progress(0.9, "Saving redacted file") if is_pdf(file_path) is False: out_redacted_pdf_file_path = ( output_folder + pdf_file_name_without_ext + "_redacted.png" ) # Add page range suffix if partial processing out_redacted_pdf_file_path = add_page_range_suffix_to_file_path( out_redacted_pdf_file_path, page_min, current_loop_page, number_of_pages, page_max, ) # pymupdf_doc is an image list in this case if isinstance(pymupdf_doc[-1], str): # Normalize and validate path safety before opening image normalized_path = os.path.normpath( os.path.abspath(pymupdf_doc[-1]) ) if validate_path_containment(normalized_path, INPUT_FOLDER): img = Image.open(normalized_path) else: raise ValueError( f"Unsafe image path detected: {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) if isinstance(out_redacted_pdf_file_path, str): out_file_paths.append(out_redacted_pdf_file_path) else: out_file_paths.append(out_redacted_pdf_file_path[0]) else: # Check if we have dual PDF documents to save applied_redaction_pymupdf_doc = None if RETURN_PDF_FOR_REVIEW and RETURN_REDACTED_PDF: if ( hasattr(redact_image_pdf, "_applied_redaction_pages") and redact_image_pdf._applied_redaction_pages ): # Create final document by copying the original document and replacing specific pages applied_redaction_pymupdf_doc = pymupdf.open() applied_redaction_pymupdf_doc.insert_pdf(pymupdf_doc) # Create a mapping of original page numbers to final pages applied_redaction_pages_map = {} for ( applied_redaction_page_data ) in redact_image_pdf._applied_redaction_pages: if isinstance(applied_redaction_page_data, tuple): applied_redaction_page, original_page_number = ( applied_redaction_page_data ) applied_redaction_pages_map[ original_page_number ] = applied_redaction_page else: applied_redaction_page = applied_redaction_page_data applied_redaction_pages_map[0] = ( applied_redaction_page # Default to page 0 if no original number ) # Replace pages in the final document with their final versions for ( original_page_number, applied_redaction_page, ) in applied_redaction_pages_map.items(): if ( original_page_number < applied_redaction_pymupdf_doc.page_count ): # Remove the original page and insert the final page applied_redaction_pymupdf_doc.delete_page( original_page_number ) try: applied_redaction_pymupdf_doc.insert_pdf( applied_redaction_page.parent, from_page=applied_redaction_page.number, to_page=applied_redaction_page.number, start_at=original_page_number, ) except IndexError: # Retry without link processing if it fails print( "IndexError: Retrying without link processing" ) applied_redaction_pymupdf_doc.insert_pdf( applied_redaction_page.parent, from_page=applied_redaction_page.number, to_page=applied_redaction_page.number, start_at=original_page_number, links=False, ) applied_redaction_pymupdf_doc[ original_page_number ].apply_redactions( images=APPLY_REDACTIONS_IMAGES, graphics=APPLY_REDACTIONS_GRAPHICS, text=APPLY_REDACTIONS_TEXT, ) # Clear the stored final pages delattr(redact_image_pdf, "_applied_redaction_pages") elif ( hasattr(redact_text_pdf, "_applied_redaction_pages") and redact_text_pdf._applied_redaction_pages ): # Create final document by copying the original document and replacing specific pages applied_redaction_pymupdf_doc = pymupdf.open() applied_redaction_pymupdf_doc.insert_pdf(pymupdf_doc) # Create a mapping of original page numbers to final pages applied_redaction_pages_map = {} for ( applied_redaction_page_data ) in redact_text_pdf._applied_redaction_pages: if isinstance(applied_redaction_page_data, tuple): applied_redaction_page, original_page_number = ( applied_redaction_page_data ) applied_redaction_pages_map[ original_page_number ] = applied_redaction_page else: applied_redaction_page = applied_redaction_page_data applied_redaction_pages_map[0] = ( applied_redaction_page # Default to page 0 if no original number ) # Replace pages in the final document with their final versions for ( original_page_number, applied_redaction_page, ) in applied_redaction_pages_map.items(): if ( original_page_number < applied_redaction_pymupdf_doc.page_count ): # Remove the original page and insert the final page applied_redaction_pymupdf_doc.delete_page( original_page_number ) try: applied_redaction_pymupdf_doc.insert_pdf( applied_redaction_page.parent, from_page=applied_redaction_page.number, to_page=applied_redaction_page.number, start_at=original_page_number, ) except IndexError: # Retry without link processing if it fails print( "IndexError: Retrying without link processing" ) applied_redaction_pymupdf_doc.insert_pdf( applied_redaction_page.parent, from_page=applied_redaction_page.number, to_page=applied_redaction_page.number, start_at=original_page_number, links=False, ) applied_redaction_pymupdf_doc[ original_page_number ].apply_redactions( images=APPLY_REDACTIONS_IMAGES, graphics=APPLY_REDACTIONS_GRAPHICS, text=APPLY_REDACTIONS_TEXT, ) # Clear the stored final pages delattr(redact_text_pdf, "_applied_redaction_pages") # Save final redacted PDF if we have dual outputs or if RETURN_PDF_FOR_REVIEW is False if RETURN_PDF_FOR_REVIEW is False or applied_redaction_pymupdf_doc: out_redacted_pdf_file_path = ( output_folder + pdf_file_name_without_ext + "_redacted.pdf" ) # Add page range suffix if partial processing out_redacted_pdf_file_path = add_page_range_suffix_to_file_path( out_redacted_pdf_file_path, page_min, current_loop_page, number_of_pages, page_max, ) # print("Saving redacted PDF file:", out_redacted_pdf_file_path) # Use final document if available, otherwise use main document doc_to_save = ( applied_redaction_pymupdf_doc if applied_redaction_pymupdf_doc else pymupdf_doc ) if out_redacted_pdf_file_path: save_pdf_with_or_without_compression( doc_to_save, out_redacted_pdf_file_path ) if isinstance(out_redacted_pdf_file_path, str): out_file_paths.append(out_redacted_pdf_file_path) else: out_file_paths.append(out_redacted_pdf_file_path[0]) # Always return a file for review if a pdf is given and RETURN_PDF_FOR_REVIEW is True if is_pdf(file_path) is True: if RETURN_PDF_FOR_REVIEW is True: out_review_pdf_file_path = ( output_folder + pdf_file_name_without_ext + "_redactions_for_review.pdf" ) # Add page range suffix if partial processing out_review_pdf_file_path = add_page_range_suffix_to_file_path( out_review_pdf_file_path, page_min, current_loop_page, number_of_pages, page_max, ) # print("Saving PDF file for review:", out_review_pdf_file_path) if out_review_pdf_file_path: save_pdf_with_or_without_compression( pymupdf_doc, out_review_pdf_file_path ) if isinstance(out_review_pdf_file_path, str): out_file_paths.append(out_review_pdf_file_path) else: out_file_paths.append(out_review_pdf_file_path[0]) if not all_page_line_level_ocr_results_df.empty: all_page_line_level_ocr_results_df = all_page_line_level_ocr_results_df[ ["page", "text", "left", "top", "width", "height", "line", "conf"] ] else: all_page_line_level_ocr_results_df = pd.DataFrame( columns=[ "page", "text", "left", "top", "width", "height", "line", "conf", ] ) ocr_file_path = ( output_folder + pdf_file_name_without_ext + "_ocr_output_" + file_ending + ".csv" ) # Add page range suffix if partial processing ocr_file_path = add_page_range_suffix_to_file_path( ocr_file_path, page_min, current_loop_page, number_of_pages, page_max ) all_page_line_level_ocr_results_df.sort_values(["page", "line"], inplace=True) all_page_line_level_ocr_results_df.to_csv( ocr_file_path, index=None, encoding="utf-8-sig" ) if isinstance(ocr_file_path, str): out_file_paths.append(ocr_file_path) else: duplication_file_path_outputs.append(ocr_file_path[0]) if all_page_line_level_ocr_results_with_words: all_page_line_level_ocr_results_with_words = merge_page_results( all_page_line_level_ocr_results_with_words ) 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=(",", ":"), ) all_page_line_level_ocr_results_with_words_df = ( word_level_ocr_output_to_dataframe( all_page_line_level_ocr_results_with_words ) ) all_page_line_level_ocr_results_with_words_df = ( divide_coordinates_by_page_sizes( all_page_line_level_ocr_results_with_words_df, page_sizes_df, xmin="word_x0", xmax="word_x1", ymin="word_y0", ymax="word_y1", ) ) if text_extraction_method == SELECTABLE_TEXT_EXTRACT_OPTION: # Coordinates need to be reversed for ymin and ymax to match with image annotator objects downstream if not all_page_line_level_ocr_results_with_words_df.empty: all_page_line_level_ocr_results_with_words_df["word_y0"] = ( reverse_y_coords( all_page_line_level_ocr_results_with_words_df, "word_y0" ) ) all_page_line_level_ocr_results_with_words_df["word_y1"] = ( reverse_y_coords( all_page_line_level_ocr_results_with_words_df, "word_y1" ) ) all_page_line_level_ocr_results_with_words_df["line_text"] = "" all_page_line_level_ocr_results_with_words_df["line_x0"] = "" all_page_line_level_ocr_results_with_words_df["line_x1"] = "" all_page_line_level_ocr_results_with_words_df["line_y0"] = "" all_page_line_level_ocr_results_with_words_df["line_y1"] = "" all_page_line_level_ocr_results_with_words_df.sort_values( ["page", "line", "word_x0"], inplace=True ) all_page_line_level_ocr_results_with_words_df_file_path = ( all_page_line_level_ocr_results_with_words_json_file_path.replace( ".json", ".csv" ) ) # Add page range suffix if partial processing all_page_line_level_ocr_results_with_words_df_file_path = ( add_page_range_suffix_to_file_path( all_page_line_level_ocr_results_with_words_df_file_path, page_min, current_loop_page, number_of_pages, page_max, ) ) all_page_line_level_ocr_results_with_words_df.to_csv( all_page_line_level_ocr_results_with_words_df_file_path, index=None, encoding="utf-8-sig", ) if ( all_page_line_level_ocr_results_with_words_json_file_path not in log_files_output_paths ): if isinstance( all_page_line_level_ocr_results_with_words_json_file_path, str ): log_files_output_paths.append( all_page_line_level_ocr_results_with_words_json_file_path ) else: log_files_output_paths.append( all_page_line_level_ocr_results_with_words_json_file_path[0] ) if ( all_page_line_level_ocr_results_with_words_df_file_path not in log_files_output_paths ): if isinstance( all_page_line_level_ocr_results_with_words_df_file_path, str ): log_files_output_paths.append( all_page_line_level_ocr_results_with_words_df_file_path ) else: log_files_output_paths.append( all_page_line_level_ocr_results_with_words_df_file_path[0] ) if ( all_page_line_level_ocr_results_with_words_df_file_path not in out_file_paths ): if isinstance( all_page_line_level_ocr_results_with_words_df_file_path, str ): out_file_paths.append( all_page_line_level_ocr_results_with_words_df_file_path ) else: out_file_paths.append( all_page_line_level_ocr_results_with_words_df_file_path[0] ) # Save decision process outputs if not all_pages_decision_process_table.empty: all_pages_decision_process_table_file_path = ( output_folder + pdf_file_name_without_ext + "_all_pages_decision_process_table_output_" + file_ending + ".csv" ) # Add page range suffix if partial processing all_pages_decision_process_table_file_path = ( add_page_range_suffix_to_file_path( all_pages_decision_process_table_file_path, page_min, current_loop_page, number_of_pages, page_max, ) ) all_pages_decision_process_table.to_csv( all_pages_decision_process_table_file_path, index=None, encoding="utf-8-sig", ) log_files_output_paths.append(all_pages_decision_process_table_file_path) # Save outputs from form analysis if they exist if not selection_element_results_list_df.empty: selection_element_results_list_df_file_path = ( output_folder + pdf_file_name_without_ext + "_selection_element_results_output_" + file_ending + ".csv" ) # Add page range suffix if partial processing selection_element_results_list_df_file_path = ( add_page_range_suffix_to_file_path( selection_element_results_list_df_file_path, page_min, current_loop_page, number_of_pages, page_max, ) ) selection_element_results_list_df.to_csv( selection_element_results_list_df_file_path, index=None, encoding="utf-8-sig", ) out_file_paths.append(selection_element_results_list_df_file_path) if not form_key_value_results_list_df.empty: form_key_value_results_list_df_file_path = ( output_folder + pdf_file_name_without_ext + "_form_key_value_results_output_" + file_ending + ".csv" ) # Add page range suffix if partial processing form_key_value_results_list_df_file_path = ( add_page_range_suffix_to_file_path( form_key_value_results_list_df_file_path, page_min, current_loop_page, number_of_pages, page_max, ) ) form_key_value_results_list_df.to_csv( form_key_value_results_list_df_file_path, index=None, encoding="utf-8-sig", ) out_file_paths.append(form_key_value_results_list_df_file_path) # Convert the gradio annotation boxes to relative coordinates progress(0.93, "Creating review file 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", ) if ( pii_identification_method == NO_REDACTION_PII_OPTION and not form_key_value_results_list_df.empty ): print( "Form outputs found with no redaction method selected. Creating review file from form outputs." ) review_file_state = form_key_value_results_list_df annotations_all_pages_divide = create_annotation_dicts_from_annotation_df( review_file_state, page_sizes ) review_file_path = orig_pdf_file_path + "_review_file.csv" # Add page range suffix if partial processing review_file_path = add_page_range_suffix_to_file_path( review_file_path, page_min, current_loop_page, number_of_pages, page_max ) if isinstance(review_file_path, str): review_file_state.to_csv(review_file_path, index=None, encoding="utf-8-sig") else: review_file_state.to_csv( review_file_path[0], index=None, encoding="utf-8-sig" ) if pii_identification_method != NO_REDACTION_PII_OPTION: if isinstance(review_file_path, str): out_file_paths.append(review_file_path) else: out_file_paths.append(review_file_path[0]) # Make a combined message for the file if isinstance(combined_out_message, list): combined_out_message = "\n".join(combined_out_message) elif combined_out_message is None: combined_out_message = "" if isinstance(out_message, list) and out_message: combined_out_message = combined_out_message + "\n".join(out_message) elif isinstance(out_message, str) and 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 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. Also 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() textract_metadata_filename = ( pdf_file_name_without_ext + "_textract_metadata.txt" ) # Add page range suffix if partial processing textract_metadata_filename = add_page_range_suffix_to_file_path( textract_metadata_filename, page_min, current_loop_page, number_of_pages, page_max, ) secure_file_write( output_folder, textract_metadata_filename, all_request_metadata_str, ) all_textract_request_metadata_file_path = ( output_folder + textract_metadata_filename ) # 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: if isinstance(all_textract_request_metadata_file_path, str): log_files_output_paths.append(all_textract_request_metadata_file_path) else: log_files_output_paths.append( all_textract_request_metadata_file_path[0] ) 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))) # Create OCR review files list for input_review_files component if ocr_file_path: if isinstance(ocr_file_path, str): ocr_review_files.append(ocr_file_path) else: ocr_review_files.append(ocr_file_path[0]) if all_page_line_level_ocr_results_with_words_df_file_path: if isinstance(all_page_line_level_ocr_results_with_words_df_file_path, str): ocr_review_files.append( all_page_line_level_ocr_results_with_words_df_file_path ) else: ocr_review_files.append( all_page_line_level_ocr_results_with_words_df_file_path[0] ) # 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 page_break_return = True return ( combined_out_message, out_file_paths, out_file_paths, latest_file_completed, log_files_output_paths, log_files_output_paths, estimated_time_taken_state, all_request_metadata_str, pymupdf_doc, annotations_all_pages_divide, current_loop_page, page_break_return, all_page_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, all_page_line_level_ocr_results_with_words_df, review_file_state, task_textbox, ocr_review_files, ) 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): # Normalize and validate path safety before opening image normalized_path = os.path.normpath(os.path.abspath(image)) if validate_path_containment(normalized_path, INPUT_FOLDER): image_path = normalized_path image = Image.open(image_path) else: raise ValueError(f"Unsafe image path detected: {image}") # 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: CustomImageRecognizerResult, image: Image, page_sizes_df: pd.DataFrame, custom_colours: bool = USE_GUI_BOX_COLOURS_FOR_OUTPUTS, ): """ Prepare an image annotation box and coordinates based on a CustomImageRecogniserResult, PyMuPDF page, and PIL Image. """ img_annotation_box = dict() # 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"] = ( annot.color if custom_colours is True else CUSTOM_BOX_COLOUR ) try: img_annotation_box["label"] = str(annot.entity_type) except Exception as e: print(f"Error getting entity type: {e}") 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 = 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 = dict() page_no = page.number if convert_pikepdf_to_pymupdf_coords is 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 set_cropbox_safely(page: Page, original_cropbox: Optional[Rect]): """ Sets the cropbox of a PyMuPDF page safely and defensively. If the 'original_cropbox' is valid (i.e., a pymupdf.Rect instance, not None, not empty, not infinite, and fully contained within the page's mediabox), it is set as the cropbox. Otherwise, the page's mediabox is used, and a warning is printed to explain why. Args: page: The PyMuPDF page object. original_cropbox: The Rect representing the desired cropbox. """ mediabox = page.mediabox reason_for_defaulting = "" # Check for None if original_cropbox is None: reason_for_defaulting = "the original cropbox is None." # Check for incorrect type elif not isinstance(original_cropbox, Rect): reason_for_defaulting = f"the original cropbox is not a pymupdf.Rect instance (got {type(original_cropbox)})." else: # Normalise the cropbox (ensures x0 < x1 and y0 < y1) original_cropbox.normalize() # Check for empty or infinite or out-of-bounds if original_cropbox.is_empty: reason_for_defaulting = ( f"the provided original cropbox {original_cropbox} is empty." ) elif original_cropbox.is_infinite: reason_for_defaulting = ( f"the provided original cropbox {original_cropbox} is infinite." ) elif not mediabox.contains(original_cropbox): reason_for_defaulting = ( f"the provided original cropbox {original_cropbox} is not fully contained " f"within the page's mediabox {mediabox}." ) if reason_for_defaulting: print( f"Warning (Page {page.number}): Cannot use original cropbox because {reason_for_defaulting} " f"Defaulting to the page's mediabox as the cropbox." ) page.set_cropbox(mediabox) else: page.set_cropbox(original_cropbox) def convert_color_to_range_0_1(color): return tuple(component / 255 for component in color) def define_box_colour( custom_colours: bool, img_annotation_box: dict, CUSTOM_BOX_COLOUR: tuple ): """ Determines the color for a bounding box annotation. If `custom_colours` is True, it attempts to parse the color from `img_annotation_box['color']`. It supports color strings in "(R,G,B)" format (0-255 integers) or tuples/lists of (R,G,B) where components are either 0-1 floats or 0-255 integers. If parsing fails or `custom_colours` is False, it defaults to `CUSTOM_BOX_COLOUR`. All output colors are converted to a 0.0-1.0 float range. Args: custom_colours (bool): If True, attempts to use a custom color from `img_annotation_box`. img_annotation_box (dict): A dictionary that may contain a 'color' key with the custom color. CUSTOM_BOX_COLOUR (tuple): The default color to use if custom colors are not enabled or parsing fails. Expected to be a tuple of (R, G, B) with values in the 0.0-1.0 range. Returns: tuple: A tuple (R, G, B) representing the chosen color, with components in the 0.0-1.0 float range. """ if custom_colours is True: color_input = img_annotation_box["color"] out_colour = (0, 0, 0) # Initialize with a default black color (0.0-1.0 range) if isinstance(color_input, str): # Expected format: "(R,G,B)" where R,G,B are integers 0-255 (e.g., "(255,0,0)") try: # Remove parentheses and split by comma, then convert to integers components_str = color_input.strip().strip("()").split(",") colour_tuple_int = tuple(int(c.strip()) for c in components_str) # Validate the parsed integer tuple if len(colour_tuple_int) == 3 and not all( 0 <= c <= 1 for c in colour_tuple_int ): out_colour = convert_color_to_range_0_1(colour_tuple_int) elif len(colour_tuple_int) == 3 and all( 0 <= c <= 1 for c in colour_tuple_int ): out_colour = colour_tuple_int else: print( f"Warning: Invalid color string values or length for '{color_input}'. Expected (R,G,B) with R,G,B in 0-255. Defaulting to black." ) except (ValueError, IndexError): print( f"Warning: Could not parse color string '{color_input}'. Expected '(R,G,B)' format. Defaulting to black." ) elif isinstance(color_input, (tuple, list)) and len(color_input) == 3: # Expected formats: (R,G,B) where R,G,B are either 0-1 floats or 0-255 integers if all(isinstance(c, (int, float)) for c in color_input): # Case 1: Components are already 0.0-1.0 floats if all(isinstance(c, float) and 0.0 <= c <= 1.0 for c in color_input): out_colour = tuple(color_input) # Case 2: Components are 0-255 integers elif not all( isinstance(c, float) and 0.0 <= c <= 1.0 for c in color_input ): out_colour = convert_color_to_range_0_1(color_input) else: # Numeric values but not in expected 0-1 float or 0-255 integer ranges print( f"Warning: Invalid color tuple/list values {color_input}. Expected (R,G,B) with R,G,B in 0-1 floats or 0-255 integers. Defaulting to black." ) else: # Contains non-numeric values (e.g., (1, 'a', 3)) print( f"Warning: Color tuple/list {color_input} contains non-numeric values. Defaulting to black." ) else: # Catch-all for any other unexpected format (e.g., None, dict, etc.) print( f"Warning: Unexpected color format for {color_input}. Expected string '(R,G,B)' or tuple/list (R,G,B). Defaulting to black." ) # Final safeguard: Ensure out_colour is always a valid PyMuPDF color tuple (3 floats 0.0-1.0) if not ( isinstance(out_colour, tuple) and len(out_colour) == 3 and all(isinstance(c, float) and 0.0 <= c <= 1.0 for c in out_colour) ): out_colour = ( 0, 0, 0, ) # Fallback to black if any previous logic resulted in an invalid state out_colour = img_annotation_box["color"] else: if CUSTOM_BOX_COLOUR: # Should be a tuple of three integers between 0 and 255 from config if ( isinstance(CUSTOM_BOX_COLOUR, (tuple, list)) and len(CUSTOM_BOX_COLOUR) >= 3 ): # Convert from 0-255 range to 0-1 range out_colour = tuple( float(component / 255) if component >= 1 else float(component) for component in CUSTOM_BOX_COLOUR[:3] ) else: out_colour = ( 0, 0, 0, ) # Fallback to black if no custom box colour is provided return out_colour def redact_single_box( pymupdf_page: Page, pymupdf_rect: Rect, img_annotation_box: dict, custom_colours: bool = USE_GUI_BOX_COLOURS_FOR_OUTPUTS, retain_text: bool = RETURN_PDF_FOR_REVIEW, return_pdf_end_of_redaction: bool = RETURN_REDACTED_PDF, ): """ Commit redaction boxes to a PyMuPDF page. Args: pymupdf_page (Page): The PyMuPDF page object to which the redaction will be applied. pymupdf_rect (Rect): The PyMuPDF rectangle defining the bounds of the redaction box. img_annotation_box (dict): A dictionary containing annotation details, such as label, text, and color. custom_colours (bool, optional): If True, uses custom colors for the redaction box. Defaults to USE_GUI_BOX_COLOURS_FOR_OUTPUTS. retain_text (bool, optional): If True, adds a redaction annotation but retains the underlying text. If False, the text within the redaction area is deleted. Defaults to RETURN_PDF_FOR_REVIEW. return_pdf_end_of_redaction (bool, optional): If True, returns both review and final redacted page objects. Defaults to RETURN_REDACTED_PDF. Returns: Page or Tuple[Page, Page]: If return_pdf_end_of_redaction is True and retain_text is True, returns a tuple of (review_page, applied_redaction_page). Otherwise returns a single Page. """ pymupdf_x1 = pymupdf_rect[0] pymupdf_y1 = pymupdf_rect[1] pymupdf_x2 = pymupdf_rect[2] pymupdf_y2 = pymupdf_rect[3] # Full size redaction box for covering all the text of a word full_size_redaction_box = Rect( pymupdf_x1 - 1, pymupdf_y1 - 1, pymupdf_x2 + 1, pymupdf_y2 + 1 ) # Calculate tiny height redaction box so that it doesn't delete text from adjacent lines redact_bottom_y = pymupdf_y1 + 2 redact_top_y = pymupdf_y2 - 2 # Calculate the middle y value and set a small height if default values are too close together if (redact_top_y - redact_bottom_y) < 1: middle_y = (pymupdf_y1 + pymupdf_y2) / 2 redact_bottom_y = middle_y - 1 redact_top_y = middle_y + 1 rect_small_pixel_height = Rect( pymupdf_x1 + 2, redact_bottom_y, pymupdf_x2 - 2, redact_top_y ) # Slightly smaller than outside box out_colour = define_box_colour( custom_colours, img_annotation_box, CUSTOM_BOX_COLOUR ) img_annotation_box["text"] = img_annotation_box.get("text") or "" img_annotation_box["label"] = img_annotation_box.get("label") or "Redaction" # Create a copy of the page for final redaction if needed applied_redaction_page = None if return_pdf_end_of_redaction and retain_text: # Create a deep copy of the page for final redaction applied_redaction_page = pymupdf.open() applied_redaction_page.insert_pdf( pymupdf_page.parent, from_page=pymupdf_page.number, to_page=pymupdf_page.number, ) applied_redaction_page = applied_redaction_page[0] # Handle review page first, then deal with final redacted page (retain_text = True) if retain_text is True: annot = pymupdf_page.add_redact_annot(full_size_redaction_box) annot.set_colors(stroke=out_colour, fill=out_colour, colors=out_colour) annot.set_name(img_annotation_box["label"]) annot.set_info( info=img_annotation_box["label"], title=img_annotation_box["label"], subject=img_annotation_box["label"], content=img_annotation_box["text"], creationDate=datetime.now().strftime("%Y%m%d%H%M%S"), ) annot.update(opacity=0.5, cross_out=False) # If we need both review and final pages, and the applied redaction page has been prepared, apply final redaction to the copy if return_pdf_end_of_redaction and applied_redaction_page is not None: # Apply final redaction to the copy # Add the annotation to the middle of the character line, so that it doesn't delete text from adjacent lines applied_redaction_page.add_redact_annot(rect_small_pixel_height) # Only