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from pdf2image import convert_from_path, pdfinfo_from_path
from tools.helper_functions import get_file_path_end, output_folder, detect_file_type
from PIL import Image
import os
import time
from gradio import Progress
from typing import List, Optional
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 convert_pdf_to_images(pdf_path:str, page_min:int = 0, progress=Progress(track_tqdm=True)):
# Get the number of pages in the PDF
page_count = pdfinfo_from_path(pdf_path)['Pages']
print("Number of pages in PDF: ", str(page_count))
images = []
# Open the PDF file
#for page_num in progress.tqdm(range(0,page_count), total=page_count, unit="pages", desc="Converting pages"):
for page_num in range(page_min,page_count): #progress.tqdm(range(0,page_count), total=page_count, unit="pages", desc="Converting pages"):
print("Converting page: ", str(page_num + 1))
# Convert one page to image
image = convert_from_path(pdf_path, first_page=page_num+1, last_page=page_num+1, dpi=300, use_cropbox=True, use_pdftocairo=False)
# If no images are returned, break the loop
if not image:
print("Conversion of page", str(page_num), "to file failed.")
break
# print("Conversion of page", str(page_num), "to file succeeded.")
# print("image:", image)
#image[0].save(pdf_path + "_" + str(page_num) + ".png", format="PNG")
images.extend(image)
print("PDF has been converted to images.")
# print("Images:", images)
return images
# %% Function to take in a file path, decide if it is an image or pdf, then process appropriately.
def process_file(file_path):
# 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 = [Image.open(file_path)]
# Load images from the file paths
# 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_object = convert_pdf_to_images(file_path)
else:
print(f"{file_path} is not an image or PDF file.")
img_object = ['']
return img_object
def prepare_image_or_text_pdf(
file_paths: List[str],
in_redact_method: str,
in_allow_list: Optional[List[List[str]]] = None,
latest_file_completed: int = 0,
out_message: List[str] = [],
first_loop_state: 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.
in_allow_list (Optional[List[List[str]]]): List of allowed terms for redaction.
latest_file_completed (int): Index of the last completed file.
out_message (List[str]): List to store output messages.
first_loop_state (bool): Flag indicating if this is the first iteration.
progress (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()
# If out message or out_file_paths are blank, change to a list so it can be appended to
if isinstance(out_message, str):
out_message = [out_message]
# If this is the first time around, set variables to 0/blank
if first_loop_state==True:
latest_file_completed = 0
out_message = []
out_file_paths = []
else:
print("Now attempting file:", str(latest_file_completed))
out_file_paths = []
if not file_paths:
file_paths = []
#out_file_paths = 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 >= len(file_paths):
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, out_file_paths
#in_allow_list_flat = [item for sublist in in_allow_list for item in sublist]
file_paths_loop = [file_paths[int(latest_file_completed)]]
#print("file_paths_loop:", str(file_paths_loop))
#for file in progress.tqdm(file_paths, desc="Preparing files"):
for file in file_paths_loop:
file_path = file.name
file_path_without_ext = get_file_path_end(file_path)
#print("file:", file_path)
file_extension = os.path.splitext(file_path)[1].lower()
# Check if the file is an image type
if file_extension in ['.jpg', '.jpeg', '.png']:
in_redact_method = "Image analysis"
#if file_path:
# file_path_without_ext = get_file_path_end(file_path)
if not file_path:
out_message = "No file selected"
print(out_message)
return out_message, out_file_paths
if in_redact_method == "Image analysis" or in_redact_method == "AWS Textract":
# Analyse and redact image-based pdf or image
if is_pdf_or_image(file_path) == False:
out_message = "Please upload a PDF file or image file (JPG, PNG) for image analysis."
print(out_message)
return out_message, out_file_paths
out_file_path = process_file(file_path)
#print("Out file path at image conversion step:", out_file_path)
elif in_redact_method == "Text analysis":
if is_pdf(file_path) == False:
out_message = "Please upload a PDF file for text analysis."
print(out_message)
return out_message, out_file_paths
out_file_path = file_path
out_file_paths.append(out_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)
out_message_out = '\n'.join(out_message)
return out_message_out, out_file_paths
def convert_text_pdf_to_img_pdf(in_file_path:str, out_text_file_path:List[str]):
file_path_without_ext = get_file_path_end(in_file_path)
out_file_paths = out_text_file_path
# Convert annotated text pdf back to image to give genuine redactions
print("Creating image version of redacted PDF to embed redactions.")
pdf_text_image_paths = process_file(out_text_file_path[0])
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=300.0, save_all=True, append_images=pdf_text_image_paths[1:])
# out_file_paths.append(out_text_image_file_path)
out_file_paths = [out_text_image_file_path]
out_message = "PDF " + file_path_without_ext + " converted to image-based file."
print(out_message)
#print("Out file paths:", out_file_paths)
return out_message, out_file_paths |