基于深度学习车牌识别中遮挡车牌分类

  from skimage import io,transform

    
 import glob
    
 import os
    
 import tensorflow as tf
    
 import numpy as np
    
 import time
    
  
    
 #数据集地址
    
 path='./car_train_data/'
    
 #模型保存地址
    
 model_path='./model/model.ckpt'
    
 #基础学习率
    
 LEARNING_RATE_BASE=0.001
    
 #学习率的衰减率
    
 LEARNING_RATE_DECAY=0.99
    
 #滑动平均衰减率
    
 MOVING_AVERAGE_DECAY=0.99
    
 #将所有的图片resize成100*100
    
 w=100
    
 h=100
    
 c=3
    
 gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)
    
  
    
 #读取图片
    
 def read_img(path):
    
     cate=[path+x for x in os.listdir(path) if os.path.isdir(path+x)]
    
     imgs=[]
    
     labels=[]
    
     for idx,folder in enumerate(cate):
    
     for im in glob.glob(folder+'/*.jpg'):
    
         print('reading the images:%s'%(im))
    
         img=io.imread(im)
    
         img=transform.resize(img,(w,h))
    
         imgs.append(img)
    
         labels.append(idx)
    
     return np.asarray(imgs,np.float32),np.asarray(labels,np.int32)
    
 data,label=read_img(path)
    
  
    
 #打乱顺序
    
 num_example=data.shape[0]
    
 arr=np.arange(num_example)
    
 np.random.shuffle(arr)
    
 data=data[arr]
    
 label=label[arr]
    
  
    
  
    
 #将所有数据分为训练集和验证集
    
 ratio=0.8
    
 s=np.int(num_example*ratio)
    
 x_train=data[:s]
    
 y_train=label[:s]
    
 x_val=data[s:]
    
 y_val=label[s:]
    
  
    
  
    
  
    
 #-----------------构建网络----------------------
    
 #占位符
    
 x=tf.placeholder(tf.float32,shape=[None,w,h,c],name='x')
    
 y_=tf.placeholder(tf.int32,shape=[None,],name='y_')
    
  
    
 def inference(input_tensor, train, regularizer):
    
     with tf.variable_scope('layer1-conv1'):
    
     conv1_weights = tf.get_variable("weight",[5,5,3,32],initializer=tf.truncated_normal_initializer(stddev=0.1))
    
     conv1_biases = tf.get_variable("bias", [32], initializer=tf.constant_initializer(0.0))
    
     conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')
    
     relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))
    
  
    
     with tf.name_scope("layer2-pool1"):
    
     pool1 = tf.nn.max_pool(relu1, ksize = [1,2,2,1],strides=[1,2,2,1],padding="VALID")
    
  
    
     with tf.variable_scope("layer3-conv2"):
    
     conv2_weights = tf.get_variable("weight",[5,5,32,64],initializer=tf.truncated_normal_initializer(stddev=0.1))
    
     conv2_biases = tf.get_variable("bias", [64], initializer=tf.constant_initializer(0.0))
    
     conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')
    
     relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))
    
  
    
     with tf.name_scope("layer4-pool2"):
    
     pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
    
  
    
     with tf.variable_scope("layer5-conv3"):
    
     conv3_weights = tf.get_variable("weight",[3,3,64,128],initializer=tf.truncated_normal_initializer(stddev=0.1))
    
     conv3_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))
    
     conv3 = tf.nn.conv2d(pool2, conv3_weights, strides=[1, 1, 1, 1], padding='SAME')
    
     relu3 = tf.nn.relu(tf.nn.bias_add(conv3, conv3_biases))
    
  
    
     with tf.name_scope("layer6-pool3"):
    
     pool3 = tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
    
  
    
     with tf.variable_scope("layer7-conv4"):
    
     conv4_weights = tf.get_variable("weight",[3,3,128,128],initializer=tf.truncated_normal_initializer(stddev=0.1))
    
     conv4_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))
    
     conv4 = tf.nn.conv2d(pool3, conv4_weights, strides=[1, 1, 1, 1], padding='SAME')
    
     relu4 = tf.nn.relu(tf.nn.bias_add(conv4, conv4_biases))
    
  
    
     with tf.name_scope("layer8-pool4"):
    
     pool4 = tf.nn.max_pool(relu4, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')
    
     nodes = 6*6*128
    
     reshaped = tf.reshape(pool4,[-1,nodes])
    
