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| import easyocr | |
| import numpy as np | |
| import cv2 | |
| import re | |
| reader = easyocr.Reader(['en'], gpu=False) | |
| def extract_weight_from_image(pil_img): | |
| try: | |
| img = np.array(pil_img) | |
| # Resize large image if needed | |
| max_dim = 1000 | |
| height, width = img.shape[:2] | |
| if max(height, width) > max_dim: | |
| scale = max_dim / max(height, width) | |
| img = cv2.resize(img, None, fx=scale, fy=scale, interpolation=cv2.INTER_AREA) | |
| # OCR | |
| results = reader.readtext(img) | |
| weight_candidates = [] | |
| fallback_weight = None | |
| fallback_conf = 0.0 | |
| for box, (text, conf) in results: # ✅ Correct unpacking | |
| cleaned = text.lower().strip() | |
| cleaned = cleaned.replace(",", ".") | |
| cleaned = cleaned.replace("o", "0").replace("O", "0") | |
| cleaned = cleaned.replace("s", "5").replace("S", "5") | |
| cleaned = cleaned.replace("g", "9").replace("G", "6") | |
| cleaned = cleaned.replace("kg", "").replace("kgs", "") | |
| cleaned = re.sub(r"[^0-9\.]", "", cleaned) | |
| if cleaned and cleaned.replace(".", "").isdigit() and not fallback_weight: | |
| fallback_weight = cleaned | |
| fallback_conf = conf | |
| if cleaned.count(".") <= 1 and re.fullmatch(r"\d{2,4}(\.\d{1,3})?", cleaned): | |
| weight_candidates.append((cleaned, conf)) | |
| if weight_candidates: | |
| best_weight, best_conf = sorted(weight_candidates, key=lambda x: -x[1])[0] | |
| elif fallback_weight: | |
| best_weight, best_conf = fallback_weight, fallback_conf | |
| else: | |
| return "Not detected", 0.0 | |
| # Normalize | |
| 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}" | |
| else: | |
| best_weight = best_weight.lstrip("0") or "0" | |
| return best_weight, round(best_conf * 100, 2) | |
| except Exception as e: | |
| return f"Error: {str(e)}", 0.0 | |