from pdf2image import convert_from_path, pdfinfo_from_path from PIL import Image, ImageFile import os import re import time import json import numpy as np import pymupdf from pymupdf import Document, Page, Rect import pandas as pd import shutil import zipfile from collections import defaultdict from tqdm import tqdm from gradio import Progress from typing import List, Optional, Dict, Any from concurrent.futures import ThreadPoolExecutor, as_completed from pdf2image import convert_from_path from PIL import Image from scipy.spatial import cKDTree import random import string IMAGE_NUM_REGEX = re.compile(r'_(\d+)\.png$') pd.set_option('future.no_silent_downcasting', True) from tools.config import OUTPUT_FOLDER, INPUT_FOLDER, IMAGES_DPI, LOAD_TRUNCATED_IMAGES, MAX_IMAGE_PIXELS, CUSTOM_BOX_COLOUR from tools.helper_functions import get_file_name_without_type, tesseract_ocr_option, text_ocr_option, textract_option, read_file # from tools.aws_textract import load_and_convert_textract_json image_dpi = float(IMAGES_DPI) if not MAX_IMAGE_PIXELS: Image.MAX_IMAGE_PIXELS = None else: Image.MAX_IMAGE_PIXELS = MAX_IMAGE_PIXELS ImageFile.LOAD_TRUNCATED_IMAGES = LOAD_TRUNCATED_IMAGES.lower() == "true" def is_pdf_or_image(filename): """ Check if a file name is a PDF or an image file. Args: filename (str): The name of the file. Returns: bool: True if the file name ends with ".pdf", ".jpg", or ".png", False otherwise. """ if filename.lower().endswith(".pdf") or filename.lower().endswith(".jpg") or filename.lower().endswith(".jpeg") or filename.lower().endswith(".png"): output = True else: output = False return output def is_pdf(filename): """ Check if a file name is a PDF. Args: filename (str): The name of the file. Returns: bool: True if the file name ends with ".pdf", False otherwise. """ return filename.lower().endswith(".pdf") ## Convert pdf to image if necessary def check_image_size_and_reduce(out_path:str, image:Image): ''' Check if a given image size is above around 4.5mb, and reduce size if necessary. 5mb is the maximum possible to submit to AWS Textract. ''' all_img_details = [] page_num = 0 # Check file size and resize if necessary max_size = 4.5 * 1024 * 1024 # 5 MB in bytes # 5 file_size = os.path.getsize(out_path) width = image.width height = image.height # Resize images if they are too big if file_size > max_size: # Start with the original image size print(f"Image size before {width}x{height}, original file_size: {file_size}") while file_size > max_size: # Reduce the size by a factor (e.g., 50% of the current size) new_width = int(width * 0.5) new_height = int(height * 0.5) image = image.resize((new_width, new_height), Image.Resampling.LANCZOS) # Save the resized image image.save(out_path, format="PNG", optimize=True) # Update the file size file_size = os.path.getsize(out_path) print(f"Resized to {new_width}x{new_height}, new file_size: {file_size}") else: new_width = width new_height = height all_img_details.append((page_num, image, new_width, new_height)) return image, new_width, new_height, all_img_details, out_path def process_single_page_for_image_conversion(pdf_path:str, page_num:int, image_dpi:float=image_dpi, create_images:bool = True, input_folder: str = INPUT_FOLDER) -> tuple[int, str, float, float]: out_path_placeholder = "placeholder_image_" + str(page_num) + ".png" if create_images == True: try: # Construct the full output directory path image_output_dir = os.path.join(os.getcwd(), input_folder) out_path = os.path.join(image_output_dir, f"{os.path.basename(pdf_path)}_{page_num}.png") os.makedirs(os.path.dirname(out_path), exist_ok=True) if os.path.exists(out_path): # Load existing image image = Image.open(out_path) elif pdf_path.lower().endswith(".pdf"): # Convert PDF page to image image_l = convert_from_path(pdf_path, first_page=page_num+1, last_page=page_num+1, dpi=image_dpi, use_cropbox=False, use_pdftocairo=False) image = image_l[0] image = image.convert("L") image.save(out_path, format="PNG") elif pdf_path.lower().endswith(".jpg") or pdf_path.lower().endswith(".png") or pdf_path.lower().endswith(".jpeg"): image = Image.open(pdf_path) image.save(out_path, format="PNG") width, height = image.size # Check if image size too large and reduce if necessary #print("Checking size of image and reducing if necessary.") image, width, height, all_img_details, img_path = check_image_size_and_reduce(out_path, image) return page_num, out_path, width, height except Exception as e: print(f"Error processing page {page_num + 1}: {e}") return page_num, out_path_placeholder, pd.NA, pd.NA else: # print("Not creating image for page", page_num) return page_num, out_path_placeholder, pd.NA, pd.NA def convert_pdf_to_images(pdf_path: str, prepare_for_review:bool=False, page_min: int = 0, page_max:int = 0, create_images:bool=True, image_dpi: float = image_dpi, num_threads: int = 8, input_folder: str = INPUT_FOLDER): # If preparing for review, just load the first page (not currently used) if prepare_for_review == True: page_count = pdfinfo_from_path(pdf_path)['Pages'] #1 page_min = 0 page_max = page_count else: page_count = pdfinfo_from_path(pdf_path)['Pages'] print(f"Number of pages in PDF: {page_count}") # Set page max to length of pdf if not specified if page_max == 0: page_max = page_count results = [] with ThreadPoolExecutor(max_workers=num_threads) as executor: futures = [] for page_num in range(page_min, page_max): futures.append(executor.submit(process_single_page_for_image_conversion, pdf_path, page_num, image_dpi, create_images=create_images, input_folder=input_folder)) for future in tqdm(as_completed(futures), total=len(futures), unit="pages", desc="Converting pages to image"): page_num, img_path, width, height = future.result() if img_path: results.append((page_num, img_path, width, height)) else: print(f"Page {page_num + 1} failed to process.") results.append((page_num, "placeholder_image_" + str(page_num) + ".png", pd.NA, pd.NA)) # Sort results by page number results.sort(key=lambda x: x[0]) images = [result[1] for result in results] widths = [result[2] for result in results] heights = [result[3] for result in results] #print("PDF has been converted to images.") return images, widths, heights, results # Function to take in a file path, decide if it is an image or pdf, then process appropriately. def process_file_for_image_creation(file_path:str, prepare_for_review:bool=False, input_folder:str=INPUT_FOLDER, create_images:bool=True): # Get the file extension file_extension = os.path.splitext(file_path)[1].lower() # Check if the file is an image type if file_extension in ['.jpg', '.jpeg', '.png']: print(f"{file_path} is an image file.") # Perform image processing here img_object = [file_path] #[Image.open(file_path)] # Load images from the file paths. Test to see if it is bigger than 4.5 mb and reduct if needed (Textract limit is 5mb) image = Image.open(file_path) img_object, image_sizes_width, image_sizes_height, all_img_details, img_path = check_image_size_and_reduce(file_path, image) if not isinstance(image_sizes_width, list): img_path = [img_path] image_sizes_width = [image_sizes_width] image_sizes_height = [image_sizes_height] all_img_details = [all_img_details] # Check if the file is a PDF elif file_extension == '.pdf': # print(f"{file_path} is a PDF file. Converting to image set") # Run your function for processing PDF files here img_path, image_sizes_width, image_sizes_height, all_img_details = convert_pdf_to_images(file_path, prepare_for_review, input_folder=input_folder, create_images=create_images) else: print(f"{file_path} is not an image or PDF file.") img_path = [] image_sizes_width = [] image_sizes_height = [] all_img_details = [] return img_path, image_sizes_width, image_sizes_height, all_img_details def get_input_file_names(file_input:List[str]): ''' Get list of input files to report to logs. ''' all_relevant_files = [] file_name_with_extension = "" full_file_name = "" total_pdf_page_count = 0 if isinstance(file_input, dict): file_input = os.path.abspath(file_input["name"]) if isinstance(file_input, str): file_input_list = [file_input] else: file_input_list = file_input for file in file_input_list: if isinstance(file, str): file_path = file else: file_path = file.name file_path_without_ext = get_file_name_without_type(file_path) file_extension = os.path.splitext(file_path)[1].lower() # Check if the file is in acceptable types if (file_extension in ['.jpg', '.jpeg', '.png', '.pdf', '.xlsx', '.csv', '.parquet']) & ("review_file" not in file_path_without_ext) & ("ocr_output" not in file_path_without_ext): all_relevant_files.append(file_path_without_ext) file_name_with_extension = file_path_without_ext + file_extension full_file_name = file_path # If PDF, get number of pages if (file_extension in ['.pdf']): # Open the PDF file pdf_document = pymupdf.open(file_path) # Get the number of pages page_count = pdf_document.page_count # Close the document pdf_document.close() else: page_count = 1 total_pdf_page_count += page_count all_relevant_files_str = ", ".join(all_relevant_files) return all_relevant_files_str, file_name_with_extension, full_file_name, all_relevant_files, total_pdf_page_count def convert_color_to_range_0_1(color): return tuple(component / 255 for component in color) def redact_single_box(pymupdf_page:Page, pymupdf_rect:Rect, img_annotation_box:dict, custom_colours:bool=False): ''' Commit redaction boxes to a PyMuPDF page. ''' pymupdf_x1 = pymupdf_rect[0] pymupdf_y1 = pymupdf_rect[1] pymupdf_x2 = pymupdf_rect[2] pymupdf_y2 = pymupdf_rect[3] # Calculate area to actually remove text from the pdf (different from black box size) 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, redact_bottom_y, pymupdf_x2, redact_top_y) # Slightly smaller than outside box # Add the annotation to the middle of the character line, so that it doesn't delete text from adjacent lines #page.add_redact_annot(rect)#rect_small_pixel_height) pymupdf_page.add_redact_annot(rect_small_pixel_height) # Set up drawing a black box over the whole rect shape = pymupdf_page.new_shape() shape.draw_rect(pymupdf_rect) if custom_colours == True: if img_annotation_box["color"][0] > 1: out_colour = convert_color_to_range_0_1(img_annotation_box["color"]) else: out_colour = img_annotation_box["color"] else: if CUSTOM_BOX_COLOUR == "grey": out_colour = (0.5, 0.5, 0.5) else: out_colour = (0,0,0) shape.finish(color=out_colour, fill=out_colour) # Black fill for the rectangle #shape.finish(color=(0, 0, 0)) # Black fill for the rectangle shape.commit() def convert_pymupdf_to_image_coords(pymupdf_page:Page, x1:float, y1:float, x2:float, y2:float, image: Image=None, image_dimensions:dict={}): ''' Converts coordinates from pymupdf format to image coordinates, accounting for mediabox dimensions and offset. ''' # Get rect dimensions rect = pymupdf_page.rect rect_width = rect.width rect_height = rect.height # Get mediabox dimensions and position mediabox = pymupdf_page.mediabox mediabox_width = mediabox.width mediabox_height = mediabox.height # Get target image dimensions if image: image_page_width, image_page_height = image.size elif image_dimensions: image_page_width, image_page_height = image_dimensions['image_width'], image_dimensions['image_height'] else: image_page_width, image_page_height = mediabox_width, mediabox_height # Calculate scaling factors image_to_mediabox_x_scale = image_page_width / mediabox_width image_to_mediabox_y_scale = image_page_height / mediabox_height # Adjust coordinates: # Apply scaling to match image dimensions x1_image = x1 * image_to_mediabox_x_scale x2_image = x2 * image_to_mediabox_x_scale y1_image = y1 * image_to_mediabox_y_scale y2_image = y2 * image_to_mediabox_y_scale # Correct for difference in rect and mediabox size if mediabox_width != rect_width: mediabox_to_rect_x_scale = mediabox_width / rect_width mediabox_to_rect_y_scale = mediabox_height / rect_height rect_to_mediabox_x_scale = rect_width / mediabox_width #rect_to_mediabox_y_scale = rect_height / mediabox_height mediabox_rect_x_diff = (mediabox_width - rect_width) * (image_to_mediabox_x_scale / 2) mediabox_rect_y_diff = (mediabox_height - rect_height) * (image_to_mediabox_y_scale / 2) x1_image -= mediabox_rect_x_diff x2_image -= mediabox_rect_x_diff y1_image += mediabox_rect_y_diff y2_image += mediabox_rect_y_diff # x1_image *= mediabox_to_rect_x_scale x2_image *= mediabox_to_rect_x_scale y1_image *= mediabox_to_rect_y_scale y2_image *= mediabox_to_rect_y_scale return x1_image, y1_image, x2_image, y2_image def redact_whole_pymupdf_page(rect_height:float, rect_width:float, image:Image, page:Page, custom_colours, border:float = 5, image_dimensions:dict={}): # Small border to page that remains white border = 5 # Define the coordinates for the Rect 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 # whole_page_image_x1, whole_page_image_y1, whole_page_image_x2, whole_page_image_y2 = convert_pymupdf_to_image_coords(page, whole_page_x1, whole_page_y1, whole_page_x2, whole_page_y2, image, image_dimensions=image_dimensions) # 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) # Write whole page annotation to annotation boxes whole_page_img_annotation_box = {} whole_page_img_annotation_box["xmin"] = whole_page_x1 #whole_page_image_x1 whole_page_img_annotation_box["ymin"] = whole_page_y1 #whole_page_image_y1 whole_page_img_annotation_box["xmax"] = whole_page_x2 #whole_page_image_x2 whole_page_img_annotation_box["ymax"] = whole_page_y2 #whole_page_image_y2 whole_page_img_annotation_box["color"] = (0,0,0) whole_page_img_annotation_box["label"] = "Whole page" redact_single_box(page, whole_page_rect, whole_page_img_annotation_box, custom_colours) return whole_page_img_annotation_box def create_page_size_objects(pymupdf_doc:Document, image_sizes_width:List[float], image_sizes_height:List[float], image_file_paths:List[str]): page_sizes = [] original_cropboxes = [] for page_no, page in enumerate(pymupdf_doc): reported_page_no = page_no + 1 pymupdf_page = pymupdf_doc.load_page(page_no) original_cropboxes.append(pymupdf_page.cropbox) # Save original CropBox # Create a page_sizes_object. If images have been created, then image width an height come from this value. Otherwise, they are set to the cropbox size out_page_image_sizes = { "page":reported_page_no, "mediabox_width":pymupdf_page.mediabox.width, "mediabox_height": pymupdf_page.mediabox.height, "cropbox_width":pymupdf_page.cropbox.width, "cropbox_height":pymupdf_page.cropbox.height, "original_cropbox":original_cropboxes[-1], "image_path":image_file_paths[page_no]} # cropbox_x_offset: Distance from MediaBox left edge to CropBox left edge # This is simply the difference in their x0 coordinates. out_page_image_sizes['cropbox_x_offset'] = pymupdf_page.cropbox.x0 - pymupdf_page.mediabox.x0 # cropbox_y_offset_from_top: Distance from MediaBox top edge to CropBox top edge # MediaBox top y = mediabox.y1 # CropBox top y = cropbox.y1 # The difference is mediabox.y1 - cropbox.y1 out_page_image_sizes['cropbox_y_offset_from_top'] = pymupdf_page.mediabox.y1 - pymupdf_page.cropbox.y1 if image_sizes_width and image_sizes_height: out_page_image_sizes["image_width"] = image_sizes_width[page_no] out_page_image_sizes["image_height"] = image_sizes_height[page_no] page_sizes.append(out_page_image_sizes) return page_sizes, original_cropboxes def prepare_image_or_pdf( file_paths: List[str], in_redact_method: str, latest_file_completed: int = 0, out_message: List[str] = [], first_loop_state: bool = False, number_of_pages:int = 0, all_annotations_object:List = [], prepare_for_review:bool = False, in_fully_redacted_list:List[int]=[], output_folder:str=OUTPUT_FOLDER, input_folder:str=INPUT_FOLDER, prepare_images:bool=True, page_sizes:list[dict]=[], textract_output_found:bool = False, progress: Progress = Progress(track_tqdm=True) ) -> tuple[List[str], List[str]]: """ Prepare and process image or text PDF files for redaction. This function takes a list of file paths, processes each file based on the specified redaction method, and returns the output messages and processed file paths. Args: file_paths (List[str]): List of file paths to process. in_redact_method (str): The redaction