create a box over the whole rect if we want to delete the text shape = applied_redaction_page.new_shape() shape.draw_rect(pymupdf_rect) # Use solid fill for normal redaction shape.finish(color=out_colour, fill=out_colour) shape.commit() return pymupdf_page, applied_redaction_page else: return pymupdf_page # If we don't need to retain the text, we only have one page which is the applied redaction page, so just apply the redaction to the page else: # Add the annotation to the middle of the character line, so that it doesn't delete text from adjacent lines pymupdf_page.add_redact_annot(rect_small_pixel_height) # Only create a box over the whole rect if we want to delete the text shape = pymupdf_page.new_shape() shape.draw_rect(pymupdf_rect) # Use solid fill for normal redaction shape.finish(color=out_colour, fill=out_colour) shape.commit() return pymupdf_page def redact_whole_pymupdf_page( rect_height: float, rect_width: float, page: Page, custom_colours: bool = False, border: float = 5, redact_pdf: bool = True, ): """ Redacts a whole page of a PDF document. Args: rect_height (float): The height of the page in points. rect_width (float): The width of the page in points. page (Page): The PyMuPDF page object to be redacted. custom_colours (bool, optional): If True, uses custom colors for the redaction box. border (float, optional): The border width in points. Defaults to 5. redact_pdf (bool, optional): If True, redacts the PDF document. Defaults to True. """ # Small border to page that remains white # Define the coordinates for the Rect (PDF coordinates for actual redaction) whole_page_x1, whole_page_y1 = 0 + border, 0 + border # Bottom-left corner whole_page_x2, whole_page_y2 = ( rect_width - border, rect_height - border, ) # Top-right corner # Create new image annotation element based on whole page coordinates whole_page_rect = Rect(whole_page_x1, whole_page_y1, whole_page_x2, whole_page_y2) # Calculate relative coordinates for the annotation box (0-1 range) # This ensures the coordinates are already in relative format for output files relative_border = border / min( rect_width, rect_height ) # Scale border proportionally relative_x1 = relative_border relative_y1 = relative_border relative_x2 = 1 - relative_border relative_y2 = 1 - relative_border # Write whole page annotation to annotation boxes using relative coordinates whole_page_img_annotation_box = dict() whole_page_img_annotation_box["xmin"] = relative_x1 whole_page_img_annotation_box["ymin"] = relative_y1 whole_page_img_annotation_box["xmax"] = relative_x2 whole_page_img_annotation_box["ymax"] = relative_y2 whole_page_img_annotation_box["color"] = (0, 0, 0) whole_page_img_annotation_box["label"] = "Whole page" if redact_pdf is True: redact_single_box( page, whole_page_rect, whole_page_img_annotation_box, custom_colours ) return whole_page_img_annotation_box def redact_page_with_pymupdf( page: Page, page_annotations: dict, image: Image = None, custom_colours: bool = USE_GUI_BOX_COLOURS_FOR_OUTPUTS, redact_whole_page: bool = False, convert_pikepdf_to_pymupdf_coords: bool = True, original_cropbox: List[Rect] = list(), page_sizes_df: pd.DataFrame = pd.DataFrame(), return_pdf_for_review: bool = RETURN_PDF_FOR_REVIEW, return_pdf_end_of_redaction: bool = RETURN_REDACTED_PDF, input_folder: str = INPUT_FOLDER, ): """ Applies redactions to a single PyMuPDF page based on provided annotations. This function processes various types of annotations (Gradio, CustomImageRecognizerResult, or pikepdf-like) and applies them as redactions to the given PyMuPDF page. It can also redact the entire page if specified. Args: page (Page): The PyMuPDF page object to which redactions will be applied. page_annotations (dict): A dictionary containing annotation data for the current page. Expected to have a 'boxes' key with a list of annotation boxes. image (Image, optional): A PIL Image object or path to an image file associated with the page. Used for coordinate conversions if available. Defaults to None. custom_colours (bool, optional): If True, custom box colors will be used for redactions. Defaults to USE_GUI_BOX_COLOURS_FOR_OUTPUTS. redact_whole_page (bool, optional): If True, the entire page will be redacted. Defaults to False. convert_pikepdf_to_pymupdf_coords (bool, optional): If True, coordinates from pikepdf-like annotations will be converted to PyMuPDF's coordinate system. Defaults to True. original_cropbox (List[Rect], optional): The original cropbox of the page. This is used to restore the cropbox after redactions. Defaults to an empty list. page_sizes_df (pd.DataFrame, optional): A DataFrame containing page size and image dimension information, used for coordinate scaling. Defaults to an empty DataFrame. return_pdf_for_review (bool, optional): If True, redactions are applied in a way suitable for review (e.g., not removing underlying text/images completely). Defaults to RETURN_PDF_FOR_REVIEW. return_pdf_end_of_redaction (bool, optional): If True, returns both review and final redacted page objects. Defaults to RETURN_REDACTED_PDF. Returns: Tuple[Page, dict] or Tuple[Tuple[Page, Page], dict]: A tuple containing: - page (Page or Tuple[Page, Page]): The PyMuPDF page object(s) with redactions applied. If return_pdf_end_of_redaction is True and return_pdf_for_review is True, returns a tuple of (review_page, applied_redaction_page). - out_annotation_boxes (dict): A dictionary containing the processed annotation boxes for the page, including the image path. """ 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 = dict() 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 = dict() out_annotation_boxes = dict() all_image_annotation_boxes = list() if isinstance(image, Image.Image): # Create an image path using the input folder with PDF filename # Get the PDF filename from the page's parent document pdf_filename = ( os.path.basename(page.parent.name) if hasattr(page.parent, "name") and page.parent.name else "document" ) # Normalize and validate path safety before using in file path construction normalized_filename = os.path.normpath(pdf_filename) # Ensure the filename doesn't contain path traversal characters if ( ".." in normalized_filename or "/" in normalized_filename or "\\" in normalized_filename ): normalized_filename = "document" # Fallback to safe default image_path = os.path.join( input_folder, f"{normalized_filename}_{page.number}.png" ) if not os.path.exists(image_path): image.save(image_path) elif isinstance(image, str): # Normalize and validate path safety before checking existence normalized_path = os.path.normpath(os.path.abspath(image)) if validate_path_containment(normalized_path, INPUT_FOLDER): image_path = normalized_path 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 = dict() # 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 is 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 "text" in annot 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, custom_colours ) ) # 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_result = redact_single_box( page, rect, img_annotation_box, custom_colours, return_pdf_for_review, return_pdf_end_of_redaction, ) # Handle dual page objects if returned if isinstance(redact_result, tuple): page, applied_redaction_page = redact_result # Store the final page for later use if not hasattr(redact_page_with_pymupdf, "_applied_redaction_page"): redact_page_with_pymupdf._applied_redaction_page = ( applied_redaction_page ) else: # If we already have a final page, we need to handle multiple pages # For now, we'll use the last final page redact_page_with_pymupdf._applied_redaction_page = ( applied_redaction_page ) # If whole page is to be redacted, do that here if redact_whole_page is True: whole_page_img_annotation_box = redact_whole_pymupdf_page( rect_height, rect_width, page, custom_colours, border=5 ) # Ensure the whole page annotation box has a unique ID whole_page_img_annotation_box = fill_missing_box_ids( whole_page_img_annotation_box ) all_image_annotation_boxes.append(whole_page_img_annotation_box) # Handle dual page objects for whole page redaction if needed if return_pdf_end_of_redaction and return_pdf_for_review: # Create a copy of the page for final redaction using the same approach as redact_single_box applied_redaction_doc = pymupdf.open() applied_redaction_doc.insert_pdf( page.parent, from_page=page.number, to_page=page.number, ) applied_redaction_page = applied_redaction_doc[0] # Apply the whole page redaction to the final page as well redact_whole_pymupdf_page( rect_height, rect_width, applied_redaction_page, custom_colours, border=5, ) # Store the final page with its original page number for later use if not hasattr(redact_page_with_pymupdf, "_applied_redaction_page"): redact_page_with_pymupdf._applied_redaction_page = ( applied_redaction_page, page.number, ) else: # If we already have a final page, we need to handle multiple pages # For now, we'll use the last final page redact_page_with_pymupdf._applied_redaction_page = ( applied_redaction_page, page.number, ) out_annotation_boxes = { "image": image_path, # Image.open(image_path), #image_path, "boxes": all_image_annotation_boxes, } # If we are not returning the review page, can directly remove text and all images if return_pdf_for_review is False: page.apply_redactions( images=APPLY_REDACTIONS_IMAGES, graphics=APPLY_REDACTIONS_GRAPHICS, text=APPLY_REDACTIONS_TEXT, ) set_cropbox_safely(page, original_cropbox) page.clean_contents() # Handle dual page objects if we have a final page if ( return_pdf_end_of_redaction and return_pdf_for_review and hasattr(redact_page_with_pymupdf, "_applied_redaction_page") ): applied_redaction_page_data = redact_page_with_pymupdf._applied_redaction_page # Handle both tuple format (new) and single page format (backward compatibility) if isinstance(applied_redaction_page_data, tuple): applied_redaction_page, original_page_number = applied_redaction_page_data else: applied_redaction_page = applied_redaction_page_data # Apply redactions to applied redaction page only applied_redaction_page.apply_redactions( images=APPLY_REDACTIONS_IMAGES, graphics=APPLY_REDACTIONS_GRAPHICS, text=APPLY_REDACTIONS_TEXT, ) set_cropbox_safely(applied_redaction_page, original_cropbox) applied_redaction_page.clean_contents() # Clear the stored final page delattr(redact_page_with_pymupdf, "_applied_redaction_page") return (page, applied_redaction_page), out_annotation_boxes else: return page, out_annotation_boxes ### # IMAGE-BASED OCR PDF TEXT DETECTION/REDACTION WITH TESSERACT OR AWS TEXTRACT ### def merge_img_bboxes( bboxes: list, combined_results: Dict, page_signature_recogniser_results: list = list(), page_handwriting_recogniser_results: list = list(), handwrite_signature_checkbox: List[str] = [ "Extract handwriting", "Extract signatures", ], horizontal_threshold: int = 50, vertical_threshold: int = 12, ): """ Merges bounding boxes for image annotations based on the provided results from signature and handwriting recognizers. Args: bboxes (list): A list of bounding boxes to be merged. combined_results (Dict): A dictionary containing combined results with line text and their corresponding bounding boxes. page_signature_recogniser_results (list, optional): A list of results from the signature recognizer. Defaults to an empty list. page_handwriting_recogniser_results (list, optional): A list of results from the handwriting recognizer. Defaults to an empty list. handwrite_signature_checkbox (List[str], optional): A list of options indicating whether to extract handwriting and signatures. Defaults to ["Extract handwriting", "Extract signatures"]. horizontal_threshold (int, optional): The threshold for merging bounding boxes horizontally. Defaults to 50. vertical_threshold (int, optional): The threshold for merging bounding boxes vertically. Defaults to 12. Returns: None: This function modifies the bounding boxes in place and does not return a value. """ all_bboxes = list() merged_bboxes = list() 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: # print("Extracting handwriting in merge_img_bboxes function") merged_bboxes.extend(copy.deepcopy(page_handwriting_recogniser_results)) if "Extract signatures" in handwrite_signature_checkbox: # print("Extracting signatures in merge_img_bboxes function") merged_bboxes.extend(copy.deepcopy(page_signature_recogniser_results)) # Add VLM [PERSON] and [SIGNATURE] detections from combined_results, if present try: for line_info in combined_results.values(): words = line_info.get("words", []) for word in words: text_val = word.get("text") if text_val not in ["[PERSON]", "[SIGNATURE]"]: continue x0, y0, x1, y1 = word.get("bounding_box", (0, 0, 0, 0)) width = x1 - x0 height = y1 - y0 entity_type = ( "CUSTOM_VLM_PERSON" if text_val == "[PERSON]" else "CUSTOM_VLM_SIGNATURE" ) merged_bboxes.append( CustomImageRecognizerResult( entity_type, 0, 0, float(word.get("conf", 0.0)), int(x0), int(y0), int(width), int(height), text_val, ) ) except Exception as e: print( f"Warning: Error while adding VLM [PERSON]/[SIGNATURE] boxes in merge_img_bboxes: {e}" ) # Reconstruct bounding boxes for substrings of interest reconstructed_bboxes = list() 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 = list() 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 = 