深度学习Tensorflow 基于MNIST数据集的手写数字识别源码（自己在纸上写数字可识别）

╰+哭是因爲堅強的太久メ 2021-11-16 13:48 364阅读 0赞

### 参考博客： ###

《[参考博客一][Link 1]》

《[参考博客二][Link 2]》

《[MNIST代码理解][MNIST]》

--------------------

# 所需环境： #

已安装opencv环境

下载好MNIST数据集

pycharm一些库的安装

--------------------

# 实现效果： #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNzYzOTI5_size_16_color_FFFFFF_t_70][]            ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNzYzOTI5_size_16_color_FFFFFF_t_70 1][]    ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNzYzOTI5_size_16_color_FFFFFF_t_70 2][]

这是手写的两个字，进行opencv二值化处理后，得到两张28\*28像素的图片，即可进行识别。

进行代码二处理后的数字3图片：

![20190728153441994.png][]

然后将此图片进行预测即可得出结果。

--------------------

#  代码一： #

### 前向传播，进行模型训练，训练完成后会在电脑桌面自动生成4个文件，这4个文件就是训练好的模型！ ###

from tensorflow.examples.tutorials.mnist import input_data
    
    import tensorflow as tf
    
    mnist = input_data.read_data_sets('MNIST_data', one_hot=True) # MNIST数据集所在路径
    
    x = tf.placeholder(tf.float32, [None, 784])
    
    y_ = tf.placeholder(tf.float32, [None, 10])
    
    
    def weight_variable(shape):
        initial = tf.truncated_normal(shape,stddev = 0.1)
        return tf.Variable(initial)
    
    def bias_variable(shape):
        initial = tf.constant(0.1,shape = shape)
        return tf.Variable(initial)
    
    def conv2d(x,W):
        return tf.nn.conv2d(x, W, strides = [1,1,1,1], padding = 'SAME')
    
    def max_pool_2x2(x):
        return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
    
    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    
    x_image = tf.reshape(x,[-1,28,28,1])
    
    h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)
    
    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)
    
    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])
    
    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
    
    keep_prob = tf.placeholder("float")
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
    
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])
    
    y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
    
    cross_entropy = -tf.reduce_sum(y_*tf.log(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, "float"))
    
    saver = tf.train.Saver() #定义saver
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for i in range(20000):
            batch = mnist.train.next_batch(50)
            if i % 100 == 0:
                train_accuracy = accuracy.eval(feed_dict={
                    x: batch[0], y_: batch[1], keep_prob: 1.0})
                print('step %d, training accuracy %g' % (i, train_accuracy))
            train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
        saver.save(sess, 'C:/Users/Lenovo/Desktop/model.ckpt') # 模型储存位置，这个地方要结合你#自己的储存位置进行修改
    
        print('test accuracy %g' % accuracy.eval(feed_dict={
            x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

###  训练好的模型： ###

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNzYzOTI5_size_16_color_FFFFFF_t_70 3][]

