在之前釋出的一篇博文《MNIST資料集實現車牌識別--初步演示版》中，我們演示瞭如何使用TensorFlow進行車牌識別，但是，當時採用的資料集是MNIST數字手寫體，只能分類0-9共10個數字，無法分類省份簡稱和字母，侷限性較大，無實際意義。

經過影象定位分割處理，博主收集了相關省份簡稱和26個字母的圖片資料集，結合前述博文中貼出的python+TensorFlow程式碼，實現了完整的車牌識別功能。本著分享精神，在此送上全部程式碼和車牌資料集。

省份簡稱訓練+識別程式碼（儲存檔名為train-license-province.py）（拷貝程式碼請務必注意python文字縮排，只要有一處縮排錯誤，就無法得到正確結果，或者出現異常

1. #!/usr/bin/python3.5

2. # -*- coding: utf-8 -*-

3. import sys

4. import os

5. import time

6. import random

7. import numpy as np

8. import tensorflow as tf

9. from PIL import Image

10. SIZE = 1280

11. WIDTH = 32

12. HEIGHT = 40

13. NUM_CLASSES = 6

14. iterations = 300

15. SAVER_DIR = "train-saver/province/"

16. PROVINCES = ("京","閩","粵","蘇","滬","浙")

17. nProvinceIndex = 0

18. time_begin = time.time()

19. # 定義輸入節點，對應於圖片畫素值矩陣集合和圖片標籤(即所代表的數字)

20. x = tf.placeholder(tf.float32, shape=[None, SIZE])

21. y_ = tf.placeholder(tf.float32, shape=[None, NUM_CLASSES])

22. x_image = tf.reshape(x, [-1, WIDTH, HEIGHT, 1])

23. # 定義卷積函式

24. def conv_layer(inputs, W, b, conv_strides, kernel_size, pool_strides, padding):

25. L1_conv = tf.nn.conv2d(inputs, W, strides=conv_strides, padding=padding)

26. L1_relu = tf.nn.relu(L1_conv + b)

27. return tf.nn.max_pool(L1_relu, ksize=kernel_size, strides=pool_strides, padding='SAME')

28. # 定義全連線層函式

29. def full_connect(inputs, W, b):

30. return tf.nn.relu(tf.matmul(inputs, W) + b)

31. if __name__ =='__main__' and sys.argv[1]=='train':

32. # 第一次遍歷圖片目錄是為了獲取圖片總數

33. input_count = 0

34. for i in range(0,NUM_CLASSES):

35. dir = './train_images/training-set/chinese-characters/%s/' % i # 這裡可以改成你自己的圖片目錄，i為分類標籤

36. for rt, dirs, files in os.walk(dir):

37. for filename in files:

38. input_count += 1

39. # 定義對應維數和各維長度的陣列

40. input_images = np.array([[0]*SIZE for i in range(input_count)])

41. input_labels = np.array([[0]*NUM_CLASSES for i in range(input_count)])

42. # 第二次遍歷圖片目錄是為了生成圖片資料和標籤

43. index = 0

44. for i in range(0,NUM_CLASSES):

45. dir = './train_images/training-set/chinese-characters/%s/' % i # 這裡可以改成你自己的圖片目錄，i為分類標籤

46. for rt, dirs, files in os.walk(dir):

47. for filename in files:

48. filename = dir + filename

49. img = Image.open(filename)

50. width = img.size[0]

51. height = img.size[1]

52. for h in range(0, height):

53. for w in range(0, width):

54. # 通過這樣的處理，使數字的線條變細，有利於提高識別準確率

55. if img.getpixel((w, h)) > 230:

56. input_images[index][w+h*width] = 0

57. else:

58. input_images[index][w+h*width] = 1

59. input_labels[index][i] = 1

60. index += 1

61. # 第一次遍歷圖片目錄是為了獲取圖片總數

62. val_count = 0

63. for i in range(0,NUM_CLASSES):

64. dir = './train_images/validation-set/chinese-characters/%s/' % i # 這裡可以改成你自己的圖片目錄，i為分類標籤

65. for rt, dirs, files in os.walk(dir):

66. for filename in files:

67. val_count += 1

68. # 定義對應維數和各維長度的陣列

69. val_images = np.array([[0]*SIZE for i in range(val_count)])

70. val_labels = np.array([[0]*NUM_CLASSES for i in range(val_count)])

71. # 第二次遍歷圖片目錄是為了生成圖片資料和標籤

72. index = 0

73. for i in range(0,NUM_CLASSES):

74. dir = './train_images/validation-set/chinese-characters/%s/' % i # 這裡可以改成你自己的圖片目錄，i為分類標籤

75. for rt, dirs, files in os.walk(dir):

76. for filename in files:

77. filename = dir + filename

78. img = Image.open(filename)

79. width = img.size[0]

80. height = img.size[1]

81. for h in range(0, height):

82. for w in range(0, width):

83. # 通過這樣的處理，使數字的線條變細，有利於提高識別準確率

84. if img.getpixel((w, h)) > 230:

