TensorFlow车牌识别完整版代码（含车牌数据集）

在之前发布的一篇博文《MNIST数据集实现车牌识别--初步演示版》中，我们演示了如何使用Te

在之前发布的一篇博文《MNIST数据集实现车牌识别--初步演示版》中，我们演示了如何使用TensorFlow进行车牌识别，但是，当时采用的数据集是MNIST数字手写体，只能分类0-9共10个数字，无法分类省份简称和字母，局限性较大，无实际意义。

经过图像定位分割处理，博主收集了相关省份简称和26个字母的图片数据集，结合前述博文中贴出的python+TensorFlow代码，实现了完整的车牌识别功能。本着分享精神，在此送上全部代码和车牌数据集。

车牌数据集下载地址（约4000张图片）：tf_car_license_dataset_jb51.rar

省份简称训练+识别代码（保存文件名为train-license-province.py）（拷贝代码请务必注意python文本缩进，只要有一处缩进错误，就无法得到正确结果，或者出现异常）：

#!/usr/bin/python3.5
# -*- coding: utf-8 -*- 
 
import sys
import os
import time
import random
 
import numpy as np
import tensorflow as tf
 
from PIL import Image
 
 
SIZE = 1280
WIDTH = 32
HEIGHT = 40
NUM_CLASSES = 6
iterations = 300
 
SAVER_DIR = "train-saver/province/"
 
PROVINCES = ("京","闽","粤","苏","沪","浙")
nProvinceIndex = 0
 
time_begin = time.time()
 
 
# 定义输入节点，对应于图片像素值矩阵集合和图片标签(即所代表的数字)
x = tf.placeholder(tf.float32, shape=[None, SIZE])
y_ = tf.placeholder(tf.float32, shape=[None, NUM_CLASSES])
 
x_image = tf.reshape(x, [-1, WIDTH, HEIGHT, 1])
 
 
# 定义卷积函数
def conv_layer(inputs, W, b, conv_strides, kernel_size, pool_strides, padding):
 L1_conv = tf.nn.conv2d(inputs, W, strides=conv_strides, padding=padding)
 L1_relu = tf.nn.relu(L1_conv + b)
 return tf.nn.max_pool(L1_relu, ksize=kernel_size, strides=pool_strides, padding='SAME')
 
# 定义全连接层函数
def full_connect(inputs, W, b):
 return tf.nn.relu(tf.matmul(inputs, W) + b)
 
 
if __name__ =='__main__' and sys.argv[1]=='train':
 # 第一次遍历图片目录是为了获取图片总数
 input_count = 0
 for i in range(0,NUM_CLASSES):
 dir = './train_images/training-set/chinese-characters/%s/' % i # 这里可以改成你自己的图片目录，i为分类标签
 for rt, dirs, files in os.walk(dir):
 for filename in files:
 input_count += 1
 
 # 定义对应维数和各维长度的数组
 input_images = np.array([[0]*SIZE for i in range(input_count)])
 input_labels = np.array([[0]*NUM_CLASSES for i in range(input_count)])
 
 # 第二次遍历图片目录是为了生成图片数据和标签
 index = 0
 for i in range(0,NUM_CLASSES):
 dir = './train_images/training-set/chinese-characters/%s/' % i # 这里可以改成你自己的图片目录，i为分类标签
 for rt, dirs, files in os.walk(dir):
 for filename in files:
 filename = dir + filename
 img = Image.open(filename)
 width = img.size[0]
 height = img.size[1]
 for h in range(0, height):
 for w in range(0, width):
 # 通过这样的处理，使数字的线条变细，有利于提高识别准确率
 if img.getpixel((w, h)) > 230:
 input_images[index][w+h*width] = 0
 else:
 input_images[index][w+h*width] = 1
 input_labels[index][i] = 1
 index += 1
 
 # 第一次遍历图片目录是为了获取图片总数
 val_count = 0
 for i in range(0,NUM_CLASSES):
 dir = './train_images/validation-set/chinese-characters/%s/' % i # 这里可以改成你自己的图片目录，i为分类标签
 for rt, dirs, files in os.walk(dir):
 for filename in files:
 val_count += 1
 
 # 定义对应维数和各维长度的数组
 val_images = np.array([[0]*SIZE for i in range(val_count)])
 val_labels = np.array([[0]*NUM_CLASSES for i in range(val_count)])
 
