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中文源码网。