import warnings from concurrent.futures import ThreadPoolExecutor, as_completed import time # Filter warnings about inputs not requiring gradients warnings.filterwarnings("ignore", message="None of the inputs have requires_grad=True. Gradients will be None") warnings.filterwarnings("ignore", message="torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument.") import cv2 import os import fitz # PyMuPDF import numpy as np import re import pytesseract import torch from PIL import Image from tqdm import tqdm from unilm.dit.object_detection.ditod import add_vit_config from detectron2.config import CfgNode as CN from detectron2.config import get_cfg from detectron2.utils.visualizer import ColorMode, Visualizer from detectron2.data import MetadataCatalog from detectron2.engine import DefaultPredictor # Step 1: instantiate config cfg = get_cfg() add_vit_config(cfg) cfg.merge_from_file("cascade_dit_base.yml") # Step 2: add model weights URL to config cfg.MODEL.WEIGHTS = "publaynet_dit-b_cascade.pth" # Step 3: set device cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu" #cfg.MODEL.DEVICE = "cuda" # Step 4: define model predictor = DefaultPredictor(cfg) def analyze_image(img): md = MetadataCatalog.get(cfg.DATASETS.TEST[0]) if cfg.DATASETS.TEST[0]=='icdar2019_test': md.set(thing_classes=["table"]) else: md.set(thing_classes=["text","title","list","table","figure"]) output = predictor(img)["instances"] v = Visualizer(img[:, :, ::-1], md, scale=1.0, instance_mode=ColorMode.SEGMENTATION) result = v.draw_instance_predictions(output.to("cpu")) result_image = result.get_image()[:, :, ::-1] return result_image, output, v def convert_pdf_to_jpg(pdf_path, output_folder, zoom_factor=2): doc = fitz.open(pdf_path) for page_num in range(len(doc)): page = doc.load_page(page_num) # Adjust zoom factor for higher resolution mat = fitz.Matrix(zoom_factor, zoom_factor) # Create a Matrix with the zoom factor pix = page.get_pixmap(matrix=mat) # Render the page using the matrix output_file = f"{output_folder}/page_{page_num}.jpg" pix.save(output_file) def process_jpeg_images(output_folder): for page_num in tqdm(range(len(os.listdir(output_folder))), desc="Processing the pdf"): file_path = f"{output_folder}/page_{page_num}.jpg" img = cv2.imread(file_path) if img is None: print(f"Failed to read {file_path}. Skipping.") continue result_image, output, v = analyze_image(img) # Saving logic save_extracted_instances(img, output, page_num,output_folder) def save_extracted_instances(img, output, page_num, dest_folder, confidence_threshold=0.8): class_names = { 0: "text", 1: "title", 2: "list", 3: "table", 4: "figure" } threshold_value = 0 # Standard deviation threshold min_height = 0 # Minimum height threshold instances = output.to("cpu") boxes = instances.pred_boxes.tensor.numpy() class_ids = instances.pred_classes.tolist() scores = instances.scores.tolist() # Get prediction scores image_counter = 1 for box, class_id, score in zip(boxes, class_ids, scores): # Check if the prediction score meets the confidence threshold if score >= confidence_threshold: class_name = class_names.get(class_id, "unknown") # Save only if class is 'figure' or 'table' if class_name in ["figure", "table","text"]: x1, y1, x2, y2 = map(int, box) cropped_image = img[y1:y2, x1:x2] if np.std(cropped_image) > threshold_value and (y2 - y1) > min_height: save_path = os.path.join(dest_folder, f"page_{page_num}_{class_name}_{image_counter}.jpg") cv2.imwrite(save_path, cropped_image) image_counter += 1 def delete_files_in_folder(folder_path): for filename in os.listdir(folder_path): file_path = os.path.join(folder_path, filename) if os.path.isfile(file_path): os.remove(file_path) def rename_files_sequentially(folder_path): # Regex pattern to match 'page_{page_num}_{class_name}_{image_counter}.jpg' pattern = re.compile(r'page_(\d+)_(\w+)_(\d+).jpg', re.IGNORECASE) # List files in the folder files = os.listdir(folder_path) # Filter and sort files based on the regex pattern sorted_files = sorted( [f for f in files if pattern.match(f)], key=lambda x: (int(pattern.match(x).group(1)), pattern.match(x).group(2).lower(), int(pattern.match(x).group(3))) ) # Initialize an empty dictionary for counters counters = {} for filename in sorted_files: match = pattern.match(filename) if match: page_num, class_name, _ = match.groups() class_name = class_name.lower() # Convert class name to lowercase # Initialize counter for this class if it doesn't exist if class_name not in counters: counters[class_name] = 1 # New filename format: '{class_name}_{sequential_number}.jpg' new_filename = f"{class_name}_{counters[class_name]}.jpg" counters[class_name] += 1 # Rename the file os.rename(os.path.join(folder_path, filename), os.path.join(folder_path, new_filename)) #print(f"Renamed '{filename}' to '{new_filename}'") def ocr_folder(folder_path): # Regex pattern to match 'text_{number}.jpg' pattern = re.compile(r'text_\d+\.jpg', re.IGNORECASE) # Create a subfolder for the OCR text files ocr_text_folder = os.path.join(folder_path, "ocr_results") if not os.path.exists(ocr_text_folder): os.makedirs(ocr_text_folder) for filename in os.listdir(folder_path): if pattern.match(filename): image_path = os.path.join(folder_path, filename) text = ocr_image(image_path) # Save the OCR result to a text file in the subfolder text_file_name = filename.replace('.jpg', '.txt') text_file_path = os.path.join(ocr_text_folder, text_file_name) with open(text_file_path, 'w') as file: file.write(text) #print(f"OCR result for {filename} saved to {text_file_path}\n") def ocr_image(image_path): image = Image.open(image_path) text = pytesseract.image_to_string(image) return text