 # 第二次遍历图片目录是为了生成图片数据和标签
 index = 0
 for i in range(0,NUM_CLASSES):
 dir = './train_images/validation-set/chinese-characters/%s/' % i # 这里可以改成你自己的图片目录，i为分类标签
 for rt, dirs, files in os.walk(dir):
 for filename in files:
 filename = dir + filename
 img = Image.open(filename)
 width = img.size[0]
 height = img.size[1]
 for h in range(0, height):
 for w in range(0, width):
 # 通过这样的处理，使数字的线条变细，有利于提高识别准确率
 if img.getpixel((w, h)) > 230:
 val_images[index][w+h*width] = 0
 else:
 val_images[index][w+h*width] = 1
 val_labels[index][i] = 1
 index += 1
 
 with tf.Session() as sess:
 # 第一个卷积层
 W_conv1 = tf.Variable(tf.truncated_normal([8, 8, 1, 16], stddev=0.1), name="W_conv1")
 b_conv1 = tf.Variable(tf.constant(0.1, shape=[16]), name="b_conv1")
 conv_strides = [1, 1, 1, 1]
 kernel_size = [1, 2, 2, 1]
 pool_strides = [1, 2, 2, 1]
 L1_pool = conv_layer(x_image, W_conv1, b_conv1, conv_strides, kernel_size, pool_strides, padding='SAME')
 
 # 第二个卷积层
 W_conv2 = tf.Variable(tf.truncated_normal([5, 5, 16, 32], stddev=0.1), name="W_conv2")
 b_conv2 = tf.Variable(tf.constant(0.1, shape=[32]), name="b_conv2")
 conv_strides = [1, 1, 1, 1]
 kernel_size = [1, 1, 1, 1]
 pool_strides = [1, 1, 1, 1]
 L2_pool = conv_layer(L1_pool, W_conv2, b_conv2, conv_strides, kernel_size, pool_strides, padding='SAME')
 
 
 # 全连接层
 W_fc1 = tf.Variable(tf.truncated_normal([16 * 20 * 32, 512], stddev=0.1), name="W_fc1")
 b_fc1 = tf.Variable(tf.constant(0.1, shape=[512]), name="b_fc1")
 h_pool2_flat = tf.reshape(L2_pool, [-1, 16 * 20*32])
 h_fc1 = full_connect(h_pool2_flat, W_fc1, b_fc1)
 
 
 # dropout
 keep_prob = tf.placeholder(tf.float32)
 
 h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
 
 
 # readout层
 W_fc2 = tf.Variable(tf.truncated_normal([512, NUM_CLASSES], stddev=0.1), name="W_fc2")
 b_fc2 = tf.Variable(tf.constant(0.1, shape=[NUM_CLASSES]), name="b_fc2")
 
 # 定义优化器和训练op
 y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
 train_step = tf.train.AdamOptimizer((1e-4)).minimize(cross_entropy)
 
 correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 
 # 初始化saver
 saver = tf.train.Saver()
 
 sess.run(tf.global_variables_initializer())
 
 time_elapsed = time.time() - time_begin
 print("读取图片文件耗费时间：%d秒" % time_elapsed)
 time_begin = time.time()
 
 print ("一共读取了 %s 个训练图像， %s 个标签" % (input_count, input_count))
 
 # 设置每次训练op的输入个数和迭代次数，这里为了支持任意图片总数，定义了一个余数remainder，譬如，如果每次训练op的输入个数为60，图片总数为150张，则前面两次各输入60张，最后一次输入30张（余数30）
 batch_size = 60
 iterations = iterations
 batches_count = int(input_count / batch_size)
 remainder = input_count % batch_size
 print ("训练数据集分成 %s 批, 前面每批 %s 个数据，最后一批 %s 个数据" % (batches_count+1, batch_size, remainder))
 
 # 执行训练迭代
 for it in range(iterations):
 # 这里的关键是要把输入数组转为np.array
 for n in range(batches_count):
 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})
 if remainder > 0:
 start_index = batches_count * batch_size;
 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})
 
 # 每完成五次迭代，判断准确度是否已达到100%，达到则退出迭代循环
 iterate_accuracy = 0
 if it%5 == 0:
 iterate_accuracy = accuracy.eval(feed_dict={x: val_images, y_: val_labels, keep_prob: 1.0})
 print ('第 %d 次训练迭代: 准确率 %0.5f%%' % (it, iterate_accuracy*100))
 if iterate_accuracy >= 0.9999 and it >= 150:
 break;
 
 print ('完成训练!')
 time_elapsed = time.time() - time_begin
 print ("训练耗费时间：%d秒" % time_elapsed)
 time_begin = time.time()
 
