python车牌识别使用训练集_TensorFlow车牌识别完整版代码（含车牌数据集）
在之前發(fā)布的一篇博文《MNIST數(shù)據(jù)集實現(xiàn)車牌識別--初步演示版》中，我們演示了如何使用TensorFlow進行車牌識別，但是，當時采用的數(shù)據(jù)集是MNIST數(shù)字手寫體，只能分類0-9共10個數(shù)字，無法分類省份簡稱和字母，局限性較大，無實際意義。
經(jīng)過圖像定位分割處理，博主收集了相關省份簡稱和26個字母的圖片數(shù)據(jù)集，結合前述博文中貼出的python+TensorFlow代碼，實現(xiàn)了完整的車牌識別功能。本著分享精神，在此送上全部代碼和車牌數(shù)據(jù)集。
省份簡稱訓練+識別代碼(保存文件名為train-license-province.py)(拷貝代碼請務必注意python文本縮進，只要有一處縮進錯誤，就無法得到正確結果，或者出現(xiàn)異常)：
#!/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","閩","粵","蘇","滬","浙")
nProvinceIndex = 0
time_begin = time.time()
# 定義輸入節(jié)點，對應于圖片像素值矩陣集合和圖片標簽(即所代表的數(shù)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])
# 定義卷積函數(shù)
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')
# 定義全連接層函數(shù)
def full_connect(inputs, W, b):
return tf.nn.relu(tf.matmul(inputs, W) + b)
if __name__ =='__main__' and sys.argv[1]=='train':
# 第一次遍歷圖片目錄是為了獲取圖片總數(shù)
input_count = 0
for i in range(0,NUM_CLASSES):
dir = './train_images/training-set/chinese-characters/%s/' % i # 這里可以改成你自己的圖片目錄，i為分類標簽
for rt, dirs, files in os.walk(dir):
for filename in files:
input_count += 1
# 定義對應維數(shù)和各維長度的數(shù)組
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)])
# 第二次遍歷圖片目錄是為了生成圖片數(shù)據(jù)和標簽
index = 0
for i in range(0,NUM_CLASSES):
dir = './train_images/training-set/chinese-characters/%s/' % i # 這里可以改成你自己的圖片目錄，i為分類標簽
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):
# 通過這樣的處理，使數(shù)字的線條變細，有利于提高識別準確率
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
# 第一次遍歷圖片目錄是為了獲取圖片總數(shù)
val_count = 0
for i in range(0,NUM_CLASSES):
dir = './train_images/validation-set/chinese-characters/%s/' % i # 這里可以改成你自己的圖片目錄，i為分類標簽
for rt, dirs, files in os.walk(dir):
for filename in files:
val_count += 1
# 定義對應維數(shù)和各維長度的數(shù)組
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)])
# 第二次遍歷圖片目錄是為了生成圖片數(shù)據(jù)和標簽
index = 0
for i in range(0,NUM_CLASSES):
dir = './train_images/validation-set/chinese-characters/%s/' % i # 這里可以改成你自己的圖片目錄，i為分類標簽
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):
# 通過這樣的處理，使數(shù)字的線條變細，有利于提高識別準確率
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:
# 第一個卷積層
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')
# 第二個卷積層
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')
# 全連接層
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層
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")
# 定義優(yōu)化器和訓練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("讀取圖片文件耗費時間：%d秒" % time_elapsed)
time_begin = time.time()
print ("一共讀取了 %s 個訓練圖像， %s 個標簽" % (input_count, input_count))
# 設置每次訓練op的輸入個數(shù)和迭代次數(shù)，這里為了支持任意圖片總數(shù)，定義了一個余數(shù)remainder，譬如，如果每次訓練op的輸入個數(shù)為60，圖片總數(shù)為150張，則前面兩次各輸入60張，最后一次輸入30張(余數(shù)30)
batch_size = 60
iterations = iterations
batches_count = int(input_count / batch_size)
remainder = input_count % batch_size
print ("訓練數(shù)據(jù)集分成 %s 批, 前面每批 %s 個數(shù)據(jù)，最后一批 %s 個數(shù)據(jù)" % (batches_count+1, batch_size, remainder))
# 執(zhí)行訓練迭代
for it in range(iterations):
# 這里的關鍵是要把輸入數(shù)組轉(zhuǎn)為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，判斷準確度是否已達到100%，達到則退出迭代循環(huán)
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 次訓練迭代: 準確率 %0.5f%%' % (it, iterate_accuracy*100))
if iterate_accuracy >= 0.9999 and it >= 150:
break;
print ('完成訓練!')
