ocr_engine.py

import easyocr
import numpy as np
import cv2
import re
import logging
from datetime import datetime
import os
from PIL import Image, ImageEnhance
from scipy.signal import convolve2d

# Set up logging for detailed debugging
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')

# Initialize EasyOCR with English (enable GPU if available)
easyocr_reader = easyocr.Reader(['en'], gpu=False)

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

def save_debug_image(img, filename_suffix, prefix=""):
    """Saves an image to the debug directory with a 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 len(img.shape) == 3:  # Color image
        cv2.imwrite(filename, img)
    else:  # Grayscale image
        cv2.imwrite(filename, img)
    logging.debug(f"Saved debug image: {filename}")

def estimate_brightness(img):
    """Estimate image brightness to adjust processing"""
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    brightness = np.mean(gray)
    logging.debug(f"Estimated brightness: {brightness}")
    return brightness

def deblur_image(img):
    """Apply deconvolution to reduce blur (approximate Wiener filter)"""
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # Create a simple point spread function (PSF) for deblurring
    psf = np.ones((5, 5)) / 25
    # Normalize image to float32
    img_float = gray.astype(np.float32) / 255.0
    # Convolve with PSF (simulate blur)
    img_blurred = convolve2d(img_float, psf, mode='same')
    # Avoid division by zero
    img_blurred = np.where(img_blurred == 0, 1e-10, img_blurred)
    # Deconvolve
    img_deblurred = img_float / img_blurred
    img_deblurred = np.clip(img_deblurred * 255, 0, 255).astype(np.uint8)
    save_debug_image(img_deblurred, "00_deblurred")
    return img_deblurred

def preprocess_image(img):
    """Enhance contrast, brightness, reduce noise, and deblur for digit detection"""
    # Deblur first
    deblurred = deblur_image(img)
    
    # Convert to PIL for enhancement
    pil_img = Image.fromarray(deblurred)
    pil_img = ImageEnhance.Contrast(pil_img).enhance(2.5)  # Aggressive contrast
    pil_img = ImageEnhance.Brightness(pil_img).enhance(1.5)  # Stronger brightness
    img_enhanced = np.array(pil_img)
    save_debug_image(img_enhanced, "00_preprocessed_pil")

    # Apply CLAHE for local contrast enhancement
    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
    enhanced = clahe.apply(img_enhanced)
    save_debug_image(enhanced, "00_clahe_enhanced")

    # Aggressive noise reduction
    filtered = cv2.bilateralFilter(enhanced, d=15, sigmaColor=150, sigmaSpace=150)
    save_debug_image(filtered, "00_bilateral_filtered")
    return filtered

def normalize_image(img):
    """Resize image to standard dimensions while preserving aspect ratio"""
    h, w = img.shape[:2]
    target_height = 720
    aspect_ratio = w / h
    target_width = int(target_height * aspect_ratio)
    if target_width < 320:
        target_width = 320
        target_height = int(target_width / aspect_ratio)
    resized = cv2.resize(img, (target_width, target_height), interpolation=cv2.INTER_CUBIC)
    save_debug_image(resized, "00_normalized")
    logging.debug(f"Normalized image to {target_width}x{target_height}")
    return resized

def detect_roi(img):
    """Detect the digital display region, with fallback to full image"""
    try:
        save_debug_image(img, "01_original")
        gray = preprocess_image(img)
        save_debug_image(gray, "02_preprocessed_grayscale")

        # Try multiple thresholding methods
        brightness = estimate_brightness(img)
        if brightness > 120:
            thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                           cv2.THRESH_BINARY_INV, 41, 7)  # Inverted for bright displays
            save_debug_image(thresh, "03_roi_adaptive_threshold_high")
        else:
            _, thresh = cv2.threshold(gray, 20, 255, cv2.THRESH_BINARY_INV)  # Low threshold for dim displays
            save_debug_image(thresh, "03_roi_simple_threshold_low")