 # 保存训练结果
 if not os.path.exists(SAVER_DIR):
 print ('不存在训练数据保存目录，现在创建保存目录')
 os.makedirs(SAVER_DIR)
 saver_path = saver.save(sess, "%smodel.ckpt"%(SAVER_DIR))
 
 
 
if __name__ =='__main__' and sys.argv[1]=='predict':
 saver = tf.train.import_meta_graph("%smodel.ckpt.meta"%(SAVER_DIR))
 with tf.Session() as sess:
 model_file=tf.train.latest_checkpoint(SAVER_DIR)
 saver.restore(sess, model_file)
 
 # 第一个卷积层
 W_conv1 = sess.graph.get_tensor_by_name("W_conv1:0")
 b_conv1 = sess.graph.get_tensor_by_name("b_conv1:0")
 conv_strides = [1, 1, 1, 1]
 kernel_size = [1, 2, 2, 1]
 pool_strides = [1, 2, 2, 1]
 L1_pool = conv_layer(x_image, W_conv1, b_conv1, conv_strides, kernel_size, pool_strides, padding='SAME')
 
 # 第二个卷积层
 W_conv2 = sess.graph.get_tensor_by_name("W_conv2:0")
 b_conv2 = sess.graph.get_tensor_by_name("b_conv2:0")
 conv_strides = [1, 1, 1, 1]
 kernel_size = [1, 1, 1, 1]
 pool_strides = [1, 1, 1, 1]
 L2_pool = conv_layer(L1_pool, W_conv2, b_conv2, conv_strides, kernel_size, pool_strides, padding='SAME')
 
 
 # 全连接层
 W_fc1 = sess.graph.get_tensor_by_name("W_fc1:0")
 b_fc1 = sess.graph.get_tensor_by_name("b_fc1:0")
 h_pool2_flat = tf.reshape(L2_pool, [-1, 16 * 20*32])
 h_fc1 = full_connect(h_pool2_flat, W_fc1, b_fc1)
 
 
 # dropout
 keep_prob = tf.placeholder(tf.float32)
 
 h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
 
 
 # readout层
 W_fc2 = sess.graph.get_tensor_by_name("W_fc2:0")
 b_fc2 = sess.graph.get_tensor_by_name("b_fc2:0")
 
 # 定义优化器和训练op
 conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
 
 for n in range(1,2):
 path = "test_images/%s.bmp" % (n)
 img = Image.open(path)
 width = img.size[0]
 height = img.size[1]
 
 img_data = [[0]*SIZE for i in range(1)]
 for h in range(0, height):
 for w in range(0, width):
 if img.getpixel((w, h)) < 190:
 img_data[0][w+h*width] = 1
 else:
 img_data[0][w+h*width] = 0
 
 result = sess.run(conv, feed_dict = {x: np.array(img_data), keep_prob: 1.0})
 max1 = 0
 max2 = 0
 max3 = 0
 max1_index = 0
 max2_index = 0
 max3_index = 0
 for j in range(NUM_CLASSES):
 if result[0][j] > max1:
 max1 = result[0][j]
 max1_index = j
 continue
 if (result[0][j]>max2) and (result[0][j]<=max1):
 max2 = result[0][j]
 max2_index = j
 continue
 if (result[0][j]>max3) and (result[0][j]<=max2):
 max3 = result[0][j]
 max3_index = j
 continue
 
 nProvinceIndex = max1_index
 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))
 
 print ("省份简称是: %s" % PROVINCES[nProvinceIndex])

城市代号训练+识别代码（保存文件名为train-license-letters.py）：

#!/usr/bin/python3.5
# -*- coding: utf-8 -*- 
 
import sys
import os
import time
import random
 
import numpy as np
import tensorflow as tf
 
from PIL import Image
 
 
SIZE = 1280
WIDTH = 32
HEIGHT = 40
NUM_CLASSES = 26
iterations = 500
 
SAVER_DIR = "train-saver/letters/"
 
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")
license_num = ""
 
time_begin = time.time()
 
 
# 定义输入节点，对应于图片像素值矩阵集合和图片标签(即所代表的数字)
x = tf.placeholder(tf.float32, shape=[None, SIZE])
y_ = tf.placeholder(tf.float32, shape=[None, NUM_CLASSES])
 
x_image = tf.reshape(x, [-1, WIDTH, HEIGHT, 1])
 
 
# 定义卷积函数
def conv_layer(inputs, W, b, conv_strides, kernel_size, pool_strides, padding):
 L1_conv = tf.nn.conv2d(inputs, W, strides=conv_strides, padding=padding)
 L1_relu = tf.nn.relu(L1_conv + b)
 return tf.nn.max_pool(L1_relu, ksize=kernel_size, strides=pool_strides, padding='SAME')
 
