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

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

def test_rotation_ocr():
    target = "威凯检测技术有限公司"
    img_path = "seal_cropped.png"
    if not os.path.exists(img_path):
        print(f"Error: {img_path} not found")
        return

    img = cv2.imread(img_path)
    h, w = img.shape[:2]
    cx, cy = w // 2, h // 2

    ocr = PaddleOCR(use_angle_cls=True, lang='ch')
    
    results = []
    # Test angles from -90 to 90 with step 15
    for angle in range(-120, 121, 15):
        M = cv2.getRotationMatrix2D((cx, cy), angle, 1.0)
        rotated = cv2.warpAffine(img, M, (w, h), borderValue=(255, 255, 255))
        
        # We can also try different scales or enhancements
        res = ocr.ocr(rotated)
        
        if res:
            # Handle list of dicts (PaddleX format)
            for page in res:
                if 'rec_texts' in page:
                    for text in page['rec_texts']:
                        clean_text = text.replace(" ", "")
                        sim = similarity(target, clean_text)
                        if sim > 0.1:
                            print(f"Angle: {angle} | Sim: {sim:.4f} | Text: {clean_text}")
                            results.append({
                                "angle": angle,
                                "text": clean_text,
                                "sim": sim
                            })
                # Handle standard PaddleOCR format just in case
                elif isinstance(page, list):
                    for line in page:
                        if isinstance(line, list) and len(line) > 1:
                            clean_text = line[1][0].replace(" ", "")
                            sim = similarity(target, clean_text)
                            if sim > 0.1:
                                print(f"Angle: {angle} | Sim: {sim:.4f} | Text: {clean_text}")
                                results.append({
                                    "angle": angle,
                                    "text": clean_text,
                                    "sim": sim
                                })

    results.sort(key=lambda x: x['sim'], reverse=True)
    with open("rotation_ocr_results.json", "w", encoding="utf-8") as f:
        json.dump(results, f, ensure_ascii=False, indent=2)

if __name__ == "__main__":
    test_rotation_ocr()