ocr_engine.py

import easyocr
import numpy as np
import cv2
import re
import logging
from datetime import datetime
import os

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

# Initialize EasyOCR
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.info(f"Saved debug image: {filename}")

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

def preprocess_image(img, scale=1.0, method='clahe'):
    """Preprocess image for better OCR accuracy."""
    if scale != 1.0:
        img = cv2.resize(img, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)
        save_debug_image(img, f"01_preprocess_scaled_{scale}")
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # Gentle denoising
    denoised = cv2.bilateralFilter(gray, 7, 10, 10)
    save_debug_image(denoised, "02_preprocess_bilateral")
    # Enhance contrast
    if method == 'clahe':
        clahe = cv2.createCLAHE(clipLimit=3.5, tileGridSize=(8, 8))
        enhanced = clahe.apply(denoised)
    else:  # Histogram equalization
        enhanced = cv2.equalizeHist(denoised)
    save_debug_image(enhanced, f"03_preprocess_{method}")
    # Sharpen
    kernel_sharpening = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
    sharpened = cv2.filter2D(enhanced, -1, kernel_sharpening)
    save_debug_image(sharpened, "04_preprocess_sharpened")
    return sharpened

def correct_rotation(img):
    """Correct image rotation using Hough Transform."""
    try:
        edges = cv2.Canny(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), 50, 150)
        lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=50, minLineLength=40, 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) > 2:
                (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 correction: {angle:.2f} degrees")
        return img
    except Exception as e:
        logging.error(f"Rotation correction failed: {str(e)}")
        return img

def detect_roi(img):
    """Detect and crop the region of interest (likely the digital display)."""
    try:
        save_debug_image(img, "05_original")
        brightness_map = cv2.GaussianBlur(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), (15, 15), 0)
        
        # Try multiple scales and methods
        scales = [1.0, 1.5, 0.5]
        methods = ['clahe', 'hist']
        for scale in scales:
            for method in methods:
                preprocessed = preprocess_image(img, scale, method)
                block_size = max(9, min(31, int(img.shape[0] / 25) * 2 + 1))
                thresh = cv2.adaptiveThreshold(preprocessed, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                               cv2.THRESH_BINARY_INV, block_size, 3)
                _, otsu_thresh = cv2.threshold(preprocessed, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
                combined_thresh = cv2.bitwise_and(thresh, otsu_thresh)
                save_debug_image(combined_thresh, f"06_roi_combined_threshold_scale_{scale}_{method}")
                
                # Morphological operations
                kernel = np.ones((3, 3), np.uint8)
                dilated = cv2.dilate(combined_thresh, kernel, iterations=2)
                eroded = cv2.erode(dilated, kernel, iterations=1)
                save_debug_image(eroded, f"07_roi_morphological_scale_{scale}_{method}")
                
                contours, _ = cv2.findContours(eroded, 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)
                        x, y, w, h = cv2.boundingRect(c)
                        roi_brightness = np.mean(brightness_map[y:y+h, x:x+w] if scale == 1.0 else cv2.resize(brightness_map, (img.shape[1], img.shape[0])))
                        aspect_ratio = w / h
                        if (100 < area < (img_area * 0.95) and 
                            0.3 <= aspect_ratio <= 20.0 and w > 40 and h > 15 and roi_brightness > 50):
                            valid_contours.append((c, roi_brightness))
                            logging.debug(f"Contour: Scale={scale}, Method={method}, 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)
                        if scale != 1.0:
                            x, y, w, h = [int(v / scale) for v in (x, y, w, h)]
                        padding = 150
                        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, f"08_detected_roi_scale_{scale}_{method}")
                        logging.info(f"Detected ROI with dimensions: ({x}, {y}, {w}, {h}) at scale {scale}, method {method}")
                        return roi_img, (x, y, w, h)
        
        logging.info("No suitable ROI found, attempting fallback criteria.")
        # Fallback with relaxed criteria
        preprocessed = preprocess_image(img, method='clahe')
        thresh = cv2.adaptiveThreshold(preprocessed, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                       cv2.THRESH_BINARY_INV, block_size, 5)
        save_debug_image(thresh, "06_roi_fallback_threshold")
        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        valid_contours = [c for c in contours if 50 < cv2.contourArea(c) < (img.shape[0] * img.shape[1] * 0.95) and 
                          0.2 <= cv2.boundingRect(c)[2]/cv2.boundingRect(c)[3] <= 25.0]
        if valid_contours:
            contour = max(valid_contours, key=cv2.contourArea)
            x, y, w, h = cv2.boundingRect(contour)
            padding = 150
            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, "08_detected_roi_fallback")
            logging.info(f"Detected fallback ROI with dimensions: ({x}, {y}, {w}, {h})")
            return roi_img, (x, y, w, h)
        
        logging.info("No suitable ROI found, returning original image.")
        save_debug_image(img, "08_no_roi_original_fallback")
        return img, None
    except Exception as e:
        logging.error(f"ROI detection failed: {str(e)}")
        save_debug_image(img, "08_roi_detection_error_fallback")
        return img, None

def detect_segments(digit_img, brightness):
    """Detect seven-segment patterns in a digit image."""
    h, w = digit_img.shape
    if h < 8 or w < 6:
        return None