# 定义全连接层函数
def full_connect(inputs, W, b):
 return tf.nn.relu(tf.matmul(inputs, W) + b)
 
 
if __name__ =='__main__' and sys.argv[1]=='train':
 # 第一次遍历图片目录是为了获取图片总数
 input_count = 0
 for i in range(0+10,NUM_CLASSES+10):
 dir = './train_images/training-set/letters/%s/' % i # 这里可以改成你自己的图片目录，i为分类标签
 for rt, dirs, files in os.walk(dir):
 for filename in files:
 input_count += 1
 
 # 定义对应维数和各维长度的数组
 input_images = np.array([[0]*SIZE for i in range(input_count)])
 input_labels = np.array([[0]*NUM_CLASSES for i in range(input_count)])
 
 # 第二次遍历图片目录是为了生成图片数据和标签
 index = 0
 for i in range(0+10,NUM_CLASSES+10):
 dir = './train_images/training-set/letters/%s/' % i # 这里可以改成你自己的图片目录，i为分类标签
 for rt, dirs, files in os.walk(dir):
 for filename in files:
 filename = dir + filename
 img = Image.open(filename)
 width = img.size[0]
 height = img.size[1]
 for h in range(0, height):
 for w in range(0, width):
 # 通过这样的处理，使数字的线条变细，有利于提高识别准确率
 if img.getpixel((w, h)) > 230:
 input_images[index][w+h*width] = 0
 else:
 input_images[index][w+h*width] = 1
 #print ("i=%d, index=%d" % (i, index))
 input_labels[index][i-10] = 1
 index += 1
 
 # 第一次遍历图片目录是为了获取图片总数
 val_count = 0
 for i in range(0+10,NUM_CLASSES+10):
 dir = './train_images/validation-set/%s/' % i # 这里可以改成你自己的图片目录，i为分类标签
 for rt, dirs, files in os.walk(dir):
 for filename in files:
 val_count += 1
 
 # 定义对应维数和各维长度的数组
 val_images = np.array([[0]*SIZE for i in range(val_count)])
 val_labels = np.array([[0]*NUM_CLASSES for i in range(val_count)])
 
 # 第二次遍历图片目录是为了生成图片数据和标签
 index = 0
 for i in range(0+10,NUM_CLASSES+10):
 dir = './train_images/validation-set/%s/' % i # 这里可以改成你自己的图片目录，i为分类标签
 for rt, dirs, files in os.walk(dir):
 for filename in files:
 filename = dir + filename
 img = Image.open(filename)
 width = img.size[0]
 height = img.size[1]
 for h in range(0, height):
 for w in range(0, width):
 # 通过这样的处理，使数字的线条变细，有利于提高识别准确率
 if img.getpixel((w, h)) > 230:
 val_images[index][w+h*width] = 0
 else:
 val_images[index][w+h*width] = 1
 val_labels[index][i-10] = 1
 index += 1
 
 with tf.Session() as sess:
 # 第一个卷积层
 W_conv1 = tf.Variable(tf.truncated_normal([8, 8, 1, 16], stddev=0.1), name="W_conv1")
 b_conv1 = tf.Variable(tf.constant(0.1, shape=[16]), name="b_conv1")
 conv_strides = [1, 1, 1, 1]
 kernel_size = [1, 2, 2, 1]
 pool_strides = [1, 2, 2, 1]
 L1_pool = conv_layer(x_image, W_conv1, b_conv1, conv_strides, kernel_size, pool_strides, padding='SAME')
 
 # 第二个卷积层
 W_conv2 = tf.Variable(tf.truncated_normal([5, 5, 16, 32], stddev=0.1), name="W_conv2")
 b_conv2 = tf.Variable(tf.constant(0.1, shape=[32]), name="b_conv2")
 conv_strides = [1, 1, 1, 1]
 kernel_size = [1, 1, 1, 1]
 pool_strides = [1, 1, 1, 1]
 L2_pool = conv_layer(L1_pool, W_conv2, b_conv2, conv_strides, kernel_size, pool_strides, padding='SAME')
 
 
 # 全连接层
 W_fc1 = tf.Variable(tf.truncated_normal([16 * 20 * 32, 512], stddev=0.1), name="W_fc1")
 b_fc1 = tf.Variable(tf.constant(0.1, shape=[512]), name="b_fc1")
 h_pool2_flat = tf.reshape(L2_pool, [-1, 16 * 20*32])
 h_fc1 = full_connect(h_pool2_flat, W_fc1, b_fc1)
 
