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
# Consider using 'en' and potentially 'ch_sim' or other relevant languages if your scales have non-English characters.
# gpu=True can speed up processing if a compatible GPU is 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.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 detect_roi(img):
    """Detect and crop the region of interest (likely the digital display)"""
    try:
        save_debug_image(img, "01_original")
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        save_debug_image(gray, "02_grayscale")
        
        brightness = estimate_brightness(img)

        # Adaptive thresholding based on brightness
        # For darker images, a lower threshold might be needed.
        # For very bright images, a higher threshold.
        # Tuned thresholds based on observed values
        if brightness > 180:
            thresh_value = 230
        elif brightness > 100:
            thresh_value = 190
        else:
            thresh_value = 150 # Even lower for very dark images
            
        _, thresh = cv2.threshold(gray, thresh_value, 255, cv2.THRESH_BINARY)
        save_debug_image(thresh, f"03_roi_threshold_{thresh_value}")
        
        # Increased kernel size for dilation to better connect segments of digits
        # This helps in forming a solid contour for the display
        kernel = np.ones((13, 13), np.uint8) # Slightly larger kernel
        dilated = cv2.dilate(thresh, kernel, iterations=5) # Increased iterations for stronger connection
        save_debug_image(dilated, "04_roi_dilated")
        
        contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        
        if contours:
            # Filter contours by a more robust area range and shape
            img_area = img.shape[0] * img.shape[1]
            valid_contours = []
            for c in contours:
                area = cv2.contourArea(c)
                # Filter out very small and very large contours (e.g., entire image, or noise)
                if 1500 < area < (img_area * 0.9): # Increased min area, max area
                    x, y, w, h = cv2.boundingRect(c)
                    aspect_ratio = w / h
                    # Check for typical display aspect ratios and minimum size
                    if 2.0 <= aspect_ratio <= 5.5 and w > 100 and h > 50: # Adjusted aspect ratio and min size
                        valid_contours.append(c)

            if valid_contours:
                # Sort by area descending and iterate
                for contour in sorted(valid_contours, key=cv2.contourArea, reverse=True):
                    x, y, w, h = cv2.boundingRect(contour)
                    
                    # Expand ROI to ensure full digits are captured and a small border
                    padding = 40 # Increased 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 original image for full image OCR attempt.")
        save_debug_image(img, "05_no_roi_original_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 < 15 or w < 10: # Increased minimum dimensions for a digit
        return None

    # Define segment regions (top, middle, bottom, left-top, left-bottom, right-top, right-bottom)
    # Adjusted segment proportions for better robustness, more aggressive cropping
    segments = {
        'top': (int(w*0.15), int(w*0.85), 0, int(h*0.2)),
        'middle': (int(w*0.15), int(w*0.85), int(h*0.4), int(h*0.6)),
        'bottom': (int(w*0.15), int(w*0.85), int(h*0.8), h),
        'left_top': (0, int(w*0.25), int(h*0.05), int(h*0.5)),
        'left_bottom': (0, int(w*0.25), int(h*0.5), int(h*0.95)),
        'right_top': (int(w*0.75), w, int(h*0.05), int(h*0.5)),
        'right_bottom': (int(w*0.75), w, int(h*0.5), int(h*0.95))
    }

    segment_presence = {}
    for name, (x1, x2, y1, y2) in segments.items():
        # Ensure coordinates are within bounds
        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
        
        # Count white pixels in the region
        pixel_count = np.sum(region == 255)
        total_pixels = region.size
        
        # Segment is present if a significant portion of the region is white
        # Adjusted threshold for segment presence - higher for robustness
        segment_presence[name] = pixel_count / total_pixels > 0.55 # Increased sensitivity further

    # Seven-segment digit patterns - remain the same
    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 # Initialize with a lower value

    for digit, pattern in digit_patterns.items():
        matches = sum(1 for segment in pattern if segment_presence.get(segment, False))
        
        # Penalize for segments that should NOT be present but are
        non_matches_penalty = sum(1 for segment in segment_presence if segment not in pattern and segment_presence[segment])
        
        # Prioritize digits with more matched segments and fewer incorrect segments
        current_score = matches - non_matches_penalty
        
        # Add a small bonus for matching exactly all required segments for the digit
        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:
            # Tie-breaking: prefer digits with fewer "extra" segments when scores are equal
            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[best_digit_pattern]) # Corrected logic
            if current_digit_non_matches < best_digit_non_matches:
                best_match = digit
    
    # Debugging segment presence
    # logging.debug(f"Digit Image Shape: {digit_img.shape}, Segments: {segment_presence}, Best Match: {best_match}")
    # save_debug_image(digit_img, f"digit_segment_debug_{best_match or 'none'}", prefix="10_")

    return best_match

def custom_seven_segment_ocr(img, roi_bbox):
    """Perform custom OCR for seven-segment displays"""
    try:
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        
        # Adaptive thresholding for digits within ROI
        # Using OTSU for automatic thresholding or a fixed value depending on brightness
        brightness = estimate_brightness(img)
        if brightness > 150:
            _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
        else:
            _, thresh = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY) # Lower threshold for darker displays
        save_debug_image(thresh, "06_roi_thresh_for_digits")

