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):
    """Preprocess image for better OCR accuracy."""
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # Apply bilateral filter to preserve edges
    denoised = cv2.bilateralFilter(gray, 11, 17, 17)
    save_debug_image(denoised, "01_preprocess_bilateral")
    # Enhance contrast using CLAHE
    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
    enhanced = clahe.apply(denoised)
    save_debug_image(enhanced, "02_preprocess_clahe")
    return enhanced

def detect_roi(img):
    """Detect and crop the region of interest (likely the digital display)."""
    try:
        save_debug_image(img, "03_original")
        preprocessed = preprocess_image(img)
        brightness_map = cv2.GaussianBlur(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), (15, 15), 0)
        
        # Dynamic adaptive thresholding
        block_size = max(11, min(31, int(img.shape[0] / 20) * 2 + 1))
        thresh = cv2.adaptiveThreshold(preprocessed, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                       cv2.THRESH_BINARY_INV, block_size, 5)
        _, 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, "04_roi_combined_threshold")
        
        # Morphological operations to connect digits
        kernel = np.ones((5, 5), np.uint8)
        dilated = cv2.dilate(combined_thresh, kernel, iterations=2)
        eroded = cv2.erode(dilated, kernel, iterations=1)
        save_debug_image(eroded, "05_roi_morphological")
        
        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])
                aspect_ratio = w / h
                if (1000 < area < (img_area * 0.9) and 
                    1.0 <= aspect_ratio <= 10.0 and w > 80 and h > 40 and roi_brightness > 100):
                    valid_contours.append((c, roi_brightness))
                    logging.debug(f"Contour: Area={area}, Aspect={aspect_ratio:.2f}, Brightness={roi_brightness:.2f}")
            
            if valid_contours:
                contour, _ = max(valid_contours, key=lambda x: x[1])  # Max brightness
                x, y, w, h = cv2.boundingRect(contour)
                padding = 80
                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 with dimensions: ({x}, {y}, {w}, {h})")
                return roi_img, (x, y, w, h)
        
        logging.info("No suitable ROI found, attempting fallback criteria.")
        # Fallback with relaxed criteria
        valid_contours = [c for c in contours if 500 < cv2.contourArea(c) < (img_area * 0.95) and 
                          0.8 <= cv2.boundingRect(c)[2]/cv2.boundingRect(c)[3] <= 12.0]
        if valid_contours:
            contour = max(valid_contours, key=cv2.contourArea)
            x, y, w, h = cv2.boundingRect(contour)
            padding = 80
            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_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, "06_no_roi_original_fallback")
        return img, None
    except Exception as e:
        logging.error(f"ROI detection failed: {str(e)}")
        save_debug_image(img, "06_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 < 20 or w < 15:
        return None

    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*0.45), int(h*0.55)),
        'bottom': (int(w*0.15), int(w*0.85), int(h*0.8), h),
        'left_top': (0, int(w*0.25), int(h*0.15), int(h*0.5)),
        'left_bottom': (0, int(w*0.25), int(h*0.5), int(h*0.85)),
        'right_top': (int(w*0.75), w, int(h*0.15), int(h*0.5)),
        'right_bottom': (int(w*0.75), w, int(h*0.5), int(h*0.85))
    }

    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 > (0.3 if brightness < 100 else 0.5)

    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 - 0.3 * non_matches_penalty
        if matches >= len(pattern) * 0.8:
            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)
        brightness = estimate_brightness(img)
        thresh_value = 100 if brightness < 100 else 0
        _, thresh = cv2.threshold(preprocessed, thresh_value, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
        save_debug_image(thresh, "07_roi_thresh_for_digits")

        # Morphological operations to enhance digit segments
        kernel = np.ones((3, 3), np.uint8)
        thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)
        save_debug_image(thresh, "08_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=0.3, adjust_contrast=1.0, 
                                          text_threshold=0.6, mag_ratio=3.0, 
                                          allowlist='0123456789.', batch_size=batch_size, y_ths=0.2)
        
        logging.info(f"EasyOCR results: {results}")
        if not results:
            logging.info("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) == 1 and (text.isdigit() or text == '.') and h_bbox > 10:
                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"09_digit_crop_{idx}_{easyocr_char}")
            if easyocr_conf > 0.95 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")

        # Rotation correction
        edges = cv2.Canny(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), 100, 200)
        lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=100, minLineLength=100, maxLineGap=10)
        if lines is not None:
            angle = np.mean([np.arctan2(line[0][3] - line[0][1], line[0][2] - line[0][0]) * 180 / np.pi for line in lines])
            if abs(angle) > 5:
                (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")

        brightness = estimate_brightness(img)
        conf_threshold = 0.7 if brightness > 150 else (0.6 if brightness > 80 else 0.4)
        roi_img, roi_bbox = detect_roi(img)
        if roi_bbox:
            roi_area = roi_bbox[2] * roi_bbox[3]
            conf_threshold *= 1.2 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:
            try:
                weight = float(custom_result)
                if 0.01 <= weight <= 500:
                    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)
        kernel_sharpening = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
        sharpened_roi = cv2.filter2D(preprocessed_roi, -1, kernel_sharpening)
        save_debug_image(sharpened_roi, "10_fallback_sharpened")
        block_size = max(11, min(31, int(roi_img.shape[0] / 20) * 2 + 1))
        final_roi = cv2.adaptiveThreshold(sharpened_roi, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                          cv2.THRESH_BINARY_INV, block_size, 8)
        save_debug_image(final_roi, "11_fallback_adaptive_thresh")

        batch_size = max(4, min(16, int(roi_img.shape[0] * roi_img.shape[1] / 100000)))
        results = easyocr_reader.readtext(final_roi, detail=1, paragraph=False, 
                                          contrast_ths=0.4, adjust_contrast=1.2, 
                                          text_threshold=0.5, mag_ratio=4.0, 
                                          allowlist='0123456789. kglb', batch_size=batch_size, y_ths=0.2)

        best_weight = None
        best_conf = 0.0
        best_score = 0.0
        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  # Convert grams to kilograms
                    elif unit == 'lb':
                        weight *= 0.453592  # Convert pounds to kilograms
                    range_score = 1.5 if 0.01 <= weight <= 500 else 0.8
                    digit_count = len(text.replace('.', ''))
                    digit_score = 1.3 if 2 <= digit_count <= 6 else 0.9
                    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 < 0.05:
                            score *= 0.6
                    if score > best_score and conf > conf_threshold:
                        best_weight = text
                        best_conf = conf
                        best_score = score
                        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.")

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

        # 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 < 0.01 or final_weight > 500:
                best_conf *= 0.7
        except ValueError:
            pass

        logging.info(f"Final detected weight: {best_weight}, Unit: {unit or 'none'}, 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