 
 # dropout
 keep_prob = tf.placeholder(tf.float32)
 
 h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
 
 
 # readout层
 W_fc2 = tf.Variable(tf.truncated_normal([512, NUM_CLASSES], stddev=0.1), name="W_fc2")
 b_fc2 = tf.Variable(tf.constant(0.1, shape=[NUM_CLASSES]), name="b_fc2")
 
 # 定义优化器和训练op
 y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
 train_step = tf.train.AdamOptimizer((1e-4)).minimize(cross_entropy)
 
 correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 
 sess.run(tf.global_variables_initializer())
 
 time_elapsed = time.time() - time_begin
 print("读取图片文件耗费时间：%d秒" % time_elapsed)
 time_begin = time.time()
 
 print ("一共读取了 %s 个训练图像， %s 个标签" % (input_count, input_count))
 
 # 设置每次训练op的输入个数和迭代次数，这里为了支持任意图片总数，定义了一个余数remainder，譬如，如果每次训练op的输入个数为60，图片总数为150张，则前面两次各输入60张，最后一次输入30张（余数30）
 batch_size = 60
 iterations = iterations
 batches_count = int(input_count / batch_size)
 remainder = input_count % batch_size
 print ("训练数据集分成 %s 批, 前面每批 %s 个数据，最后一批 %s 个数据" % (batches_count+1, batch_size, remainder))
 
 # 执行训练迭代
 for it in range(iterations):
 # 这里的关键是要把输入数组转为np.array
 for n in range(batches_count):
 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})
 if remainder > 0:
 start_index = batches_count * batch_size;
 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})
 
 # 每完成五次迭代，判断准确度是否已达到100%，达到则退出迭代循环
 iterate_accuracy = 0
 if it%5 == 0:
 iterate_accuracy = accuracy.eval(feed_dict={x: val_images, y_: val_labels, keep_prob: 1.0})
 print ('第 %d 次训练迭代: 准确率 %0.5f%%' % (it, iterate_accuracy*100))
 if iterate_accuracy >= 0.9999 and it >= iterations:
 break;
 
 print ('完成训练!')
 time_elapsed = time.time() - time_begin
 print ("训练耗费时间：%d秒" % time_elapsed)
 time_begin = time.time()
 
 # 保存训练结果
 if not os.path.exists(SAVER_DIR):
 print ('不存在训练数据保存目录，现在创建保存目录')
 os.makedirs(SAVER_DIR)
 # 初始化saver
 saver = tf.train.Saver() 
 saver_path = saver.save(sess, "%smodel.ckpt"%(SAVER_DIR))
 
 
 
if __name__ =='__main__' and sys.argv[1]=='predict':
 saver = tf.train.import_meta_graph("%smodel.ckpt.meta"%(SAVER_DIR))
 with tf.Session() as sess:
 model_file=tf.train.latest_checkpoint(SAVER_DIR)
 saver.restore(sess, model_file)
 
 # 第一个卷积层
 W_conv1 = sess.graph.get_tensor_by_name("W_conv1:0")
 b_conv1 = sess.graph.get_tensor_by_name("b_conv1:0")
 conv_strides = [1, 1, 1, 1]
 kernel_size = [1, 2, 2, 1]
 pool_strides = [1, 2, 2, 1]
 L1_pool = conv_layer(x_image, W_conv1, b_conv1, conv_strides, kernel_size, pool_strides, padding='SAME')
 
 # 第二个卷积层
 W_conv2 = sess.graph.get_tensor_by_name("W_conv2:0")
 b_conv2 = sess.graph.get_tensor_by_name("b_conv2:0")
 conv_strides = [1, 1, 1, 1]
 kernel_size = [1, 1, 1, 1]
 pool_strides = [1, 1, 1, 1]
 L2_pool = conv_layer(L1_pool, W_conv2, b_conv2, conv_strides, kernel_size, pool_strides, padding='SAME')
 
 
 # 全连接层
 W_fc1 = sess.graph.get_tensor_by_name("W_fc1:0")
 b_fc1 = sess.graph.get_tensor_by_name("b_fc1:0")
 h_pool2_flat = tf.reshape(L2_pool, [-1, 16 * 20*32])
 h_fc1 = full_connect(h_pool2_flat, W_fc1, b_fc1)
 