    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 > (0.15 if brightness < 80 else 0.35)

    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])
        score = matches - 0.15 * non_matches_penalty
        if matches >= len(pattern) * 0.65:
            score += 1.0
        if score > max_score:
            max_score = score
            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:
        preprocessed = preprocess_image(img, method='clahe')
        brightness = estimate_brightness(img)
        thresh_value = 60 if brightness < 80 else 0
        _, thresh = cv2.threshold(preprocessed, thresh_value, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
        save_debug_image(thresh, "09_roi_thresh_for_digits")

        # Morphological operations
        kernel = np.ones((3, 3), np.uint8)
        thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)
        save_debug_image(thresh, "10_morph_closed")

        batch_size = max(4, min(16, int(img.shape[0] * img.shape[1] / 100000)))
        results = easyocr_reader.readtext(thresh, detail=1, paragraph=False, 
                                          contrast_ths=0.1, adjust_contrast=1.3, 
                                          text_threshold=0.3, mag_ratio=6.0, 
                                          allowlist='0123456789.', batch_size=batch_size, y_ths=0.4)
        
        logging.info(f"EasyOCR results (seven-segment): {results}")
        if not results:
            logging.info("EasyOCR found no digits in seven-segment OCR.")
            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 (text.isdigit() or text == '.') and h_bbox > 5:
                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"11_digit_crop_{idx}_{easyocr_char}")
            if easyocr_conf > 0.85 or easyocr_char == '.':
                recognized_text += easyocr_char
            else:
                digit_from_segments = detect_segments(digit_img_crop, brightness)
                recognized_text += digit_from_segments if digit_from_segments else easyocr_char
        
        logging.info(f"Before validation, recognized_text: {recognized_text}")
        text = re.sub(r"[^\d\.]", "", recognized_text)
        if text.count('.') > 1:
            text = text.replace('.', '', text.count('.') - 1)
        if text and re.fullmatch(r"^\d*\.?\d*$", text):
            text = text.strip('.')
            if text == '':
                return None
            return text.lstrip('0') or '0'
        logging.info(f"Custom OCR text '{recognized_text}' failed validation.")
        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")

        # Apply rotation correction
        img = correct_rotation(img)

        brightness = estimate_brightness(img)
        conf_threshold = 0.65 if brightness > 150 else (0.45 if brightness > 80 else 0.25)

        roi_img, roi_bbox = detect_roi(img)
        if roi_bbox:
            roi_area = roi_bbox[2] * roi_bbox[3]
            conf_threshold *= 1.1 if roi_area > (img.shape[0] * img.shape[1] * 0.5) else 1.0

        custom_result = custom_seven_segment_ocr(roi_img, roi_bbox)
        if custom_result and custom_result != '0':
            try:
                weight = float(custom_result)
                if 0.0001 <= weight <= 5000:
                    logging.info(f"Custom OCR result: {custom_result}, Confidence: 95.0%")
                    return custom_result, 95.0
                else:
                    logging.warning(f"Custom OCR result {custom_result} outside typical weight range.")
            except ValueError:
                logging.warning(f"Custom OCR result '{custom_result}' is not a valid number.")

        logging.info("Custom OCR failed or invalid, falling back to enhanced EasyOCR.")
        preprocessed_roi = preprocess_image(roi_img, method='hist')
        block_size = max(9, min(31, int(roi_img.shape[0] / 25) * 2 + 1))
        final_roi = cv2.adaptiveThreshold(preprocessed_roi, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                          cv2.THRESH_BINARY_INV, block_size, 5)
        save_debug_image(final_roi, "12_fallback_adaptive_thresh")

        batch_size = max(4, min(16, int(roi_img.shape[0] * roi_img.shape[1] / 100000)))
        ocr_passes = [
            {'contrast_ths': 0.2, 'text_threshold': 0.3, 'mag_ratio': 6.0, 'y_ths': 0.4, 'label': 'first'},
            {'contrast_ths': 0.1, 'text_threshold': 0.2, 'mag_ratio': 7.0, 'y_ths': 0.5, 'label': 'second'},
            {'contrast_ths': 0.05, 'text_threshold': 0.1, 'mag_ratio': 8.0, 'y_ths': 0.6, 'label': 'third'}
        ]
        candidates = []

