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Update ocr_engine.py
Browse files- ocr_engine.py +33 -20
ocr_engine.py
CHANGED
@@ -5,38 +5,51 @@ import re
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reader = easyocr.Reader(['en'], gpu=False)
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def extract_weight_from_image(pil_img):
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try:
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img = np.array(pil_img)
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gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
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gray = cv2.bilateralFilter(gray, 11, 17, 17)
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_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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results = reader.readtext(thresh)
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ocr_raw_texts = []
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weight_candidates = []
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for _, text, conf in results:
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t = t.replace("s", "5").replace("S", "5")
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t = t.replace("g", "9").replace("G", "6")
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t = re.sub(r"[^\d\.]", "", t)
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if re.fullmatch(r"\d{2,4}(\.\d{1,2})?",
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weight_candidates.append((
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if not weight_candidates:
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return "Not detected", 0.0, "\n".join(
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best_weight, best_conf = sorted(weight_candidates, key=lambda x: -x[1])[0]
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return best_weight, round(best_conf * 100, 2), "\n".join(
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except Exception as e:
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return f"Error: {str(e)}", 0.0, "OCR failed"
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reader = easyocr.Reader(['en'], gpu=False)
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def enhance_image(img):
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# Resize to make text clearer
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img = cv2.resize(img, None, fx=4, fy=4, interpolation=cv2.INTER_CUBIC)
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# Convert to gray
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gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
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# Denoise
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gray = cv2.fastNlMeansDenoising(gray, h=15)
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# Sharpen
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kernel = np.array([[0, -1, 0],
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[-1, 5,-1],
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[0, -1, 0]])
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sharp = cv2.filter2D(gray, -1, kernel)
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# Contrast enhancement (CLAHE)
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clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
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contrast = clahe.apply(sharp)
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return contrast
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def extract_weight_from_image(pil_img):
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try:
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img = np.array(pil_img)
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enhanced = enhance_image(img)
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results = reader.readtext(enhanced)
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ocr_texts = [text for _, text, _ in results]
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weight_candidates = []
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for _, text, conf in results:
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cleaned = text.lower()
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cleaned = cleaned.replace("kg", "").replace("kgs", "")
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cleaned = cleaned.replace("o", "0").replace("s", "5").replace("g", "9")
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cleaned = re.sub(r"[^\d\.]", "", cleaned)
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if re.fullmatch(r"\d{2,4}(\.\d{1,2})?", cleaned):
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weight_candidates.append((cleaned, conf))
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if not weight_candidates:
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return "Not detected", 0.0, "\n".join(ocr_texts)
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best_weight, best_conf = sorted(weight_candidates, key=lambda x: -x[1])[0]
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return best_weight, round(best_conf * 100, 2), "\n".join(ocr_texts)
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except Exception as e:
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return f"Error: {str(e)}", 0.0, "OCR failed"
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