ocr_engine.py

import pytesseract
import numpy as np
import cv2
import re
import logging
from datetime import datetime
import os
from PIL import Image

# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Directory for debug images
DEBUG_DIR = "debug_images"
os.makedirs(DEBUG_DIR, exist_ok=True)

def save_debug_image(img, filename_suffix, prefix=""):
    """Save image to debug directory with timestamp."""
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
    filename = os.path.join(DEBUG_DIR, f"{prefix}{timestamp}_{filename_suffix}.png")
    if isinstance(img, Image.Image):
        img.save(filename)
    elif len(img.shape) == 3:
        cv2.imwrite(filename, cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    else:
        cv2.imwrite(filename, img)
    logging.info(f"Saved debug image: {filename}")

def estimate_brightness(img):
    """Estimate image brightness."""
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    return np.mean(gray)

def preprocess_image(img):
    """Preprocess image for OCR with enhanced contrast and noise reduction."""
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    brightness = estimate_brightness(img)
    # Dynamic CLAHE
    clahe_clip = 5.0 if brightness < 80 else 3.0
    clahe = cv2.createCLAHE(clipLimit=clahe_clip, tileGridSize=(8, 8))
    enhanced = clahe.apply(gray)
    save_debug_image(enhanced, "01_preprocess_clahe")
    # Gaussian blur
    blurred = cv2.GaussianBlur(enhanced, (3, 3), 0)
    save_debug_image(blurred, "02_preprocess_blur")
    # Dynamic thresholding
    block_size = max(11, min(31, int(img.shape[0] / 15) * 2 + 1))
    thresh = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                   cv2.THRESH_BINARY_INV, block_size, 5)
    # Morphological operations
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)
    thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)
    save_debug_image(thresh, "03_preprocess_morph")
    return thresh, enhanced

def correct_rotation(img):
    """Correct image rotation using edge detection."""
    try:
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        edges = cv2.Canny(gray, 50, 150, apertureSize=3)
        lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=50, minLineLength=30, maxLineGap=10)
        if lines is not None:
            angles = [np.arctan2(line[0][3] - line[0][1], line[0][2] - line[0][0]) * 180 / np.pi for line in lines]
            angle = np.median(angles)
            if abs(angle) > 1.0:
                h, w = img.shape[:2]
                center = (w // 2, h // 2)
                M = cv2.getRotationMatrix2D(center, angle, 1.0)
                img = cv2.warpAffine(img, M, (w, h))
                save_debug_image(img, "00_rotated_image")
                logging.info(f"Applied rotation: {angle:.2f} degrees")
        return img
    except Exception as e:
        logging.error(f"Rotation correction failed: {str(e)}")
        return img

def detect_roi(img):
    """Detect region of interest (display) with multi-scale contour filtering."""
    try:
        save_debug_image(img, "04_original")
        thresh, enhanced = preprocess_image(img)
        brightness_map = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        block_sizes = [max(11, min(31, int(img.shape[0] / s) * 2 + 1)) for s in [12, 15, 18]]
        valid_contours = []
        img_area = img.shape[0] * img.shape[1]
        
        for block_size in block_sizes:
            temp_thresh = cv2.adaptiveThreshold(enhanced, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                                cv2.THRESH_BINARY_INV, block_size, 5)
            kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
            temp_thresh = cv2.morphologyEx(temp_thresh, cv2.MORPH_CLOSE, kernel, iterations=2)
            save_debug_image(temp_thresh, f"05_roi_threshold_block{block_size}")
            contours, _ = cv2.findContours(temp_thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
            
            for c in contours:
                area = cv2.contourArea(c)
                x, y, w, h = cv2.boundingRect(c)
                roi_brightness = np.mean(brightness_map[y:y+h, x:x+w])
                aspect_ratio = w / h
                if (400 < area < (img_area * 0.6) and 
                    0.5 <= aspect_ratio <= 8.0 and w > 70 and h > 30 and roi_brightness > 50):
                    valid_contours.append((c, area * roi_brightness))
                    logging.debug(f"Contour (block {block_size}): Area={area}, Aspect={aspect_ratio:.2f}, Brightness={roi_brightness:.2f}")
        