        # Morphological operations to connect digits
        kernel = np.ones((7, 7), np.uint8)
        thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=3)
        save_debug_image(thresh, "03_roi_morph_cleaned")

        kernel = np.ones((15, 15), np.uint8)
        dilated = cv2.dilate(thresh, kernel, iterations=6)
        save_debug_image(dilated, "04_roi_dilated")

        contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        if contours:
            img_area = img.shape[0] * img.shape[1]
            valid_contours = []
            for c in contours:
                area = cv2.contourArea(c)
                if 100 < area < (img_area * 0.999):  # Extremely relaxed area filter
                    x, y, w, h = cv2.boundingRect(c)
                    aspect_ratio = w / h if h > 0 else 0
                    if 0.3 <= aspect_ratio <= 15.0 and w > 20 and h > 10:  # Very relaxed filters
                        valid_contours.append(c)

            if valid_contours:
                contour = max(valid_contours, key=cv2.contourArea)
                x, y, w, h = cv2.boundingRect(contour)
                padding = 120  # Very generous padding
                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, "05_detected_roi")
                logging.info(f"Detected ROI with dimensions: ({x}, {y}, {w}, {h})")
                return roi_img, (x, y, w, h)

        logging.info("No suitable ROI found, returning full image.")
        save_debug_image(img, "05_no_roi_full_fallback")
        return img, None
    except Exception as e:
        logging.error(f"ROI detection failed: {str(e)}")
        save_debug_image(img, "05_roi_detection_error_fallback")
        return img, None

def detect_segments(digit_img):
    """Detect seven-segment patterns in a digit image"""
    h, w = digit_img.shape
    if h < 6 or w < 3:  # Extremely relaxed size constraints
        logging.debug(f"Digit image too small: {w}x{h}")
        return None

    segments = {
        'top': (int(w*0.05), int(w*0.95), 0, int(h*0.3)),
        'middle': (int(w*0.05), int(w*0.95), int(h*0.35), int(h*0.65)),
        'bottom': (int(w*0.05), int(w*0.95), int(h*0.7), h),
        'left_top': (0, int(w*0.35), int(h*0.05), int(h*0.55)),
        'left_bottom': (0, int(w*0.35), int(h*0.45), int(h*0.95)),
        'right_top': (int(w*0.65), w, int(h*0.05), int(h*0.55)),
        'right_bottom': (int(w*0.65), w, int(h*0.45), int(h*0.95))
    }

    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 > 0.25  # Very low 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 = None
    max_score = -1
    for digit, pattern in digit_patterns.items():
        matches = sum(1 for segment in pattern if segment_presence.get(segment, False))
        non_matches_penalty = sum(1 for segment in segment_presence if segment not in pattern and segment_presence[segment])
        current_score = matches - non_matches_penalty
        if all(segment_presence.get(s, False) for s in pattern):
            current_score += 0.5
        if current_score > max_score:
            max_score = current_score
            best_match = digit
        elif current_score == max_score and best_match is not None:
            current_digit_non_matches = sum(1 for segment in segment_presence if segment not in pattern and segment_presence[segment])
            best_digit_pattern = digit_patterns[best_match]
            best_digit_non_matches = sum(1 for segment in segment_presence if segment not in best_digit_pattern and segment_presence[segment])
            if current_digit_non_matches < best_digit_non_matches:
                best_match = digit
    
    logging.debug(f"Segment presence: {segment_presence}, Detected digit: {best_match}")
    return best_match

def custom_seven_segment_ocr(img, roi_bbox):
    """Perform custom OCR for seven-segment displays"""
    try:
        gray = preprocess_image(img)
        brightness = estimate_brightness(img)
        # Multiple thresholding approaches
        if brightness > 120:
            _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
            save_debug_image(thresh, "06_roi_otsu_threshold")
        else:
            _, thresh = cv2.threshold(gray, 15, 255, cv2.THRESH_BINARY_INV)  # Very low threshold
            save_debug_image(thresh, "06_roi_simple_threshold")
        
