import boto3 from PIL import Image import io import json import pikepdf # Example: converting this single page to an image from pdf2image import convert_from_bytes from tools.custom_image_analyser_engine import OCRResult, CustomImageRecognizerResult def extract_textract_metadata(response): """Extracts metadata from an AWS Textract response.""" print("Document metadata:", response['DocumentMetadata']) request_id = response['ResponseMetadata']['RequestId'] pages = response['DocumentMetadata']['Pages'] #number_of_pages = response['DocumentMetadata']['NumberOfPages'] return str({ 'RequestId': request_id, 'Pages': pages #, #'NumberOfPages': number_of_pages }) def analyse_page_with_textract(pdf_page_bytes, page_no, client=""): ''' Analyse page with AWS Textract ''' if client == "": try: client = boto3.client('textract') except: print("Cannot connect to AWS Textract") return [], "" # Return an empty list and an empty string print("Analysing page with AWS Textract") response = client.analyze_document(Document={'Bytes': pdf_page_bytes}, FeatureTypes=["SIGNATURES"]) # Wrap the response with the page number in the desired format wrapped_response = { 'page_no': page_no, 'data': response } request_metadata = extract_textract_metadata(response) # Metadata comes out as a string # Return a list containing the wrapped response and the metadata return wrapped_response, request_metadata # Return as a list to match the desired structure def convert_pike_pdf_page_to_bytes(pdf, page_num): # Create a new empty PDF new_pdf = pikepdf.Pdf.new() # Specify the page number you want to extract (0-based index) page_num = 0 # Example: first page # Extract the specific page and add it to the new PDF new_pdf.pages.append(pdf.pages[page_num]) # Save the new PDF to a bytes buffer buffer = io.BytesIO() new_pdf.save(buffer) # Get the PDF bytes pdf_bytes = buffer.getvalue() # Now you can use the `pdf_bytes` to convert it to an image or further process buffer.close() #images = convert_from_bytes(pdf_bytes) #image = images[0] return pdf_bytes def json_to_ocrresult(json_data, page_width, page_height, page_no): ''' Convert the json response from textract to the OCRResult format used elsewhere in the code. Looks for lines, words, and signatures. Handwriting and signatures are set aside especially for later in case the user wants to override the default behaviour and redact all handwriting/signatures. ''' all_ocr_results = [] signature_or_handwriting_recogniser_results = [] signature_recogniser_results = [] handwriting_recogniser_results = [] signatures = [] handwriting = [] ocr_results_with_children = {} text_block={} i = 1 # Assuming json_data is structured as a dictionary with a "pages" key #if "pages" in json_data: # Find the specific page data page_json_data = json_data #next((page for page in json_data["pages"] if page["page_no"] == page_no), None) if "Blocks" in page_json_data: # Access the data for the specific page text_blocks = page_json_data["Blocks"] # Access the Blocks within the page data # This is a new page elif "page_no" in page_json_data: text_blocks = page_json_data["data"]["Blocks"] is_signature = False is_handwriting = False for text_block in text_blocks: if (text_block['BlockType'] == 'LINE') | (text_block['BlockType'] == 'SIGNATURE'): # (text_block['BlockType'] == 'WORD') | # Extract text and bounding box for the line line_bbox = text_block["Geometry"]["BoundingBox"] line_left = int(line_bbox["Left"] * page_width) line_top = int(line_bbox["Top"] * page_height) line_right = int((line_bbox["Left"] + line_bbox["Width"]) * page_width) line_bottom = int((line_bbox["Top"] + line_bbox["Height"]) * page_height) width_abs = int(line_bbox["Width"] * page_width) height_abs = int(line_bbox["Height"] * page_height) if text_block['BlockType'] == 'LINE': # Extract text and bounding box for the line line_text = text_block.get('Text', '') words = [] if 'Relationships' in text_block: for relationship in text_block['Relationships']: if relationship['Type'] == 'CHILD': for child_id in relationship['Ids']: child_block = next((block for block in text_blocks if block['Id'] == child_id), None) if child_block and child_block['BlockType'] == 'WORD': word_text = child_block.get('Text', '') word_bbox = child_block["Geometry"]["BoundingBox"] confidence = child_block.get('Confidence','') word_left = int(word_bbox["Left"] * page_width) word_top = int(word_bbox["Top"] * page_height) word_right = int((word_bbox["Left"] + word_bbox["Width"]) * page_width) word_bottom = int((word_bbox["Top"] + word_bbox["Height"]) * page_height) # Extract BoundingBox details word_width = word_bbox["Width"] word_height = word_bbox["Height"] # Convert proportional coordinates to absolute coordinates word_width_abs = int(word_width * page_width) word_height_abs = int(word_height * page_height) words.append({ 'text': word_text, 'bounding_box': (word_left, word_top, word_right, word_bottom) }) # Check for handwriting text_type = child_block.get("TextType", '') if text_type == "HANDWRITING": is_handwriting = True entity_name = "HANDWRITING" word_end = len(entity_name) recogniser_result = CustomImageRecognizerResult(entity_type=entity_name, text= word_text, score= confidence, start=0, end=word_end, left=word_left, top=word_top, width=word_width_abs, height=word_height_abs) if recogniser_result not in handwriting: handwriting.append(recogniser_result) print("Handwriting found:", handwriting[-1]) # If handwriting or signature, add to bounding box elif (text_block['BlockType'] == 'SIGNATURE'): line_text = "SIGNATURE" is_signature = True entity_name = "SIGNATURE" confidence = text_block['Confidence'] word_end = len(entity_name) recogniser_result = CustomImageRecognizerResult(entity_type=entity_name, text= line_text, score= confidence, start=0, end=word_end, left=line_left, top=line_top, width=width_abs, height=height_abs) if recogniser_result not in signatures: signatures.append(recogniser_result) #print("Signature found:", signatures[-1]) words = [] words.append({ 'text': line_text, 'bounding_box': (line_left, line_top, line_right, line_bottom) }) ocr_results_with_children["text_line_" + str(i)] = { "line": i, 'text': line_text, 'bounding_box': (line_left, line_top, line_right, line_bottom), 'words': words } # Create OCRResult with absolute coordinates ocr_result = OCRResult(line_text, line_left, line_top, width_abs, height_abs) all_ocr_results.append(ocr_result) is_signature_or_handwriting = is_signature | is_handwriting # If it is signature or handwriting, will overwrite the default behaviour of the PII analyser if is_signature_or_handwriting: if recogniser_result not in signature_or_handwriting_recogniser_results: signature_or_handwriting_recogniser_results.append(recogniser_result) if is_signature: if recogniser_result not in signature_recogniser_results: signature_recogniser_results.append(recogniser_result) if is_handwriting: if recogniser_result not in handwriting_recogniser_results: handwriting_recogniser_results.append(recogniser_result) i += 1 return all_ocr_results, signature_or_handwriting_recogniser_results, signature_recogniser_results, handwriting_recogniser_results, ocr_results_with_children