ocr_engine.py

import easyocr
import numpy as np
import cv2
import re
import logging

# 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)

def estimate_blur(img):
    """Estimate image blur using Laplacian variance"""
    try:
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        return cv2.Laplacian(gray, cv2.CV_64F).var()
    except Exception as e:
        logging.error(f"Blur estimation failed: {str(e)}")
        return 100  # Default value for fallback

def detect_roi(img):
    """Detect and crop the region of interest (likely the digital display)"""
    try:
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        # Adaptive thresholding to handle varying lighting
        thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                      cv2.THRESH_BINARY_INV, 11, 2)
        # Dilate to connect text regions
        kernel = np.ones((5, 5), np.uint8)
        dilated = cv2.dilate(thresh, kernel, iterations=1)
        # Find contours
        contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        if contours:
            # Get the largest contour with reasonable size
            valid_contours = [c for c in contours if cv2.contourArea(c) > 1000]
            if valid_contours:
                largest_contour = max(valid_contours, key=cv2.contourArea)
                x, y, w, h = cv2.boundingRect(largest_contour)
                # Add padding and ensure bounds
                x, y = max(0, x-20), max(0, y-20)
                w, h = min(w+40, img.shape[1]-x), min(h+40, img.shape[0]-y)
                if w > 50 and h > 30:  # Minimum size for valid ROI
                    return img[y:y+h, x:x+w]
        return img  # Fallback to original image
    except Exception as e:
        logging.error(f"ROI detection failed: {str(e)}")
        return img

def enhance_image(img, mode="standard"):
    """Enhance image with different modes for multi-scale processing"""
    try:
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

        if mode == "high_contrast":
            # Stronger denoising and contrast for blurry images
            denoised = cv2.bilateralFilter(gray, d=11, sigmaColor=100, sigmaSpace=100)
            clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
        elif mode == "low_noise":
            # Gentle denoising for clear but noisy images
            denoised = cv2.bilateralFilter(gray, d=7, sigmaColor=50, sigmaSpace=50)
            clahe = cv2.createCLAHE(clipLimit=1.5, tileGridSize=(8, 8))
        else:
            # Standard preprocessing
            denoised = cv2.bilateralFilter(gray, d=9, sigmaColor=75, sigmaSpace=75)
            clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))

        contrast = clahe.apply(denoised)

        # Adaptive thresholding
        thresh = cv2.adaptiveThreshold(contrast, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
                                      cv2.THRESH_BINARY, 11, 2)

        # Morphological operations
        kernel = np.ones((3, 3), np.uint8)
        morphed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=1)

        # Adaptive sharpening
        blur_score = estimate_blur(img)
        sharpen_strength = 5 if blur_score < 100 else 3
        sharpen_kernel = np.array([[0, -1, 0], [-1, sharpen_strength, -1], [0, -1, 0]])
        sharpened = cv2.filter2D(morphed, -1, sharpen_kernel)

        # Dynamic resizing
        h, w = sharpened.shape
        target_size = 800
        scale_factor = min(target_size / max(h, w), 2.0) if max(h, w) < 300 else min(target_size / max(h, w), 1.0)
        if scale_factor != 1.0:
            sharpened = cv2.resize(sharpened, None, fx=scale_factor, fy=scale_factor, 
                                 interpolation=cv2.INTER_CUBIC if scale_factor > 1 else cv2.INTER_AREA)

        return sharpened
    except Exception as e:
        logging.error(f"Image enhancement failed (mode={mode}): {str(e)}")
        return img

def extract_weight_from_image(pil_img):
    try:
        img = np.array(pil_img)
        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

        # Estimate blur for adaptive thresholding
        blur_score = estimate_blur(img)
        conf_threshold = 0.35 if blur_score < 100 else 0.55  # Slightly stricter thresholds

        # Detect ROI
        roi_img = detect_roi(img)

        # Process multiple image versions
        images_to_process = [
            ("standard", enhance_image(roi_img, mode="standard"), {}),
            ("high_contrast", enhance_image(roi_img, mode="high_contrast"), {}),
            ("low_noise", enhance_image(roi_img, mode="low_noise"), {}),
            ("original", roi_img, {'allowlist': '0123456789.'})  # Restrict to digits and decimal
        ]

        best_weight = None
        best_conf = 0.0
        best_score = 0.0

        for mode, proc_img, ocr_params in images_to_process:
            # EasyOCR detection
            results = easyocr_reader.readtext(proc_img, detail=1, paragraph=False, **ocr_params)
            
            for (bbox, text, conf) in results:
                original_text = text
                text = text.lower().strip()

                # Fix common OCR errors
                text = text.replace(",", ".").replace(";", ".")
                text = text.replace("o", "0").replace("O", "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")
                text = text.replace("b", "8").replace("B", "8")
                text = text.replace("z", "2").replace("Z", "2")
                text = text.replace("q", "9").replace("Q", "9")
                text = text.replace("kgs", "").replace("kg", "").replace("k", "")
                text = re.sub(r"[^\d\.]", "", text)

                # Regex for weight (0.0 to 9999.999)
                if re.fullmatch(r"\d{1,4}(\.\d{0,3})?", text):
                    try:
                        weight = float(text)
                        # Score based on realistic weight range (0.1–500 kg)
                        range_score = 1.0 if 0.1 <= weight <= 500 else 0.3
                        score = conf * range_score
                        if score > best_score and conf > conf_threshold:
                            best_weight = text
                            best_conf = conf
                            best_score = score
                    except ValueError:
                        continue

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

        # Format output
        if "." in best_weight:
            int_part, dec_part = best_weight.split(".")
            int_part = int_part.lstrip("0") or "0"
            best_weight = f"{int_part}.{dec_part.rstrip('0')}"
        else:
            best_weight = best_weight.lstrip('0') or "0"

        return best_weight, round(best_conf * 100, 2)

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