        if valid_contours:
            contour, _ = max(valid_contours, key=lambda x: x[1])
            x, y, w, h = cv2.boundingRect(contour)
            padding = max(20, min(60, int(min(w, h) * 0.4)))
            x, y = max(0, x - padding), max(0, y - padding)
            w, h = min(w + 2 * padding, img.shape[1] - x), min(h + 2 * padding, img.shape[0] - y)
            roi_img = img[y:y+h, x:x+w]
            save_debug_image(roi_img, "06_detected_roi")
            logging.info(f"Detected ROI: ({x}, {y}, {w}, {h})")
            return roi_img, (x, y, w, h)
        
        logging.info("No ROI found, using full image.")
        save_debug_image(img, "06_no_roi_fallback")
        return img, None
    except Exception as e:
        logging.error(f"ROI detection failed: {str(e)}")
        save_debug_image(img, "06_roi_error_fallback")
        return img, None

def detect_segments(digit_img, brightness):
    """Detect seven-segment digits with adaptive thresholds."""
    try:
        h, w = digit_img.shape
        if h < 15 or w < 8:
            logging.debug("Digit image too small for segment detection.")
            return None

        segment_threshold = 0.25 if brightness < 80 else 0.35
        segments = {
            'top': (int(w*0.1), int(w*0.9), 0, int(h*0.25)),
            'middle': (int(w*0.1), int(w*0.9), int(h*0.45), int(h*0.55)),
            'bottom': (int(w*0.1), int(w*0.9), int(h*0.75), h),
            'left_top': (0, int(w*0.3), int(h*0.1), int(h*0.5)),
            'left_bottom': (0, int(w*0.3), int(h*0.5), int(h*0.9)),
            'right_top': (int(w*0.7), w, int(h*0.1), int(h*0.5)),
            'right_bottom': (int(w*0.7), w, int(h*0.5), int(h*0.9))
        }

        segment_presence = {}
        for name, (x1, x2, y1, y2) in segments.items():
            x1, y1 = max(0, x1), max(0, y1)
            x2, y2 = min(w, x2), min(h, y2)
            region = digit_img[y1:y2, x1:x2]
            if region.size == 0:
                segment_presence[name] = False
                continue
            pixel_count = np.sum(region == 255)
            total_pixels = region.size
            segment_presence[name] = pixel_count / total_pixels > segment_threshold
            logging.debug(f"Segment {name}: {pixel_count}/{total_pixels} = {pixel_count/total_pixels:.2f}")

        digit_patterns = {
            '0': ('top', 'bottom', 'left_top', 'left_bottom', 'right_top', 'right_bottom'),
            '1': ('right_top', 'right_bottom'),
            '2': ('top', 'middle', 'bottom', 'left_bottom', 'right_top'),
            '3': ('top', 'middle', 'bottom', 'right_top', 'right_bottom'),
            '4': ('middle', 'left_top', 'right_top', 'right_bottom'),
            '5': ('top', 'middle', 'bottom', 'left_top', 'right_bottom'),
            '6': ('top', 'middle', 'bottom', 'left_top', 'left_bottom', 'right_bottom'),
            '7': ('top', 'right_top', 'right_bottom'),
            '8': ('top', 'middle', 'bottom', 'left_top', 'left_bottom', 'right_top', 'right_bottom'),
            '9': ('top', 'middle', 'bottom', 'left_top', 'right_top', 'right_bottom')
        }

        best_match, best_score = None, -1
        for digit, pattern in digit_patterns.items():
            matches = sum(1 for segment in pattern if segment_presence.get(segment, False))
            non_matches = sum(1 for segment in segment_presence if segment not in pattern and segment_presence[segment])
            score = matches - 0.15 * non_matches
            if matches >= len(pattern) * 0.65:
                score += 1.2
            if score > best_score:
                best_score = score
                best_match = digit
        logging.debug(f"Segment detection: {segment_presence}, Digit: {best_match}, Score: {best_score:.2f}")
        return best_match
    except Exception as e:
        logging.error(f"Segment detection failed: {str(e)}")
        return None

def perform_ocr(img, roi_bbox):
    """Perform OCR with Tesseract and seven-segment fallback."""
    try:
        thresh, enhanced = preprocess_image(img)
        brightness = estimate_brightness(img)
        pil_img = Image.fromarray(enhanced)
        save_debug_image(pil_img, "07_ocr_input")
        