0, text_extraction_method: str = TESSERACT_TEXT_EXTRACT_OPTION, handwrite_signature_checkbox: List[str] = [ "Extract handwriting", "Extract signatures", ], textract_request_metadata: list = list(), current_loop_page: int = 0, page_break_return: bool = False, annotations_all_pages: List = list(), all_page_line_level_ocr_results_df: pd.DataFrame = pd.DataFrame( columns=["page", "text", "left", "top", "width", "height", "line", "conf"] ), 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 = list(), pii_identification_method: str = "Local", comprehend_query_number: int = 0, comprehend_client: str = "", textract_client: str = "", in_deny_list: List[str] = list(), redact_whole_page_list: List[str] = list(), 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, textract_output_found: bool = False, all_page_line_level_ocr_results=list(), all_page_line_level_ocr_results_with_words=list(), chosen_local_ocr_model: str = CHOSEN_LOCAL_OCR_MODEL, page_break_val: int = int(PAGE_BREAK_VALUE), log_files_output_paths: List = list(), out_file_paths: List = list(), max_time: int = int(MAX_TIME_VALUE), nlp_analyser: AnalyzerEngine = nlp_analyser, output_folder: str = OUTPUT_FOLDER, input_folder: str = INPUT_FOLDER, progress=Progress(track_tqdm=True), ): """ This function redacts sensitive information from a PDF document. It takes the following parameters in order: - 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 0. - text_extraction_method (str, optional): The type of analysis to perform on the PDF. Defaults to TESSERACT_TEXT_EXTRACT_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. - current_loop_page (int, optional): The current page being processed. Defaults to 0. - 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_page_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. - in_deny_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. - textract_output_found (bool, optional): Boolean is true when a textract OCR output for the file has been found. - all_page_line_level_ocr_results (optional): List of all page line level OCR results. - all_page_line_level_ocr_results_with_words (optional): List of all page line level OCR results with words. - chosen_local_ocr_model (str, optional): The local model chosen for OCR. Defaults to CHOSEN_LOCAL_OCR_MODEL, other choices are "paddle" for PaddleOCR, or "hybrid-paddle" for a combination of both. - page_break_val (int, optional): The value at which to trigger a page break. Defaults to PAGE_BREAK_VALUE. - log_files_output_paths (List, optional): List of file paths used for saving redaction process logging results. - out_file_paths (List, optional): List of file paths used for saving redaction process output 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. - nlp_analyser (AnalyzerEngine, optional): The nlp_analyser object to use for entity detection. Defaults to nlp_analyser. - 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. - input_folder (str, optional): The folder for file inputs. 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 selection_element_results_list_df = pd.DataFrame() form_key_value_results_list_df = pd.DataFrame() textract_json_file_path = "" textract_client_not_found = False # Try updating the supported languages for the spacy analyser try: nlp_analyser = create_nlp_analyser(language, existing_nlp_analyser=nlp_analyser) # Check list of nlp_analyser recognisers and languages if language != "en": gr.Info( f"Language: {language} only supports the following entity detection: {str(nlp_analyser.registry.get_supported_entities(languages=[language]))}" ) except Exception as e: print(f"Error creating nlp_analyser for {language}: {e}") raise Exception(f"Error creating nlp_analyser for {language}: {e}") # Update custom word list analyser object with any new words that have been added to the custom deny list if in_deny_list: nlp_analyser.registry.remove_recognizer("CUSTOM") new_custom_recogniser = custom_word_list_recogniser(in_deny_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=in_deny_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) # Only load in PaddleOCR models if not running Textract if text_extraction_method == TEXTRACT_TEXT_EXTRACT_OPTION: image_analyser = CustomImageAnalyzerEngine( analyzer_engine=nlp_analyser, ocr_engine="tesseract", language=language, output_folder=output_folder, ) else: image_analyser = CustomImageAnalyzerEngine( analyzer_engine=nlp_analyser, ocr_engine=chosen_local_ocr_model, language=language, output_folder=output_folder, ) 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_TEXT_EXTRACT_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." textract_client_not_found = True 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_TEXT_EXTRACT_OPTION: # Generate suffix based on checkbox options textract_suffix = get_textract_file_suffix(handwrite_signature_checkbox) textract_json_file_path = ( output_folder + file_name + textract_suffix + "_textract.json" ) if OVERWRITE_EXISTING_OCR_RESULTS: # Skip loading existing results, start fresh textract_data = {} is_missing = True else: textract_data, is_missing, log_files_output_paths = ( load_and_convert_textract_json( textract_json_file_path, log_files_output_paths, page_sizes_df ) ) if textract_data: textract_output_found = True original_textract_data = textract_data.copy() if textract_client_not_found and is_missing: print( "No existing Textract results file found and no Textract client found. Redaction will not continue." ) raise Exception( "No existing Textract results file found and no Textract client found. Redaction will not continue." ) # If running local OCR option, check if file already exists. If it does, load in existing data if text_extraction_method == TESSERACT_TEXT_EXTRACT_OPTION: all_page_line_level_ocr_results_with_words_json_file_path = ( output_folder + file_name + "_ocr_results_with_words_local_ocr.json" ) if OVERWRITE_EXISTING_OCR_RESULTS: # Skip loading existing results, start fresh all_page_line_level_ocr_results_with_words = [] is_missing = True else: ( 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() ) ### if current_loop_page == 0: page_loop_start = page_min else: page_loop_start = current_loop_page page_loop_end = page_max progress_bar = tqdm( range(page_loop_start, page_loop_end), unit="pages remaining", desc="Redacting pages", ) # If there's data from a previous run (passed in via the DataFrame parameters), add it all_line_level_ocr_results_list = list() all_pages_decision_process_list = list() selection_element_results_list = list() form_key_value_results_list = list() if not all_page_line_level_ocr_results_df.empty: all_line_level_ocr_results_list.extend( all_page_line_level_ocr_results_df.to_dict("records") ) if not all_pages_decision_process_table.empty: all_pages_decision_process_list.extend( all_pages_decision_process_table.to_dict("records") ) # Go through each page for page_no in progress_bar: reported_page_number = str(page_no + 1) print(f"Current page: {reported_page_number}") handwriting_or_signature_boxes = list() page_signature_recogniser_results = list() page_handwriting_recogniser_results = list() page_line_level_ocr_results_with_words = list() page_break_return = False # 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): # Normalize and validate path safety before checking existence normalized_path = os.path.normpath(os.path.abspath(image_path)) if validate_path_containment(normalized_path, input_folder): image = Image.open(normalized_path) page_width, page_height = image.size else: # If validation fails and input file is an image file, try using file_path as fallback if ( is_pdf(file_path) is False and isinstance(file_path, str) and file_path ): normalized_file_path = os.path.normpath( os.path.abspath(file_path) ) # Check if it's a Gradio temporary file (often in temp directories) is_gradio_temp = ( "gradio" in normalized_file_path.lower() and "temp" in normalized_file_path.lower() ) if is_gradio_temp or validate_path_containment( normalized_file_path, input_folder ): try: image = Image.open(normalized_file_path) page_width, page_height = image.size except Exception as e: print( f"Could not open image from file_path {file_path}: {e}" ) image = None page_width = pymupdf_page.mediabox.width page_height = pymupdf_page.mediabox.height else: # For image files, at least keep image_path as a string for later use image = None page_width = pymupdf_page.mediabox.width page_height = pymupdf_page.mediabox.height 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): # If image_path is not a string or Image, and input file is an image file, try file_path if ( is_pdf(file_path) is False and isinstance(file_path, str) and file_path ): normalized_file_path = os.path.normpath(os.path.abspath(file_path)) is_gradio_temp = ( "gradio" in normalized_file_path.lower() and "temp" in normalized_file_path.lower() ) if is_gradio_temp or validate_path_containment( normalized_file_path, input_folder ): try: image = Image.open(normalized_file_path) page_width, page_height = image.size except Exception as e: print( f"Could not open image from file_path {file_path}: {e}" ) image = None page_width = pymupdf_page.mediabox.width page_height = pymupdf_page.mediabox.height else: image = None page_width = pymupdf_page.mediabox.width page_height = pymupdf_page.mediabox.height else: 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 if image is None: # Check if image_path is a placeholder and create the actual image if isinstance(image_path, str) and "placeholder_image" in image_path: # print(f"Detected placeholder image path: {image_path}") try: # Extract page number from placeholder path page_num_from_placeholder = int( image_path.split("_")[-1].split(".")[0] ) # Create the actual image using process_single_page_for_image_conversion _, created_image_path, page_width, page_height = ( process_single_page_for_image_conversion( pdf_path=file_path, page_num=page_num_from_placeholder, image_dpi=IMAGES_DPI, create_images=True, input_folder=input_folder, ) ) # Load the created image if os.path.exists(created_image_path): image = Image.open(created_image_path) # print( # f"Successfully created and loaded image from: {created_image_path}" # ) else: # print(f"Failed to create image at: {created_image_path}") page_width = pymupdf_page.mediabox.width page_height = pymupdf_page.mediabox.height except Exception as e: print(f"Error creating image from placeholder: {e}") page_width = pymupdf_page.mediabox.width page_height = pymupdf_page.mediabox.height else: try: # Create the actual image using process_single_page_for_image_conversion _, created_image_path, page_width, page_height = ( process_single_page_for_image_conversion( pdf_path=file_path, page_num=page_no, image_dpi=IMAGES_DPI, create_images=True, input_folder=input_folder, ) ) # Load the created image if os.path.exists(created_image_path): image = Image.open(created_image_path) # print( # f"Successfully created and loaded image from: {created_image_path}" # ) else: # print(f"Failed to create image at: {created_image_path}") page_width = pymupdf_page.mediabox.width page_height = pymupdf_page.mediabox.height # print( # "Image is None and not a placeholder - using mediabox coordinates" # ) except Exception as e: print(f"Error creating image from file_path: {e}") page_width = pymupdf_page.mediabox.width page_height = pymupdf_page.mediabox.height if image is None: print("Image is None - using mediabox coordinates") page_width = pymupdf_page.mediabox.width page_height = pymupdf_page.mediabox.height # Step 1: Perform OCR. Either with Tesseract, or with AWS Textract # If using Tesseract if text_extraction_method == TESSERACT_TEXT_EXTRACT_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 = list() 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 ) if all_page_line_level_ocr_results_with_words is None: all_page_line_level_ocr_results_with_words = list() all_page_line_level_ocr_results_with_words.append( page_line_level_ocr_results_with_words ) # Optional additional VLM / inference-server pass to detect people # and inject [PERSON] entries into the word-level OCR structure. # Supports pure and hybrid VLM/inference-server local OCR models. if ( chosen_local_ocr_model in [ "vlm", "inference-server", "hybrid-vlm", "hybrid-paddle-vlm", "hybrid-paddle-inference-server", ] and "CUSTOM_VLM_PERSON" in chosen_redact_entities and isinstance(page_line_level_ocr_results_with_words, dict) and page_line_level_ocr_results_with_words.get("results") and image is not None ): try: image_name = ( os.path.basename(image_path) if isinstance(image_path, str) else f"{file_name}_{reported_page_number}.png" ) # Decide which backend to use for people detection if chosen_local_ocr_model in [ "vlm", "hybrid-vlm", "hybrid-paddle-vlm", ]: people_ocr = _vlm_page_ocr_predict( image, image_name=image_name, normalised_coords_range=999, output_folder=output_folder, detect_people_only=True, ) else: # inference-server based hybrids people_ocr = _inference_server_page_ocr_predict( image, image_name=image_name, normalised_coords_range=999, output_folder=output_folder, detect_people_only=True, ) # Convert people_ocr outputs into additional word-level entries texts = people_ocr.get("text", []) lefts = people_ocr.get("left", []) tops = people_ocr.get("top", []) widths = people_ocr.get("width", []) heights = people_ocr.get("height", []) confs = people_ocr.get("conf", []) results_dict = page_line_level_ocr_results_with_words["results"] # Determine a valid starting line number for synthetic [PERSON] lines existing_lines = [] for _line_key, _line_data in results_dict.items(): line_val = _line_data.get("line") if isinstance(line_val, (int, float, str)): try: existing_lines.append(int(line_val)) except Exception: continue next_line_number = ( max(existing_lines) if existing_lines else 0 ) + 1 existing_keys = list(results_dict.keys()) person_index_start = len(existing_keys) + 1 for idx, text in enumerate(texts): if text != "[PERSON]": continue try: left = int(lefts[idx]) top = int(tops[idx]) width = int(widths[idx]) height = int(heights[idx]) conf = float(confs[idx]) if idx < len(confs) else 0.0 except Exception: continue key = f"person_line_{person_index_start + idx}" bbox = (left, top, left + width, top + height) results_dict[key] = { "line": int(next_line_number), "text": "[PERSON]", "bounding_box": bbox, "words": [ { "text": "[PERSON]", "bounding_box": bbox, "conf": conf, "model": chosen_local_ocr_model, } ], "conf": conf, } next_line_number += 1 except Exception as e: print( f"Warning: VLM person detection failed on page {reported_page_number}: {e}" ) # Optional additional VLM / inference-server pass to detect signatures # and inject [SIGNATURE] entries into the word-level OCR structure. # Supports pure and hybrid VLM/inference-server local OCR models. if ( chosen_local_ocr_model in [ "vlm", "inference-server", "hybrid-vlm", "hybrid-paddle-vlm", "hybrid-paddle-inference-server", ] and "CUSTOM_VLM_SIGNATURE" in chosen_redact_entities and isinstance(page_line_level_ocr_results_with_words, dict) and page_line_level_ocr_results_with_words.get("results") and image is not None ): try: image_name = ( os.path.basename(image_path) if isinstance(image_path, str) else f"{file_name}_{reported_page_number}.png" ) # Decide which backend to use for signature detection if chosen_local_ocr_model in [ "vlm", "hybrid-vlm", "hybrid-paddle-vlm", ]: sig_ocr = _vlm_page_ocr_predict( image, image_name=image_name, normalised_coords_range=999, output_folder=output_folder, detect_signatures_only=True, ) else: # inference-server based hybrids sig_ocr = _inference_server_page_ocr_predict( image, image_name=image_name, normalised_coords_range=999, output_folder=output_folder, detect_signatures_only=True, ) # Convert sig_ocr outputs into additional word-level entries texts = sig_ocr.get("text", []) lefts = sig_ocr.get("left", []) tops = sig_ocr.get("top", []) widths = sig_ocr.get("width", []) heights = sig_ocr.get("height", []) confs = sig_ocr.get("conf", []) results_dict = page_line_level_ocr_results_with_words["results"] # Determine a valid starting line number for synthetic [SIGNATURE] lines existing_lines = [] for _line_key, _line_data in results_dict.items(): line_val = _line_data.get("line") if isinstance(line_val, (int, float, str)): try: existing_lines.append(int(line_val)) except Exception: continue next_line_number = ( max(existing_lines) if existing_lines else 0 ) + 1 existing_keys = list(results_dict.keys()) sig_index_start = len(existing_keys) + 1 for idx, text in enumerate(texts): if text != "[SIGNATURE]": continue try: left = int(lefts[idx]) top = int(tops[idx]) width = int(widths[idx]) height = int(heights[idx]) conf = float(confs[idx]) if idx < len(confs) else 0.0 except Exception: continue key = f"signature_line_{sig_index_start + idx}" bbox = (left, top, left + width, top + height) results_dict[key] = { "line": int(next_line_number), "text": "[SIGNATURE]", "bounding_box": bbox, "words": [ { "text": "[SIGNATURE]", "bounding_box": bbox, "conf": conf, "model": chosen_local_ocr_model, } ], "conf": conf, } next_line_number += 1 except Exception as e: print( f"Warning: VLM signature detection failed on page {reported_page_number}: {e}" ) # Check if page exists in existing textract data. If not, send to service to analyse if text_extraction_method == TEXTRACT_TEXT_EXTRACT_OPTION: text_blocks = list() page_exists = False if not textract_data: try: # print(f"Image object: {image}") # 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: out_message = ( "Textract extraction for page " + reported_page_number + " failed due to:" + str(e) ) textract_data = {"pages": []} new_textract_request_metadata = "Failed Textract API call" raise Exception(out_message) 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: if not image: page_num, image_path, width, height = ( process_single_page_for_image_conversion( file_path, page_no ) ) # Normalize and validate path safety before opening image normalized_path = os.path.normpath( os.path.abspath(image_path) ) if validate_path_containment( normalized_path, input_folder ): image = Image.open(normalized_path) else: raise ValueError( f"Unsafe image path detected: {image_path}" ) # 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"] = list() # 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 = list() 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"] = list() 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 ) # Check if existing Textract output for this page if textract_output_found and page_exists: use_mediabox_for_textract = True else: use_mediabox_for_textract = False if use_mediabox_for_textract: # Whole-document Textract: use mediabox dimensions textract_page_width = pymupdf_page.mediabox.width textract_page_height = pymupdf_page.mediabox.height # print( # f"Using mediabox dimensions for Textract: {textract_page_width}x{textract_page_height}" # ) else: # Individual image Textract: use image dimensions (current behavior) textract_page_width = page_width textract_page_height = page_height # print( # f"Using image dimensions for Textract: {textract_page_width}x{textract_page_height}" # ) # textract_page_width = page_width # textract_page_height = page_height ( page_line_level_ocr_results, handwriting_or_signature_boxes, page_signature_recogniser_results, page_handwriting_recogniser_results, page_line_level_ocr_results_with_words, selection_element_results, form_key_value_results, ) = json_to_ocrresult( text_blocks, textract_page_width, textract_page_height, reported_page_number, ) if all_page_line_level_ocr_results_with_words is None: all_page_line_level_ocr_results_with_words = list() all_page_line_level_ocr_results_with_words.append( page_line_level_ocr_results_with_words ) if selection_element_results: selection_element_results_list.extend(selection_element_results) if form_key_value_results: form_key_value_results_list.extend(form_key_value_results) # 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, "line": result.line, "conf": result.conf, } for result in page_line_level_ocr_results["results"] ] ) if not line_level_ocr_results_df.empty: # Ensure there are records to add all_line_level_ocr_results_list.extend( line_level_ocr_results_df.to_dict("records") ) # Save OCR visualization with bounding boxes (works for all OCR methods) if ( text_extraction_method == TEXTRACT_TEXT_EXTRACT_OPTION and SAVE_PAGE_OCR_VISUALISATIONS is True ) or ( text_extraction_method == TESSERACT_TEXT_EXTRACT_OPTION and SAVE_PAGE_OCR_VISUALISATIONS is True ): if ( page_line_level_ocr_results_with_words and "results" in page_line_level_ocr_results_with_words ): # Ensure image is set - if None, try to use image_path or file_path for image files image_for_visualization = image if image_for_visualization is None: # If image is None and input file is an image file, try to use image_path or file_path if is_pdf(file_path) is False: if isinstance(image_path, str) and image_path: # Try to use image_path if it's a valid string image_for_visualization = image_path elif isinstance(file_path, str) and file_path: # Fall back to using the original file_path for image files image_for_visualization = file_path else: # For PDF files, we need an image object or image path if isinstance(image_path, str) and image_path: image_for_visualization = image_path # Only proceed if we have a valid image or image path if image_for_visualization is not None: # Store the length before the call to detect new additions log_files_output_paths_length_before = len( log_files_output_paths ) log_files_output_paths = visualise_ocr_words_bounding_boxes( image_for_visualization, page_line_level_ocr_results_with_words["results"], image_name=f"{file_name}_{reported_page_number}", output_folder=output_folder, text_extraction_method=text_extraction_method, chosen_local_ocr_model=chosen_local_ocr_model, log_files_output_paths=log_files_output_paths, ) # If config is enabled and a new visualization file was added, add it to out_file_paths if ( INCLUDE_OCR_VISUALISATION_IN_OUTPUT_FILES and log_files_output_paths is not None and len(log_files_output_paths) > log_files_output_paths_length_before ): # Get the newly added visualization file path (last item in the list) new_visualisation_path = log_files_output_paths[-1] if new_visualisation_path not in out_file_paths: out_file_paths.append(new_visualisation_path) else: print( f"Warning: Could not determine image for visualization at page {reported_page_number}. Skipping visualization." ) if ( pii_identification_method != NO_REDACTION_PII_OPTION or RETURN_PDF_FOR_REVIEW is True ): page_redaction_bounding_boxes = list() comprehend_query_number = 0 comprehend_query_number_new = 0 redact_whole_page = False if pii_identification_method != NO_REDACTION_PII_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, custom_entities=chosen_redact_entities, language=language, allow_list=allow_list, score_threshold=score_threshold, nlp_analyser=nlp_analyser, ) ) comprehend_query_number = ( comprehend_query_number + comprehend_query_number_new ) else: page_redaction_bounding_boxes = list() # 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 = list() if is_pdf(file_path) is 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 # Check if there are question answer boxes if form_key_value_results_list: page_merged_redaction_bboxes.extend( convert_page_question_answer_to_custom_image_recognizer_results( form_key_value_results_list, page_sizes_df, reported_page_number, ) ) # 3. Draw the merged boxes ## Apply annotations to pdf with pymupdf redact_result = 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, input_folder=input_folder, ) # Handle dual page objects if returned if isinstance(redact_result[0], tuple): ( pymupdf_page, pymupdf_applied_redaction_page, ), page_image_annotations = redact_result # Store the final page with its original page number for later use if not hasattr(redact_image_pdf, "_applied_redaction_pages"): redact_image_pdf._applied_redaction_pages = list() redact_image_pdf._applied_redaction_pages.append( (pymupdf_applied_redaction_page, page_no) ) else: pymupdf_page, page_image_annotations = redact_result # If an image_path file, draw onto the image_path elif is_pdf(file_path) is False: if isinstance(image_path, str): # Normalise and validate path safety before checking existence normalized_path = os.path.normpath(os.path.abspath(image_path)) # Check if it's a Gradio temporary file is_gradio_temp = ( "gradio" in normalized_path.lower() and "temp" in normalized_path.lower() ) if is_gradio_temp or validate_path_containment( normalized_path, INPUT_FOLDER ): image = Image.open(normalized_path) else: print(f"Path validation failed for: {normalized_path}") # You might want to handle this case differently continue # or raise an exception elif isinstance(image_path, Image.Image): image = image_path else: # Assume image_path is an image image = image_path fill = CUSTOM_BOX_COLOUR # Fill colour for redactions draw = ImageDraw.Draw(image) all_image_annotations_boxes = list() 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": CUSTOM_BOX_COLOUR, "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, } redacted_image = image.copy() # 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_list.append(decision_process_table.to_dict('records')) if not decision_process_table.empty: # Ensure there are records to add all_pages_decision_process_list.extend( decision_process_table.to_dict("records") ) decision_process_table = fill_missing_ids(decision_process_table) 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) is 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) # Save word level options if text_extraction_method == TEXTRACT_TEXT_EXTRACT_OPTION: if original_textract_data != textract_data: # Write the updated existing textract data back to the JSON file secure_file_write( output_folder, file_name + textract_suffix + "_textract.json", json.dumps(textract_data, separators=(",", ":")), ) 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.DataFrame( all_pages_decision_process_list ) all_line_level_ocr_results_df = pd.DataFrame( all_line_level_ocr_results_list ) if selection_element_results_list: selection_element_results_list_df = pd.DataFrame( selection_element_results_list ) if form_key_value_results_list: pd.DataFrame(form_key_value_results_list) form_key_value_results_list_df = ( convert_question_answer_to_dataframe( form_key_value_results_list, page_sizes_df ) ) 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, selection_element_results_list_df, form_key_value_results_list_df, out_file_paths, ) # If it's an image file if is_pdf(file_path) is 