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| import easyocr | |
| import numpy as np | |
| import cv2 | |
| import re | |
| reader = easyocr.Reader(['en'], gpu=False) | |
| def enhance_image(img): | |
| gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| blur = cv2.GaussianBlur(gray, (5, 5), 0) | |
| _, thresh = cv2.threshold(blur, 120, 255, cv2.THRESH_BINARY_INV) | |
| # Find contours to crop region with max text | |
| contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) | |
| height, width = gray.shape | |
| digit_region = gray # default | |
| if contours: | |
| largest = max(contours, key=cv2.contourArea) | |
| x, y, w, h = cv2.boundingRect(largest) | |
| pad = 10 | |
| x = max(x - pad, 0) | |
| y = max(y - pad, 0) | |
| w = min(w + 2 * pad, width - x) | |
| h = min(h + 2 * pad, height - y) | |
| digit_region = gray[y:y+h, x:x+w] | |
| # Resize and sharpen | |
| digit_region = cv2.resize(digit_region, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC) | |
| kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]]) | |
| digit_region = cv2.filter2D(digit_region, -1, kernel) | |
| return digit_region | |
| def extract_weight_from_image(pil_img): | |
| try: | |
| img = np.array(pil_img) | |
| img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) | |
| processed = enhance_image(img) | |
| results = reader.readtext(processed) | |
| best_weight = None | |
| best_conf = 0.0 | |
| for (_, text, conf) in results: | |
| text = text.lower().strip() | |
| text = text.replace(",", ".").replace("o", "0").replace("s", "5") | |
| text = text.replace("kgs", "").replace("kg", "") | |
| text = re.sub(r"[^\d\.]", "", text) | |
| if len(text) >= 2 and re.fullmatch(r"\d{1,4}(\.\d{1,3})?", text): | |
| if conf > best_conf: | |
| best_weight = text | |
| best_conf = conf | |
| if not best_weight: | |
| return "Not detected", 0.0 | |
| if "." in best_weight: | |
| int_part, dec_part = best_weight.split(".") | |
| best_weight = f"{int_part.lstrip('0') or '0'}.{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 | |