**我把这4个文件放在了一个名为   “SAVE” 的文件夹，文件夹放在桌面。**

--------------------

# 代码二： #

### 将自己的手写图片进行二值化处理，生成新的符合代码要求的28\*28像素的图片。 ###

### 代码运行后，需要用鼠标在图片上进行截图，如果弹出的图片过大，可以先在画图工具中将图片的像素缩小再进行截图。 ###

### 记住：图片要放在你的python文件的同一个工程目录下。 ###

用鼠标右键在工程目录新建一个txt文档，将名字改为temp，后缀名改为png，那么这就是一个新的空白的png文件了，用于储存二值化之后的结果图片。

# coding=gbk
    import cv2
    
    global img
    global point1, point2
    def on_mouse(event, x, y, flags, param):
        global img, point1, point2
        img2 = img.copy()
        if event == cv2.EVENT_LBUTTONDOWN:         #左键点击
            point1 = (x,y)
            cv2.circle(img2, point1, 10, (0,255,0), 5)
            cv2.imshow('image', img2)
        elif event == cv2.EVENT_MOUSEMOVE and (flags & cv2.EVENT_FLAG_LBUTTON):   #按住左键拖曳
            cv2.rectangle(img2, point1, (x,y), (255,0,0), 5) # 图像，矩形顶点，相对顶点，颜色，粗细
            cv2.imshow('image', img2)
        elif event == cv2.EVENT_LBUTTONUP:         #左键释放
            point2 = (x,y)
            cv2.rectangle(img2, point1, point2, (0,0,255), 5)
            cv2.imshow('image', img2)
            min_x = min(point1[0], point2[0])
            min_y = min(point1[1], point2[1])
            width = abs(point1[0] - point2[0])
            height = abs(point1[1] -point2[1])
            cut_img = img[min_y:min_y+height, min_x:min_x+width]
            resize_img = cv2.resize(cut_img, (28,28)) # 调整图像尺寸为28*28
            ret, thresh_img = cv2.threshold(resize_img,127,255,cv2.THRESH_BINARY) # 二值化
            cv2.imshow('result', thresh_img)
            cv2.imwrite('C:\\Users\\Lenovo\\PycharmProjects\\untitled5\\temp.png', thresh_img)  # 预处理后图像保存位置，用鼠标右键新建一个txt文档，将名字改为temp，后缀名改为png，#那么这就是一个新的空白的png文件了，用于储存二值化之后的结果图片。
    
    def main():
        global img
        img = cv2.imread('C:\\Users\\Lenovo\\PycharmProjects\\untitled5\\MyNum9.jpg')  # 手写数字图像所在位置
        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转换图像为单通道(灰度图)
        cv2.namedWindow('image')
        cv2.setMouseCallback('image', on_mouse) # 调用回调函数
        cv2.imshow('image', img)
        cv2.waitKey(0)
    
    if __name__ == '__main__':
        main()

### 通过代码处理过的图片为： ###

![20190728152541140.jpg][]

--------------------

# 代码三： #

### 将处理过的图片和已经训练好的模型文件，进行代码预测。 ###

from PIL import Image, ImageFilter
    import tensorflow as tf
    import matplotlib.pyplot as plt
    
    def imageprepare():
        im = Image.open('C:\\Users\\Lenovo\\PycharmProjects\\untitled5\\MyNum9.png') #读取的图片所在路径，注意是已经处理好的28*28像素的图片，其他图片不行。
       
        im = im.convert('L')
        tv = list(im.getdata())
        tva = [(255-x)*1.0/255.0 for x in tv]
        return tva
    
    result=imageprepare()
    x = tf.placeholder(tf.float32, [None, 784])
    
    y_ = tf.placeholder(tf.float32, [None, 10])
    
    def weight_variable(shape):
        initial = tf.truncated_normal(shape,stddev = 0.1)
        return tf.Variable(initial)
    
    def bias_variable(shape):
        initial = tf.constant(0.1,shape = shape)
        return tf.Variable(initial)
    
    def conv2d(x,W):
        return tf.nn.conv2d(x, W, strides = [1,1,1,1], padding = 'SAME')
    
    def max_pool_2x2(x):
        return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
    
    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    
    x_image = tf.reshape(x,[-1,28,28,1])
    
    h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)
    
    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)
    
    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])
    
    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
    
    keep_prob = tf.placeholder("float")
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
    
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])
    
    y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
    
    cross_entropy = -tf.reduce_sum(y_*tf.log(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, "float"))
    
    saver = tf.train.Saver()
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        saver.restore(sess, "C:/Users/Lenovo/Desktop/SAVE/model.ckpt") #使用模型，参数和之前的代码保持一致
    
        prediction=tf.argmax(y_conv,1)
        predint=prediction.eval(feed_dict={x: [result],keep_prob: 1.0}, session=sess)
    
        print('识别结果为:')
        print(predint[0])

# 这样结果就出来了，成功识别！ #

[Link 1]: https://www.cnblogs.com/qiuhlee/p/9866222.html
[Link 2]: https://blog.csdn.net/qq_38269418/article/details/78991649
[MNIST]: http://www.tensorfly.cn/tfdoc/tutorials/mnist_beginners.html
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[20190728153441994.png]: /images/20211116/cabc084374b049e88ee7230ca57a539e.png
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[20190728152541140.jpg]: /images/20211116/3518af7941fc49d69ea4dbbf502ee675.png