time_elapsed = time.time() - time_begin
print ("訓練耗費時間：%d秒" % time_elapsed)
time_begin = time.time()
# 保存訓練結果
if not os.path.exists(SAVER_DIR):
print ('不存在訓練數(shù)據(jù)保存目錄，現(xiàn)在創(chuàng)建保存目錄')
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)
# 第一個卷積層
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')
# 第二個卷積層
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')
# 全連接層
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層
W_fc2 = sess.graph.get_tensor_by_name("W_fc2:0")
b_fc2 = sess.graph.get_tensor_by_name("b_fc2:0")
# 定義優(yōu)化器和訓練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 ("省份簡稱是: %s" % PROVINCES[nProvinceIndex])
城市代號訓練+識別代碼(保存文件名為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()
# 定義輸入節(jié)點，對應于圖片像素值矩陣集合和圖片標簽(即所代表的數(shù)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])
# 定義卷積函數(shù)
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')
# 定義全連接層函數(shù)
def full_connect(inputs, W, b):
return tf.nn.relu(tf.matmul(inputs, W) + b)
if __name__ =='__main__' and sys.argv[1]=='train':
# 第一次遍歷圖片目錄是為了獲取圖片總數(shù)
input_count = 0
for i in range(0+10,NUM_CLASSES+10):
dir = './train_images/training-set/letters/%s/' % i # 這里可以改成你自己的圖片目錄，i為分類標簽
for rt, dirs, files in os.walk(dir):
for filename in files:
input_count += 1
# 定義對應維數(shù)和各維長度的數(shù)組
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)])
# 第二次遍歷圖片目錄是為了生成圖片數(shù)據(jù)和標簽
index = 0
for i in range(0+10,NUM_CLASSES+10):
dir = './train_images/training-set/letters/%s/' % i # 這里可以改成你自己的圖片目錄，i為分類標簽
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):
# 通過這樣的處理，使數(shù)字的線條變細，有利于提高識別準確率
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
# 第一次遍歷圖片目錄是為了獲取圖片總數(shù)
val_count = 0
for i in range(0+10,NUM_CLASSES+10):
dir = './train_images/validation-set/%s/' % i # 這里可以改成你自己的圖片目錄，i為分類標簽
for rt, dirs, files in os.walk(dir):
for filename in files:
val_count += 1
# 定義對應維數(shù)和各維長度的數(shù)組
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)])
# 第二次遍歷圖片目錄是為了生成圖片數(shù)據(jù)和標簽
index = 0
for i in range(0+10,NUM_CLASSES+10):
dir = './train_images/validation-set/%s/' % i # 這里可以改成你自己的圖片目錄，i為分類標簽
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):
# 通過這樣的處理，使數(shù)字的線條變細，有利于提高識別準確率
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:
# 第一個卷積層
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')
# 第二個卷積層
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')
# 全連接層
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層
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")
# 定義優(yōu)化器和訓練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("讀取圖片文件耗費時間：%d秒" % time_elapsed)
time_begin = time.time()
print ("一共讀取了 %s 個訓練圖像， %s 個標簽" % (input_count, input_count))
# 設置每次訓練op的輸入個數(shù)和迭代次數(shù)，這里為了支持任意圖片總數(shù)，定義了一個余數(shù)remainder，譬如，如果每次訓練op的輸入個數(shù)為60，圖片總數(shù)為150張，則前面兩次各輸入60張，最后一次輸入30張(余數(shù)30)
batch_size = 60
iterations = iterations
batches_count = int(input_count / batch_size)
remainder = input_count % batch_size
print ("訓練數(shù)據(jù)集分成 %s 批, 前面每批 %s 個數(shù)據(jù)，最后一批 %s 個數(shù)據(jù)" % (batches_count+1, batch_size, remainder))
# 執(zhí)行訓練迭代
for it in range(iterations):
# 這里的關鍵是要把輸入數(shù)組轉(zhuǎn)為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，判斷準確度是否已達到100%，達到則退出迭代循環(huán)
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 次訓練迭代: 準確率 %0.5f%%' % (it, iterate_accuracy*100))
if iterate_accuracy >= 0.9999 and it >= iterations:
break;
print ('完成訓練!')