        for ocr_pass in ocr_passes:
            results = easyocr_reader.readtext(final_roi, detail=1, paragraph=False, 
                                              contrast_ths=ocr_pass['contrast_ths'], 
                                              adjust_contrast=1.4, 
                                              text_threshold=ocr_pass['text_threshold'], 
                                              mag_ratio=ocr_pass['mag_ratio'], 
                                              allowlist='0123456789. kglb', 
                                              batch_size=batch_size, 
                                              y_ths=ocr_pass['y_ths'])
            logging.info(f"EasyOCR results ({ocr_pass['label']} pass): {results}")
            save_debug_image(final_roi, f"12_fallback_adaptive_thresh_{ocr_pass['label']}_pass")
            
            unit = None
            for (bbox, text, conf) in results:
                if 'kg' in text.lower():
                    unit = 'kg'
                    continue
                elif 'g' in text.lower():
                    unit = 'g'
                    continue
                elif 'lb' in text.lower():
                    unit = 'lb'
                    continue
                text = re.sub(r"[^\d\.]", "", text)
                if text.count('.') > 1:
                    text = text.replace('.', '', text.count('.') - 1)
                text = text.strip('.')
                if re.fullmatch(r"^\d*\.?\d*$", text):
                    try:
                        weight = float(text)
                        if unit == 'g':
                            weight /= 1000
                        elif unit == 'lb':
                            weight *= 0.453592
                        range_score = 1.5 if 0.0001 <= weight <= 5000 else 0.6
                        digit_count = len(text.replace('.', ''))
                        digit_score = 1.4 if 1 <= digit_count <= 8 else 0.7
                        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 < 0.02:
                                score *= 0.4
                        candidates.append((text, conf, score, unit))
                        logging.info(f"Candidate EasyOCR weight: '{text}', Unit: {unit or 'none'}, Conf: {conf}, Score: {score}")
                    except ValueError:
                        logging.warning(f"Could not convert '{text}' to float during EasyOCR fallback.")

        # Fallback to full image if no candidates
        if not candidates:
            logging.info("No candidates from ROI, trying full image.")
            preprocessed_full = preprocess_image(img, method='hist')
            final_full = cv2.adaptiveThreshold(preprocessed_full, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                               cv2.THRESH_BINARY_INV, block_size, 5)
            save_debug_image(final_full, "12_fallback_full_image")
            results = easyocr_reader.readtext(final_full, detail=1, paragraph=False, 
                                              contrast_ths=0.1, adjust_contrast=1.5, 
                                              text_threshold=0.2, mag_ratio=7.0, 
                                              allowlist='0123456789. kglb', batch_size=batch_size, y_ths=0.5)
            logging.info(f"EasyOCR results (full image): {results}")
            
            unit = None
            for (bbox, text, conf) in results:
                if 'kg' in text.lower():
                    unit = 'kg'
                    continue
                elif 'g' in text.lower():
                    unit = 'g'
                    continue
                elif 'lb' in text.lower():
                    unit = 'lb'
                    continue
                text = re.sub(r"[^\d\.]", "", text)
                if text.count('.') > 1:
                    text = text.replace('.', '', text.count('.') - 1)
                text = text.strip('.')
                if re.fullmatch(r"^\d*\.?\d*$", text):
                    try:
                        weight = float(text)
                        if unit == 'g':
                            weight /= 1000
                        elif unit == 'lb':
                            weight *= 0.453592
                        range_score = 1.2 if 0.0001 <= weight <= 5000 else 0.5
                        digit_count = len(text.replace('.', ''))
                        digit_score = 1.2 if 1 <= digit_count <= 8 else 0.6
                        score = conf * range_score * digit_score * 0.8  # Penalty for full image
                        candidates.append((text, conf, score, unit))
                        logging.info(f"Candidate EasyOCR weight (full image): '{text}', Unit: {unit or 'none'}, Conf: {conf}, Score: {score}")
                    except ValueError:
                        logging.warning(f"Could not convert '{text}' to float during full image fallback.")

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

        # Select best candidate
        best_weight, best_conf, best_score, best_unit = max(candidates, key=lambda x: x[2])
        
        # Format the weight
        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')
            best_weight = f"{int_part}.{dec_part}" if dec_part else int_part
        else:
            best_weight = best_weight.lstrip('0') or "0"

        try:
            final_weight = float(best_weight)
            if final_weight < 0.0001 or final_weight > 5000:
                best_conf *= 0.5
            elif final_weight == 0 and best_conf < 0.95:
                best_conf *= 0.6  # Penalize zero weights
        except ValueError:
            pass

        logging.info(f"Final detected weight: {best_weight} kg, Confidence: {round(best_conf * 100, 2)}%, Unit: {best_unit or 'none'}")
        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