        # Morphological cleaning
        kernel = np.ones((5, 5), np.uint8)
        thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
        save_debug_image(thresh, "06_roi_morph_cleaned")

        results = easyocr_reader.readtext(thresh, detail=1, paragraph=False, 
                                         contrast_ths=0.05, adjust_contrast=1.2, 
                                         text_threshold=0.2, mag_ratio=6.0, 
                                         allowlist='0123456789.-', y_ths=0.7)
        
        logging.info(f"Custom OCR EasyOCR results: {results}")
        if not results:
            logging.info("Custom OCR EasyOCR found no digits.")
            return None

        digits_info = []
        for (bbox, text, conf) in results:
            (x1, y1), (x2, y2), (x3, y3), (x4, y4) = bbox
            h_bbox = max(y1, y2, y3, y4) - min(y1, y2, y3, y4)
            if len(text) <= 2 and any(c in '0123456789.-' for c in text) and h_bbox > 3:
                x_min, x_max = int(min(x1, x4)), int(max(x2, x3))
                y_min, y_max = int(min(y1, y2)), int(max(y3, y4))
                digits_info.append((x_min, x_max, y_min, y_max, text, conf))

        digits_info.sort(key=lambda x: x[0])
        recognized_text = ""
        for idx, (x_min, x_max, y_min, y_max, easyocr_char, easyocr_conf) 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_img_crop = thresh[y_min:y_max, x_min:x_max]
            save_debug_image(digit_img_crop, f"07_digit_crop_{idx}_{easyocr_char}")
            if easyocr_conf > 0.7 or easyocr_char in '.-' or digit_img_crop.shape[0] < 6 or digit_img_crop.shape[1] < 3:
                recognized_text += easyocr_char
            else:
                digit_from_segments = detect_segments(digit_img_crop)
                if digit_from_segments:
                    recognized_text += digit_from_segments
                else:
                    recognized_text += easyocr_char
        
        logging.info(f"Custom OCR before validation, recognized_text: {recognized_text}")
        if recognized_text:
            return recognized_text
        logging.info(f"Custom OCR text '{recognized_text}' is empty.")
        return None
    except Exception as e:
        logging.error(f"Custom seven-segment OCR failed: {str(e)}")
        return None

def extract_weight_from_image(pil_img):
    """Extract weight from a PIL image of a digital scale display"""
    try:
        img = np.array(pil_img)
        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
        save_debug_image(img, "00_input_image")

        # Normalize image dimensions
        img = normalize_image(img)
        brightness = estimate_brightness(img)
        conf_threshold = 0.2 if brightness > 120 else 0.1

        roi_img, roi_bbox = detect_roi(img)
        custom_result = custom_seven_segment_ocr(roi_img, roi_bbox)
        if custom_result:
            logging.info(f"Raw custom OCR result: {custom_result}")
            # Minimal cleaning
            text = re.sub(r"[^\d\.\-]", "", custom_result)  # Allow negative signs
            if text.count('.') > 1:
                text = text.replace('.', '', text.count('.') - 1)
            if text:
                if text.startswith('.'):
                    text = "0" + text
                if text.endswith('.'):
                    text = text.rstrip('.')
                if text == '.' or text == '':
                    logging.warning(f"Custom OCR result '{text}' is invalid after cleaning.")
                else:
                    try:
                        weight = float(text)
                        logging.info(f"Custom OCR result: {text}, Confidence: 90.0%")
                        return text, 90.0
                    except ValueError:
                        logging.warning(f"Custom OCR result '{text}' is not a valid number, falling back.")
            logging.warning(f"Custom OCR result '{custom_result}' failed cleaning, falling back.")

        logging.info("Custom OCR failed or invalid, falling back to general EasyOCR.")
        processed_roi_img = preprocess_image(roi_img)
        
        # Multiple thresholding approaches
        if brightness > 120:
            thresh = cv2.adaptiveThreshold(processed_roi_img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                           cv2.THRESH_BINARY_INV, 51, 9)
            save_debug_image(thresh, "09_fallback_adaptive_thresh")
        else:
            _, thresh = cv2.threshold(processed_roi_img, 15, 255, cv2.THRESH_BINARY_INV)
            save_debug_image(thresh, "09_fallback_simple_thresh")