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: print( f"current_loop_page: {current_loop_page} is a multiple of page_break_val: {page_break_val}, breaking loop" ) page_break_return = True progress.close(_tqdm=progress_bar) tqdm._instances.clear() if text_extraction_method == TEXTRACT_TEXT_EXTRACT_OPTION: # Write the updated existing textract data back to the JSON file if original_textract_data != textract_data: secure_file_write( output_folder, file_name + textract_suffix + "_textract.json", json.dumps(textract_data, separators=(",", ":")), ) 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_TEXT_EXTRACT_OPTION: if ( original_all_page_line_level_ocr_results_with_words != all_page_line_level_ocr_results_with_words ): # Write the updated existing local OCR 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.DataFrame( all_pages_decision_process_list ) all_line_level_ocr_results_df = pd.DataFrame( all_line_level_ocr_results_list ) if selection_element_results_list: selection_element_results_list_df = pd.DataFrame( selection_element_results_list ) if form_key_value_results_list: pd.DataFrame(form_key_value_results_list) form_key_value_results_list_df = convert_question_answer_to_dataframe( form_key_value_results_list, page_sizes_df ) 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, selection_element_results_list_df, form_key_value_results_list_df, out_file_paths, ) if text_extraction_method == TEXTRACT_TEXT_EXTRACT_OPTION: # Write the updated existing textract data back to the JSON file if OVERWRITE_EXISTING_OCR_RESULTS or original_textract_data != textract_data: secure_file_write( output_folder, file_name + textract_suffix + "_textract.json", json.dumps(textract_data, separators=(",", ":")), ) 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_TEXT_EXTRACT_OPTION: # print( # f"Writing updated existing local OCR data back to the JSON file: {all_page_line_level_ocr_results_with_words_json_file_path}" # ) if ( OVERWRITE_EXISTING_OCR_RESULTS or 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.DataFrame(all_pages_decision_process_list) all_line_level_ocr_results_df = pd.DataFrame(all_line_level_ocr_results_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", ) if selection_element_results_list: selection_element_results_list_df = pd.DataFrame(selection_element_results_list) if form_key_value_results_list: pd.DataFrame(form_key_value_results_list) form_key_value_results_list_df = convert_question_answer_to_dataframe( form_key_value_results_list, page_sizes_df ) 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, selection_element_results_list_df, form_key_value_results_list_df, out_file_paths, ) ### # 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_line_level_ocr_results_from_characters( char_objects: List, line_number: int ) -> Tuple[List[OCRResult], List[List]]: """ Create OCRResult objects based on a list of pdfminer LTChar objects. This version is corrected to use the specified OCRResult class definition. """ line_level_results_out = list() line_level_characters_out = list() character_objects_out = list() full_text = "" # [x0, y0, x1, y1] overall_bbox = [float("inf"), float("inf"), float("-inf"), float("-inf")] for char in char_objects: character_objects_out.append(char) if isinstance(char, LTAnno): added_text = char.get_text() full_text += added_text if "\n" in added_text: if full_text.strip(): # Create OCRResult for line line_level_results_out.append( OCRResult( text=full_text.strip(), left=round(overall_bbox[0], 2), top=round(overall_bbox[1], 2), width=round(overall_bbox[2] - overall_bbox[0], 2), height=round(overall_bbox[3] - overall_bbox[1], 2), line=line_number, ) ) line_level_characters_out.append(character_objects_out) # Reset for the next line character_objects_out = list() full_text = "" overall_bbox = [ float("inf"), float("inf"), float("-inf"), float("-inf"), ] line_number += 1 continue # This part handles LTChar objects added_text = clean_unicode_text(char.get_text()) full_text += added_text x0, y0, x1, y1 = char.bbox overall_bbox[0] = min(overall_bbox[0], x0) overall_bbox[1] = min(overall_bbox[1], y0) overall_bbox[2] = max(overall_bbox[2], x1) overall_bbox[3] = max(overall_bbox[3], y1) # Process the last line if full_text.strip(): line_number += 1 line_ocr_result = OCRResult( text=full_text.strip(), left=round(overall_bbox[0], 2), top=round(overall_bbox[1], 2), width=round(overall_bbox[2] - overall_bbox[0], 2), height=round(overall_bbox[3] - overall_bbox[1], 2), line=line_number, ) line_level_results_out.append(line_ocr_result) line_level_characters_out.append(character_objects_out) return line_level_results_out, line_level_characters_out def generate_words_for_line(line_chars: List) -> List[Dict[str, Any]]: """ Generates word-level results for a single, pre-defined line of characters. This robust version correctly identifies word breaks by: 1. Treating specific punctuation characters as standalone words. 2. Explicitly using space characters (' ') as a primary word separator. 3. Using a geometric gap between characters as a secondary, heuristic separator. Args: line_chars: A list of pdfminer.six LTChar/LTAnno objects for one line. Returns: A list of dictionaries, where each dictionary represents an individual word. """ # We only care about characters with coordinates and text for word building. text_chars = [c for c in line_chars if hasattr(c, "bbox") and c.get_text()] if not text_chars: return [] # Sort characters by horizontal position for correct processing. text_chars.sort(key=lambda c: c.bbox[0]) # NEW: Define punctuation that should be split into separate words. # The hyphen '-' is intentionally excluded to keep words like 'high-tech' together. PUNCTUATION_TO_SPLIT = {".", ",", "?", "!", ":", ";", "(", ")", "[", "]", "{", "}"} line_words = list() current_word_text = "" current_word_bbox = [float("inf"), float("inf"), -1, -1] # [x0, y0, x1, y1] prev_char = None def finalize_word(): nonlocal current_word_text, current_word_bbox # Only add the word if it contains non-space text if current_word_text.strip(): # bbox from [x0, y0, x1, y1] to your required format final_bbox = [ round(current_word_bbox[0], 2), round(current_word_bbox[3], 2), # Note: using y1 from pdfminer bbox round(current_word_bbox[2], 2), round(current_word_bbox[1], 2), # Note: using y0 from pdfminer bbox ] line_words.append( { "text": current_word_text.strip(), "bounding_box": final_bbox, "conf": 100.0, } ) # Reset for the next word current_word_text = "" current_word_bbox = [float("inf"), float("inf"), -1, -1] for char in text_chars: char_text = clean_unicode_text(char.get_text()) # 1. NEW: Check for splitting punctuation first. if char_text in PUNCTUATION_TO_SPLIT: # Finalize any word that came immediately before the punctuation. finalize_word() # Treat the punctuation itself as a separate word. px0, py0, px1, py1 = char.bbox punc_bbox = [round(px0, 2), round(py1, 2), round(px1, 2), round(py0, 2)] line_words.append( {"text": char_text, "bounding_box": punc_bbox, "conf": 100.0} ) prev_char = char continue # Skip to the next character # 2. Primary Signal: Is the character a space? if char_text.isspace(): finalize_word() # End the preceding word prev_char = char continue # Skip to the next character, do not add the space to any word # 3. Secondary Signal: Is there a large geometric gap? if prev_char: # A gap is considered a word break if it's larger than a fraction of the font size. space_threshold = prev_char.size * 0.25 # 25% of the char size min_gap = 1.0 # Or at least 1.0 unit gap = ( char.bbox[0] - prev_char.bbox[2] ) # gap = current_char.x0 - prev_char.x1 if gap > max(space_threshold, min_gap): finalize_word() # Found a gap, so end the previous word. # Append the character's text and update the bounding box for the current word current_word_text += char_text x0, y0, x1, y1 = char.bbox current_word_bbox[0] = min(current_word_bbox[0], x0) current_word_bbox[1] = min(current_word_bbox[3], y0) # pdfminer y0 is bottom current_word_bbox[2] = max(current_word_bbox[2], x1) current_word_bbox[3] = max(current_word_bbox[1], y1) # pdfminer y1 is top prev_char = char # After the loop, finalize the last word that was being built. finalize_word() return line_words def process_page_to_structured_ocr( all_char_objects: List, page_number: int, text_line_number: int, # This will now be treated as the STARTING line number ) -> Tuple[Dict[str, Any], List[OCRResult], List[List]]: """ Orchestrates the OCR process, correctly handling multiple lines. Returns: A tuple containing: 1. A dictionary with detailed line/word results for the page. 2. A list of the complete OCRResult objects for each line. 3. A list of lists, containing the character objects for each line. """ page_data = {"page": str(page_number), "results": {}} # Step 1: Get definitive lines and their character groups. # This function correctly returns all lines found in the input characters. line_results, lines_char_groups = create_line_level_ocr_results_from_characters( all_char_objects, text_line_number ) if not line_results: return {}, [], [] # Step 2: Iterate through each found line and generate its words. for i, (line_info, char_group) in enumerate(zip(line_results, lines_char_groups)): current_line_number = line_info.line # text_line_number + i word_level_results = generate_words_for_line(char_group) # Create a unique, incrementing line number for each iteration. line_key = f"text_line_{current_line_number}" line_bbox = [ line_info.left, line_info.top, line_info.left + line_info.width, line_info.top + line_info.height, ] # Now, each line is added to the dictionary with its own unique key. page_data["results"][line_key] = { "line": current_line_number, # Use the unique line number "text": line_info.text, "bounding_box": line_bbox, "words": word_level_results, "conf": 100.0, } # The list of OCRResult objects is already correct. line_level_ocr_results_list = line_results # Return the structured dictionary, the list of OCRResult objects, and the character groups return page_data, line_level_ocr_results_list, lines_char_groups 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 = list() 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, S=Name.S # Border width: 1 point # Border style: solid ), ) pikepdf_redaction_annotations_on_page.append(annotation) return pikepdf_redaction_annotations_on_page def redact_text_pdf( file_path: 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 = 0, # 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(), # List of annotations across all pages all_line_level_ocr_results_df: pd.DataFrame = pd.DataFrame( columns=["page", "text", "left", "top", "width", "height", "line", "conf"] ), # 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(), # List of PyMuPDF documents all_page_line_level_ocr_results_with_words: List = list(), pii_identification_method: str = "Local", comprehend_query_number: int = 0, comprehend_client="", in_deny_list: List[str] = list(), redact_whole_page_list: List[str] = list(), 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] = list(), text_extraction_only: bool = False, output_folder: str = OUTPUT_FOLDER, input_folder: str = INPUT_FOLDER, page_break_val: int = int(PAGE_BREAK_VALUE), # Value for page break max_time: int = int(MAX_TIME_VALUE), nlp_analyser: AnalyzerEngine = nlp_analyser, 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: - file_path: 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. - in_deny_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 of PDF page sizes in PDF or image format. - 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. - language (str, optional): The language to do AWS Comprehend calls. Defaults to value of language if not provided. - output_folder (str, optional): The output folder for the function - input_folder (str, optional): The folder for file inputs. - 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. - nlp_analyser (AnalyzerEngine, optional): The nlp_analyser object to use for entity detection. Defaults to nlp_analyser. - progress: Progress tracking object """ tic = time.perf_counter() if isinstance(all_line_level_ocr_results_df, pd.DataFrame): all_line_level_ocr_results_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_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) # Try updating the supported languages for the spacy analyser try: nlp_analyser = create_nlp_analyser(language, existing_nlp_analyser=nlp_analyser) # Check list of nlp_analyser recognisers and languages if language != "en": gr.Info( f"Language: {language} only supports the following entity detection: {str(nlp_analyser.registry.get_supported_entities(languages=[language]))}" ) except Exception as e: print(f"Error creating nlp_analyser for {language}: {e}") raise Exception(f"Error creating nlp_analyser for {language}: {e}") # Update custom word list analyser object with any new words that have been added to the custom deny list if in_deny_list: nlp_analyser.registry.remove_recognizer("CUSTOM") new_custom_recogniser = custom_word_list_recogniser(in_deny_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=in_deny_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(file_path) number_of_pages = len(pikepdf_pdf.pages) # file_name = get_file_name_without_type(file_path) if not all_page_line_level_ocr_results_with_words: