Spaces:
Running
Running
| import easyocr | |
| import numpy as np | |
| import re | |
| import cv2 | |
| reader = easyocr.Reader(['en'], gpu=False) | |
| def extract_weight_from_image(pil_img): | |
| try: | |
| img = np.array(pil_img) | |
| # Step 1: Convert to grayscale | |
| gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) | |
| # Step 2: Apply adaptive threshold to handle lighting | |
| thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, | |
| cv2.THRESH_BINARY_INV, 11, 2) | |
| # Step 3: Dilate to make digits thicker | |
| kernel = np.ones((2, 2), np.uint8) | |
| dilated = cv2.dilate(thresh, kernel, iterations=1) | |
| # Step 4: OCR on the preprocessed image | |
| result = reader.readtext(dilated, detail=0) | |
| text = " ".join(result).strip() | |
| print("OCR Text:", text) | |
| # Step 5: Match numeric values like 52.30 or 003.25 | |
| match = re.search(r"\b\d{2,4}\.?\d{0,2}\b", text) | |
| if match: | |
| return match.group(), 95.0 | |
| else: | |
| return "No weight detected", 0.0 | |
| except Exception as e: | |
| return f"Error: {str(e)}", 0.0 | |