 
 # dropout
 keep_prob = tf.placeholder(tf.float32)
 
 h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
 
 
 # readout层
 W_fc2 = sess.graph.get_tensor_by_name("W_fc2:0")
 b_fc2 = sess.graph.get_tensor_by_name("b_fc2:0")
 
 # 定义优化器和训练op
 conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
 
 for n in range(2,3):
 path = "test_images/%s.bmp" % (n)
 img = Image.open(path)
 width = img.size[0]
 height = img.size[1]
 
 img_data = [[0]*SIZE for i in range(1)]
 for h in range(0, height):
 for w in range(0, width):
 if img.getpixel((w, h)) < 190:
 img_data[0][w+h*width] = 1
 else:
 img_data[0][w+h*width] = 0
 
 result = sess.run(conv, feed_dict = {x: np.array(img_data), keep_prob: 1.0})
 
 max1 = 0
 max2 = 0
 max3 = 0
 max1_index = 0
 max2_index = 0
 max3_index = 0
 for j in range(NUM_CLASSES):
 if result[0][j] > max1:
 max1 = result[0][j]
 max1_index = j
 continue
 if (result[0][j]>max2) and (result[0][j]<=max1):
 max2 = result[0][j]
 max2_index = j
 continue
 if (result[0][j]>max3) and (result[0][j]<=max2):
 max3 = result[0][j]
 max3_index = j
 continue
 
 if n == 3:
 license_num += "-"
 license_num = license_num + LETTERS_DIGITS[max1_index]
 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))
 
 print ("城市代号是: 【%s】" % license_num)

车牌编号训练+识别代码（保存文件名为train-license-digits.py）：

#!/usr/bin/python3.5
# -*- coding: utf-8 -*- 
 
import sys
import os
import time
import random
 
import numpy as np
import tensorflow as tf
 
from PIL import Image
 
 
SIZE = 1280
WIDTH = 32
HEIGHT = 40
NUM_CLASSES = 34
iterations = 1000
 
SAVER_DIR = "train-saver/digits/"
 
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")
license_num = ""
 
time_begin = time.time()
 
 
# 定义输入节点，对应于图片像素值矩阵集合和图片标签(即所代表的数字)
x = tf.placeholder(tf.float32, shape=[None, SIZE])
y_ = tf.placeholder(tf.float32, shape=[None, NUM_CLASSES])
 
x_image = tf.reshape(x, [-1, WIDTH, HEIGHT, 1])
 
 
# 定义卷积函数
def conv_layer(inputs, W, b, conv_strides, kernel_size, pool_strides, padding):
 L1_conv = tf.nn.conv2d(inputs, W, strides=conv_strides, padding=padding)
 L1_relu = tf.nn.relu(L1_conv + b)
 return tf.nn.max_pool(L1_relu, ksize=kernel_size, strides=pool_strides, padding='SAME')
 
# 定义全连接层函数
def full_connect(inputs, W, b):
 return tf.nn.relu(tf.matmul(inputs, W) + b)
 
 
if __name__ =='__main__' and sys.argv[1]=='train':
 # 第一次遍历图片目录是为了获取图片总数
 input_count = 0
 for i in range(0,NUM_CLASSES):
 dir = './train_images/training-set/%s/' % i # 这里可以改成你自己的图片目录，i为分类标签
 for rt, dirs, files in os.walk(dir):
 for filename in files:
 input_count += 1
 
 # 定义对应维数和各维长度的数组
 input_images = np.array([[0]*SIZE for i in range(input_count)])
 input_labels = np.array([[0]*NUM_CLASSES for i in range(input_count)])
 
 # 第二次遍历图片目录是为了生成图片数据和标签
 index = 0
 for i in range(0,NUM_CLASSES):
 dir = './train_images/training-set/%s/' % i # 这里可以改成你自己的图片目录，i为分类标签
 for rt, dirs, files in os.walk(dir):
 for filename in files:
 filename = dir + filename
 img = Image.open(filename)
 width = img.size[0]
 height = img.size[1]
 for h in range(0, height):
 for w in range(0, width):
 # 通过这样的处理，使数字的线条变细，有利于提高识别准确率
 if img.getpixel((w, h)) > 230:
 input_images[index][w+h*width] = 0
 else:
 input_images[index][w+h*width] = 1
 input_labels[index][i] = 1
 index += 1
 
 # 第一次遍历图片目录是为了获取图片总数
 val_count = 0
 for i in range(0,NUM_CLASSES):
 dir = './train_images/validation-set/%s/' % i # 这里可以改成你自己的图片目录，i为分类标签
 for rt, dirs, files in os.walk(dir):
 for filename in files:
 val_count += 1
 