all_page_line_level_ocr_results_with_words = list() # 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 ### if current_loop_page == 0: page_loop_start = page_min else: page_loop_start = current_loop_page page_loop_end = page_max print("Page range is", str(page_loop_start + 1), "to", str(page_loop_end)) # Run through each page in document to 1. Extract text and then 2. Create redaction boxes progress_bar = tqdm( range(page_loop_start, page_loop_end), 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( file_path, page_numbers=[page_no], maxpages=1 ): all_page_line_text_extraction_characters = list() all_page_line_level_text_extraction_results_list = list() page_analyser_results = list() page_redaction_bounding_boxes = list() characters = list() pikepdf_redaction_annotations_on_page = list() page_decision_process_table = pd.DataFrame( columns=[ "image_path", "page", "label", "xmin", "xmax", "ymin", "ymax", "text", "id", ] ) page_text_ocr_outputs = pd.DataFrame( columns=[ "page", "text", "left", "top", "width", "height", "line", "conf", ] ) page_text_ocr_outputs_list = list() text_line_no = 1 for n, text_container in enumerate(page_layout): characters = list() if isinstance(text_container, LTTextContainer) or isinstance( text_container, LTAnno ): characters = get_text_container_characters(text_container) # text_line_no += 1 # Create dataframe for all the text on the page # line_level_text_results_list, line_characters = create_line_level_ocr_results_from_characters(characters) # line_level_ocr_results_with_words = generate_word_level_ocr(characters, page_number=int(reported_page_number), text_line_number=text_line_no) ( line_level_ocr_results_with_words, line_level_text_results_list, line_characters, ) = process_page_to_structured_ocr( characters, page_number=int(reported_page_number), text_line_number=text_line_no, ) text_line_no += len(line_level_text_results_list) ### 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, "line": result.line, "conf": 100.0, } for result in line_level_text_results_list ] ) page_text_ocr_outputs_list.append(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) all_page_line_level_ocr_results_with_words.append( line_level_ocr_results_with_words ) if page_text_ocr_outputs_list: # Filter out empty DataFrames before concatenation to avoid FutureWarning non_empty_ocr_outputs = [ df for df in page_text_ocr_outputs_list if not df.empty ] if non_empty_ocr_outputs: page_text_ocr_outputs = pd.concat( non_empty_ocr_outputs, ignore_index=True ) else: page_text_ocr_outputs = pd.DataFrame( columns=[ "page", "text", "left", "top", "width", "height", "line", "conf", ] ) ### REDACTION if pii_identification_method != NO_REDACTION_PII_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 = list() # 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 redact_result = 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, input_folder=input_folder, ) # Handle dual page objects if returned if isinstance(redact_result[0], tuple): ( pymupdf_page, pymupdf_applied_redaction_page, ), page_image_annotations = redact_result # Store the final page with its original page number for later use if not hasattr(redact_text_pdf, "_applied_redaction_pages"): redact_text_pdf._applied_redaction_pages = list() redact_text_pdf._applied_redaction_pages.append( (pymupdf_applied_redaction_page, page_no) ) else: pymupdf_page, page_image_annotations = redact_result # 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_list.append( page_decision_process_table ) # Else, user chose not to run redaction else: pass # 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( ["line"] ).reset_index(drop=True) page_text_ocr_outputs = page_text_ocr_outputs.loc[ :, [ "page", "text", "left", "top", "width", "height", "line", "conf", ], ] all_line_level_ocr_results_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 # Filter out empty DataFrames before concatenation to avoid FutureWarning non_empty_decision_process = [ df for df in all_pages_decision_process_list if not df.empty ] if non_empty_decision_process: all_pages_decision_process_table = pd.concat( non_empty_decision_process, ignore_index=True ) else: all_pages_decision_process_table = pd.DataFrame( columns=[ "text", "xmin", "ymin", "xmax", "ymax", "label", "start", "end", "score", "page", "id", ] ) non_empty_ocr_results = [ df for df in all_line_level_ocr_results_list if not df.empty ] if non_empty_ocr_results: all_line_level_ocr_results_df = pd.concat( non_empty_ocr_results, ignore_index=True ) else: all_line_level_ocr_results_df = pd.DataFrame( columns=[ "page", "text", "left", "top", "width", "height", "line", "conf", ] ) 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, all_page_line_level_ocr_results_with_words, ) # 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 # Filter out empty DataFrames before concatenation to avoid FutureWarning non_empty_decision_process = [ df for df in all_pages_decision_process_list if not df.empty ] if non_empty_decision_process: all_pages_decision_process_table = pd.concat( non_empty_decision_process, ignore_index=True ) else: all_pages_decision_process_table = pd.DataFrame( columns=[ "text", "xmin", "ymin", "xmax", "ymax", "label", "start", "end", "score", "page", "id", ] ) 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, all_page_line_level_ocr_results_with_words, ) # Write all page outputs # Filter out empty DataFrames before concatenation to avoid FutureWarning non_empty_decision_process = [ df for df in all_pages_decision_process_list if not df.empty ] if non_empty_decision_process: all_pages_decision_process_table = pd.concat( non_empty_decision_process, ignore_index=True ) else: all_pages_decision_process_table = pd.DataFrame( columns=[ "text", "xmin", "ymin", "xmax", "ymax", "label", "start", "end", "score", "page", "id", ] ) non_empty_ocr_results = [ df for df in all_line_level_ocr_results_list if not df.empty ] if non_empty_ocr_results: all_line_level_ocr_results_df = pd.concat( non_empty_ocr_results, ignore_index=True ) else: all_line_level_ocr_results_df = pd.DataFrame( columns=["page", "text", "left", "top", "width", "height", "line", "conf"] ) if not all_pages_decision_process_table.empty: # 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"] = reverse_y_coords( all_pages_decision_process_table, "ymin" ) all_pages_decision_process_table["ymax"] = reverse_y_coords( all_pages_decision_process_table, "ymax" ) # Convert decision table to relative coordinates if not all_line_level_ocr_results_df.empty: 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 if not all_line_level_ocr_results_df.empty: all_line_level_ocr_results_df["top"] = reverse_y_coords( all_line_level_ocr_results_df, "top" ) # Remove empty dictionary items from ocr results with words all_page_line_level_ocr_results_with_words = [ d for d in all_page_line_level_ocr_results_with_words if d ] 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, all_page_line_level_ocr_results_with_words, ) def visualise_ocr_words_bounding_boxes( image: Union[str, Image.Image], ocr_results: Dict[str, Any], image_name: str = None, output_folder: str = OUTPUT_FOLDER, text_extraction_method: str = None, visualisation_folder: str = None, add_legend: bool = True, chosen_local_ocr_model: str = None, log_files_output_paths: List[str] = list(), ) -> None: """ Visualizes OCR bounding boxes with confidence-based colors and a legend. Handles word-level OCR results from Textract and Tesseract. Args: image: The PIL Image object or image path ocr_results: Dictionary containing word-level OCR results image_name: Optional name for the saved image file output_folder: Output folder path text_extraction_method: The text extraction method being used (determines folder name) visualisation_folder: Subfolder name for visualizations (auto-determined if not provided) add_legend: Whether to add a legend to the visualization log_files_output_paths: List of file paths used for saving redaction process logging results. """ # Determine visualization folder based on text extraction method # Initialize base_model_name with a default value base_model_name = "OCR" # Default fallback value if visualisation_folder is None: if text_extraction_method == TEXTRACT_TEXT_EXTRACT_OPTION: base_model_name = "Textract" visualisation_folder = "textract_visualisations" elif ( text_extraction_method == TESSERACT_TEXT_EXTRACT_OPTION and chosen_local_ocr_model == "tesseract" ): base_model_name = "Tesseract" visualisation_folder = "tesseract_visualisations" elif ( text_extraction_method == TESSERACT_TEXT_EXTRACT_OPTION and chosen_local_ocr_model == "hybrid-paddle" ): base_model_name = "Tesseract" visualisation_folder = "hybrid_paddle_visualisations" elif ( text_extraction_method == TESSERACT_TEXT_EXTRACT_OPTION and chosen_local_ocr_model == "paddle" ): base_model_name = "Paddle" visualisation_folder = "paddle_visualisations" elif ( text_extraction_method == TESSERACT_TEXT_EXTRACT_OPTION and chosen_local_ocr_model == "hybrid-vlm" ): base_model_name = "Tesseract" visualisation_folder = "hybrid_vlm_visualisations" elif ( text_extraction_method == TESSERACT_TEXT_EXTRACT_OPTION and chosen_local_ocr_model == "hybrid-paddle-vlm" ): base_model_name = "Paddle" visualisation_folder = "hybrid_paddle_vlm_visualisations" elif ( text_extraction_method == TESSERACT_TEXT_EXTRACT_OPTION and chosen_local_ocr_model == "hybrid-paddle-inference-server" ): base_model_name = "Paddle" visualisation_folder = "hybrid_paddle_inference_server_visualisations" elif ( text_extraction_method == TESSERACT_TEXT_EXTRACT_OPTION and chosen_local_ocr_model == "vlm" ): base_model_name = "VLM" visualisation_folder = "vlm_visualisations" elif ( text_extraction_method == TESSERACT_TEXT_EXTRACT_OPTION and chosen_local_ocr_model == "inference-server" ): base_model_name = "Inference server" visualisation_folder = "inference_server_visualisations" else: base_model_name = "OCR" visualisation_folder = "ocr_visualisations" if not ocr_results: return log_files_output_paths if isinstance(image, str): image = Image.open(image) # Convert PIL image to OpenCV format image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) # Get image dimensions height, width = image_cv.shape[:2] # Detect if coordinates need conversion from PyMuPDF to image space # This happens when Textract uses mediabox dimensions (PyMuPDF coordinates) # instead of image pixel dimensions # For non-Textract methods (VLM/inference-server), coordinates should already be in image pixel space, # but we need to check if there's a size mismatch between coordinate space and visualization image needs_coordinate_conversion = False source_width = width source_height = height if text_extraction_method == TEXTRACT_TEXT_EXTRACT_OPTION: # Collect all bounding box coordinates to detect coordinate system all_x_coords = [] all_y_coords = [] for line_key, line_data in ocr_results.items(): if not isinstance(line_data, dict) or "words" not in line_data: continue words = line_data.get("words", []) for word_data in words: if not isinstance(word_data, dict): continue bbox = word_data.get("bounding_box", (0, 0, 0, 0)) if len(bbox) == 4: x1, y1, x2, y2 = bbox all_x_coords.extend([x1, x2]) all_y_coords.extend([y1, y2]) # Check if coordinates appear to be in PyMuPDF range (typically 0-1200 points) # and image is much larger (indicating coordinate system mismatch) # if all_x_coords and all_y_coords: # max_x = max(all_x_coords) # max_y = max(all_y_coords) # # PyMuPDF coordinates are typically in points (0-1200 range) # # If max coordinates are much smaller than image dimensions, likely need conversion # if ( # max_x < width * 0.6 # and max_y < height * 0.6 # and max_x < 1500 # and max_y < 1500 # ): # # Estimate source dimensions from actual coordinate range # # Add some padding to account for coordinates not reaching edges # source_width = max( # max_x * 1.1, 612 # ) # Default to US Letter width if too small # source_height = max( # max_y * 1.1, 792 # ) # Default to US Letter height if too small # needs_coordinate_conversion = True else: # For non-Textract methods (Tesseract, VLM, inference-server, etc.), # coordinates should be in image pixel space, but check if there's a size mismatch # Collect all bounding box coordinates to detect coordinate space all_x_coords = [] all_y_coords = [] for line_key, line_data in ocr_results.items(): if not isinstance(line_data, dict) or "words" not in line_data: continue words = line_data.get("words", []) for word_data in words: if not isinstance(word_data, dict): continue bbox = word_data.get("bounding_box", (0, 0, 0, 0)) if len(bbox) == 4: x1, y1, x2, y2 = bbox all_x_coords.extend([x1, x2]) all_y_coords.extend([y1, y2]) # Calculate scaling factors if conversion is needed if needs_coordinate_conversion: scale_x = width / source_width scale_y = height / source_height else: scale_x = 1.0 scale_y = 1.0 # Define confidence ranges and colors for bounding boxes (bright colors) confidence_ranges = [ (80, 100, (0, 255, 0), "High (80-100%)"), # Green (50, 79, (0, 165, 255), "Medium (50-79%)"), # Orange (0, 49, (0, 0, 255), "Low (0-49%)"), # Red ] # Define darker colors for text on white background text_confidence_ranges = [ (80, 100, (0, 150, 0), "High (80-100%)"), # Dark Green (50, 79, (0, 100, 200), "Medium (50-79%)"), # Dark Orange (0, 49, (0, 0, 180), "Low (0-49%)"), # Dark Red ] # Process each line's words for line_key, line_data in ocr_results.items(): if not isinstance(line_data, dict) or "words" not in line_data: continue words = line_data.get("words", []) # Process each word in the line for word_data in words: if not isinstance(word_data, dict): continue text = word_data.get("text", "") # Handle both 'conf' and 'confidence' field names for compatibility conf = int(word_data.get("conf", word_data.get("confidence", 0))) # Skip empty text or invalid confidence if not text.strip() or conf == -1: continue # Get bounding box coordinates bbox = word_data.get("bounding_box", (0, 0, 0, 0)) if len(bbox) != 4: continue x1, y1, x2, y2 = bbox # Convert coordinates if needed (from PyMuPDF to image space) if needs_coordinate_conversion: x1 = x1 * scale_x y1 = y1 * scale_y x2 = x2 * scale_x y2 = y2 * scale_y # Ensure coordinates are within image bounds x1 = max(0, min(int(x1), width)) y1 = max(0, min(int(y1), height)) x2 = max(0, min(int(x2), width)) y2 = max(0, min(int(y2), height)) # Skip if bounding box is invalid if x2 <= x1 or y2 <= y1: continue # Check if word was replaced by a different model model = word_data.get("model", None) is_replaced = model and model.lower() != base_model_name.lower() # Determine bounding box color: grey for replaced words, otherwise based on confidence # if is_replaced: # box_color = (128, 128, 128) # Grey for model replacements (bounding box only) # else: box_color = (0, 0, 255) # Default to red for min_conf, max_conf, conf_color, _ in confidence_ranges: if min_conf <= conf <= max_conf: box_color = conf_color break # Draw bounding box cv2.rectangle(image_cv, (x1, y1), (x2, y2), box_color, 1) # Add legend if add_legend: add_confidence_legend(image_cv, confidence_ranges, show_model_replacement=False) # Create second page with text overlay text_page = np.ones((height, width, 3), dtype=np.uint8) * 255 # White background # Process each line's words for text overlay for line_key, line_data in ocr_results.items(): if not isinstance(line_data, dict) or "words" not in line_data: continue words = line_data.get("words", []) # Group words by bounding box (to handle cases where multiple words share the same box) # Use a small tolerance to consider boxes as "the same" if they're very close bbox_tolerance = 5 # pixels bbox_groups = {} # Maps (x1, y1, x2, y2) to list of word_data for word_data in words: if not isinstance(word_data, dict): continue text = word_data.get("text", "") # Handle both 'conf' and 'confidence' field names for compatibility conf = int(word_data.get("conf", word_data.get("confidence", 0))) # Skip empty text or invalid confidence if not text.strip() or conf == -1: continue # Get bounding box coordinates bbox = word_data.get("bounding_box", (0, 0, 0, 0)) if len(bbox) != 4: continue x1, y1, x2, y2 = bbox # Convert coordinates if needed (from PyMuPDF to image space) if needs_coordinate_conversion: x1 = x1 * scale_x y1 = y1 * scale_y x2 = x2 * scale_x y2 = y2 * scale_y # Ensure coordinates are within image bounds x1 = max(0, min(int(x1), width)) y1 = max(0, min(int(y1), height)) x2 = max(0, min(int(x2), width)) y2 = max(0, min(int(y2), height)) # Skip if bounding box is invalid if x2 <= x1 or y2 <= y1: continue # Round coordinates to nearest tolerance to group similar boxes x1_rounded = (x1 // bbox_tolerance) * bbox_tolerance y1_rounded = (y1 // bbox_tolerance) * bbox_tolerance x2_rounded = (x2 // bbox_tolerance) * bbox_tolerance y2_rounded = (y2 // bbox_tolerance) * bbox_tolerance bbox_key = (x1_rounded, y1_rounded, x2_rounded, y2_rounded) if bbox_key not in bbox_groups: bbox_groups[bbox_key] = [] bbox_groups[bbox_key].append( {"word_data": word_data, "original_bbox": (x1, y1, x2, y2)} ) # Process each group of words for bbox_key, word_group in bbox_groups.items(): if not word_group: continue # Use the first word's bounding box as the reference (they should all be similar) x1, y1, x2, y2 = word_group[0]["original_bbox"] box_width = x2 - x1 box_height = y2 - y1 # If only one word in the box, process it normally if len(word_group) == 1: word_data = word_group[0]["word_data"] text = word_data.get("text", "") conf = int(word_data.get("conf", word_data.get("confidence", 0))) # Check if word was replaced by a different model model = word_data.get("model", None) is_replaced = model and model.lower() != base_model_name.lower() # Text color always based on confidence text_color = (0, 0, 180) # Default to dark red for min_conf, max_conf, conf_color, _ in text_confidence_ranges: if min_conf <= conf <= max_conf: text_color = conf_color break # Calculate font size to fit text within bounding box font_scale = 0.5 font_thickness = 1 font = cv2.FONT_HERSHEY_SIMPLEX # Get text size and adjust to fit (text_width, text_height), baseline = cv2.getTextSize( text, font, font_scale, font_thickness ) # Scale font to fit width (with some padding) if text_width > 0: width_scale = (box_width * 0.9) / text_width else: width_scale = 1.0 # Scale font to fit height (with some padding) if text_height > 0: height_scale = (box_height * 0.8) / text_height else: height_scale = 1.0 # Use the smaller scale to ensure text fits both dimensions font_scale = min( font_scale * min(width_scale, height_scale), 2.0 ) # Cap at 2.0 # Recalculate text size with adjusted font scale (text_width, text_height), baseline = cv2.getTextSize( text, font, font_scale, font_thickness ) # Center text within bounding box text_x = x1 + (box_width - text_width) // 2 text_y = y1 + (box_height + text_height) // 2 # Baseline adjustment # Draw text cv2.putText( text_page, text, (text_x, text_y), font, font_scale, text_color, font_thickness, cv2.LINE_AA, ) # Draw grey bounding box for replaced words on text page if is_replaced: box_color = (128, 128, 128) # Grey for model replacements cv2.rectangle(text_page, (x1, y1), (x2, y2), box_color, 1) else: # Multiple words in the same box - arrange them side by side # Extract texts and determine colors for each word word_texts = [] word_colors = [] word_is_replaced = [] for item in word_group: word_data = item["word_data"] text = word_data.get("text", "") conf = int(word_data.get("conf", word_data.get("confidence", 0))) model = word_data.get("model", None) is_replaced = model and model.lower() != base_model_name.lower() # Text color based on confidence text_color = (0, 0, 180) # Default to dark red for min_conf, max_conf, conf_color, _ in text_confidence_ranges: if min_conf <= conf <= max_conf: text_color = conf_color break word_texts.append(text) word_colors.append(text_color) word_is_replaced.append(is_replaced) # Calculate font size to fit all words side by side font_scale = 0.5 font_thickness = 1 font = cv2.FONT_HERSHEY_SIMPLEX # Start with a reasonable font scale and reduce if needed max_font_scale = 2.0 min_font_scale = 0.1 font_scale = max_font_scale # Binary search or iterative approach to find the right font size for _ in range(20): # Max iterations # Calculate total width needed for all words with spaces total_width = 0 max_text_height = 0 for i, text in enumerate(word_texts): (text_width, text_height), baseline = cv2.getTextSize( text, font, font_scale, font_thickness ) total_width += text_width max_text_height = max(max_text_height, text_height) # Add space width between words (except last word) if i < len(word_texts) - 1: (space_width, _), _ = cv2.getTextSize( " ", font, font_scale, font_thickness ) total_width += space_width # Check if it fits width_fits = total_width <= box_width * 0.9 height_fits = max_text_height <= box_height * 0.8 if width_fits and height_fits: break # Reduce font scale font_scale *= 0.9 if font_scale < min_font_scale: font_scale = min_font_scale break # Recalculate total width and max height with final font scale total_width = 0 max_text_height = 0 for i, text in enumerate(word_texts): (text_width, text_height), baseline = cv2.getTextSize( text, font, font_scale, font_thickness ) total_width += text_width max_text_height = max(max_text_height, text_height) # Add space width between words (except last word) if i < len(word_texts) - 1: (space_width, _), _ = cv2.getTextSize( " ", font, font_scale, font_thickness ) total_width += space_width # Now draw each word side by side current_x = ( x1 + (box_width - total_width) // 2 ) # Center the combined text text_y = y1 + (box_height + max_text_height) // 2 # Baseline adjustment for i, (text, text_color) in enumerate(zip(word_texts, word_colors)): # Get text size with final font scale (text_width, text_height), baseline = cv2.getTextSize( text, font, font_scale, font_thickness ) # Draw text cv2.putText( text_page, text, (int(current_x), text_y), font, font_scale, text_color, font_thickness, cv2.LINE_AA, ) # Move to next position current_x += text_width # Add space between words (except last word) if i < len(word_texts) - 1: (space_width, _), _ = cv2.getTextSize( " ", font, font_scale, font_thickness ) current_x += space_width # Draw grey bounding box if any word was replaced if any(word_is_replaced): box_color = (128, 128, 128) # Grey for model replacements cv2.rectangle(text_page, (x1, y1), (x2, y2), box_color, 1) # Add legend to second page if add_legend: add_confidence_legend( text_page, text_confidence_ranges, show_model_replacement=True ) # Concatenate images horizontally combined_image = np.hstack([image_cv, text_page]) # Save the visualization if output_folder: textract_viz_folder = os.path.join(output_folder, visualisation_folder) # Double-check the constructed path is safe if not validate_folder_containment(textract_viz_folder, OUTPUT_FOLDER): raise ValueError( f"Unsafe textract visualisations folder path: {textract_viz_folder}" ) os.makedirs(textract_viz_folder, exist_ok=True) # Generate filename if image_name: # Remove file extension if present base_name = os.path.splitext(image_name)[0] filename = f"{base_name}_{visualisation_folder}.jpg" else: timestamp = int(time.time()) filename = f"{visualisation_folder}_{timestamp}.jpg" output_path = os.path.join(textract_viz_folder, filename) # Save the combined image cv2.imwrite(output_path, combined_image) log_files_output_paths.append(output_path) return log_files_output_paths def add_confidence_legend( image_cv: np.ndarray, confidence_ranges: List[Tuple], show_model_replacement: bool = False, ) -> None: """ Adds a confidence legend to the visualization image. Args: image_cv: OpenCV image array confidence_ranges: List of tuples containing (min_conf, max_conf, color, label) show_model_replacement: Whether to include a legend entry for model replacements (grey) """ height, width = image_cv.shape[:2] # Calculate legend height based on number of items num_items = len(confidence_ranges) if show_model_replacement: num_items += 1 # Add one more for model replacement entry # Legend parameters legend_width = 200 legend_height = 70 + (num_items * 25) # Dynamic height based on number of items legend_x = width - legend_width - 20 legend_y = 20 # Draw legend background # Draw a translucent (semi-transparent) white rectangle for the legend background overlay = image_cv.copy() cv2.rectangle( overlay, (legend_x, legend_y), (legend_x + legend_width, legend_y + legend_height), (255, 255, 255), # White background -1, ) alpha = 0.5 # Opacity: 1.0 = opaque, 0.0 = fully transparent cv2.addWeighted(overlay, alpha, image_cv, 1 - alpha, 0, image_cv) # cv2.rectangle( # image_cv, # (legend_x, legend_y), # (legend_x + legend_width, legend_y + legend_height), # (0, 0, 0), # Black border # 2, # ) # Add title title_text = "Confidence Levels" font_scale = 0.6 font_thickness = 1 (title_width, title_height), _ = cv2.getTextSize( title_text, cv2.FONT_HERSHEY_SIMPLEX, font_scale, font_thickness ) title_x = legend_x + (legend_width - title_width) // 2 title_y = legend_y + title_height + 10 cv2.putText( image_cv, title_text, (title_x, title_y), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 0), # Black text font_thickness, ) # Add confidence range items item_spacing = 25 start_y = title_y + 25 item_index = 0 # Add model replacement entry first if enabled if show_model_replacement: item_y = start_y + item_index * item_spacing item_index += 1 # Draw grey color box box_size = 15 box_x = legend_x + 10 box_y = item_y - box_size replacement_color = (128, 128, 128) # Grey in BGR cv2.rectangle( image_cv, (box_x, box_y), (box_x + box_size, box_y + box_size), replacement_color, -1, ) cv2.rectangle( image_cv, (box_x, box_y), (box_x + box_size, box_y + box_size), (0, 0, 0), # Black border 1, ) # Add label text label_x = box_x + box_size + 10 label_y = item_y - 5 cv2.putText( image_cv, "Model Replacement", (label_x, label_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), # Black text 1, ) # Add confidence range items for i, (min_conf, max_conf, color, label) in enumerate(confidence_ranges): item_y = start_y + (item_index + i) * item_spacing # Draw color box box_size = 15 box_x = legend_x + 10 box_y = item_y - box_size cv2.rectangle( image_cv, (box_x, box_y), (box_x + box_size, box_y + box_size), color, -1 ) cv2.rectangle( image_cv, (box_x, box_y), (box_x + box_size, box_y + box_size), (0, 0, 0), # Black border 1, ) # Add label text label_x = box_x + box_size + 10 label_y = item_y - 5 cv2.putText( image_cv, label, (label_x, label_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), # Black text 1, )