method to use. latest_file_completed (optional, int): Index of the last completed file. out_message (optional, List[str]): List to store output messages. first_loop_state (optional, bool): Flag indicating if this is the first iteration. number_of_pages (optional, int): integer indicating the number of pages in the document all_annotations_object(optional, List of annotation objects): All annotations for current document prepare_for_review(optional, bool): Is this preparation step preparing pdfs and json files to review current redactions? in_fully_redacted_list(optional, List of int): A list of pages to fully redact output_folder (optional, str): The output folder for file save prepare_images (optional, bool): A boolean indicating whether to create images for each PDF page. Defaults to True. page_sizes(optional, List[dict]): A list of dicts containing information about page sizes in various formats. textract_output_found (optional, bool): A boolean indicating whether textract output has already been found . Defaults to False. progress (optional, Progress): Progress tracker for the operation Returns: tuple[List[str], List[str]]: A tuple containing the output messages and processed file paths. """ tic = time.perf_counter() json_from_csv = False original_cropboxes = [] # Store original CropBox values converted_file_paths = [] image_file_paths = [] pymupdf_doc = [] all_img_details = [] review_file_csv = pd.DataFrame() all_line_level_ocr_results_df = pd.DataFrame() out_textract_path = "" combined_out_message = "" final_out_message = "" if isinstance(in_fully_redacted_list, pd.DataFrame): if not in_fully_redacted_list.empty: in_fully_redacted_list = in_fully_redacted_list.iloc[:,0].tolist() # If this is the first time around, set variables to 0/blank if first_loop_state==True: latest_file_completed = 0 out_message = [] all_annotations_object = [] else: print("Now redacting file", str(latest_file_completed)) # If combined out message or converted_file_paths are blank, change to a list so it can be appended to if isinstance(out_message, str): out_message = [out_message] if not file_paths: file_paths = [] if isinstance(file_paths, dict): file_paths = os.path.abspath(file_paths["name"]) if isinstance(file_paths, str): file_path_number = 1 else: file_path_number = len(file_paths) latest_file_completed = int(latest_file_completed) # If we have already redacted the last file, return the input out_message and file list to the relevant components if latest_file_completed >= file_path_number: print("Last file reached, returning files:", str(latest_file_completed)) if isinstance(out_message, list): final_out_message = '\n'.join(out_message) else: final_out_message = out_message return final_out_message, converted_file_paths, image_file_paths, number_of_pages, number_of_pages, pymupdf_doc, all_annotations_object, review_file_csv, original_cropboxes, page_sizes, textract_output_found, all_img_details, all_line_level_ocr_results_df progress(0.1, desc='Preparing file') if isinstance(file_paths, str): file_paths_list = [file_paths] file_paths_loop = file_paths_list else: file_paths_list = file_paths file_paths_loop = sorted(file_paths_list, key=lambda x: (os.path.splitext(x)[1] != '.pdf', os.path.splitext(x)[1] != '.json')) # Loop through files to load in for file in file_paths_loop: converted_file_path = [] image_file_path = [] if isinstance(file, str): file_path = file else: file_path = file.name file_path_without_ext = get_file_name_without_type(file_path) file_name_with_ext = os.path.basename(file_path) if not file_path: out_message = "Please select a file." print(out_message) raise Exception(out_message) file_extension = os.path.splitext(file_path)[1].lower() # If a pdf, load as a pymupdf document if is_pdf(file_path): pymupdf_doc = pymupdf.open(file_path) pymupdf_pages = pymupdf_doc.page_count converted_file_path = file_path if prepare_images==True: image_file_paths, image_sizes_width, image_sizes_height, all_img_details = process_file_for_image_creation(file_path, prepare_for_review, input_folder, create_images=True) else: image_file_paths, image_sizes_width, image_sizes_height, all_img_details = process_file_for_image_creation(file_path, prepare_for_review, input_folder, create_images=False) page_sizes, original_cropboxes = create_page_size_objects(pymupdf_doc, image_sizes_width, image_sizes_height, image_file_paths) #Create base version of the annotation object that doesn't have any annotations in it if (not all_annotations_object) & (prepare_for_review == True): all_annotations_object = [] for image_path in image_file_paths: annotation = {} annotation["image"] = image_path annotation["boxes"] = [] all_annotations_object.append(annotation) elif is_pdf_or_image(file_path): # Alternatively, if it's an image # Check if the file is an image type and the user selected text ocr option if file_extension in ['.jpg', '.jpeg', '.png'] and in_redact_method == text_ocr_option: in_redact_method = tesseract_ocr_option # Convert image to a pymupdf document pymupdf_doc = pymupdf.open() # Create a new empty document img = Image.open(file_path) # Open the image file rect = pymupdf.Rect(0, 0, img.width, img.height) # Create a rectangle for the image pymupdf_page = pymupdf_doc.new_page(width=img.width, height=img.height) # Add a new page pymupdf_page.insert_image(rect, filename=file_path) # Insert the image into the page pymupdf_page = pymupdf_doc.load_page(0) file_path_str = str(file_path) image_file_paths, image_sizes_width, image_sizes_height, all_img_details = process_file_for_image_creation(file_path_str, prepare_for_review, input_folder, create_images=True) # Create a page_sizes_object page_sizes, original_cropboxes = create_page_size_objects(pymupdf_doc, image_sizes_width, image_sizes_height, image_file_paths) converted_file_path = output_folder + file_name_with_ext pymupdf_doc.save(converted_file_path, garbage=4, deflate=True, clean=True) elif file_extension in ['.csv']: if '_review_file' in file_path_without_ext: #print("file_path:", file_path) review_file_csv = read_file(file_path) all_annotations_object = convert_review_df_to_annotation_json(review_file_csv, image_file_paths, page_sizes) json_from_csv = True print("Converted CSV review file to image annotation object") elif '_ocr_output' in file_path_without_ext: all_line_level_ocr_results_df = read_file(file_path) json_from_csv = False # NEW IF STATEMENT # If the file name ends with .json, check if we are loading for review. If yes, assume it is an annoations object, overwrite the current annotations object. If false, assume this is a Textract object, load in to Textract if (file_extension in ['.json']) | (json_from_csv == True): if (file_extension in ['.json']) & (prepare_for_review == True): if isinstance(file_path, str): with open(file_path, 'r') as json_file: all_annotations_object = json.load(json_file) else: # Assuming file_path is a NamedString or similar all_annotations_object = json.loads(file_path) # Use loads for string content # Assume it's a textract json elif (file_extension in ['.json']) and (prepare_for_review != True): print("Saving Textract output") # Copy it to the output folder so it can be used later. output_textract_json_file_name = file_path_without_ext if not file_path.endswith("_textract.json"): output_textract_json_file_name = file_path_without_ext + "_textract.json" else: output_textract_json_file_name = file_path_without_ext + ".json" out_textract_path = os.path.join(output_folder, output_textract_json_file_name) # Use shutil to copy the file directly shutil.copy2(file_path, out_textract_path) # Preserves metadata textract_output_found = True continue # NEW IF STATEMENT # If you have an annotations object from the above code if all_annotations_object: # Get list of page numbers image_file_paths_pages = [ int(re.search(r'_(\d+)\.png$', os.path.basename(s)).group(1)) for s in image_file_paths if re.search(r'_(\d+)\.png$', os.path.basename(s)) ] image_file_paths_pages = [int(i) for i in image_file_paths_pages] # If PDF pages have been converted to image files, replace the