85. val_images[index][w+h*width] = 0

86. else:

87. val_images[index][w+h*width] = 1

88. val_labels[index][i] = 1

89. index += 1

90. with tf.Session() as sess:

91. # 第一個卷積層

92. W_conv1 = tf.Variable(tf.truncated_normal([8, 8, 1, 16], stddev=0.1), name="W_conv1")

93. b_conv1 = tf.Variable(tf.constant(0.1, shape=[16]), name="b_conv1")

94. conv_strides = [1, 1, 1, 1]

95. kernel_size = [1, 2, 2, 1]

96. pool_strides = [1, 2, 2, 1]

97. L1_pool = conv_layer(x_image, W_conv1, b_conv1, conv_strides, kernel_size, pool_strides, padding='SAME')

98. # 第二個卷積層

99. W_conv2 = tf.Variable(tf.truncated_normal([5, 5, 16, 32], stddev=0.1), name="W_conv2")

100. b_conv2 = tf.Variable(tf.constant(0.1, shape=[32]), name="b_conv2")

101. conv_strides = [1, 1, 1, 1]

102. kernel_size = [1, 1, 1, 1]

103. pool_strides = [1, 1, 1, 1]

104. L2_pool = conv_layer(L1_pool, W_conv2, b_conv2, conv_strides, kernel_size, pool_strides, padding='SAME')

105. # 全連線層

106. W_fc1 = tf.Variable(tf.truncated_normal([16 * 20 * 32, 512], stddev=0.1), name="W_fc1")

107. b_fc1 = tf.Variable(tf.constant(0.1, shape=[512]), name="b_fc1")

108. h_pool2_flat = tf.reshape(L2_pool, [-1, 16 * 20*32])

109. h_fc1 = full_connect(h_pool2_flat, W_fc1, b_fc1)

110. # dropout

111. keep_prob = tf.placeholder(tf.float32)

112. h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

113. # readout層

114. W_fc2 = tf.Variable(tf.truncated_normal([512, NUM_CLASSES], stddev=0.1), name="W_fc2")

115. b_fc2 = tf.Variable(tf.constant(0.1, shape=[NUM_CLASSES]), name="b_fc2")

116. # 定義優化器和訓練op

117. y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

118. cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))

119. train_step = tf.train.AdamOptimizer((1e-4)).minimize(cross_entropy)

120. correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))

121. accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

122. # 初始化saver

123. saver = tf.train.Saver()

124. sess.run(tf.global_variables_initializer())

125. time_elapsed = time.time() - time_begin

126. print("讀取圖片檔案耗費時間：%d秒" % time_elapsed)

127. time_begin = time.time()

128. print ("一共讀取了 %s 個訓練影象， %s 個標籤" % (input_count, input_count))

129. # 設定每次訓練op的輸入個數和迭代次數，這裡為了支援任意圖片總數，定義了一個餘數remainder，譬如，如果每次訓練op的輸入個數為60，圖片總數為150張，則前面兩次各輸入60張，最後一次輸入30張（餘數30）

130. batch_size = 60

131. iterations = iterations

132. batches_count = int(input_count / batch_size)

133. remainder = input_count % batch_size

134. print ("訓練資料集分成 %s 批, 前面每批 %s 個數據，最後一批 %s 個數據" % (batches_count+1, batch_size, remainder))

135. # 執行訓練迭代

136. for it in range(iterations):

137. # 這裡的關鍵是要把輸入陣列轉為np.array

138. for n in range(batches_count):

139. train_step.run(feed_dict={x: input_images[n*batch_size:(n+1)*batch_size], y_: input_labels[n*batch_size:(n+1)*batch_size], keep_prob: 0.5})

140. if remainder > 0:

141. start_index = batches_count * batch_size;

142. train_step.run(feed_dict={x: input_images[start_index:input_count-1], y_: input_labels[start_index:input_count-1], keep_prob: 0.5})

143. # 每完成五次迭代，判斷準確度是否已達到100%，達到則退出迭代迴圈

144. iterate_accuracy = 0

145. if it%5 == 0:

146. iterate_accuracy = accuracy.eval(feed_dict={x: val_images, y_: val_labels, keep_prob: 1.0})

147. print ('第 %d 次訓練迭代: 準確率 %0.5f%%' % (it, iterate_accuracy*100))

148. if iterate_accuracy >= 0.9999 and it >= 150:

149. break;

150. print ('完成訓練!')