 # 定义对应维数和各维长度的数组
 val_images = np.array([[0]*SIZE for i in range(val_count)])
 val_labels = np.array([[0]*NUM_CLASSES for i in range(val_count)])
 
 # 第二次遍历图片目录是为了生成图片数据和标签
 index = 0
 for i in range(0,NUM_CLASSES):
 dir = './train_images/validation-set/%s/' % i # 这里可以改成你自己的图片目录，i为分类标签
 for rt, dirs, files in os.walk(dir):
 for filename in files:
 filename = dir + filename
 img = Image.open(filename)
 width = img.size[0]
 height = img.size[1]
 for h in range(0, height):
 for w in range(0, width):
 # 通过这样的处理，使数字的线条变细，有利于提高识别准确率
 if img.getpixel((w, h)) > 230:
 val_images[index][w+h*width] = 0
 else:
 val_images[index][w+h*width] = 1
 val_labels[index][i] = 1
 index += 1
 
 with tf.Session() as sess:
 # 第一个卷积层
 W_conv1 = tf.Variable(tf.truncated_normal([8, 8, 1, 16], stddev=0.1), name="W_conv1")
 b_conv1 = tf.Variable(tf.constant(0.1, shape=[16]), name="b_conv1")
 conv_strides = [1, 1, 1, 1]
 kernel_size = [1, 2, 2, 1]
 pool_strides = [1, 2, 2, 1]
 L1_pool = conv_layer(x_image, W_conv1, b_conv1, conv_strides, kernel_size, pool_strides, padding='SAME')
 
 # 第二个卷积层
 W_conv2 = tf.Variable(tf.truncated_normal([5, 5, 16, 32], stddev=0.1), name="W_conv2")
 b_conv2 = tf.Variable(tf.constant(0.1, shape=[32]), name="b_conv2")
 conv_strides = [1, 1, 1, 1]
 kernel_size = [1, 1, 1, 1]
 pool_strides = [1, 1, 1, 1]
 L2_pool = conv_layer(L1_pool, W_conv2, b_conv2, conv_strides, kernel_size, pool_strides, padding='SAME')
 
 
 # 全连接层
 W_fc1 = tf.Variable(tf.truncated_normal([16 * 20 * 32, 512], stddev=0.1), name="W_fc1")
 b_fc1 = tf.Variable(tf.constant(0.1, shape=[512]), name="b_fc1")
 h_pool2_flat = tf.reshape(L2_pool, [-1, 16 * 20*32])
 h_fc1 = full_connect(h_pool2_flat, W_fc1, b_fc1)
 
 
 # dropout
 keep_prob = tf.placeholder(tf.float32)
 
 h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
 
 
 # readout层
 W_fc2 = tf.Variable(tf.truncated_normal([512, NUM_CLASSES], stddev=0.1), name="W_fc2")
 b_fc2 = tf.Variable(tf.constant(0.1, shape=[NUM_CLASSES]), name="b_fc2")
 
 # 定义优化器和训练op
 y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
 train_step = tf.train.AdamOptimizer((1e-4)).minimize(cross_entropy)
 
 correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 
 sess.run(tf.global_variables_initializer())
 
 time_elapsed = time.time() - time_begin
 print("读取图片文件耗费时间：%d秒" % time_elapsed)
 time_begin = time.time()
 
 print ("一共读取了 %s 个训练图像， %s 个标签" % (input_count, input_count))
 
 # 设置每次训练op的输入个数和迭代次数，这里为了支持任意图片总数，定义了一个余数remainder，譬如，如果每次训练op的输入个数为60，图片总数为150张，则前面两次各输入60张，最后一次输入30张（余数30）
 batch_size = 60
 iterations = iterations
 batches_count = int(input_count / batch_size)
 remainder = input_count % batch_size
 print ("训练数据集分成 %s 批, 前面每批 %s 个数据，最后一批 %s 个数据" % (batches_count+1, batch_size, remainder))
 
 # 执行训练迭代
 for it in range(iterations):
 # 这里的关键是要把输入数组转为np.array
 for n in range(batches_count):
 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})
 if remainder > 0:
 start_index = batches_count * batch_size;
 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})
 
 # 每完成五次迭代，判断准确度是否已达到100%，达到则退出迭代循环
 iterate_accuracy = 0
 if it%5 == 0:
 iterate_accuracy = accuracy.eval(feed_dict={x: val_images, y_: val_labels, keep_prob: 1.0})
 print ('第 %d 次训练迭代: 准确率 %0.5f%%' % (it, iterate_accuracy*100))
 if iterate_accuracy >= 0.9999 and it >= iterations:
 break;
 
 print ('完成训练!')
 time_elapsed = time.time() - time_begin
 print ("训练耗费时间：%d秒" % time_elapsed)
 time_begin = time.time()
 