current image paths in the json to this. if image_file_paths: for i, image_file_path in enumerate(image_file_paths): if i < len(all_annotations_object): annotation = all_annotations_object[i] else: annotation = {} all_annotations_object.append(annotation) try: if not annotation: annotation = {"image":"", "boxes": []} annotation_page_number = int(re.search(r'_(\d+)\.png$', image_file_path).group(1)) else: annotation_page_number = int(re.search(r'_(\d+)\.png$', annotation["image"]).group(1)) except Exception as e: print("Extracting page number from image failed due to:", e) annotation_page_number = 0 # Check if the annotation page number exists in the image file paths pages if annotation_page_number in image_file_paths_pages: # Set the correct image page directly since we know it's in the list correct_image_page = annotation_page_number annotation["image"] = image_file_paths[correct_image_page] else: print("Page", annotation_page_number, "image file not found.") all_annotations_object[i] = annotation if isinstance(in_fully_redacted_list, list): in_fully_redacted_list = pd.DataFrame(data={"fully_redacted_pages_list":in_fully_redacted_list}) # Get list of pages that are to be fully redacted and redact them if not in_fully_redacted_list.empty: print("Redacting whole pages") for i, image in enumerate(image_file_paths): page = pymupdf_doc.load_page(i) rect_height = page.rect.height rect_width = page.rect.width whole_page_img_annotation_box = redact_whole_pymupdf_page(rect_height, rect_width, image, page, custom_colours = False, border = 5, image_dimensions={"image_width":image_sizes_width[i], "image_height":image_sizes_height[i]}) all_annotations_object.append(whole_page_img_annotation_box) # Write the response to a JSON file in output folder out_folder = output_folder + file_path_without_ext + ".json" # with open(out_folder, 'w') as json_file: # json.dump(all_annotations_object, json_file, separators=(",", ":")) continue # If it's a zip, it could be extract from a Textract bulk API call. Check it's this, and load in json if found elif file_extension in ['.zip']: # Assume it's a Textract response object. Copy it to the output folder so it can be used later. out_folder = os.path.join(output_folder, file_path_without_ext + "_textract.json") # Use shutil to copy the file directly # Open the ZIP file to check its contents with zipfile.ZipFile(file_path, 'r') as zip_ref: json_files = [f for f in zip_ref.namelist() if f.lower().endswith('.json')] if len(json_files) == 1: # Ensure only one JSON file exists json_filename = json_files[0] # Extract the JSON file to the same directory as the ZIP file extracted_path = os.path.join(os.path.dirname(file_path), json_filename) zip_ref.extract(json_filename, os.path.dirname(file_path)) # Move the extracted JSON to the intended output location shutil.move(extracted_path, out_folder) textract_output_found = True else: print(f"Skipping {file_path}: Expected 1 JSON file, found {len(json_files)}") elif file_extension in ['.csv'] and "ocr_output" in file_path: continue # Must be something else, return with error message else: if in_redact_method == tesseract_ocr_option or in_redact_method == textract_option: if is_pdf_or_image(file_path) == False: out_message = "Please upload a PDF file or image file (JPG, PNG) for image analysis." print(out_message) raise Exception(out_message) elif in_redact_method == text_ocr_option: if is_pdf(file_path) == False: out_message = "Please upload a PDF file for text analysis." print(out_message) raise Exception(out_message) converted_file_paths.append(converted_file_path) image_file_paths.extend(image_file_path) toc = time.perf_counter() out_time = f"File '{file_path_without_ext}' prepared in {toc - tic:0.1f} seconds." print(out_time) out_message.append(out_time) combined_out_message = '\n'.join(out_message) number_of_pages = len(page_sizes)#len(image_file_paths) return combined_out_message, converted_file_paths, image_file_paths, number_of_pages, number_of_pages, pymupdf_doc, all_annotations_object, review_file_csv, original_cropboxes, page_sizes, textract_output_found, all_img_details, all_line_level_ocr_results_df def convert_text_pdf_to_img_pdf(in_file_path:str, out_text_file_path:List[str], image_dpi:float=image_dpi, output_folder:str=OUTPUT_FOLDER, input_folder:str=INPUT_FOLDER): file_path_without_ext = get_file_name_without_type(in_file_path) out_file_paths = out_text_file_path # Convert annotated text pdf back to image to give genuine redactions pdf_text_image_paths, image_sizes_width, image_sizes_height, all_img_details = process_file_for_image_creation(out_file_paths[0], input_folder=input_folder) out_text_image_file_path = output_folder + file_path_without_ext + "_text_redacted_as_img.pdf" pdf_text_image_paths[0].save(out_text_image_file_path, "PDF" ,resolution=image_dpi, save_all=True, append_images=pdf_text_image_paths[1:]) out_file_paths = [out_text_image_file_path] out_message = "PDF " + file_path_without_ext + " converted to image-based file." print(out_message) return out_message, out_file_paths def join_values_within_threshold(df1:pd.DataFrame, df2:pd.DataFrame): # Threshold for matching threshold = 5 # Perform a cross join df1['key'] = 1 df2['key'] = 1 merged = pd.merge(df1, df2, on='key').drop(columns=['key']) # Apply conditions for all columns conditions = ( (abs(merged['xmin_x'] - merged['xmin_y']) <= threshold) & (abs(merged['xmax_x'] - merged['xmax_y']) <= threshold) & (abs(merged['ymin_x'] - merged['ymin_y']) <= threshold) & (abs(merged['ymax_x'] - merged['ymax_y']) <= threshold) ) # Filter rows that satisfy all conditions filtered = merged[conditions] # Drop duplicates if needed (e.g., keep only the first match for each row in df1) result = filtered.drop_duplicates(subset=['xmin_x', 'xmax_x', 'ymin_x', 'ymax_x']) # Merge back into the original DataFrame (if necessary) final_df = pd.merge(df1, result, left_on=['xmin', 'xmax', 'ymin', 'ymax'], right_on=['xmin_x', 'xmax_x', 'ymin_x', 'ymax_x'], how='left') # Clean up extra columns final_df = final_df.drop(columns=['key']) def remove_duplicate_images_with_blank_boxes(data: List[dict]) -> List[dict]: ''' Remove items from the annotator object where the same page exists twice. ''' # Group items by 'image' image_groups = defaultdict(list) for item in data: image_groups[item['image']].append(item) # Process each group to prioritize items with non-empty boxes result = [] for image, items in image_groups.items(): # Filter items with non-empty boxes non_empty_boxes = [item for item in items if item.get('boxes')] # Remove 'text' elements from boxes (deprecated) #for item in non_empty_boxes: # if 'boxes' in item: # item['boxes'] = [{k: v for k, v in box.items() if k != 'text'} for box in item['boxes']] if non_empty_boxes: # Keep the first entry with non-empty boxes result.append(non_empty_boxes[0]) else: # If all items have empty or missing boxes, keep the first item result.append(items[0]) return result def divide_coordinates_by_page_sizes(review_file_df:pd.DataFrame, page_sizes_df:pd.DataFrame, xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax"): '''Convert data to same coordinate system. If all coordinates all greater than one, this is a absolute image coordinates - change back to relative coordinates.''' review_file_df_out = review_file_df if xmin in review_file_df.columns and not review_file_df.empty: coord_cols = [xmin, xmax, ymin, ymax] for col in coord_cols: review_file_df.loc[:, col] = pd.to_numeric(review_file_df[col], errors="coerce") review_file_df_orig = review_file_df.copy().loc[(review_file_df[xmin] <= 1) & (review_file_df[xmax] <= 1) & (review_file_df[ymin] <= 1) & (review_file_df[ymax] <= 1),:] #print("review_file_df_orig:", review_file_df_orig) review_file_df_div = review_file_df.loc[(review_file_df[xmin] > 1) & (review_file_df[xmax] > 1) & (review_file_df[ymin] > 1) & (review_file_df[ymax] > 1),:] #print("review_file_df_div:", review_file_df_div) review_file_df_div.loc[:, "page"] = pd.to_numeric(review_file_df_div["page"], errors="coerce") if "image_width" not in review_file_df_div.columns