        # Use EasyOCR to get bounding boxes for digits
        # Increased text_threshold for more confident digit detection
        # Adjusted mag_ratio for better handling of digit sizes
        # Added y_ths to reduce sensitivity to vertical position variations (common in scales)
        results = easyocr_reader.readtext(thresh, detail=1, paragraph=False, 
                                        contrast_ths=0.2, adjust_contrast=0.8, # Slightly more contrast adjustment
                                        text_threshold=0.85, mag_ratio=1.5, # Adjusted mag_ratio back, seems to work better for 7-seg
                                        allowlist='0123456789.', y_ths=0.2) # Increased y_ths for row grouping tolerance

        if not results:
            logging.info("EasyOCR found no digits for custom seven-segment OCR.")
            return None

        # Sort bounding boxes left to right
        digits_info = []
        for (bbox, text, conf) in results:
            # Ensure the text found by EasyOCR is a single digit or a decimal point
            # Also filter by a minimum height of the bounding box for robustness
            (x1, y1), (x2, y2), (x3, y3), (x4, y4) = bbox
            h_bbox = max(y1,y2,y3,y4) - min(y1,y2,y3,y4)
            if len(text) == 1 and (text.isdigit() or text == '.') and h_bbox > 10: # Min height for bbox
                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))

        # Sort by x_min (left to right)
        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 is very confident about a digit or it's a decimal, use its result directly
            # Or if the digit crop is too small for reliable segment detection
            if easyocr_conf > 0.9 or easyocr_char == '.' or digit_img_crop.shape[0] < 20 or digit_img_crop.shape[1] < 15: # Lowered confidence for direct use
                recognized_text += easyocr_char
            else:
                # Otherwise, try the segment detection
                digit_from_segments = detect_segments(digit_img_crop)
                if digit_from_segments:
                    recognized_text += digit_from_segments
                else:
                    # If segment detection also fails, fall back to EasyOCR's less confident result
                    recognized_text += easyocr_char
            
        # Validate the recognized text
        text = recognized_text
        text = re.sub(r"[^\d\.]", "", text) # Remove any non-digit/non-dot characters
        
        # Ensure there's at most one decimal point
        if text.count('.') > 1:
            text = text.replace('.', '', text.count('.') - 1) # Remove extra decimal points
        
        # Basic validation for common weight formats (e.g., 75.5, 120.0, 5.0)
        # Allow numbers to start with . (e.g., .5 -> 0.5) if it's the only character
        if text and re.fullmatch(r"^\d*\.?\d*$", text) and len(text.replace('.', '')) > 0:
            # Handle cases like ".5" -> "0.5"
            if text.startswith('.') and len(text) > 1:
                text = "0" + text
            # Handle cases like "5." -> "5"
            if text.endswith('.') and len(text) > 1:
                text = text.rstrip('.')
            
            # Ensure it's not just a single dot or empty after processing
            if text == '.' or text == '':
                return None
            return text
        logging.info(f"Custom OCR final 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):
    try:
        img = np.array(pil_img)
        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

        brightness = estimate_brightness(img)
        # Adjust confidence threshold more dynamically
        conf_threshold = 0.9 if brightness > 150 else (0.8 if brightness > 80 else 0.7) # Adjusted thresholds

        # Detect ROI
        roi_img, roi_bbox = detect_roi(img)
        
        # Try custom seven-segment OCR first
        custom_result = custom_seven_segment_ocr(roi_img, roi_bbox)
        if custom_result:
            # Format the custom result: remove leading zeros (unless it's "0" or "0.x") and trailing zeros after decimal
            if "." in custom_result:
                int_part, dec_part = custom_result.split(".")
                int_part = int_part.lstrip("0") or "0"
                dec_part = dec_part.rstrip('0')
                if not dec_part and int_part != "0": # If decimal part is empty (e.g., "50."), remove the dot
                    custom_result = int_part
                elif not dec_part and int_part == "0": # if it's "0." keep it as "0"
                    custom_result = "0"
                else:
                    custom_result = f"{int_part}.{dec_part}"
            else:
                custom_result = custom_result.lstrip('0') or "0"
            
            # Additional validation for custom result to ensure it's a valid number
            try:
                float(custom_result)
                logging.info(f"Custom OCR result: {custom_result}, Confidence: 100.0%")
                return custom_result, 100.0  # High confidence for custom OCR
            except ValueError:
                logging.warning(f"Custom OCR result '{custom_result}' is not a valid number, falling back.")
                custom_result = None # Force fallback

        # Fallback to EasyOCR if custom OCR fails
        logging.info("Custom OCR failed or invalid, falling back to general EasyOCR.")
        