time_elapsed = time.time() - time_begin
print ("訓練耗費時間：%d秒" % time_elapsed)
time_begin = time.time()
# 保存訓練結果
if not os.path.exists(SAVER_DIR):
print ('不存在訓練數(shù)據(jù)保存目錄，現(xiàn)在創(chuàng)建保存目錄')
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)
# 第一個卷積層
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')
# 第二個卷積層
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')
# 全連接層
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層
W_fc2 = sess.graph.get_tensor_by_name("W_fc2:0")
b_fc2 = sess.graph.get_tensor_by_name("b_fc2:0")
# 定義優(yōu)化器和訓練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 ("城市代號是: 【%s】" % license_num)
車牌編號訓練+識別代碼(保存文件名為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()
# 定義輸入節(jié)點，對應于圖片像素值矩陣集合和圖片標簽(即所代表的數(shù)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])
# 定義卷積函數(shù)
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')
# 定義全連接層函數(shù)
def full_connect(inputs, W, b):
return tf.nn.relu(tf.matmul(inputs, W) + b)
if __name__ =='__main__' and sys.argv[1]=='train':
# 第一次遍歷圖片目錄是為了獲取圖片總數(shù)
input_count = 0
for i in range(0,NUM_CLASSES):
dir = './train_images/training-set/%s/' % i # 這里可以改成你自己的圖片目錄，i為分類標簽
for rt, dirs, files in os.walk(dir):
for filename in files:
input_count += 1
# 定義對應維數(shù)和各維長度的數(shù)組
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)])
# 第二次遍歷圖片目錄是為了生成圖片數(shù)據(jù)和標簽
index = 0
for i in range(0,NUM_CLASSES):
dir = './train_images/training-set/%s/' % i # 這里可以改成你自己的圖片目錄，i為分類標簽
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):
# 通過這樣的處理，使數(shù)字的線條變細，有利于提高識別準確率
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
# 第一次遍歷圖片目錄是為了獲取圖片總數(shù)
val_count = 0
for i in range(0,NUM_CLASSES):
dir = './train_images/validation-set/%s/' % i # 這里可以改成你自己的圖片目錄，i為分類標簽
for rt, dirs, files in os.walk(dir):
for filename in files:
val_count += 1
# 定義對應維數(shù)和各維長度的數(shù)組
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)])
# 第二次遍歷圖片目錄是為了生成圖片數(shù)據(jù)和標簽
index = 0
for i in range(0,NUM_CLASSES):
dir = './train_images/validation-set/%s/' % i # 這里可以改成你自己的圖片目錄，i為分類標簽
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):
# 通過這樣的處理，使數(shù)字的線條變細，有利于提高識別準確率
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:
# 第一個卷積層
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')
# 第二個卷積層
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')
# 全連接層
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層
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")
# 定義優(yōu)化器和訓練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("讀取圖片文件耗費時間：%d秒" % time_elapsed)
time_begin = time.time()
print ("一共讀取了 %s 個訓練圖像， %s 個標簽" % (input_count, input_count))
# 設置每次訓練op的輸入個數(shù)和迭代次數(shù)，這里為了支持任意圖片總數(shù)，定義了一個余數(shù)remainder，譬如，如果每次訓練op的輸入個數(shù)為60，圖片總數(shù)為150張，則前面兩次各輸入60張，最后一次輸入30張(余數(shù)30)
batch_size = 60
iterations = iterations
batches_count = int(input_count / batch_size)
remainder = input_count % batch_size
print ("訓練數(shù)據(jù)集分成 %s 批, 前面每批 %s 個數(shù)據(jù)，最后一批 %s 個數(shù)據(jù)" % (batches_count+1, batch_size, remainder))
# 執(zhí)行訓練迭代
for it in range(iterations):
# 這里的關鍵是要把輸入數(shù)組轉(zhuǎn)為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，判斷準確度是否已達到100%，達到則退出迭代循環(huán)
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 次訓練迭代: 準確率 %0.5f%%' % (it, iterate_accuracy*100))
if iterate_accuracy >= 0.9999 and it >= iterations:
break;
print ('完成訓練!')
time_elapsed = time.time() - time_begin
print ("訓練耗費時間：%d秒" % time_elapsed)
time_begin = time.time()
# 保存訓練結果
if not os.path.exists(SAVER_DIR):
print ('不存在訓練數(shù)據(jù)保存目錄，現(xiàn)在創(chuàng)建保存目錄')
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)
# 第一個卷積層
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')
# 第二個卷積層
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')
# 全連接層
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層
W_fc2 = sess.graph.get_tensor_by_name("W_fc2:0")
b_fc2 = sess.graph.get_tensor_by_name("b_fc2:0")
# 定義優(yōu)化器和訓練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 ("車牌編號是: 【%s】" % license_num)
保存好上面三個python腳本后，我們首先進行省份簡稱訓練。在運行代碼之前，需要先把數(shù)據(jù)集解壓到訓練腳本所在目錄。然后，在命令行中進入腳本所在目錄，輸入執(zhí)行如下命令：
python train-license-province.py train
訓練結果如下：
然后進行省份簡稱識別，在命令行輸入執(zhí)行如下命令：
python train-license-province.py predict
執(zhí)行城市代號訓練(相當于訓練26個字母)：
python train-license-letters.py train
識別城市代號：
python train-license-letters.py predict
執(zhí)行車牌編號訓練(相當于訓練24個字母+10個數(shù)字，我國交通法規(guī)規(guī)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：
以上就是本文的全部內(nèi)容，希望對大家的學習有所幫助，也希望大家多多支持腳本之家。
總結
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