151. time_elapsed = time.time() - time_begin

152. print ("訓練耗費時間：%d秒" % time_elapsed)

153. time_begin = time.time()

154. # 儲存訓練結果

155. if not os.path.exists(SAVER_DIR):

156. print ('不存在訓練資料儲存目錄，現在建立儲存目錄')

157. os.makedirs(SAVER_DIR)

158. saver_path = saver.save(sess, "%smodel.ckpt"%(SAVER_DIR))

159. if __name__ =='__main__' and sys.argv[1]=='predict':

160. saver = tf.train.import_meta_graph("%smodel.ckpt.meta"%(SAVER_DIR))

161. with tf.Session() as sess:

162. model_file=tf.train.latest_checkpoint(SAVER_DIR)

163. saver.restore(sess, model_file)

164. # 第一個卷積層

165. W_conv1 = sess.graph.get_tensor_by_name("W_conv1:0")

166. b_conv1 = sess.graph.get_tensor_by_name("b_conv1:0")

167. conv_strides = [1, 1, 1, 1]

168. kernel_size = [1, 2, 2, 1]

169. pool_strides = [1, 2, 2, 1]

170. L1_pool = conv_layer(x_image, W_conv1, b_conv1, conv_strides, kernel_size, pool_strides, padding='SAME')

171. # 第二個卷積層

172. W_conv2 = sess.graph.get_tensor_by_name("W_conv2:0")

173. b_conv2 = sess.graph.get_tensor_by_name("b_conv2:0")

174. conv_strides = [1, 1, 1, 1]

175. kernel_size = [1, 1, 1, 1]

176. pool_strides = [1, 1, 1, 1]

177. L2_pool = conv_layer(L1_pool, W_conv2, b_conv2, conv_strides, kernel_size, pool_strides, padding='SAME')

178. # 全連線層

179. W_fc1 = sess.graph.get_tensor_by_name("W_fc1:0")

180. b_fc1 = sess.graph.get_tensor_by_name("b_fc1:0")

181. h_pool2_flat = tf.reshape(L2_pool, [-1, 16 * 20*32])

182. h_fc1 = full_connect(h_pool2_flat, W_fc1, b_fc1)

183. # dropout

184. keep_prob = tf.placeholder(tf.float32)

185. h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

186. # readout層

187. W_fc2 = sess.graph.get_tensor_by_name("W_fc2:0")

188. b_fc2 = sess.graph.get_tensor_by_name("b_fc2:0")

189. # 定義優化器和訓練op

190. conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

191. for n in range(1,2):

192. path = "test_images/%s.bmp" % (n)

193. img = Image.open(path)

194. width = img.size[0]

195. height = img.size[1]

196. img_data = [[0]*SIZE for i in range(1)]

197. for h in range(0, height):

198. for w in range(0, width):

199. if img.getpixel((w, h)) < 190:

200. img_data[0][w+h*width] = 1

201. else:

202. img_data[0][w+h*width] = 0

203. result = sess.run(conv, feed_dict = {x: np.array(img_data), keep_prob: 1.0})

204. max1 = 0

205. max2 = 0

206. max3 = 0

207. max1_index = 0

208. max2_index = 0

209. max3_index = 0

210. for j in range(NUM_CLASSES):

211. if result[0][j] > max1:

212. max1 = result[0][j]

213. max1_index = j

214. continue

215. if (result[0][j]>max2) and (result[0][j]<=max1):

216. max2 = result[0][j]

217. max2_index = j

218. continue

219. if (result[0][j]>max3) and (result[0][j]<=max2):

220. max3 = result[0][j]

221. max3_index = j

222. continue

223. nProvinceIndex = max1_index

224. print ("概率： [%s %0.2f%%] [%s %0.2f%%] [%s %0.2f%%]" % (PROVINCES[max1_index],max1*100, PROVINCES[max2_index],max2*100, PROVINCES[max3_index],max3*100))

225. print ("省份簡稱是: %s" % PROVINCES[nProvinceIndex])

城市代號訓練+識別程式碼（儲存檔名為train-license-letters.py）：

1. #!/usr/bin/python3.5

2. # -*- coding: utf-8 -*-

3. import sys

4. import os

5. import time

6. import random

7. import numpy as np

8. import tensorflow as tf

9. from PIL import Image

10. SIZE = 1280

11. WIDTH = 32

12. HEIGHT = 40

13. NUM_CLASSES = 26

14. iterations = 500

15. SAVER_DIR = "train-saver/letters/"

16. LETTERS_DIGITS = ("A","B","C","D","E","F","G","H","J","K","L","M","N","P","Q","R","S","T","U","V","W","X","Y","Z","I","O")

17. license_num = ""

18. time_begin = time.time()

19. # 定義輸入節點，對應於圖片畫素值矩陣集合和圖片標籤(即所代表的數字)

20. x = tf.placeholder(tf.float32, shape=[None, SIZE])

21. y_ = tf.placeholder(tf.float32, shape=[None, NUM_CLASSES])

22. x_image = tf.reshape(x, [-1, WIDTH, HEIGHT, 1])

23. # 定義卷積函式

24. def conv_layer(inputs, W, b, conv_strides, kernel_size, pool_strides, padding):

25. L1_conv = tf.nn.conv2d(inputs, W, strides=conv_strides, padding=padding)

26. L1_relu = tf.nn.relu(L1_conv + b)

27. return tf.nn.max_pool(L1_relu, ksize=kernel_size, strides=pool_strides, padding='SAME')

28. # 定義全連線層函式

29. def full_connect(inputs, W, b):

