import cv2
import os
import difflib
import numpy as np
from paddleocr import PaddleOCR

def similarity(s1, s2):
    return difflib.SequenceMatcher(None, s1, s2).ratio()

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

if __name__ == "__main__":
    rapid_test()