  
    
     with tf.variable_scope('layer9-fc1'):
    
     fc1_weights = tf.get_variable("weight", [nodes, 1024],
    
                                   initializer=tf.truncated_normal_initializer(stddev=0.1))
    
     if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights))
    
     fc1_biases = tf.get_variable("bias", [1024], initializer=tf.constant_initializer(0.1))
    
  
    
     fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)
    
     if train: fc1 = tf.nn.dropout(fc1, 0.5)
    
  
    
     with tf.variable_scope('layer10-fc2'):
    
     fc2_weights = tf.get_variable("weight", [1024, 512],
    
                                   initializer=tf.truncated_normal_initializer(stddev=0.1))
    
     if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights))
    
     fc2_biases = tf.get_variable("bias", [512], initializer=tf.constant_initializer(0.1))
    
  
    
     fc2 = tf.nn.relu(tf.matmul(fc1, fc2_weights) + fc2_biases)
    
     if train: fc2 = tf.nn.dropout(fc2, 0.5)
    
  
    
     with tf.variable_scope('layer11-fc3'):
    
     fc3_weights = tf.get_variable("weight", [512, 2],
    
                                   initializer=tf.truncated_normal_initializer(stddev=0.1))
    
     if regularizer != None: tf.add_to_collection('losses', regularizer(fc3_weights))
    
     fc3_biases = tf.get_variable("bias", [2], initializer=tf.constant_initializer(0.1))
    
     logit = tf.matmul(fc2, fc3_weights) + fc3_biases
    
  
    
     return logit
    
  
    
 #---------------------------网络结束---------------------------
    
 regularizer = tf.contrib.layers.l2_regularizer(0.0001)
    
 logits = inference(x,True,regularizer)
    
 batch_size=16
    
 n_epoch=70
    
 #(小处理)将logits乘以1赋值给logits_eval，定义name，方便在后续调用模型时通过tensor名字调用输出tensor
    
 b = tf.constant(value=1,dtype=tf.float32)
    
 logits_eval = tf.multiply(logits,b,name='logits_eval')
    
 #增加平均滑动和衰减学习率
    
 # global_step=tf.Variable(0,trainable=False)
    
 # variable_averages=tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step)
    
 # variable_averages_op=variable_averages.apply(tf.trainable_variables())
    
 # learning_rate=tf.train.exponential_decay(LEARNING_RATE_BASE,global_step,len(data)/batch_size,LEARNING_RATE_DECAY)
    
  
    
 cross_entropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y_)
    
 cross_entropy_mean=tf.reduce_mean(cross_entropy)
    
 loss=cross_entropy_mean+tf.add_n(tf.get_collection('losses'))
    
 # train_step=tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)
    
 train_op=tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
    
 # with tf.control_dependencies([train_step,variable_averages_op]):
    
 #    train_op=tf.no_op(name='train')
    
 correct_prediction = tf.equal(tf.cast(tf.argmax(logits,1),tf.int32), y_)
    
 acc= tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    
  
    
  
    
 #定义一个函数，按批次取数据
    
 def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
    
     assert len(inputs) == len(targets)
    
     if shuffle:
    
     indices = np.arange(len(inputs))
    
     np.random.shuffle(indices)
    
     for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
    
     if shuffle:
    
         excerpt = indices[start_idx:start_idx + batch_size]
    
     else:
    
         excerpt = slice(start_idx, start_idx + batch_size)
    
     yield inputs[excerpt], targets[excerpt]
    
  
    
  
    
 #训练和测试数据，可将n_epoch设置更大一些
    
  
    
  
    
  
    
 saver=tf.train.Saver()
    
 # sess=tf.Session()
    
 sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
    
 sess.run(tf.global_variables_initializer())
    
  
    
 for epoch in range(n_epoch):
    
     start_time = time.time()
    
  
    
     #training
    
     train_loss, train_acc, n_batch = 0, 0, 0
    
     for x_train_a, y_train_a in minibatches(x_train, y_train, batch_size, shuffle=True):
    