30. return tf.nn.relu(tf.matmul(inputs, W) + b)

31. if __name__ =='__main__' and sys.argv[1]=='train':

32. # 第一次遍歷圖片目錄是為了獲取圖片總數

33. input_count = 0

34. for i in range(0+10,NUM_CLASSES+10):

35. dir = './train_images/training-set/letters/%s/' % i # 這裡可以改成你自己的圖片目錄，i為分類標籤

36. for rt, dirs, files in os.walk(dir):

37. for filename in files:

38. input_count += 1

39. # 定義對應維數和各維長度的陣列

40. input_images = np.array([[0]*SIZE for i in range(input_count)])

41. input_labels = np.array([[0]*NUM_CLASSES for i in range(input_count)])

42. # 第二次遍歷圖片目錄是為了生成圖片資料和標籤

43. index = 0

44. for i in range(0+10,NUM_CLASSES+10):

45. dir = './train_images/training-set/letters/%s/' % i # 這裡可以改成你自己的圖片目錄，i為分類標籤

46. for rt, dirs, files in os.walk(dir):

47. for filename in files:

48. filename = dir + filename

49. img = Image.open(filename)

50. width = img.size[0]

51. height = img.size[1]

52. for h in range(0, height):

53. for w in range(0, width):

54. # 通過這樣的處理，使數字的線條變細，有利於提高識別準確率

55. if img.getpixel((w, h)) > 230:

56. input_images[index][w+h*width] = 0

57. else:

58. input_images[index][w+h*width] = 1

59. #print ("i=%d, index=%d" % (i, index))

60. input_labels[index][i-10] = 1

61. index += 1

62. # 第一次遍歷圖片目錄是為了獲取圖片總數

63. val_count = 0

64. for i in range(0+10,NUM_CLASSES+10):

65. dir = './train_images/validation-set/%s/' % i # 這裡可以改成你自己的圖片目錄，i為分類標籤

66. for rt, dirs, files in os.walk(dir):

67. for filename in files:

68. val_count += 1

69. # 定義對應維數和各維長度的陣列

70. val_images = np.array([[0]*SIZE for i in range(val_count)])

71. val_labels = np.array([[0]*NUM_CLASSES for i in range(val_count)])

72. # 第二次遍歷圖片目錄是為了生成圖片資料和標籤

73. index = 0

74. for i in range(0+10,NUM_CLASSES+10):

75. dir = './train_images/validation-set/%s/' % i # 這裡可以改成你自己的圖片目錄，i為分類標籤

76. for rt, dirs, files in os.walk(dir):

77. for filename in files:

78. filename = dir + filename

79. img = Image.open(filename)

80. width = img.size[0]

81. height = img.size[1]

82. for h in range(0, height):

83. for w in range(0, width):

84. # 通過這樣的處理，使數字的線條變細，有利於提高識別準確率

85. if img.getpixel((w, h)) > 230:

86. val_images[index][w+h*width] = 0

87. else:

88. val_images[index][w+h*width] = 1

89. val_labels[index][i-10] = 1

90. index += 1

91. with tf.Session() as sess:

92. # 第一個卷積層

93. W_conv1 = tf.Variable(tf.truncated_normal([8, 8, 1, 16], stddev=0.1), name="W_conv1")

94. b_conv1 = tf.Variable(tf.constant(0.1, shape=[16]), name="b_conv1")

95. conv_strides = [1, 1, 1, 1]

96. kernel_size = [1, 2, 2, 1]

97. pool_strides = [1, 2, 2, 1]

98. L1_pool = conv_layer(x_image, W_conv1, b_conv1, conv_strides, kernel_size, pool_strides, padding='SAME')

99. # 第二個卷積層

100. W_conv2 = tf.Variable(tf.truncated_normal([5, 5, 16, 32], stddev=0.1), name="W_conv2")

101. b_conv2 = tf.Variable(tf.constant(0.1, shape=[32]), name="b_conv2")

102. conv_strides = [1, 1, 1, 1]

103. kernel_size = [1, 1, 1, 1]

104. pool_strides = [1, 1, 1, 1]

105. L2_pool = conv_layer(L1_pool, W_conv2, b_conv2, conv_strides, kernel_size, pool_strides, padding='SAME')

106. # 全連線層

107. W_fc1 = tf.Variable(tf.truncated_normal([16 * 20 * 32, 512], stddev=0.1), name="W_fc1")

108. b_fc1 = tf.Variable(tf.constant(0.1, shape=[512]), name="b_fc1")

109. h_pool2_flat = tf.reshape(L2_pool, [-1, 16 * 20*32])

110. h_fc1 = full_connect(h_pool2_flat, W_fc1, b_fc1)

111. # dropout

112. keep_prob = tf.placeholder(tf.float32)

113. h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

114. # readout層

115. W_fc2 = tf.Variable(tf.truncated_normal([512, NUM_CLASSES], stddev=0.1), name="W_fc2")

116. b_fc2 = tf.Variable(tf.constant(0.1, shape=[NUM_CLASSES]), name="b_fc2")

