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。
标签：
TensorFlow,车牌识别
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一口气升级7个大模型SaaS应用，百度智能云：突出一个“开箱即用” - 2024/11/26
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这一波大模型产业落地浪潮里，不少企业其实处在 “干瞪眼“的状态。
一种情况是，很多大模型产品看得见却摸不着，在台上一个个遥遥领先——今天Sora技精四座，明天英伟达的机器人又赢得满堂彩，可是到了台下一问：啥时候能用上啊？答曰：遥遥无期。
另一种情况是，企业想用上大模型，却又难免瞻前顾后——既要考虑场景融合，又得兼顾安全性，还要考虑打通现有系统，再加上各种部署成本和繁琐的采购流程……最后只能拂袖：罢了，再等等吧。
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这里面让他访问的是一个国服的战网网址，com.cn和后面的zh都非常明白地表明这就是国服战网。
而他在复制这个网址并且进行登录之后，确实是网易的网址，也就是我们熟悉的停服之后国服发布的暴雪游戏产品运营到期开放退款的说明。这是一件比较奇怪的事情，因为以前都没有出现这样的情况，现在突然提示跳转到国服战网的网址，是不是说明了简体中文客户端已经开始进行更新了呢？
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