import os import subprocess # Ensure poppler-utils and tesseract-ocr are installed def install_dependencies(): try: subprocess.run(["/bin/bash", "setup.sh"], check=True) except subprocess.CalledProcessError as e: print(f"An error occurred while installing dependencies: {e}") raise install_dependencies() import cv2 as cv import numpy as np import pytesseract from pdf2image import convert_from_path import gradio as gr import json # Function to rescale the frame def rescaleFrame(frame, scale=0.75): width = int(frame.shape[1] * scale) height = int(frame.shape[0] * scale) dimensions = (width, height) return cv.resize(frame, dimensions, interpolation=cv.INTER_AREA) # Function to apply gamma correction def apply_gamma(image, gamma=1.0): invGamma = 1.0 / gamma table = np.array([((i / 255.0) ** invGamma) * 255 for i in np.arange(0, 256)]).astype("uint8") return cv.LUT(image, table) # Function to apply adaptive thresholding def adaptive_threshold(image): gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY) return cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 11, 2) # Function to apply edge detection def edge_detection(image): gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY) return cv.Canny(gray, 50, 150) # Function to apply morphological transformations def morphological_transformation(image): gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY) _, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU) kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3)) return cv.morphologyEx(binary, cv.MORPH_CLOSE, kernel) # Function to process image for text extraction def process_image(img, method='default'): resized_image = rescaleFrame(img) if method == 'default': gray = cv.cvtColor(resized_image, cv.COLOR_BGR2GRAY) blur = cv.GaussianBlur(gray, (3, 3), 0) gamma_corrected = apply_gamma(blur, gamma=0.3) _, thresh = cv.threshold(gamma_corrected, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU) kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3)) return cv.morphologyEx(thresh, cv.MORPH_CLOSE, kernel) elif method == 'adaptive_threshold': return adaptive_threshold(resized_image) elif method == 'edge_detection': return edge_detection(resized_image) elif method == 'morphological': return morphological_transformation(resized_image) # Function to extract text from processed image def extract_text_from_image(image, langs='tel'): return pytesseract.image_to_string(image, lang=langs) output_dir = "output" if not os.path.exists(output_dir): os.makedirs(output_dir) all_texts = {} def save_and_next(page_num, text, extracted_texts, original_images, total_pages): page_num = int(page_num) # Ensure page_num is an integer total_pages = int(total_pages) # Ensure total_pages is an integer formatted_text = { f"Page number: {page_num}": { "Content": [ line for line in text.split('\n') if line.strip() != '' ] } } all_texts.update(formatted_text) json_path = os.path.join(output_dir, "all_texts.json") with open(json_path, 'w', encoding='utf-8') as f: json.dump(all_texts, f, ensure_ascii=False, indent=4) next_page_num = page_num + 1 # Increment to next page if next_page_num <= total_pages: next_page_image = original_images[next_page_num - 1] methods = ['default', 'adaptive_threshold', 'edge_detection', 'morphological'] best_text = "" max_confidence = -1 for method in methods: processed_image = process_image(next_page_image, method=method) text = extract_text_from_image(processed_image, langs='tel') confidence = len(text) if confidence > max_confidence: max_confidence = confidence best_text = text extracted_texts.append(best_text) return gr.update(value=best_text), next_page_num, gr.update(value=next_page_image, height=None, width=None), json_path else: return "All pages processed", page_num, None, json_path def skip_page(page_num, extracted_texts, original_images, total_pages): next_page_num = int(page_num) + 1 # Ensure page_num is an integer and increment to next page total_pages = int(total_pages) # Ensure total_pages is an integer if next_page_num <= total_pages: next_page_image = original_images[next_page_num - 1] methods = ['default', 'adaptive_threshold', 'edge_detection', 'morphological'] best_text = "" max_confidence = -1 for method in methods: processed_image = process_image(next_page_image, method=method) text = extract_text_from_image(processed_image, langs='tel') confidence = len(text) if confidence > max_confidence: max_confidence = confidence best_text = text extracted_texts.append(best_text) return gr.update(value=best_text), next_page_num, gr.update(value=next_page_image, height=None, width=None) else: return "All pages processed", page_num, None def upload_pdf(pdf): pdf_path = pdf.name pages = convert_from_path(pdf_path) first_page = np.array(pages[0]) methods = ['default', 'adaptive_threshold', 'edge_detection', 'morphological'] best_text = "" max_confidence = -1 for method in methods: processed_image = process_image(first_page, method=method) text = extract_text_from_image(processed_image, langs='tel') confidence = len(text) if confidence > max_confidence: max_confidence = confidence best_text = text original_images = [np.array(page) for page in pages] extracted_texts = [best_text] return gr.update(value=original_images[0], height=None, width=None), gr.update(value=best_text), 1, extracted_texts, original_images, len(pages) def navigate_to_page(page_num, extracted_texts, original_images): if 0 <= page_num - 1 < len(original_images): return gr.update(value=original_images[page_num - 1], height=None, width=None), gr.update(value=extracted_texts[page_num - 1]), page_num else: return gr.update(value="Invalid Page Number"), None, page_num def display_pdf_and_text(): with gr.Blocks() as demo: gr.Markdown("## PDF Viewer and Text Editor") pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"]) with gr.Row(): image_output = gr.Image(label="Page Image", type="numpy") text_editor = gr.Textbox(label="Extracted Text", lines=10, interactive=True) page_num = gr.Number(value=1, label="Page Number", visible=True) extracted_texts = gr.State() original_images = gr.State() total_pages = gr.State() save_next_button = gr.Button("Save and Next") skip_button = gr.Button("Skip") pdf_input.upload(upload_pdf, inputs=pdf_input, outputs=[image_output, text_editor, page_num, extracted_texts, original_images, total_pages]) save_next_button.click(fn=save_and_next, inputs=[page_num, text_editor, extracted_texts, original_images, total_pages], outputs=[text_editor, page_num, image_output, gr.File(label="Download JSON")]) skip_button.click(fn=skip_page, inputs=[page_num, extracted_texts, original_images, total_pages], outputs=[text_editor, page_num, image_output]) page_buttons = gr.Row() def update_page_buttons(total_pages, extracted_texts, original_images): page_buttons.clear() # Clear previous buttons if any buttons = [] for i in range(1, total_pages + 1): button = gr.Button(str(i), variant="primary", size="small") button.click(navigate_to_page, inputs=[i, extracted_texts, original_images], outputs=[image_output, text_editor, page_num]) buttons.append(button) return buttons total_pages.change(fn=update_page_buttons, inputs=[total_pages, extracted_texts, original_images], outputs=[page_buttons]) return demo iface = display_pdf_and_text() iface.launch()