117. # 定義優化器和訓練op

118. y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

119. cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))

120. train_step = tf.train.AdamOptimizer((1e-4)).minimize(cross_entropy)

121. correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))

122. accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

123. sess.run(tf.global_variables_initializer())

124. time_elapsed = time.time() - time_begin

125. print("讀取圖片檔案耗費時間：%d秒" % time_elapsed)

126. time_begin = time.time()

127. print ("一共讀取了 %s 個訓練影象， %s 個標籤" % (input_count, input_count))

128. # 設定每次訓練op的輸入個數和迭代次數，這裡為了支援任意圖片總數，定義了一個餘數remainder，譬如，如果每次訓練op的輸入個數為60，圖片總數為150張，則前面兩次各輸入60張，最後一次輸入30張（餘數30）

129. batch_size = 60

130. iterations = iterations

131. batches_count = int(input_count / batch_size)

132. remainder = input_count % batch_size

133. print ("訓練資料集分成 %s 批, 前面每批 %s 個數據，最後一批 %s 個數據" % (batches_count+1, batch_size, remainder))

134. # 執行訓練迭代

135. for it in range(iterations):

136. # 這裡的關鍵是要把輸入陣列轉為np.array

137. for n in range(batches_count):

138. train_step.run(feed_dict={x: input_images[n*batch_size:(n+1)*batch_size], y_: input_labels[n*batch_size:(n+1)*batch_size], keep_prob: 0.5})

139. if remainder > 0:

140. start_index = batches_count * batch_size;

141. train_step.run(feed_dict={x: input_images[start_index:input_count-1], y_: input_labels[start_index:input_count-1], keep_prob: 0.5})

142. # 每完成五次迭代，判斷準確度是否已達到100%，達到則退出迭代迴圈

143. iterate_accuracy = 0

144. if it%5 == 0:

145. iterate_accuracy = accuracy.eval(feed_dict={x: val_images, y_: val_labels, keep_prob: 1.0})

146. print ('第 %d 次訓練迭代: 準確率 %0.5f%%' % (it, iterate_accuracy*100))

147. if iterate_accuracy >= 0.9999 and it >= iterations:

148. break;

149. print ('完成訓練!')

150. time_elapsed = time.time() - time_begin

151. print ("訓練耗費時間：%d秒" % time_elapsed)

152. time_begin = time.time()

153. # 儲存訓練結果

154. if not os.path.exists(SAVER_DIR):

155. print ('不存在訓練資料儲存目錄，現在建立儲存目錄')

156. os.makedirs(SAVER_DIR)

157. # 初始化saver

158. saver = tf.train.Saver()

159. saver_path = saver.save(sess, "%smodel.ckpt"%(SAVER_DIR))

160. if __name__ =='__main__' and sys.argv[1]=='predict':

161. saver = tf.train.import_meta_graph("%smodel.ckpt.meta"%(SAVER_DIR))

162. with tf.Session() as sess:

163. model_file=tf.train.latest_checkpoint(SAVER_DIR)

164. saver.restore(sess, model_file)

165. # 第一個卷積層

166. W_conv1 = sess.graph.get_tensor_by_name("W_conv1:0")

167. b_conv1 = sess.graph.get_tensor_by_name("b_conv1:0")

168. conv_strides = [1, 1, 1, 1]

169. kernel_size = [1, 2, 2, 1]

170. pool_strides = [1, 2, 2, 1]

171. L1_pool = conv_layer(x_image, W_conv1, b_conv1, conv_strides, kernel_size, pool_strides, padding='SAME')

172. # 第二個卷積層

173. W_conv2 = sess.graph.get_tensor_by_name("W_conv2:0")

174. b_conv2 = sess.graph.get_tensor_by_name("b_conv2:0")

175. conv_strides = [1, 1, 1, 1]

176. kernel_size = [1, 1, 1, 1]

177. pool_strides = [1, 1, 1, 1]

178. L2_pool = conv_layer(L1_pool, W_conv2, b_conv2, conv_strides, kernel_size, pool_strides, padding='SAME')

179. # 全連線層

180. W_fc1 = sess.graph.get_tensor_by_name("W_fc1:0")

181. b_fc1 = sess.graph.get_tensor_by_name("b_fc1:0")

182. h_pool2_flat = tf.reshape(L2_pool, [-1, 16 * 20*32])

183. h_fc1 = full_connect(h_pool2_flat, W_fc1, b_fc1)

184. # dropout

185. keep_prob = tf.placeholder(tf.float32)

186. h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

187. # readout層

188. W_fc2 = sess.graph.get_tensor_by_name("W_fc2:0")

189. b_fc2 = sess.graph.get_tensor_by_name("b_fc2:0")

190. # 定義優化器和訓練op

191. conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

192. for n in range(2,3):

193. path = "test_images/%s.bmp" % (n)

194. img = Image.open(path)

195. width = img.size[0]

196. height = img.size[1]

197. img_data = [[0]*SIZE for i in range(1)]

