AutoWeightLogger1/ocr_engine.py

ocr_engine.py

@@ -1,41 +0,0 @@
-import cv2
-import numpy as np
-import re
-from PIL import Image
-
-def extract_weight_from_image(pil_img):
-    try:
-        img = np.array(pil_img)
-
-        # Convert to grayscale
-        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-
-        # Threshold image
-        _, thresh = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY)
-
-        # Invert if needed
-        if np.mean(thresh > 127) < 0.5:
-            thresh = cv2.bitwise_not(thresh)
-
-        # Resize to make digits bigger
-        scale_factor = 4
-        resized = cv2.resize(thresh, None, fx=scale_factor, fy=scale_factor, interpolation=cv2.INTER_LINEAR)
-
-        # OCR-style region crop: focus on left part of display
-        height, width = resized.shape
-        digit_region = resized[0:height, 0:int(width * 0.7)]  # ignore 'kg'
-
-        # Use pytesseract as fallback OCR for just digits
-        import pytesseract
-        config = "--psm 7 -c tessedit_char_whitelist=0123456789."
-        result = pytesseract.image_to_string(digit_region, config=config)
-        print("Raw OCR:", result)
-
-        match = re.search(r"(\d{1,4}(?:\.\d{1,2})?)", result)
-        if match:
-            return f"{match.group()} kg", 100.0
-        else:
-            return "No weight detected kg", 0.0
-
-    except Exception as e:
-        return f"Error: {str(e)}", 0.0