Tensorflow实现简易的汽车检测car-detect

本文作为一个汽车检测的小Demo，没啥技术含量，内容搜集自网络。数据集及程序来自网络，最原始的项目应该在这里，http://cogcomp.org/Data/Car/。

我整理到一起，大家一起学习学习：（内容是基于tensorflow实现简易的汽车检测car-detect，包含Training和Testing数据集，以及训练和测试的代码，python3.5+jupyter实现）

源码下载链接：https://bbs.cdsy.xyz/thread-9434-1-1.html

训练部分的代码：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

#定义加载数据的函数，注意训练数据的存储位置
def load_carDats():
    import cv2
    import os
    file_path = './TrainImages/'
    files = os.listdir(file_path)
    samples = []
    for file_name in files:
        data = cv2.imread(file_path + file_name, 0).reshape(-1) / 255
        label = 0 if file_name.split('-')[0] == 'neg' else 1
        samples.append((data, label))
    return samples
#加载数据
datas = load_carDats()
#随机打乱数据
np.random.shuffle(datas)
#划分数据，xs、ys 用来训练网络，x_test、y_test 用来测试网络训练效果
xs = [i[0] for i in datas[:1000]]
ys = np.reshape([i[1] for i in datas[:1000]], newshape=(-1,1))
x_test = [i[0] for i in datas[1000:]]
y_test = np.reshape([i[1] for i in datas[1000:]], newshape=(-1,1))

#----------------定义网络中频繁使用的函数，将其重构-----------------#
#权重变量
def weight_variables(shape):
    weights = tf.truncated_normal(shape, stddev=0.1, dtype=tf.float32)
    return tf.Variable(weights)

#偏置变量
def biase_variables(shape):
    biases = tf.constant(value=1.0, shape=shape)
    return tf.Variable(biases)

#卷积
def conv2d(x, W):
    '''计算卷积，x为输入层（shape=[-1,width,height,channel]）,
    W为f*f的共享权重矩阵shape=[f,f,in_layers_num, out_layers_num]，
    水平和垂直方向上的步长都为1'''
    return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding="VALID")

#最大值池化
def max_pooling(x):
    '''计算最大值混合，x为输入层(一般是卷积结果)shape=[-1,width,height,channels]
    ksize为混合pooling的核大小2*2，水平和垂直方向上的步长都为2'''
    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding="VALID")

#---------------------网络前向传播部分------------------#
def deepnn(x, keep_prop):
    '''定义深层卷积网络，包含了两个卷积-混合层和三个卷积层'''
    #step1:将原始一维得得数据转换成2维, 第一个表示样本数，第二三个是行列，最后一个是通道数
#     x = tf.reshape(x, shape=[-1, 40, 100, 1])
    #step2:定义第一的卷积-混合层
    with tf.name_scope("conv-pooling1"):
        W_conv1 = weight_variables([5,5,1,6])
        b_conv1 = biase_variables([6])
        ret_conv1 = tf.nn.relu(conv2d(x,W_conv1) + b_conv1)  #计算卷积，并使用修正单元对卷积结果进一步处理
        ret_pooling1 = max_pooling(ret_conv1)  #执行混合操作

    #step3:定义第二个卷积-混合层
    with tf.name_scope("conv-pooling2"):
        W_conv2 = weight_variables([5,5,6,16])
        b_conv2 = biase_variables([16])
        ret_conv2 = tf.nn.relu(conv2d(ret_pooling1, W_conv2) + b_conv2)
        ret_pooling2 = max_pooling(ret_conv2)

    #step4:定义第三个卷积层
    with tf.name_scope("conv-pooling3"):
        W_conv3 = weight_variables([5,5,16,32])
        b_conv3 = biase_variables([32])
        ret_conv3 = tf.nn.relu(conv2d(ret_pooling2, W_conv3) + b_conv3)

    #step5:定义第四个卷积层
    with tf.name_scope("conv4"):
        W_conv4 = weight_variables([3,18,32,64])
        b_conv4 = biase_variables([64])
        ret_conv4 = tf.nn.relu(conv2d(ret_conv3, W_conv4) + b_conv4)

    #step6:定义第五个卷积层
    with tf.name_scope("conv5"):
        W_conv5 = weight_variables([1,1,64,1])
        b_conv5 = biase_variables([1])
        ret_conv5 = conv2d(ret_conv4, W_conv5) + b_conv5

    return ret_conv5

#---------------------训练网络前的准备-----------------------#
#申明输入数据和标签的占位符
x = tf.placeholder(dtype=tf.float32, shape=[None,None, None, 1], name="x-input")
labels = tf.placeholder(dtype=tf.float32, shape=[None, 1], name="y-output")

#申明弃权的占位符
keep_prop = tf.placeholder(dtype=tf.float32, name="kprob")

#创建分类模型
ret = deepnn(x, keep_prop)
#此时的返回值是 -1*1*1*1的， 为了得到方便运算的结果，这里将reshape
y = tf.reshape(ret, shape=[-1,1])

#定义损失函数
with tf.name_scope("loss_function"):
    loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=y, labels=labels)
cost = tf.reduce_mean(loss)
#定义训练模型（优化模型）
with tf.name_scope("optimizor"):
    train = tf.train.AdamOptimizer(0.0005).minimize(cost)

#定义验证模型精度的方法
with tf.name_scope("accuracy"):
    y_hat = tf.nn.sigmoid(y)
    accuracy_rate = tf.abs(y_hat - labels) < 0.5
    accuracy_rate = tf.cast(accuracy_rate, dtype=tf.float32)
accuracy = tf.reduce_mean(accuracy_rate)

#--------------开始训练网络，并将训练结果保存到文件中---------------#
saver = tf.train.Saver()
sess = tf.Session()
sess.run(tf.global_variables_initializer())  #初始化变量

for i in range(20):
    skip = 10
    for k in range(0,1000,skip):
        x_train = np.reshape(xs[k:k+skip], newshape=(-1, 40, 100, 1))
        sess.run(train, feed_dict={x:x_train, labels:ys[k:k+skip], keep_prop:0.5}) # 训练模型
    # if (i+1) % 10 == 0:
    train_accuracy = sess.run(accuracy, feed_dict = {x: np.reshape(xs, (-1,40,100,1)), labels: ys, keep_prop:1.0})
    print('step %d, train accuracy %g' % (i, train_accuracy))
    saver.save(sess, "./models/carDetect_model.ckpt", global_step=i)

输出结果如下：

验证部分：

import cv2
#导入图片
pic = cv2.imread("./TestImages/test-113.pgm", 0)
size = pic.shape

img  = np.reshape(pic, (-1,size[0], size[1], 1))
#利用上面训练好的网络，开始在新的图片中检测
result = sess.run(ret, feed_dict={x:img})

#将检测结果显示
pt1 = np.array([result.argmax()//result.shape[2], result.argmax()%result.shape[2]]) * 4
pt2 = pt1 + np.array([40, 100])

pic_2 = cv2.rectangle(pic, (pt1[1], pt1[0]), (pt2[1], pt2[0]), 0, 2)

plt.imshow(pic_2, "gray")
plt.show()

结果如下：