198. for h in range(0, height):

199. for w in range(0, width):

200. if img.getpixel((w, h)) < 190:

201. img_data[0][w+h*width] = 1

202. else:

203. img_data[0][w+h*width] = 0

204. result = sess.run(conv, feed_dict = {x: np.array(img_data), keep_prob: 1.0})

205. max1 = 0

206. max2 = 0

207. max3 = 0

208. max1_index = 0

209. max2_index = 0

210. max3_index = 0

211. for j in range(NUM_CLASSES):

212. if result[0][j] > max1:

213. max1 = result[0][j]

214. max1_index = j

215. continue

216. if (result[0][j]>max2) and (result[0][j]<=max1):

217. max2 = result[0][j]

218. max2_index = j

219. continue

220. if (result[0][j]>max3) and (result[0][j]<=max2):

221. max3 = result[0][j]

222. max3_index = j

223. continue

224. if n == 3:

225. license_num += "-"

226. license_num = license_num + LETTERS_DIGITS[max1_index]

227. print ("概率： [%s %0.2f%%] [%s %0.2f%%] [%s %0.2f%%]" % (LETTERS_DIGITS[max1_index],max1*100, LETTERS_DIGITS[max2_index],max2*100, LETTERS_DIGITS[max3_index],max3*100))

228. print ("城市代號是: 【%s】" % license_num)

車牌編號訓練+識別程式碼（儲存檔名為train-license-digits.py）：

1. #!/usr/bin/python3.5

2. # -*- coding: utf-8 -*-

3. import sys

4. import os

5. import time

6. import random

7. import numpy as np

8. import tensorflow as tf

9. from PIL import Image

10. SIZE = 1280

11. WIDTH = 32

12. HEIGHT = 40

13. NUM_CLASSES = 34

14. iterations = 1000

15. SAVER_DIR = "train-saver/digits/"

16. LETTERS_DIGITS = ("0","1","2","3","4","5","6","7","8","9","A","B","C","D","E","F","G","H","J","K","L","M","N","P","Q","R","S","T","U","V","W","X","Y","Z")

17. license_num = ""

18. time_begin = time.time()

19. # 定義輸入節點，對應於圖片畫素值矩陣集合和圖片標籤(即所代表的數字)

20. x = tf.placeholder(tf.float32, shape=[None, SIZE])

21. y_ = tf.placeholder(tf.float32, shape=[None, NUM_CLASSES])

22. x_image = tf.reshape(x, [-1, WIDTH, HEIGHT, 1])

23. # 定義卷積函式

24. def conv_layer(inputs, W, b, conv_strides, kernel_size, pool_strides, padding):

25. L1_conv = tf.nn.conv2d(inputs, W, strides=conv_strides, padding=padding)

26. L1_relu = tf.nn.relu(L1_conv + b)

27. return tf.nn.max_pool(L1_relu, ksize=kernel_size, strides=pool_strides, padding='SAME')

28. # 定義全連線層函式

29. def full_connect(inputs, W, b):

30. return tf.nn.relu(tf.matmul(inputs, W) + b)

31. if __name__ =='__main__' and sys.argv[1]=='train':

32. # 第一次遍歷圖片目錄是為了獲取圖片總數

33. input_count = 0

34. for i in range(0,NUM_CLASSES):

35. dir = './train_images/training-set/%s/' % i # 這裡可以改成你自己的圖片目錄，i為分類標籤

36. for rt, dirs, files in os.walk(dir):

37. for filename in files:

38. input_count += 1

39. # 定義對應維數和各維長度的陣列

40. input_images = np.array([[0]*SIZE for i in range(input_count)])

41. input_labels = np.array([[0]*NUM_CLASSES for i in range(input_count)])

42. # 第二次遍歷圖片目錄是為了生成圖片資料和標籤

43. index = 0

44. for i in range(0,NUM_CLASSES):

45. dir = './train_images/training-set/%s/' % i # 這裡可以改成你自己的圖片目錄，i為分類標籤

46. for rt, dirs, files in os.walk(dir):

47. for filename in files:

48. filename = dir + filename

49. img = Image.open(filename)

50. width = img.size[0]

51. height = img.size[1]

52. for h in range(0, height):

53. for w in range(0, width):

54. # 通過這樣的處理，使數字的線條變細，有利於提高識別準確率

55. if img.getpixel((w, h)) > 230:

56. input_images[index][w+h*width] = 0

57. else:

58. input_images[index][w+h*width] = 1

59. input_labels[index][i] = 1

60. index += 1

61. # 第一次遍歷圖片目錄是為了獲取圖片總數

62. val_count = 0

63. for i in range(0,NUM_CLASSES):

64. dir = './train_images/validation-set/%s/' % i # 這裡可以改成你自己的圖片目錄，i為分類標籤

65. for rt, dirs, files in os.walk(dir):

66. for filename in files:

67. val_count += 1

68. # 定義對應維數和各維長度的陣列

69. val_images = np.array([[0]*SIZE for i in range(val_count)])

