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47 lines
2.0 KiB
47 lines
2.0 KiB
6 months ago
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import cv2
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import paddle as paddle
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import numpy as np
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license_plate = cv2.imread('./车牌.png')
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gray_plate = cv2.cvtColor(license_plate, cv2.COLOR_RGB2GRAY)
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ret, binary_plate = cv2.threshold(gray_plate, 175, 255, cv2.THRESH_BINARY)
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result = []
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for col in range(binary_plate.shape[1]):
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result.append(0)
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for row in range(binary_plate.shape[0]):
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result[col] = result[col] + binary_plate[row][col] / 255
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character_dict = {}
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num = 0
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i = 0
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while i < len(result):
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if result[i] == 0:
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i += 1
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else:
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index = i + 1
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while result[index] != 0:
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index += 1
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character_dict[num] = [i, index - 1]
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num += 1
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i = index
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for i in range(8):
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if i == 2:
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continue
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padding = (170 - (character_dict[i][1] - character_dict[i][0])) / 2
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ndarray = np.pad(binary_plate[:, character_dict[i][0]:character_dict[i][1]], ((0, 0), (int(padding), int(padding))),
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'constant', constant_values=(0, 0))
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ndarray = cv2.resize(ndarray, (20, 20))
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cv2.imwrite('./' + str(i) + '.png', ndarray)
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def load_image(path):
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img = paddle.dataset.image.load_image(file=path, is_color=False)
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img = img.astype('float32')
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img = img[np.newaxis,] / 255.0
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return img
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match = {'A':'A','B':'B','C':'C','D':'D','E':'E','F':'F','G':'G','H':'H','I':'I','J':'J','K':'K','L':'L','M':'M','N':'N',
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'O':'O','P':'P','Q':'Q','R':'R','S':'S','T':'T','U':'U','V':'V','W':'W','X':'X','Y':'Y','Z':'Z',
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'yun':'云','cuan':'川','hei':'黑','zhe':'浙','ning':'宁','jin':'津','gan':'赣','hu':'沪','liao':'辽','jl':'吉','qing':'青','zang':'藏',
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'e1':'鄂','meng':'蒙','gan1':'甘','qiong':'琼','shan':'陕','min':'闽','su':'苏','xin':'新','wan':'皖','jing':'京','xiang':'湘','gui':'贵',
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'yu1':'渝','yu':'豫','ji':'冀','yue':'粤','gui1':'桂','sx':'晋','lu':'鲁',
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'0':'0','1':'1','2':'2','3':'3','4':'4','5':'5','6':'6','7':'7','8':'8','9':'9'}
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