61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
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import cv2
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import os
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import difflib
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import numpy as np
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from paddleocr import PaddleOCR
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def similarity(s1, s2):
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return difflib.SequenceMatcher(None, s1, s2).ratio()
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def rapid_test():
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target = "威凯检测技术有限公司"
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img_path = "seal_cropped.png"
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img = cv2.imread(img_path)
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if img is None: return
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h_orig, w_orig = img.shape[:2]
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cx, cy = w_orig // 2, h_orig // 2
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radius = min(cx, cy)
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ocr = PaddleOCR(use_angle_cls=True, lang='ch')
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# We'll test a few angles and two factors
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angles = [0, 90, 180, 270]
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factors = [1.0, 1.2]
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for angle in angles:
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M_rot = cv2.getRotationMatrix2D((cx, cy), angle, 1.0)
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rotated = cv2.warpAffine(img, M_rot, (w_orig, h_orig), borderValue=(255, 255, 255))
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for factor in factors:
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out_w = int(radius * 2 * np.pi * factor)
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out_h = radius
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unwarped = cv2.warpPolar(rotated, (out_w, out_h), (cx, cy), radius, cv2.WARP_POLAR_LINEAR)
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# Annulus for outer text
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strip = unwarped[int(out_h * 0.75):int(out_h * 0.95), :]
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# Padding
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padded = cv2.copyMakeBorder(strip, 30, 30, 0, 0, cv2.BORDER_CONSTANT, value=[255, 255, 255])
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# OCR
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res = ocr.ocr(padded)
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best_text = ""
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best_sim = 0.0
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if res:
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for page in res:
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if 'rec_texts' in page:
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for t in page['rec_texts']:
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ct = t.replace(" ", "")
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s = similarity(target, ct)
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if s > best_sim:
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best_sim = s
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best_text = ct
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print(f"Angle: {angle} | Factor: {factor} | Sim: {best_sim:.4f} | Text: {best_text}")
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if __name__ == "__main__":
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rapid_test()
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