70. val_labels = np.array([[0]*NUM_CLASSES for i in range(val_count)])

71. # 第二次遍歷圖片目錄是為了生成圖片資料和標籤

72. index = 0

73. for i in range(0,NUM_CLASSES):

74. dir = './train_images/validation-set/%s/' % i # 這裡可以改成你自己的圖片目錄，i為分類標籤

75. for rt, dirs, files in os.walk(dir):

76. for filename in files:

77. filename = dir + filename

78. img = Image.open(filename)

79. width = img.size[0]

80. height = img.size[1]

81. for h in range(0, height):

82. for w in range(0, width):

83. # 通過這樣的處理，使數字的線條變細，有利於提高識別準確率

84. if img.getpixel((w, h)) > 230:

85. val_images[index][w+h*width] = 0

86. else:

87. val_images[index][w+h*width] = 1

88. val_labels[index][i] = 1

89. index += 1

90. with tf.Session() as sess:

91. # 第一個卷積層

92. W_conv1 = tf.Variable(tf.truncated_normal([8, 8, 1, 16], stddev=0.1), name="W_conv1")

93. b_conv1 = tf.Variable(tf.constant(0.1, shape=[16]), name="b_conv1")

94. conv_strides = [1, 1, 1, 1]

95. kernel_size = [1, 2, 2, 1]

96. pool_strides = [1, 2, 2, 1]

97. L1_pool = conv_layer(x_image, W_conv1, b_conv1, conv_strides, kernel_size, pool_strides, padding='SAME')

98. # 第二個卷積層

99. W_conv2 = tf.Variable(tf.truncated_normal([5, 5, 16, 32], stddev=0.1), name="W_conv2")

100. b_conv2 = tf.Variable(tf.constant(0.1, shape=[32]), name="b_conv2")

101. conv_strides = [1, 1, 1, 1]

102. kernel_size = [1, 1, 1, 1]

103. pool_strides = [1, 1, 1, 1]

104. L2_pool = conv_layer(L1_pool, W_conv2, b_conv2, conv_strides, kernel_size, pool_strides, padding='SAME')

105. # 全連線層

106. W_fc1 = tf.Variable(tf.truncated_normal([16 * 20 * 32, 512], stddev=0.1), name="W_fc1")

107. b_fc1 = tf.Variable(tf.constant(0.1, shape=[512]), name="b_fc1")

108. h_pool2_flat = tf.reshape(L2_pool, [-1, 16 * 20*32])

109. h_fc1 = full_connect(h_pool2_flat, W_fc1, b_fc1)

110. # dropout

111. keep_prob = tf.placeholder(tf.float32)

112. h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

113. # readout層

114. W_fc2 = tf.Variable(tf.truncated_normal([512, NUM_CLASSES], stddev=0.1), name="W_fc2")

115. b_fc2 = tf.Variable(tf.constant(0.1, shape=[NUM_CLASSES]), name="b_fc2")

116. # 定義優化器和訓練op

117. y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

118. cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))

119. train_step = tf.train.AdamOptimizer((1e-4)).minimize(cross_entropy)

120. correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))

121. accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

122. sess.run(tf.global_variables_initializer())

123. time_elapsed = time.time() - time_begin

124. print("讀取圖片檔案耗費時間：%d秒" % time_elapsed)

125. time_begin = time.time()

126. print ("一共讀取了 %s 個訓練影象， %s 個標籤" % (input_count, input_count))

127. # 設定每次訓練op的輸入個數和迭代次數，這裡為了支援任意圖片總數，定義了一個餘數remainder，譬如，如果每次訓練op的輸入個數為60，圖片總數為150張，則前面兩次各輸入60張，最後一次輸入30張（餘數30）

128. batch_size = 60

129. iterations = iterations

130. batches_count = int(input_count / batch_size)

131. remainder = input_count % batch_size

132. print ("訓練資料集分成 %s 批, 前面每批 %s 個數據，最後一批 %s 個數據" % (batches_count+1, batch_size, remainder))

133. # 執行訓練迭代

134. for it in range(iterations):

135. # 這裡的關鍵是要把輸入陣列轉為np.array

136. for n in range(batches_count):

137. train_step.run(feed_dict={x: input_images[n*batch_size:(n+1)*batch_size], y_: input_labels[n*batch_size:(n+1)*batch_size], keep_prob: 0.5})

138. if remainder > 0:

139. start_index = batches_count * batch_size;

140. train_step.run(feed_dict={x: input_images[start_index:input_count-1], y_: input_labels[start_index:input_count-1], keep_prob: 0.5})

141. # 每完成五次迭代，判斷準確度是否已達到100%，達到則退出迭代迴圈

142. iterate_accuracy = 0

143. if it%5 == 0:

144. iterate_accuracy = accuracy.eval(feed_dict={x: val_images, y_: val_labels, keep_prob: 1.0})

145. print ('第 %d 次訓練迭代: 準確率 %0.5f%%' % (it, iterate_accuracy*100))

146. if iterate_accuracy >= 0.9999 and it >= iterations:

147. break;

148. print ('完成訓練!')

