#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Final extraction using all templates with proper vertical spacing """ import cv2 import numpy as np from PIL import Image import pytesseract import pandas as pd import re import os class FinalVoterExtractor: def __init__(self): # Load ALL templates self.templates = { 'name': cv2.imread('wider_templates/name_label.png', cv2.IMREAD_GRAYSCALE), 'voter_id': cv2.imread('wider_templates/voter_id_label.png', cv2.IMREAD_GRAYSCALE), 'father': cv2.imread('wider_templates/father_label.png', cv2.IMREAD_GRAYSCALE), 'mother': cv2.imread('wider_templates/mother_label.png', cv2.IMREAD_GRAYSCALE), 'profession': cv2.imread('wider_templates/profession_label.png', cv2.IMREAD_GRAYSCALE), 'address': cv2.imread('wider_templates/address_label.png', cv2.IMREAD_GRAYSCALE), 'dob': cv2.imread('wider_templates/dob_label.png', cv2.IMREAD_GRAYSCALE), } print("Templates loaded:") for name, template in self.templates.items(): if template is not None: print(f" {name}: {template.shape}") def find_template(self, image_gray, template, threshold=0.7): """Find template in image""" if template is None: return None result = cv2.matchTemplate(image_gray, template, cv2.TM_CCOEFF_NORMED) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result) if max_val >= threshold: h, w = template.shape return (*max_loc, w, h, max_val) return None def extract_text_region(self, image, x, y, w, h): """Extract text from region using OCR""" # Handle negative y (clip to 0) if y < 0: h = h + y # Reduce height y = 0 # Handle out of bounds img_h, img_w = image.shape[:2] if x < 0: x = 0 if y < 0: y = 0 if x + w > img_w: w = img_w - x if y + h > img_h: h = img_h - y if w <= 0 or h <= 0: return "" # Crop region region = image[y:y+h, x:x+w] region_pil = Image.fromarray(region) # OCR text = pytesseract.image_to_string(region_pil, lang='ben+eng', config=r'--oem 3 --psm 7') return text.strip() def bengali_to_english_number(self, text): """Convert Bengali numerals to English""" if not text: return text trans = str.maketrans('০১২৩৪৫৬৭৮৯', '0123456789') return text.translate(trans) def clean_text(self, text): """Clean extracted text""" if not text: return text text = re.sub(r'[|\\<>{}[\]()"\']', '', text) text = re.sub(r'\s+', ' ', text).strip() return text def extract_cell(self, cell_path): """Extract voter data from a single cell""" cell_color = cv2.imread(cell_path) cell_gray = cv2.cvtColor(cell_color, cv2.COLOR_BGR2GRAY) cell_h, cell_w = cell_color.shape[:2] voter = {} # Find all template positions positions = {} for template_name, template in self.templates.items(): match = self.find_template(cell_gray, template) if match: positions[template_name] = match # Extract based on found templates # 1. SERIAL NUMBER - left of name label if 'name' in positions: name_x, name_y, name_w, name_h, name_score = positions['name'] serial_x = 0 serial_y = name_y # Use exact line position serial_w = name_x serial_h = name_h # Use template height serial_text = self.extract_text_region(cell_color, serial_x, serial_y, serial_w, serial_h) serial_match = re.search(r'([০-৯0-9]{1,4})', serial_text) if serial_match: serial = self.bengali_to_english_number(serial_match.group(1)) voter['serial_no'] = serial.zfill(4) # 2. NAME - right of name label if 'name' in positions: name_x, name_y, name_w, name_h, name_score = positions['name'] value_x = name_x + name_w value_y = name_y # Exact position value_w = cell_w - value_x - 5 value_h = name_h # Use template height name_text = self.extract_text_region(cell_color, value_x, value_y, value_w, value_h) name_clean = self.clean_text(name_text) if len(name_clean) >= 3: voter['name'] = name_clean # 3. VOTER ID - right of voter_id label if 'voter_id' in positions: label_x, label_y, label_w, label_h, score = positions['voter_id'] value_x = label_x + label_w value_y = label_y value_w = cell_w - value_x - 5 value_h = label_h text = self.extract_text_region(cell_color, value_x, value_y, value_w, value_h) vid_clean = re.sub(r'[^\d]', '', self.bengali_to_english_number(text)) if 10 <= len(vid_clean) <= 14: voter['voter_id'] = vid_clean # 4. FATHER - right of father label if 'father' in positions: label_x, label_y, label_w, label_h, score = positions['father'] value_x = label_x + label_w value_y = label_y value_w = cell_w - value_x - 5 value_h = label_h text = self.extract_text_region(cell_color, value_x, value_y, value_w, value_h) father = self.clean_text(text) if len(father) >= 3: voter['father_name'] = father # 5. MOTHER - right of mother label if 'mother' in positions: label_x, label_y, label_w, label_h, score = positions['mother'] value_x = label_x + label_w value_y = label_y value_w = cell_w - value_x - 5 value_h = label_h text = self.extract_text_region(cell_color, value_x, value_y, value_w, value_h) mother = self.clean_text(text) if len(mother) >= 3: voter['mother_name'] = mother # 6. PROFESSION - right of profession label, before DOB label if 'profession' in positions: prof_x, prof_y, prof_w, prof_h, score = positions['profession'] # If DOB found, stop before it; otherwise go to end if 'dob' in positions: dob_x, dob_y, dob_w, dob_h, dob_score = positions['dob'] value_w = dob_x - (prof_x + prof_w) - 5 else: value_w = cell_w - (prof_x + prof_w) - 5 value_x = prof_x + prof_w value_y = prof_y value_h = prof_h text = self.extract_text_region(cell_color, value_x, value_y, value_w, value_h) profession = self.clean_text(text) # Remove common OCR artifacts profession = re.sub(r'[,।]', '', profession).strip() if len(profession) >= 2: voter['profession'] = profession # 7. DATE OF BIRTH - right of dob label if 'dob' in positions: label_x, label_y, label_w, label_h, score = positions['dob'] value_x = label_x + label_w value_y = label_y value_w = cell_w - value_x - 5 value_h = label_h text = self.extract_text_region(cell_color, value_x, value_y, value_w, value_h) dob_match = re.search(r'([০-৯0-9]{2}[/.]?[০-৯0-9]{2}[/.]?[০-৯0-9]{4})', text) if dob_match: dob = self.bengali_to_english_number(dob_match.group(1)) dob = dob.replace('.', '/') voter['date_of_birth'] = dob # 8. ADDRESS - right of address label (USE FULL REMAINING HEIGHT) if 'address' in positions: label_x, label_y, label_w, label_h, score = positions['address'] value_x = label_x + label_w value_y = label_y value_w = cell_w - value_x - 5 # USE REMAINING HEIGHT FROM ADDRESS LINE TO BOTTOM OF CELL value_h = cell_h - label_y - 5 # Go to bottom of cell text = self.extract_text_region(cell_color, value_x, value_y, value_w, value_h) address = self.clean_text(text) if len(address) >= 5: voter['address'] = address return voter if voter else None # Test on first 5 cells extractor = FinalVoterExtractor() cell_files = sorted([f for f in os.listdir('page6_cells') if f.startswith('cell_') and f.endswith('.png')]) print(f"\nTesting extraction on first 5 cells...\n") for cell_file in cell_files[:5]: cell_path = f'page6_cells/{cell_file}' print(f"{cell_file}:") voter = extractor.extract_cell(cell_path) if voter: for key, value in voter.items(): print(f" {key}: {value}") else: print(" (empty/migrated)") print()