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model_predict.py

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