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）

参考博客：

《参考博客一》

《参考博客二》

《MNIST代码理解》

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所需环境：

已安装opencv环境

下载好MNIST数据集

pycharm一些库的安装

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实现效果：

                 

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：

 然后将此图片进行预测即可得出结果。

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 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：

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。

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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：

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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！