149. time_elapsed = time.time() - time_begin

150. print ("訓練耗費時間：%d秒" % time_elapsed)

151. time_begin = time.time()

152. # 儲存訓練結果

153. if not os.path.exists(SAVER_DIR):

154. print ('不存在訓練資料儲存目錄，現在建立儲存目錄')

155. os.makedirs(SAVER_DIR)

156. # 初始化saver

157. saver = tf.train.Saver()

158. saver_path = saver.save(sess, "%smodel.ckpt"%(SAVER_DIR))

159. if __name__ =='__main__' and sys.argv[1]=='predict':

160. saver = tf.train.import_meta_graph("%smodel.ckpt.meta"%(SAVER_DIR))

161. with tf.Session() as sess:

162. model_file=tf.train.latest_checkpoint(SAVER_DIR)

163. saver.restore(sess, model_file)

164. # 第一個卷積層

165. W_conv1 = sess.graph.get_tensor_by_name("W_conv1:0")

166. b_conv1 = sess.graph.get_tensor_by_name("b_conv1:0")

167. conv_strides = [1, 1, 1, 1]

168. kernel_size = [1, 2, 2, 1]

169. pool_strides = [1, 2, 2, 1]

170. L1_pool = conv_layer(x_image, W_conv1, b_conv1, conv_strides, kernel_size, pool_strides, padding='SAME')

171. # 第二個卷積層

172. W_conv2 = sess.graph.get_tensor_by_name("W_conv2:0")

173. b_conv2 = sess.graph.get_tensor_by_name("b_conv2:0")

174. conv_strides = [1, 1, 1, 1]

175. kernel_size = [1, 1, 1, 1]

176. pool_strides = [1, 1, 1, 1]

177. L2_pool = conv_layer(L1_pool, W_conv2, b_conv2, conv_strides, kernel_size, pool_strides, padding='SAME')

178. # 全連線層

179. W_fc1 = sess.graph.get_tensor_by_name("W_fc1:0")

180. b_fc1 = sess.graph.get_tensor_by_name("b_fc1:0")

181. h_pool2_flat = tf.reshape(L2_pool, [-1, 16 * 20*32])

182. h_fc1 = full_connect(h_pool2_flat, W_fc1, b_fc1)

183. # dropout

184. keep_prob = tf.placeholder(tf.float32)

185. h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

186. # readout層

187. W_fc2 = sess.graph.get_tensor_by_name("W_fc2:0")

188. b_fc2 = sess.graph.get_tensor_by_name("b_fc2:0")

189. # 定義優化器和訓練op

190. conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

191. for n in range(3,8):

192. path = "test_images/%s.bmp" % (n)

193. img = Image.open(path)

194. width = img.size[0]

195. height = img.size[1]

196. img_data = [[0]*SIZE for i in range(1)]

197. for h in range(0, height):

198. for w in range(0, width):

199. if img.getpixel((w, h)) < 190:

200. img_data[0][w+h*width] = 1

201. else:

202. img_data[0][w+h*width] = 0

203. result = sess.run(conv, feed_dict = {x: np.array(img_data), keep_prob: 1.0})

204. max1 = 0

205. max2 = 0

206. max3 = 0

207. max1_index = 0

208. max2_index = 0

209. max3_index = 0

210. for j in range(NUM_CLASSES):

211. if result[0][j] > max1:

212. max1 = result[0][j]

213. max1_index = j

214. continue

215. if (result[0][j]>max2) and (result[0][j]<=max1):

216. max2 = result[0][j]

217. max2_index = j

218. continue

219. if (result[0][j]>max3) and (result[0][j]<=max2):

220. max3 = result[0][j]

221. max3_index = j

222. continue

223. license_num = license_num + LETTERS_DIGITS[max1_index]

224. print ("概率： [%s %0.2f%%] [%s %0.2f%%] [%s %0.2f%%]" % (LETTERS_DIGITS[max1_index],max1*100, LETTERS_DIGITS[max2_index],max2*100, LETTERS_DIGITS[max3_index],max3*100))

225. print ("車牌編號是: 【%s】" % license_num)

儲存好上面三個python指令碼後，我們首先進行省份簡稱訓練。在執行程式碼之前，需要先把資料集解壓到訓練指令碼所在目錄。然後，在命令列中進入指令碼所在目錄，輸入執行如下命令：

python train-license-province.py train

訓練結果如下：

然後進行省份簡稱識別，在命令列輸入執行如下命令：

python train-license-province.py predict

執行城市代號訓練（相當於訓練26個字母）：

python train-license-letters.py train

識別城市代號：

python train-license-letters.py predict

執行車牌編號訓練（相當於訓練24個字母+10個數字，我國交通法規規定車牌編號中不包含字母I和O）：

python train-license-digits.py train

識別車牌編號：

python train-license-digits.py predict

可以看到，在測試圖片上，識別準確率很高。識別結果是閩O-1672Q。

下圖是測試圖片的車牌原圖：