and not page_sizes_df.empty: page_sizes_df["image_width"] = page_sizes_df["image_width"].replace("", pd.NA) page_sizes_df["image_height"] = page_sizes_df["image_height"].replace("", pd.NA) review_file_df_div = review_file_df_div.merge(page_sizes_df[["page", "image_width", "image_height", "mediabox_width", "mediabox_height"]], on="page", how="left") if "image_width" in review_file_df_div.columns: if review_file_df_div["image_width"].isna().all(): # Check if all are NaN values. If so, assume we only have mediabox coordinates available review_file_df_div["image_width"] = review_file_df_div["image_width"].fillna(review_file_df_div["mediabox_width"]).infer_objects() review_file_df_div["image_height"] = review_file_df_div["image_height"].fillna(review_file_df_div["mediabox_height"]).infer_objects() convert_type_cols = ["image_width", "image_height", xmin, xmax, ymin, ymax] review_file_df_div[convert_type_cols] = review_file_df_div[convert_type_cols].apply(pd.to_numeric, errors="coerce") review_file_df_div[xmin] = review_file_df_div[xmin] / review_file_df_div["image_width"] review_file_df_div[xmax] = review_file_df_div[xmax] / review_file_df_div["image_width"] review_file_df_div[ymin] = review_file_df_div[ymin] / review_file_df_div["image_height"] review_file_df_div[ymax] = review_file_df_div[ymax] / review_file_df_div["image_height"] # Concatenate the original and modified DataFrames dfs_to_concat = [df for df in [review_file_df_orig, review_file_df_div] if not df.empty] if dfs_to_concat: # Ensure there's at least one non-empty DataFrame review_file_df_out = pd.concat(dfs_to_concat) else: review_file_df_out = review_file_df # Return an original DataFrame instead of raising an error # Only sort if the DataFrame is not empty and contains the required columns required_sort_columns = {"page", xmin, ymin} if not review_file_df_out.empty and required_sort_columns.issubset(review_file_df_out.columns): review_file_df_out.sort_values(["page", ymin, xmin], inplace=True) review_file_df_out.drop(["image_width", "image_height", "mediabox_width", "mediabox_height"], axis=1, errors="ignore") return review_file_df_out def multiply_coordinates_by_page_sizes(review_file_df: pd.DataFrame, page_sizes_df: pd.DataFrame, xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax"): if xmin in review_file_df.columns and not review_file_df.empty: coord_cols = [xmin, xmax, ymin, ymax] for col in coord_cols: review_file_df.loc[:, col] = pd.to_numeric(review_file_df[col], errors="coerce") # Separate absolute vs relative coordinates review_file_df_orig = review_file_df.loc[ (review_file_df[xmin] > 1) & (review_file_df[xmax] > 1) & (review_file_df[ymin] > 1) & (review_file_df[ymax] > 1), :].copy() review_file_df = review_file_df.loc[ (review_file_df[xmin] <= 1) & (review_file_df[xmax] <= 1) & (review_file_df[ymin] <= 1) & (review_file_df[ymax] <= 1), :].copy() if review_file_df.empty: return review_file_df_orig # If nothing is left, return the original absolute-coordinates DataFrame review_file_df.loc[:, "page"] = pd.to_numeric(review_file_df["page"], errors="coerce") if "image_width" not in review_file_df.columns and not page_sizes_df.empty: page_sizes_df[['image_width', 'image_height']] = page_sizes_df[['image_width','image_height']].replace("", pd.NA) # Ensure proper NA handling review_file_df = review_file_df.merge(page_sizes_df, on="page", how="left") if "image_width" in review_file_df.columns: # Split into rows with/without image size info review_file_df_not_na = review_file_df.loc[review_file_df["image_width"].notna()].copy() review_file_df_na = review_file_df.loc[review_file_df["image_width"].isna()].copy() if not review_file_df_not_na.empty: convert_type_cols = ["image_width", "image_height", xmin, xmax, ymin, ymax] review_file_df_not_na[convert_type_cols] = review_file_df_not_na[convert_type_cols].apply(pd.to_numeric, errors="coerce") # Multiply coordinates by image sizes review_file_df_not_na[xmin] *= review_file_df_not_na["image_width"] review_file_df_not_na[xmax] *= review_file_df_not_na["image_width"] review_file_df_not_na[ymin] *= review_file_df_not_na["image_height"] review_file_df_not_na[ymax] *= review_file_df_not_na["image_height"] # Concatenate the modified and unmodified data review_file_df = pd.concat([df for df in [review_file_df_not_na, review_file_df_na] if not df.empty]) # Merge with the original absolute-coordinates DataFrame dfs_to_concat = [df for df in [review_file_df_orig, review_file_df] if not df.empty] if dfs_to_concat: # Ensure there's at least one non-empty DataFrame review_file_df = pd.concat(dfs_to_concat) else: review_file_df = pd.DataFrame() # Return an empty DataFrame instead of raising an error # Only sort if the DataFrame is not empty and contains the required columns required_sort_columns = {"page", "xmin", "ymin"} if not review_file_df.empty and required_sort_columns.issubset(review_file_df.columns): review_file_df.sort_values(["page", "xmin", "ymin"], inplace=True) return review_file_df def do_proximity_match_by_page_for_text(df1:pd.DataFrame, df2:pd.DataFrame): ''' Match text from one dataframe to another based on proximity matching of coordinates page by page. ''' if not 'text' in df2.columns: df2['text'] = '' if not 'text' in df1.columns: df1['text'] = '' # Create a unique key based on coordinates and label for exact merge merge_keys = ['xmin', 'ymin', 'xmax', 'ymax', 'label', 'page'] df1['key'] = df1[merge_keys].astype(str).agg('_'.join, axis=1) df2['key'] = df2[merge_keys].astype(str).agg('_'.join, axis=1) # Attempt exact merge first merged_df = df1.merge(df2[['key', 'text']], on='key', how='left', suffixes=('', '_duplicate')) # If a match is found, keep that text; otherwise, keep the original df1 text merged_df['text'] = np.where( merged_df['text'].isna() | (merged_df['text'] == ''), merged_df.pop('text_duplicate'), merged_df['text'] ) # Define tolerance for proximity matching tolerance = 0.02 # Precompute KDTree for each page in df2 page_trees = {} for page in df2['page'].unique(): df2_page = df2[df2['page'] == page] coords = df2_page[['xmin', 'ymin', 'xmax', 'ymax']].values if np.all(np.isfinite(coords)) and len(coords) > 0: page_trees[page] = (cKDTree(coords), df2_page) # Perform proximity matching for i, row in df1.iterrows(): page_number = row['page'] if page_number in page_trees: tree, df2_page = page_trees[page_number] # Query KDTree for nearest neighbor dist, idx = tree.query([row[['xmin', 'ymin', 'xmax', 'ymax']].values], distance_upper_bound=tolerance) if dist[0] < tolerance and idx[0] < len(df2_page): merged_df.at[i, 'text'] = df2_page.iloc[idx[0]]['text'] # Drop the temporary key column merged_df.drop(columns=['key'], inplace=True) return merged_df def do_proximity_match_all_pages_for_text(df1:pd.DataFrame, df2:pd.DataFrame, threshold:float=0.03): ''' Match text from one dataframe to another based on proximity matching of coordinates across all pages. ''' if not 'text' in df2.columns: df2['text'] = '' if not 'text' in df1.columns: df1['text'] = '' for col in ['xmin', 'ymin', 'xmax', 'ymax']: df1[col] = pd.to_numeric(df1[col], errors='coerce') for col in ['xmin', 'ymin', 'xmax', 'ymax']: df2[col] = pd.to_numeric(df2[col], errors='coerce') # Create a unique key based on coordinates and label for exact merge merge_keys = ['xmin', 'ymin', 'xmax', 'ymax', 'label', 'page'] df1['key'] = df1[merge_keys].astype(str).agg('_'.join, axis=1) df2['key'] = df2[merge_keys].astype(str).agg('_'.join, axis=1) # Attempt exact merge first, renaming df2['text'] to avoid suffixes merged_df = df1.merge(df2[['key', 'text']], on='key', how='left', suffixes=('', '_duplicate')) # If a match is found, keep that text; otherwise, keep the original df1 text merged_df['text'] = np.where( merged_df['text'].isna() | (merged_df['text'] == ''), merged_df.pop('text_duplicate'), merged_df['text'] ) # Handle missing matches using a proximity-based approach # Convert coordinates to numpy arrays for KDTree lookup query_coords = np.array(df1[['xmin', 'ymin', 'xmax', 'ymax']].values, dtype=float) # Check for NaN or infinite values in query_coords