        # Morphological cleaning
        kernel = np.ones((5, 5), np.uint8)
        thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
        save_debug_image(thresh, "09_fallback_morph_cleaned")

        results = easyocr_reader.readtext(thresh, detail=1, paragraph=False, 
                                         contrast_ths=0.05, adjust_contrast=1.2, 
                                         text_threshold=0.1, mag_ratio=7.0, 
                                         allowlist='0123456789.-', batch_size=4, y_ths=0.8)

        best_weight = None
        best_conf = 0.0
        best_score = 0.0
        for (bbox, text, conf) in results:
            logging.info(f"Fallback EasyOCR raw text: {text}, Confidence: {conf}")
            text = text.lower().strip()
            text = text.replace(",", ".").replace(";", ".").replace(":", ".").replace(" ", "")
            text = text.replace("o", "0").replace("O", "0").replace("q", "0").replace("Q", "0")
            text = text.replace("s", "5").replace("S", "5")
            text = text.replace("g", "9").replace("G", "6")
            text = text.replace("l", "1").replace("I", "1").replace("|", "1")
            text = text.replace("b", "8").replace("B", "8")
            text = text.replace("z", "2").replace("Z", "2")
            text = text.replace("a", "4").replace("A", "4")
            text = text.replace("e", "3")
            text = text.replace("t", "7")
            text = text.replace("~", "").replace("`", "")
            text = re.sub(r"(kgs|kg|k|lb|g|gr|pounds|lbs)\b", "", text)
            text = re.sub(r"[^\d\.\-]", "", text)
            if text.count('.') > 1:
                parts = text.split('.')
                text = parts[0] + '.' + ''.join(parts[1:])
            text = text.strip('.')
            if len(text.replace('.', '').replace('-', '')) > 0:
                try:
                    weight = float(text)
                    range_score = 1.0
                    if -1000 <= weight <= 1000:  # Allow negative weights
                        range_score = 1.5
                    elif weight > 1000 and weight <= 2000:
                        range_score = 1.0
                    else:
                        range_score = 0.5
                    digit_count = len(text.replace('.', '').replace('-', ''))
                    digit_score = 1.0
                    if digit_count >= 2 and digit_count <= 6:
                        digit_score = 1.3
                    elif digit_count == 1:
                        digit_score = 0.8
                    score = conf * range_score * digit_score
                    if roi_bbox:
                        (x_roi, y_roi, w_roi, h_roi) = roi_bbox
                        roi_area = w_roi * h_roi
                        x_min, y_min = int(min(b[0] for b in bbox)), int(min(b[1] for b in bbox))
                        x_max, y_max = int(max(b[0] for b in bbox)), int(max(b[1] for b in bbox))
                        bbox_area = (x_max - x_min) * (y_max - y_min)
                        if roi_area > 0 and bbox_area / roi_area < 0.01:
                            score *= 0.5
                        bbox_aspect_ratio = (x_max - x_min) / (y_max - y_min) if (y_max - y_min) > 0 else 0
                        if bbox_aspect_ratio < 0.05:
                            score *= 0.7
                    if score > best_score and conf > conf_threshold:
                        best_weight = text
                        best_conf = conf
                        best_score = score
                        logging.info(f"Candidate EasyOCR weight: '{text}', Conf: {conf}, Score: {score}")
                except ValueError:
                    logging.warning(f"Could not convert '{text}' to float during EasyOCR fallback.")
                    continue

        if not best_weight:
            logging.info("No valid weight detected after all attempts.")
            return "Not detected", 0.0

        if "." in best_weight:
            int_part, dec_part = best_weight.split(".")
            int_part = int_part.lstrip("0") or "0"
            dec_part = dec_part.rstrip('0')
            if not dec_part and int_part != "0":
                best_weight = int_part
            elif not dec_part and int_part == "0":
                best_weight = "0"
            else:
                best_weight = f"{int_part}.{dec_part}"
        else:
            best_weight = best_weight.lstrip('0') or "0"

        logging.info(f"Final detected weight: {best_weight}, Confidence: {round(best_conf * 100, 2)}%")
        return best_weight, round(best_conf * 100, 2)

    except Exception as e:
        logging.error(f"Weight extraction failed unexpectedly: {str(e)}")
        return "Not detected", 0.0