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predict_plate.py
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import tensorflow as tf
import numpy as np
from keras.utils import to_categorical
import matplotlib.pyplot as plt
import cv2
import extract_figures
from collections import OrderedDict
import os
from matplotlib.font_manager import FontProperties
import sys
def predict_plate(plates):
characters_ref = OrderedDict().fromkeys([
'0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J',
'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T',
'U', 'V', 'W', 'X', 'Y', 'Z',
'藏', '川', '鄂', '甘', '赣', '广', '桂', '贵', '黑',
'沪', '吉', '冀', '津', '晋', '京', '辽', '鲁', '蒙',
'闽', '宁', '青', '琼', '陕', '苏', '皖', '湘', '新',
'渝', '豫', '粤', '云', '浙'
])
characters_ref_keys = list(characters_ref.keys())
# y_train = []
# x_train = []
y_test = np.zeros((7, 68), dtype=np.uint8)
x_test = np.array(plates)
# print("y_test.shape={}".format(y_test.shape))
# print("x_test.shape={}".format(x_test.shape))
class_num = y_test.shape[-1]
sess = tf.InteractiveSession()
x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, class_num])
# 把x更改为4维张量，第1维代表样本数量，第2维和第3维代表图像长宽， 第4维代表图像通道数, 1表示黑白
x_image = tf.reshape(x, [-1, 28, 28, 1])
# 第一层：卷积层
conv1_weights = tf.get_variable(
"conv1_weights",
[5, 5, 1, 32],
initializer=tf.truncated_normal_initializer(stddev=0.1))
# 过滤器大小为5*5, 当前层深度为1， 过滤器的深度为32
conv1_biases = tf.get_variable("conv1_biases", [32],
initializer=tf.constant_initializer(0.0))
conv1 = tf.nn.conv2d(x_image, conv1_weights, strides=[1, 1, 1, 1],
padding='SAME')
# 移动步长为1, 使用全0填充
relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases)) # 激活函数Relu去线性化
# 第二层：最大池化层
# 池化层过滤器的大小为2*2, 移动步长为2，使用全0填充
pool1 = tf.nn.max_pool(relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
padding='SAME')
# 第三层：卷积层
conv2_weights = tf.get_variable(
"conv2_weights",
[5, 5, 32, 64],
initializer=tf.truncated_normal_initializer(stddev=0.1))
# 过滤器大小为5*5, 当前层深度为32， 过滤器的深度为64
conv2_biases = tf.get_variable(
"conv2_biases", [64], initializer=tf.constant_initializer(0.0))
conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1],
padding='SAME')
# 移动步长为1, 使用全0填充
relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))
# 第四层：最大池化层
# 池化层过滤器的大小为2*2, 移动步长为2，使用全0填充
pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
padding='SAME')
# 第五层：全连接层
fc1_weights = tf.get_variable("fc1_weights", [7 * 7 * 64, 1024],
initializer=tf.truncated_normal_initializer(
stddev=0.1))
# 7*7*64=3136把前一层的输出变成特征向量
fc1_biases = tf.get_variable(
"fc1_biases", [1024], initializer=tf.constant_initializer(0.1))
pool2_vector = tf.reshape(pool2, [-1, 7 * 7 * 64])
fc1 = tf.nn.relu(tf.matmul(pool2_vector, fc1_weights) + fc1_biases)
# 为了减少过拟合，加入Dropout层
keep_prob = tf.placeholder(tf.float32)
fc1_dropout = tf.nn.dropout(fc1, keep_prob)
# 第六层：全连接层
fc2_weights = tf.get_variable("fc2_weights", [1024, class_num],
initializer=tf.truncated_normal_initializer(
stddev=0.1))
# 神经元节点数1024, 分类节点10
fc2_biases = tf.get_variable(
"fc2_biases", [class_num], initializer=tf.constant_initializer(0.1))
fc2 = tf.matmul(fc1_dropout, fc2_weights) + fc2_biases
# 第七层：输出层
# softmax
y_conv = tf.nn.softmax(fc2)
pred_class_index = tf.argmax(y_conv, 1)
# tf.argmax()返回的是某一维度上其数据最大所在的索引值，在这里即代表预测值和真实值
# 判断预测值y和真实值y_中最大数的索引是否一致，y的值为1-class_num概率
# correct_prediction = tf.equal(pred_class_index, tf.argmax(y_, 1))
# 用平均值来统计测试准确率
# accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# 开始训练
saver = tf.train.Saver()
# sess.run(tf.global_variables_initializer())
saver.restore(sess, './my_model/model.ckpt')
# pred_value = sess.run([pred_class_index], feed_dict={
# x: x_test, y_: y_test, keep_prob: 1.0
# })
# print("pred_value=" + str(pred_value))
# acc_test = sess.run(accuracy, feed_dict={
# x: x_test, y_: y_test, keep_prob: 1.0
# })
#
batch_size_test = 1
if not y_test.shape[0] % batch_size_test:
epoch_test = y_test.shape[0] // batch_size_test
else:
epoch_test = y_test.shape[0] // batch_size_test + 1
pred_values = []
for i in range(epoch_test):
if (i*batch_size_test % x_test.shape[0]) > (((i+1)*batch_size_test) %
x_test.shape[0]):
x_data_test = np.vstack((
x_test[i*batch_size_test % x_test.shape[0]:],
x_test[:(i+1)*batch_size_test % x_test.shape[0]]))
y_data_test = np.vstack((
y_test[i*batch_size_test % y_test.shape[0]:],
y_test[:(i+1)*batch_size_test % y_test.shape[0]]))
else:
x_data_test = x_test[
i*batch_size_test % x_test.shape[0]:
(i+1)*batch_size_test % x_test.shape[0]]
y_data_test = y_test[
i*batch_size_test % y_test.shape[0]:
(i+1)*batch_size_test % y_test.shape[0]]
# plt.imshow(x_data_test[0].reshape(28, 28), cmap="gray")
# plt.show()
# Calculate batch loss and accuracy
pred_value = to_categorical(np.squeeze(
sess.run([pred_class_index], feed_dict={
x: x_data_test, y_: y_data_test, keep_prob: 1.0})), 68)
# print("{}-th pred_value={}".format(i, pred_value))
pred_values.append(characters_ref_keys[(np.argmax(pred_value))])
return pred_values
def main():
# matplotlib 显示中文
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
myfont = FontProperties(fname='/usr/share/fonts/truetype/simhei.ttf',
size=20)
image = sys.argv[1]
plate = extract_figures.extract_figures(image)
pred_values = predict_plate(plate)
pred_values.insert(2, '·')
print("The License Plate is: {}".format(pred_values))
plt.imshow(cv2.cvtColor(cv2.imread(image), cv2.COLOR_BGR2RGB))
plt.title(''.join(pred_values), fontproperties=myfont)
plt.show()
if __name__ == '__main__':
main()