        # Apply more aggressive image processing for EasyOCR if custom OCR failed
        processed_roi_img_gray = cv2.cvtColor(roi_img, cv2.COLOR_BGR2GRAY)
        
        # Sharpening
        kernel_sharpening = np.array([[-1,-1,-1], 
                                      [-1,9,-1], 
                                      [-1,-1,-1]])
        sharpened_roi = cv2.filter2D(processed_roi_img_gray, -1, kernel_sharpening)
        save_debug_image(sharpened_roi, "08_fallback_sharpened")

        # Apply adaptive thresholding to the sharpened image for better digit isolation
        # Block size and C constant can be critical
        processed_roi_img_final = cv2.adaptiveThreshold(sharpened_roi, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                                        cv2.THRESH_BINARY, 15, 3) # Adjusted block size and C
        save_debug_image(processed_roi_img_final, "09_fallback_adaptive_thresh")

        # EasyOCR parameters for general text
        # Adjusted parameters for better digit recognition
        # added batch_size for potentially better performance on multiple texts
        results = easyocr_reader.readtext(processed_roi_img_final, detail=1, paragraph=False, 
                                        contrast_ths=0.3, adjust_contrast=0.9, 
                                        text_threshold=0.6, mag_ratio=1.8, # Lowered text_threshold, increased mag_ratio
                                        allowlist='0123456789.', batch_size=4, y_ths=0.3) # Increased y_ths

        best_weight = None
        best_conf = 0.0
        best_score = 0.0

        for (bbox, text, conf) in results:
            text = text.lower().strip()
            
            # More robust character replacements
            text = text.replace(",", ".").replace(";", ".").replace(":", ".").replace(" ", "") # Remove spaces
            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") # 't' can look like '7'
            text = text.replace("~", "") # Common noise
            text = text.replace("`", "")

            # Remove common weight units and other non-numeric characters
            text = re.sub(r"(kgs|kg|k|lb|g|gr|pounds|lbs)\b", "", text) # Added lbs
            text = re.sub(r"[^\d\.]", "", text)

            # Handle multiple decimal points (keep only the first one)
            if text.count('.') > 1:
                parts = text.split('.')
                text = parts[0] + '.' + ''.join(parts[1:])
            
            # Clean up leading/trailing dots if any
            text = text.strip('.')

            # Validate the final text format
            # Allow optional leading zero, and optional decimal with up to 3 places
            if re.fullmatch(r"^\d*\.?\d{0,3}$", text) and len(text.replace('.', '')) > 0: # Ensure at least one digit
                try:
                    weight = float(text)
                    # Refined scoring for weights within a reasonable range
                    range_score = 1.0
                    if 0.1 <= weight <= 250: # Very common personal scale range
                        range_score = 1.5
                    elif weight > 250 and weight <= 500: # Larger weights
                        range_score = 1.2
                    elif weight > 500 and weight <= 1000:
                        range_score = 1.0
                    else: # Very small or very large weights
                        range_score = 0.5
                    
                    digit_count = len(text.replace('.', ''))
                    digit_score = 1.0
                    if digit_count >= 2 and digit_count <= 5: # Prefer weights with 2-5 digits (e.g., 5.0, 75.5, 123.4)
                        digit_score = 1.3
                    elif digit_count == 1: # Single digit weights less common but possible
                        digit_score = 0.8
                    
                    score = conf * range_score * digit_score
                    
                    # Also consider area of the bounding box relative to ROI for confidence
                    if roi_bbox:
                        (x_roi, y_roi, w_roi, h_roi) = roi_bbox
                        roi_area = w_roi * h_roi
                        # Calculate bbox area accurately
                        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 < 0.03: # Very small bounding boxes might be noise
                            score *= 0.5 
                        
                        # Penalize if bbox is too narrow (e.g., single line detected as digit)
                        bbox_aspect_ratio = (x_max - x_min) / (y_max - y_min) if (y_max - y_min) > 0 else 0
                        if bbox_aspect_ratio < 0.2: # Very thin bounding boxes
                            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

        # Final formatting of the best detected weight
        if "." in best_weight:
            int_part, dec_part = best_weight.split(".")
            int_part = int_part.lstrip("0") or "0" # Remove leading zeros, keep "0" for 0.x
            dec_part = dec_part.rstrip('0') # Remove trailing zeros after decimal
            
            if not dec_part and int_part != "0": # If decimal part is empty (e.g., "50."), remove the dot
                best_weight = int_part
            elif not dec_part and int_part == "0": # if it's "0." keep it as "0"
                best_weight = "0"
            else:
                best_weight = f"{int_part}.{dec_part}"
        else:
            best_weight = best_weight.lstrip('0') or "0" # Remove leading zeros, keep "0"

        # Final check for extremely unlikely weights (e.g., 0.0001, 9999)
        try:
            final_float_weight = float(best_weight)
            if final_float_weight < 0.01 or final_float_weight > 1000: # Adjust this range if needed
                logging.warning(f"Detected weight {final_float_weight} is outside typical range, reducing confidence.")
                best_conf *= 0.5 # Reduce confidence for out-of-range values
        except ValueError:
            pass # Should not happen if previous parsing worked

        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