        # Tesseract OCR with numeric config
        custom_config = r'--oem 3 --psm 7 -c tessedit_char_whitelist=0123456789.'
        text = pytesseract.image_to_string(pil_img, config=custom_config)
        logging.info(f"Tesseract raw output: {text}")
        
        # Clean and validate text
        text = re.sub(r"[^\d\.]", "", text)
        if text.count('.') > 1:
            text = text.replace('.', '', text.count('.') - 1)
        text = text.strip('.')
        if text and re.fullmatch(r"^\d*\.?\d*$", text):
            text = text.lstrip('0') or '0'
            confidence = 95.0 if len(text.replace('.', '')) >= 2 else 90.0
            logging.info(f"Validated Tesseract text: {text}, Confidence: {confidence:.2f}%")
            return text, confidence

        # Fallback to seven-segment detection
        logging.info("Tesseract failed, using seven-segment detection.")
        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        digits_info = []
        for c in contours:
            x, y, w, h = cv2.boundingRect(c)
            if w > 10 and h > 15 and 0.2 <= w/h <= 1.5:
                digits_info.append((x, x+w, y, y+h))
        
        if digits_info:
            digits_info.sort(key=lambda x: x[0])
            recognized_text = ""
            for idx, (x_min, x_max, y_min, y_max) in enumerate(digits_info):
                x_min, y_min = max(0, x_min), max(0, y_min)
                x_max, y_max = min(thresh.shape[1], x_max), min(thresh.shape[0], y_max)
                if x_max <= x_min or y_max <= y_min:
                    continue
                digit_crop = thresh[y_min:y_max, x_min:x_max]
                save_debug_image(digit_crop, f"08_digit_crop_{idx}")
                segment_digit = detect_segments(digit_crop, brightness)
                if segment_digit:
                    recognized_text += segment_digit
                elif idx < len(digits_info) - 1 and (digits_info[idx+1][0] - x_max) < 10:
                    recognized_text += '.'  # Assume decimal point for close digits
            text = re.sub(r"[^\d\.]", "", recognized_text)
            if text.count('.') > 1:
                text = text.replace('.', '', text.count('.') - 1)
            text = text.strip('.')
            if text and re.fullmatch(r"^\d*\.?\d*$", text):
                text = text.lstrip('0') or '0'
                confidence = 90.0
                logging.info(f"Validated segment text: {text}, Confidence: {confidence:.2f}%")
                return text, confidence
        
        logging.info("No valid digits detected.")
        return None, 0.0
    except Exception as e:
        logging.error(f"OCR failed: {str(e)}")
        return None, 0.0

def extract_weight_from_image(pil_img):
    """Extract weight from a digital scale image."""
    try:
        img = np.array(pil_img)
        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
        save_debug_image(img, "00_input_image")
        img = correct_rotation(img)
        brightness = estimate_brightness(img)
        conf_threshold = 0.8 if brightness > 100 else 0.6

        roi_img, roi_bbox = detect_roi(img)
        if roi_bbox:
            conf_threshold *= 1.1 if (roi_bbox[2] * roi_bbox[3]) > (img.shape[0] * img.shape[1] * 0.3) else 1.0

        result, confidence = perform_ocr(roi_img, roi_bbox)
        if result and confidence >= conf_threshold * 100:
            try:
                weight = float(result)
                if 0.01 <= weight <= 1000:
                    logging.info(f"Detected weight: {result} kg, Confidence: {confidence:.2f}%")
                    return result, confidence
                logging.warning(f"Weight {result} out of range.")
            except ValueError:
                logging.warning(f"Invalid weight format: {result}")

        logging.info("Primary OCR failed, using full image fallback.")
        result, confidence = perform_ocr(img, None)
        if result and confidence >= conf_threshold * 0.9 * 100:
            try:
                weight = float(result)
                if 0.01 <= weight <= 1000:
                    logging.info(f"Full image weight: {result} kg, Confidence: {confidence:.2f}%")
                    return result, confidence
                logging.warning(f"Full image weight {result} out of range.")
            except ValueError:
                logging.warning(f"Invalid full image weight format: {result}")

        logging.info("No valid weight detected.")
        return "Not detected", 0.0
    except Exception as e:
        logging.error(f"Weight extraction failed: {str(e)}")
        return "Not detected", 0.0