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| import easyocr | |
| import numpy as np | |
| import cv2 | |
| import re | |
| reader = easyocr.Reader(['en'], gpu=False) | |
| def enhance_image(img): | |
| # Convert to grayscale | |
| gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| # Apply sharpening kernel | |
| kernel = np.array([[0, -1, 0], [-1, 5,-1], [0, -1, 0]]) | |
| sharp = cv2.filter2D(gray, -1, kernel) | |
| # Contrast Limited Adaptive Histogram Equalization (CLAHE) | |
| clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8)) | |
| contrast = clahe.apply(sharp) | |
| # Denoising | |
| denoised = cv2.fastNlMeansDenoising(contrast, h=30) | |
| # Adaptive threshold for very dim images | |
| thresh = cv2.adaptiveThreshold(denoised, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, | |
| cv2.THRESH_BINARY, 11, 2) | |
| return thresh | |
| def extract_weight_from_image(pil_img): | |
| try: | |
| img = np.array(pil_img) | |
| # Resize if too large or too small | |
| 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) | |
| elif max(height, width) < 400: | |
| scale = 2.5 # Upscale very small images | |
| img = cv2.resize(img, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC) | |
| # Enhance image for OCR | |
| preprocessed = enhance_image(img) | |
| results = reader.readtext(preprocessed) | |
| best_weight = None | |
| best_conf = 0.0 | |
| for item in results: | |
| if len(item) != 2 or not isinstance(item[1], tuple): | |
| continue | |
| text, conf = item[1] | |
| 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"[^\d\.]", "", cleaned) | |
| if re.fullmatch(r"\d{2,4}(\.\d{1,3})?", cleaned): | |
| if conf > best_conf: | |
| best_weight = cleaned | |
| best_conf = conf | |
| if not best_weight: | |
| for item in results: | |
| if len(item) != 2 or not isinstance(item[1], tuple): | |
| continue | |
| text, conf = item[1] | |
| fallback = re.sub(r"[^\d\.]", "", text) | |
| if fallback and fallback.replace(".", "").isdigit(): | |
| best_weight = fallback | |
| best_conf = conf | |
| break | |
| if not best_weight: | |
| return "Not detected", 0.0 | |
| 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 | |