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import sys
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import cv2
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import numpy as np
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from os import path
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from loguru import logger
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from numpy import average, dot, linalg
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base_path = path.join(path.split(__file__)[0], 'models')
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def similarity(img_1, img_2):
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try:
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images = [img_1, img_2]
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vectors = []
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norms = []
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for image in images:
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vector = [average(pixels) for pixels in image]
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vectors.append(vector)
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norms.append(linalg.norm(vector, 2))
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a, b = vectors
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a_norm, b_norm = norms
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return dot(a / a_norm, b / b_norm)
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except Exception:
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exceptionInformation = sys.exc_info()
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logger.warning(f'[验证码识别] | 运算出错：{exceptionInformation}')
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def recognize(img_content: bytes):
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try:
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img = cv2.imdecode(np.asarray(bytearray(img_content), dtype=np.uint8), cv2.IMREAD_COLOR)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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img = cv2.threshold(img, 200, 255, cv2.THRESH_BINARY)[1]
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models = [cv2.imread(path.join(base_path, f'{i}.png')) for i in range(10)]
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code = ''
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for i in range(4):
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code += sorted(
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[(f'{j}', similarity(img[4:24, 9 + i * 15:24 + i * 15], std)) for j, std in enumerate(models)],
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key=lambda x: x[1], reverse=True
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)[0][0]
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logger.info(f'[验证码识别] | 识别结果：{code}')
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return code
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except Exception:
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exceptionInformation = sys.exc_info()
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logger.warning(f'[验证码识别] | 识别出错：{exceptionInformation}')
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