and filter them out finite_mask = np.isfinite(query_coords).all(axis=1) if not finite_mask.all(): #print("Warning: query_coords contains non-finite values. Filtering out non-finite entries.") query_coords = query_coords[finite_mask] # Filter out rows with NaN or infinite values else: pass # Proceed only if query_coords is not empty if query_coords.size > 0: # Ensure df2 is filtered for finite values before creating the KDTree finite_mask_df2 = np.isfinite(df2[['xmin', 'ymin', 'xmax', 'ymax']].values).all(axis=1) df2_finite = df2[finite_mask_df2] # Create the KDTree with the filtered data tree = cKDTree(df2_finite[['xmin', 'ymin', 'xmax', 'ymax']].values) # Find nearest neighbors within a reasonable tolerance (e.g., 1% of page) tolerance = threshold distances, indices = tree.query(query_coords, distance_upper_bound=tolerance) # Assign text values where matches are found for i, (dist, idx) in enumerate(zip(distances, indices)): if dist < tolerance and idx < len(df2_finite): merged_df.at[i, 'text'] = df2_finite.iloc[idx]['text'] # Drop the temporary key column merged_df.drop(columns=['key'], inplace=True) return merged_df def _extract_page_number(image_path: Any) -> int: """Helper function to safely extract page number.""" if not isinstance(image_path, str): return 1 match = IMAGE_NUM_REGEX.search(image_path) if match: try: return int(match.group(1)) + 1 except (ValueError, TypeError): return 1 return 1 def convert_annotation_data_to_dataframe(all_annotations: List[Dict[str, Any]]): ''' Convert annotation list to DataFrame using Pandas explode and json_normalize. ''' if not all_annotations: # Return an empty DataFrame with the expected schema if input is empty return pd.DataFrame(columns=["image", "page", "xmin", "xmax", "ymin", "ymax", "text", "id"]) # 1. Create initial DataFrame from the list of annotations # Use list comprehensions with .get() for robustness df = pd.DataFrame({ "image": [anno.get("image") for anno in all_annotations], # Ensure 'boxes' defaults to an empty list if missing or None "boxes": [anno.get("boxes") if isinstance(anno.get("boxes"), list) else [] for anno in all_annotations] }) # 2. Calculate the page number using the helper function df['page'] = df['image'].apply(_extract_page_number) # 3. Handle empty 'boxes' lists *before* exploding. # Explode removes rows where the list is empty. We want to keep them # as rows with NA values. Replace empty lists with a list containing # a single placeholder dictionary. placeholder_box = {"xmin": pd.NA, "xmax": pd.NA, "ymin": pd.NA, "ymax": pd.NA, "text": pd.NA, "id": pd.NA} df['boxes'] = df['boxes'].apply(lambda x: x if x else [placeholder_box]) # 4. Explode the 'boxes' column. Each item in the list becomes a new row. df_exploded = df.explode('boxes', ignore_index=True) # 5. Normalize the 'boxes' column (which now contains dictionaries or the placeholder) # This turns the dictionaries into separate columns. # Check for NaNs or non-dict items just in case, though placeholder handles most cases. mask = df_exploded['boxes'].notna() & df_exploded['boxes'].apply(isinstance, args=(dict,)) normalized_boxes = pd.json_normalize(df_exploded.loc[mask, 'boxes']) # 6. Combine the base data (image, page) with the normalized box data # Use the index of the exploded frame (where mask is True) to ensure correct alignment final_df = df_exploded.loc[mask, ['image', 'page']].reset_index(drop=True).join(normalized_boxes) # --- Optional: Handle rows that might have had non-dict items in 'boxes' --- # If there were rows filtered out by 'mask', you might want to add them back # with NA values for box columns. However, the placeholder strategy usually # prevents this from being necessary. # 7. Ensure essential columns exist and set column order essential_box_cols = ["xmin", "xmax", "ymin", "ymax", "text", "id"] for col in essential_box_cols: if col not in final_df.columns: final_df[col] = pd.NA # Add column with NA if it wasn't present in any box base_cols = ["image", "page"] extra_box_cols = [col for col in final_df.columns if col not in base_cols and col not in essential_box_cols] final_col_order = base_cols + essential_box_cols + sorted(extra_box_cols) # Reindex to ensure consistent column order and presence of essential columns # Using fill_value=pd.NA isn't strictly needed here as we added missing columns above, # but it's good practice if columns could be missing for other reasons. final_df = final_df.reindex(columns=final_col_order, fill_value=pd.NA) return final_df def create_annotation_dicts_from_annotation_df( all_image_annotations_df: pd.DataFrame, page_sizes: List[Dict[str, Any]] ) -> List[Dict[str, Any]]: ''' Convert annotation DataFrame back to list of dicts using dictionary lookup. Ensures all images from page_sizes are present without duplicates. ''' # 1. Create a dictionary keyed by image path for efficient lookup & update # Initialize with all images from page_sizes. Use .get for safety. image_dict: Dict[str, Dict[str, Any]] = {} for item in page_sizes: image_path = item.get("image_path") if image_path: # Only process if image_path exists and is not None/empty image_dict[image_path] = {"image": image_path, "boxes": []} # Check if the DataFrame is empty or lacks necessary columns if all_image_annotations_df.empty or 'image' not in all_image_annotations_df.columns: #print("Warning: Annotation DataFrame is empty or missing 'image' column.") return list(image_dict.values()) # Return based on page_sizes only # 2. Define columns to extract for boxes and check availability # Make sure these columns actually exist in the DataFrame box_cols = ['xmin', 'ymin', 'xmax', 'ymax', 'color', 'label', 'text', 'id'] available_cols = [col for col in box_cols if col in all_image_annotations_df.columns] if 'text' in all_image_annotations_df.columns: all_image_annotations_df.loc[all_image_annotations_df['text'].isnull(), 'text'] = '' if not available_cols: print(f"Warning: None of the expected box columns ({box_cols}) found in DataFrame.") return list(image_dict.values()) # Return based on page_sizes only # 3. Group the DataFrame by image and update the dictionary # Drop rows where essential coordinates might be NA (adjust if NA is meaningful) coord_cols = ['xmin', 'ymin', 'xmax', 'ymax'] valid_box_df = all_image_annotations_df.dropna( subset=[col for col in coord_cols if col in available_cols] ).copy() # Use .copy() to avoid SettingWithCopyWarning if modifying later # Check if any valid boxes remain after dropping NAs if valid_box_df.empty: print("Warning: No valid annotation rows found in DataFrame after dropping NA coordinates.") return list(image_dict.values()) # Process groups try: for image_path, group in valid_box_df.groupby('image', observed=True, sort=False): # Check if this image path exists in our target dictionary (from page_sizes) if image_path in image_dict: # Convert the relevant columns of the group to a list of dicts # Using only columns that are actually available boxes = group[available_cols].to_dict(orient='records') # Update the 'boxes' list in the dictionary image_dict[image_path]['boxes'] = boxes # Else: Image found in DataFrame but not required by page_sizes; ignore it. except KeyError: # This shouldn't happen due to the 'image' column check above, but handle defensively print("Error: Issue grouping DataFrame by 'image'.") return list(image_dict.values()) # 4. Convert the dictionary values back into the final list format result = list(image_dict.values()) return result def convert_annotation_json_to_review_df(all_annotations: List[dict], redaction_decision_output: pd.DataFrame = pd.DataFrame(), page_sizes: List[dict] = [], do_proximity_match: bool = True) -> pd.DataFrame: ''' Convert the annotation json data to a dataframe format. Add on any text from the initial review_file dataframe by joining based on 'id' if available in both sources, otherwise falling back to joining on pages/co-ordinates (if option selected). ''' # 1. Convert annotations to DataFrame # Ensure convert_annotation_data_to_dataframe populates the 'id' column # if 'id' exists in the dictionaries within