 # 保存训练结果
 if not os.path.exists(SAVER_DIR):
 print ('不存在训练数据保存目录，现在创建保存目录')
 os.makedirs(SAVER_DIR)
 # 初始化saver
 saver = tf.train.Saver() 
 saver_path = saver.save(sess, "%smodel.ckpt"%(SAVER_DIR))
 
 
 
if __name__ =='__main__' and sys.argv[1]=='predict':
 saver = tf.train.import_meta_graph("%smodel.ckpt.meta"%(SAVER_DIR))
 with tf.Session() as sess:
 model_file=tf.train.latest_checkpoint(SAVER_DIR)
 saver.restore(sess, model_file)
 
 # 第一个卷积层
 W_conv1 = sess.graph.get_tensor_by_name("W_conv1:0")
 b_conv1 = sess.graph.get_tensor_by_name("b_conv1:0")
 conv_strides = [1, 1, 1, 1]
 kernel_size = [1, 2, 2, 1]
 pool_strides = [1, 2, 2, 1]
 L1_pool = conv_layer(x_image, W_conv1, b_conv1, conv_strides, kernel_size, pool_strides, padding='SAME')
 
 # 第二个卷积层
 W_conv2 = sess.graph.get_tensor_by_name("W_conv2:0")
 b_conv2 = sess.graph.get_tensor_by_name("b_conv2:0")
 conv_strides = [1, 1, 1, 1]
 kernel_size = [1, 1, 1, 1]
 pool_strides = [1, 1, 1, 1]
 L2_pool = conv_layer(L1_pool, W_conv2, b_conv2, conv_strides, kernel_size, pool_strides, padding='SAME')
 
 
 # 全连接层
 W_fc1 = sess.graph.get_tensor_by_name("W_fc1:0")
 b_fc1 = sess.graph.get_tensor_by_name("b_fc1:0")
 h_pool2_flat = tf.reshape(L2_pool, [-1, 16 * 20*32])
 h_fc1 = full_connect(h_pool2_flat, W_fc1, b_fc1)
 
 
 # dropout
 keep_prob = tf.placeholder(tf.float32)
 
 h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
 
 
 # readout层
 W_fc2 = sess.graph.get_tensor_by_name("W_fc2:0")
 b_fc2 = sess.graph.get_tensor_by_name("b_fc2:0")
 
 # 定义优化器和训练op
 conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
 
 for n in range(3,8):
 path = "test_images/%s.bmp" % (n)
 img = Image.open(path)
 width = img.size[0]
 height = img.size[1]
 
 img_data = [[0]*SIZE for i in range(1)]
 for h in range(0, height):
 for w in range(0, width):
 if img.getpixel((w, h)) < 190:
 img_data[0][w+h*width] = 1
 else:
 img_data[0][w+h*width] = 0
 
 result = sess.run(conv, feed_dict = {x: np.array(img_data), keep_prob: 1.0})
 
 max1 = 0
 max2 = 0
 max3 = 0
 max1_index = 0
 max2_index = 0
 max3_index = 0
 for j in range(NUM_CLASSES):
 if result[0][j] > max1:
 max1 = result[0][j]
 max1_index = j
 continue
 if (result[0][j]>max2) and (result[0][j]<=max1):
 max2 = result[0][j]
 max2_index = j
 continue
 if (result[0][j]>max3) and (result[0][j]<=max2):
 max3 = result[0][j]
 max3_index = j
 continue
 
 license_num = license_num + LETTERS_DIGITS[max1_index]
 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))
 
 print ("车牌编号是: 【%s】" % license_num)

保存好上面三个python脚本后，我们首先进行省份简称训练。在运行代码之前，需要先把数据集解压到训练脚本所在目录。然后，在命令行中进入脚本所在目录，输入执行如下命令：

python train-license-province.py train

训练结果如下：

然后进行省份简称识别，在命令行输入执行如下命令：

python train-license-province.py predict

执行城市代号训练（相当于训练26个字母）：

python train-license-letters.py train

识别城市代号：

python train-license-letters.py predict

执行车牌编号训练（相当于训练24个字母+10个数字，我国交通法规规定车牌编号中不包含字母I和O）：

python train-license-digits.py train

识别车牌编号：

python train-license-digits.py predict

可以看到，在测试图片上，识别准确率很高。识别结果是闽O-1672Q。

下图是测试图片的车牌原图：

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。

TensorFlow 车牌识别