Create ocrr_engine.py

ocrr_engine.py

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+import pytesseract
+import numpy as np
+import cv2
+import re
+from PIL import Image
+import logging
+import sys
+
+# Set up logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[logging.StreamHandler(sys.stdout)])
+
+def preprocess_image(img):
+    """Preprocess image for robust OCR, optimized for various weight display formats."""
+    try:
+        # Convert PIL to OpenCV format
+        img = np.array(img)
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+        
+        # Convert to grayscale
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        
+        # Enhance contrast for diverse lighting conditions
+        clahe = cv2.createCLAHE(clipLimit=4.0, tileGridSize=(8, 8))
+        enhanced = clahe.apply(gray)
+        
+        # Apply adaptive thresholding with flexible block size
+        block_size = max(11, min(31, int(img.shape[0] / 20) * 2 + 1))
+        thresh = cv2.adaptiveThreshold(
+            enhanced, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, block_size, 3
+        )
+        
+        # Sharpen to enhance edges
+        kernel = np.array([[-1, -1, -1],
+                          [-1, 9, -1],
+                          [-1, -1, -1]])
+        sharpened = cv2.filter2D(thresh, -1, kernel)
+        
+        # Denoise for noisy images
+        denoised = cv2.fastNlMeansDenoising(sharpened, h=10)
+        
+        return denoised
+    except Exception as e:
+        logging.error(f"Preprocessing failed: {str(e)}")
+        return gray  # Fallback to grayscale
+
+def extract_weight_from_image(pil_img):
+    """Extract weight and unit from a digital scale image, supporting various formats."""
+    try:
+        # Preprocess image
+        thresh = preprocess_image(pil_img)
+        
+        # Convert to PIL for Tesseract
+        pil_img = Image.fromarray(cv2.cvtColor(thresh, cv2.COLOR_GRAY2RGB))
+        
+        # Try Tesseract with optimized config for numbers and units
+        config = r'--oem 3 --psm 7 -c tessedit_char_whitelist=0123456789.,kgKg'
+        text = pytesseract.image_to_string(pil_img, config=config)
+        logging.info(f"Tesseract raw output: {text}")
+        
+        # Clean and validate text
+        text = text.strip().lower()
+        text = re.sub(r'\s+', '', text)  # Remove extra spaces
+        
+        # Extract weight and unit
+        match = re.search(r'(\d*\.?\d+)([kgkg]?)', text)
+        if match:
+            weight_str = match.group(1)
+            unit = match.group(2) if match.group(2) else "g"  # Default to grams if no unit
+            weight_str = weight_str.replace(',', '.')  # Handle decimal formats (e.g., 68,0)
+            if re.fullmatch(r'^\d*\.?\d+$', weight_str):
+                weight_str = weight_str.lstrip('0') or '0'
+                confidence = 95.0 if len(weight_str.replace('.', '')) >= 3 else 90.0
+                try:
+                    weight = float(weight_str)
+                    if 0.001 <= weight <= 5000:
+                        logging.info(f"Detected weight: {weight} {unit}, Confidence: {confidence:.2f}%")
+                        return weight_str, confidence, unit
+                except ValueError:
+                    logging.warning(f"Invalid weight format: {weight_str}")
+        
+        logging.info("No valid weight detected.")
+        return "Not detected", 0.0, ""
+    except Exception as e:
+        logging.error(f"Weight extraction failed: {str(e)}")
+        return "Not detected", 0.0, ""