all_annotations. review_file_df = convert_annotation_data_to_dataframe(all_annotations) # Only keep rows in review_df where there are coordinates review_file_df.dropna(subset='xmin', axis=0, inplace=True) # Exit early if the initial conversion results in an empty DataFrame if review_file_df.empty: # Define standard columns for an empty return DataFrame check_columns = ["image", "page", "label", "color", "xmin", "ymin", "xmax", "ymax", "text", "id"] # Ensure 'id' is included if it might have been expected return pd.DataFrame(columns=[col for col in check_columns if col != 'id' or 'id' in review_file_df.columns]) # 2. Handle page sizes if provided if not page_sizes: page_sizes_df = pd.DataFrame(page_sizes) # Ensure it's a DataFrame # Safely convert page column to numeric page_sizes_df["page"] = pd.to_numeric(page_sizes_df["page"], errors="coerce") page_sizes_df.dropna(subset=["page"], inplace=True) # Drop rows where conversion failed page_sizes_df["page"] = page_sizes_df["page"].astype(int) # Convert to int after handling errors/NaNs # Apply coordinate division if page_sizes_df is not empty after processing if not page_sizes_df.empty: # Ensure 'page' column in review_file_df is numeric for merging if 'page' in review_file_df.columns: review_file_df['page'] = pd.to_numeric(review_file_df['page'], errors='coerce') # Drop rows with invalid pages before division review_file_df.dropna(subset=['page'], inplace=True) review_file_df['page'] = review_file_df['page'].astype(int) review_file_df = divide_coordinates_by_page_sizes(review_file_df, page_sizes_df) print("review_file_df after coord divide:", review_file_df) # Also apply to redaction_decision_output if it's not empty and has page numbers if not redaction_decision_output.empty and 'page' in redaction_decision_output.columns: redaction_decision_output['page'] = pd.to_numeric(redaction_decision_output['page'], errors='coerce') # Drop rows with invalid pages before division redaction_decision_output.dropna(subset=['page'], inplace=True) redaction_decision_output['page'] = redaction_decision_output['page'].astype(int) redaction_decision_output = divide_coordinates_by_page_sizes(redaction_decision_output, page_sizes_df) print("redaction_decision_output after coord divide:", redaction_decision_output) else: print("Warning: Page sizes DataFrame became empty after processing, skipping coordinate division.") # 3. Join additional data from redaction_decision_output if provided if not redaction_decision_output.empty: # --- NEW LOGIC: Prioritize joining by 'id' --- id_col_exists_in_review = 'id' in review_file_df.columns id_col_exists_in_redaction = 'id' in redaction_decision_output.columns joined_by_id = False # Flag to track if ID join was successful if id_col_exists_in_review and id_col_exists_in_redaction: #print("Attempting to join data based on 'id' column.") try: # Ensure 'id' columns are of compatible types (e.g., string) to avoid merge errors review_file_df['id'] = review_file_df['id'].astype(str) # Make a copy to avoid SettingWithCopyWarning if redaction_decision_output is used elsewhere redaction_copy = redaction_decision_output.copy() redaction_copy['id'] = redaction_copy['id'].astype(str) # Select columns to merge from redaction output. # Primarily interested in 'text', but keep 'id' for the merge key. # Add other columns from redaction_copy if needed. cols_to_merge = ['id'] if 'text' in redaction_copy.columns: cols_to_merge.append('text') else: print("Warning: 'text' column not found in redaction_decision_output. Cannot merge text using 'id'.") # Perform a left merge to keep all annotations and add matching text # Suffixes prevent collision if 'text' already exists and we want to compare/choose original_cols = review_file_df.columns.tolist() merged_df = pd.merge( review_file_df, redaction_copy[cols_to_merge], on='id', how='left', suffixes=('', '_redaction') # Suffix applied to columns from right df if names clash ) # Update the original 'text' column. Prioritize text from redaction output. # If redaction output had 'text', a 'text_redaction' column now exists. if 'text_redaction' in merged_df.columns: if 'text' not in merged_df.columns: # If review_file_df didn't have text initially merged_df['text'] = merged_df['text_redaction'] else: # Use text from redaction where available, otherwise keep original text merged_df['text'] = merged_df['text_redaction'].combine_first(merged_df['text']) # Remove the temporary column merged_df = merged_df.drop(columns=['text_redaction']) # Ensure final columns match original expectation + potentially new 'text' final_cols = original_cols if 'text' not in final_cols and 'text' in merged_df.columns: final_cols.append('text') # Make sure text column is kept if newly added # Reorder/select columns if necessary, ensuring 'id' is kept review_file_df = merged_df[[col for col in final_cols if col in merged_df.columns] + (['id'] if 'id' not in final_cols else [])] #print("Successfully joined data using 'id'.") joined_by_id = True except Exception as e: print(f"Error during 'id'-based merge: {e}. Falling back to proximity match if enabled.") # Fall through to proximity match below if an error occurred # --- Fallback to proximity match --- if not joined_by_id and do_proximity_match: if not id_col_exists_in_review or not id_col_exists_in_redaction: print("Could not join by 'id' (column missing in one or both sources).") print("Performing proximity match to add text data.") # Match text to review file using proximity review_file_df = do_proximity_match_all_pages_for_text(df1=review_file_df.copy(), df2=redaction_decision_output.copy()) elif not joined_by_id and not do_proximity_match: print("Skipping joining text data (ID join not possible, proximity match disabled).") # --- End of join logic --- # 4. Ensure required columns exist, filling with blank if they don't # Define base required columns, 'id' might or might not be present initially required_columns = ["image", "page", "label", "color", "xmin", "ymin", "xmax", "ymax", "text"] # Add 'id' to required list if it exists in the dataframe at this point if 'id' in review_file_df.columns: required_columns.append('id') for col in required_columns: if col not in review_file_df.columns: # Decide default value based on column type (e.g., '' for text, np.nan for numeric?) # Using '' for simplicity here. review_file_df[col] = '' # Select and order the final set of columns review_file_df = review_file_df[required_columns] # 5. Final processing and sorting # If colours are saved as list, convert to tuple if 'color' in review_file_df.columns: review_file_df["color"] = review_file_df["color"].apply(lambda x: tuple(x) if isinstance(x, list) else x) # Sort the results sort_columns = ['page', 'ymin', 'xmin', 'label'] # Ensure sort columns exist before sorting valid_sort_columns = [col for col in sort_columns if col in review_file_df.columns] if valid_sort_columns: review_file_df = review_file_df.sort_values(valid_sort_columns) return review_file_df def fill_missing_box_ids(data_input: dict) -> dict: """ Generates unique alphanumeric IDs for bounding boxes in an input dictionary where the 'id' is missing, blank, or not a 12-character string. Args: data_input (dict): The input dictionary containing 'image' and 'boxes' keys. 