68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
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import cv2
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import os
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import json
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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 test_rotation_ocr():
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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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if not os.path.exists(img_path):
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print(f"Error: {img_path} not found")
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return
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img = cv2.imread(img_path)
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h, w = img.shape[:2]
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cx, cy = w // 2, h // 2
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ocr = PaddleOCR(use_angle_cls=True, lang='ch')
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results = []
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# Test angles from -90 to 90 with step 15
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for angle in range(-120, 121, 15):
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M = cv2.getRotationMatrix2D((cx, cy), angle, 1.0)
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rotated = cv2.warpAffine(img, M, (w, h), borderValue=(255, 255, 255))
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# We can also try different scales or enhancements
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res = ocr.ocr(rotated)
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if res:
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# Handle list of dicts (PaddleX format)
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for page in res:
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if 'rec_texts' in page:
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for text in page['rec_texts']:
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clean_text = text.replace(" ", "")
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sim = similarity(target, clean_text)
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if sim > 0.1:
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print(f"Angle: {angle} | Sim: {sim:.4f} | Text: {clean_text}")
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results.append({
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"angle": angle,
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"text": clean_text,
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"sim": sim
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})
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# Handle standard PaddleOCR format just in case
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elif isinstance(page, list):
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for line in page:
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if isinstance(line, list) and len(line) > 1:
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clean_text = line[1][0].replace(" ", "")
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sim = similarity(target, clean_text)
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if sim > 0.1:
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print(f"Angle: {angle} | Sim: {sim:.4f} | Text: {clean_text}")
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results.append({
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"angle": angle,
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"text": clean_text,
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"sim": sim
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})
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results.sort(key=lambda x: x['sim'], reverse=True)
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with open("rotation_ocr_results.json", "w", encoding="utf-8") as f:
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json.dump(results, f, ensure_ascii=False, indent=2)
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if __name__ == "__main__":
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test_rotation_ocr()
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