     _,err,ac=sess.run([train_op,loss,acc], feed_dict={x: x_train_a, y_: y_train_a})
    
     train_loss += err; train_acc += ac; n_batch += 1
    
     print("step: %d" % (epoch + 1))
    
     print("   train loss: %f" % (np.sum(train_loss)/ n_batch))
    
     print("   train acc: %f" % (np.sum(train_acc)/ n_batch))
    
  
    
     #validation
    
     val_loss, val_acc, n_batch = 0, 0, 0
    
     for x_val_a, y_val_a in minibatches(x_val, y_val, batch_size, shuffle=False):
    
     err, ac = sess.run([loss,acc], feed_dict={x: x_val_a, y_: y_val_a})
    
     val_loss += err; val_acc += ac; n_batch += 1
    
     print("   validation loss: %f" % (np.sum(val_loss)/ n_batch))
    
     print("   validation acc: %f" % (np.sum(val_acc)/ n_batch))
    
 saver.save(sess,model_path)
    
 sess.close()

  from skimage import io,transform

    
 import tensorflow as tf
    
 import numpy as np
    
 import os
    
 import types
    
 import glob
    
 from PIL import Image
    
 import shutil
    
 path='./car_test_data/'
    
 gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)
    
  
    
 car_dict = {0:'is7',1:'not7'}
    
  
    
 w=100
    
 h=100
    
 c=3
    
 imgs = []
    
 images = []
    
 labels = []
    
 names = []
    
 def read_one_image(path):
    
  
    
     cate = [path + x for x in os.listdir(path) if os.path.isdir(path + x)]
    
  
    
     for idx, folder in enumerate(cate):
    
     for im in glob.glob(folder + '/*.jpg'):
    
         data1 = io.imread(im)
    
         data1=transform.resize(data1, (w, h))
    
         images.append(im)
    
         names.append(im.split("/")[-1].split("\ ")[-1])
    
         imgs.append(data1)
    
         labels.append(idx)
    
     return np.asarray(imgs, np.float32), np.asarray(labels, np.int32)
    
 data,label=read_one_image(path)
    
 #定义一个函数，按批次取数据
    
 def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
    
     assert len(inputs) == len(targets)
    
     if shuffle:
    
     indices = np.arange(len(inputs))
    
     np.random.shuffle(indices)
    
     for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
    
     if shuffle:
    
         excerpt = indices[start_idx:start_idx + batch_size]
    
     else:
    
         excerpt = slice(start_idx, start_idx + batch_size)
    
     yield inputs[excerpt], targets[excerpt]
    
 with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
    
  
    
     saver = tf.train.import_meta_graph('./model/model.ckpt.meta')
    
     saver.restore(sess,tf.train.latest_checkpoint('./model/'))
    
     #设置batch_size
    
     batch_size=8
    
     graph = tf.get_default_graph()
    
     x = graph.get_tensor_by_name("x:0")
    
     y_ = graph.get_tensor_by_name("y_:0")
    
     feed_dict = {x:data,y_:label}
    
     logits = graph.get_tensor_by_name("logits_eval:0")
    
     n_batch = 0
    
     # for x_test, y_test in minibatches(data, label, batch_size, shuffle=True):
    
     #     print("The step ",n_batch+1,"to input images.")
    
  
    
     # n_batch=n_batch+1
    
     classification_result = sess.run(logits, feed_dict)
    
     correct_prediction = tf.equal(tf.cast(tf.argmax(logits, 1), tf.int32), y_)
    
     acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    
     accuracy=sess.run(acc,feed_dict=feed_dict)
    
     #打印出预测矩阵
    
     # print(classification_result)
    
     #打印出预测矩阵每一行最大值的索引
    
     # print(tf.argmax(classification_result,1).eval())
    
     #根据索引通过字典对应车的分类
    
     output = []
    
     output = tf.argmax(classification_result,1).eval()
    
     for i in range(len(output)):
    
     print("第",i+1,"张图车位数预测:"+car_dict[output[i]]+"实际: "+car_dict[labels[i]])
    
     # img=Image.open(images[i])
    
     print(names[i])
    
     if output[i]==0:
    
         shutil.copy(images[i],"./car_data/0/%s"%names[i])
    
        # img.save("./car_data/0/%s"%names[i])
    
     else:
    
         shutil.copy(images[i], "./car_data/1/%s" % names[i])
    
         # img.save("./car_data/1/%s"%names[i])
    
     print("accuracy=%g" % accuracy)