'boxes' should be a list of dictionaries, each potentially with an 'id' key. Returns: dict: The input dictionary with missing/invalid box IDs filled. Note: The function modifies the input dictionary in place. """ # --- Input Validation --- if not isinstance(data_input, dict): raise TypeError("Input 'data_input' must be a dictionary.") #if 'boxes' not in data_input or not isinstance(data_input.get('boxes'), list): # raise ValueError("Input dictionary must contain a 'boxes' key with a list value.") boxes = data_input#['boxes'] id_length = 12 character_set = string.ascii_letters + string.digits # a-z, A-Z, 0-9 # --- Get Existing IDs to Ensure Uniqueness --- # Collect all valid existing IDs first existing_ids = set() #for box in boxes: # Check if 'id' exists, is a string, and is the correct length box_id = boxes.get('id') if isinstance(box_id, str) and len(box_id) == id_length: existing_ids.add(box_id) # --- Identify and Fill Rows Needing IDs --- generated_ids_set = set() # Keep track of IDs generated *in this run* num_filled = 0 #for box in boxes: box_id = boxes.get('id') # Check if ID needs to be generated # Needs ID if: key is missing, value is None, value is not a string, # value is an empty string after stripping whitespace, or value is a string # but not of the correct length. needs_new_id = ( box_id is None or not isinstance(box_id, str) or box_id.strip() == "" or len(box_id) != id_length ) if needs_new_id: # Generate a unique ID attempts = 0 while True: candidate_id = ''.join(random.choices(character_set, k=id_length)) # Check against *all* existing valid IDs and *newly* generated ones in this run if candidate_id not in existing_ids and candidate_id not in generated_ids_set: generated_ids_set.add(candidate_id) boxes['id'] = candidate_id # Assign the new ID directly to the box dict num_filled += 1 break # Found a unique ID attempts += 1 # Safety break for unlikely infinite loop (though highly improbable with 12 chars) if attempts > len(boxes) * 100 + 1000: raise RuntimeError(f"Failed to generate a unique ID after {attempts} attempts. Check ID length or existing IDs.") if num_filled > 0: pass #print(f"Successfully filled {num_filled} missing or invalid box IDs.") else: pass #print("No missing or invalid box IDs found.") # The input dictionary 'data_input' has been modified in place return data_input def fill_missing_ids(df: pd.DataFrame, column_name: str = 'id', length: int = 12) -> pd.DataFrame: """ Generates unique alphanumeric IDs for rows in a DataFrame column where the value is missing (NaN, None) or an empty string. Args: df (pd.DataFrame): The input Pandas DataFrame. column_name (str): The name of the column to check and fill (defaults to 'id'). This column will be added if it doesn't exist. length (int): The desired length of the generated IDs (defaults to 12). Cannot exceed the limits that guarantee uniqueness based on the number of IDs needed and character set size. Returns: pd.DataFrame: The DataFrame with missing/empty IDs filled in the specified column. Note: The function modifies the DataFrame in place. """ # --- Input Validation --- if not isinstance(df, pd.DataFrame): raise TypeError("Input 'df' must be a Pandas DataFrame.") if not isinstance(column_name, str) or not column_name: raise ValueError("'column_name' must be a non-empty string.") if not isinstance(length, int) or length <= 0: raise ValueError("'length' must be a positive integer.") # --- Ensure Column Exists --- if column_name not in df.columns: print(f"Column '{column_name}' not found. Adding it to the DataFrame.") df[column_name] = np.nan # Initialize with NaN # --- Identify Rows Needing IDs --- # Check for NaN, None, or empty strings ('') # Convert to string temporarily for robust empty string check, handle potential errors try: df[column_name] = df[column_name].astype(str) #handles NaN/None conversion, .str.strip() removes whitespace is_missing_or_empty = ( df[column_name].isna() #| (df[column_name].astype(str).str.strip() == '') #| (df[column_name] == "nan") | (df[column_name].astype(str).str.len() != length) ) except Exception as e: # Fallback if conversion to string fails (e.g., column contains complex objects) print(f"Warning: Could not perform reliable empty string check on column '{column_name}' due to data type issues. Checking for NaN/None only. Error: {e}") is_missing_or_empty = df[column_name].isna() rows_to_fill_index = df.index[is_missing_or_empty] num_needed = len(rows_to_fill_index) if num_needed == 0: #print(f"No missing or empty values found in column '{column_name}'.") return df print(f"Found {num_needed} rows requiring a unique ID in column '{column_name}'.") # --- Get Existing IDs to Ensure Uniqueness --- try: # Get all non-missing, non-empty string values from the column existing_ids = set(df.loc[~is_missing_or_empty, column_name].astype(str)) except Exception as e: print(f"Warning: Could not reliably get all existing string IDs from column '{column_name}' due to data type issues. Uniqueness check might be less strict. Error: {e}") # Fallback: Get only non-NaN IDs, potential type issues ignored existing_ids = set(df.loc[df[column_name].notna(), column_name]) # --- Generate Unique IDs --- character_set = string.ascii_letters + string.digits # a-z, A-Z, 0-9 generated_ids_set = set() # Keep track of IDs generated *in this run* new_ids_list = [] # Store the generated IDs in order max_possible_ids = len(character_set) ** length if num_needed > max_possible_ids: raise ValueError(f"Cannot generate {num_needed} unique IDs with length {length}. Maximum possible is {max_possible_ids}.") # Add a check for practical limits if needed, e.g., if num_needed is very close to max_possible_ids, generation could be slow. #print(f"Generating {num_needed} unique IDs of length {length}...") for i in range(num_needed): attempts = 0 while True: candidate_id = ''.join(random.choices(character_set, k=length)) # Check against *all* existing IDs and *newly* generated ones if candidate_id not in existing_ids and candidate_id not in generated_ids_set: generated_ids_set.add(candidate_id) new_ids_list.append(candidate_id) break # Found a unique ID attempts += 1 if attempts > num_needed * 100 and attempts > 1000 : # Safety break for unlikely infinite loop raise RuntimeError(f"Failed to generate a unique ID after {attempts} attempts. Check length and character set or existing IDs.") # Optional progress update for large numbers if (i + 1) % 1000 == 0: print(f"Generated {i+1}/{num_needed} IDs...") # --- Assign New IDs --- # Use the previously identified index to assign the new IDs correctly df.loc[rows_to_fill_index, column_name] = new_ids_list #print(f"Successfully filled {len(new_ids_list)} missing values in column '{column_name}'.") # The DataFrame 'df' has been modified in place return df def convert_review_df_to_annotation_json(review_file_df:pd.DataFrame, image_paths:List[Image.Image], page_sizes:List[dict]=[]) -> List[dict]: ''' Convert a review csv to a json file for use by the Gradio Annotation object. ''' # Make sure all relevant cols are float float_cols = ["page", "xmin", "xmax", "ymin", "ymax"] for col in float_cols: review_file_df.loc[:, col] = pd.to_numeric(review_file_df.loc[:, col], errors='coerce') # Convert relative co-ordinates into image coordinates for the image annotation output object if page_sizes: page_sizes_df = pd.DataFrame(page_sizes) page_sizes_df[["page"]] = page_sizes_df[["page"]].apply(pd.to_numeric, errors="coerce") review_file_df = multiply_coordinates_by_page_sizes(review_file_df, page_sizes_df) review_file_df = fill_missing_ids(review_file_df) if 'id' not in review_file_df.columns: review_file_df['id'] = '' review_file_df['id'] = review_file_df['id'].astype(str) # Keep only necessary columns review_file_df = review_file_df[["image", "page", "label", "color", "xmin", "ymin", "xmax", "ymax", "id", "text"]].drop_duplicates(subset=["image", "page", "xmin", "ymin", "xmax", "ymax", "label", "id"]) # If colours are saved as list, convert to tuple review_file_df.loc[:, "color"] = review_file_df.loc[:,"color"].apply(lambda x: tuple(x) if isinstance(x, list) else x) # Group the DataFrame by the 'image' column grouped_csv_pages = review_file_df.groupby('page') # Create a list to hold the JSON data json_data = [] for page_no, pdf_image_path in enumerate(page_sizes_df["image_path"]): reported_page_number = int(page_no + 1) if reported_page_number in review_file_df["page"].values: # Convert each relevant group to a list of box dictionaries selected_csv_pages = grouped_csv_pages.get_group(reported_page_number) annotation_boxes = selected_csv_pages.drop(columns=['image', 'page']).to_dict(orient='records') annotation = { "image": pdf_image_path, "boxes": annotation_boxes } else: annotation = {} annotation["image"] = pdf_image_path annotation["boxes"] = [] # Append the structured data to the json_data list json_data.append(annotation) return json_data