Tensorflow训练识别手写数字0-9

1.安装环境

    这个比较简单，

    1.1 安装cnetos7 这个版本中直接代有python2.7.5版本，（下载ISO安装包安装即可我用的是vmware12.5）

     1.2 安装 tensorflow     

           安装pip

           yum update -y && yum install -y python python-devel epel-release.noarch python-pip 

           使用pip安装tensorflow

           pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl

     1.3 安装 python flaskapi

           pip install flask(这个不记得了，不行就度娘吧)          

      1.5 下载MNIST训练库

            mnist库

                         https://files.cnblogs.com/files/keim/train-images-idx3-ubyte.gz.rar 这个文件后缀Rar去掉

                         https://files.cnblogs.com/files/keim/MNIST_data1.rar   解压和上面的放一起即可

2.训练代码

    如下是训练代码，其中mnist_data为上面的MNIST库的位置

  

#coding=utf-8
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)


import tensorflow as tf

sess = tf.InteractiveSession()


x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))


sess.run(tf.global_variables_initializer())

y = tf.matmul(x,W) + b

cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

for _ in range(1000):
  batch = mnist.train.next_batch(100)
  train_step.run(feed_dict={x: batch[0], y_: batch[1]})
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

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(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

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))

saver = tf.train.Saver()  # defaults to saving all variables

sess.run(tf.global_variables_initializer())
for i in range(5000000):
  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})

writer=tf.summary.FileWriter("Scripts",tf.get_default_graph())
writer.close()
print ('save file')
saver.save(sess, 'learning_tensorflow/model.ckpt')  #保存模型参数，注意把这里改为自己的路径
print ('save file ok')
#print("test accuracy %g"%accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

3.测试代码

  

#coding=utf-8
from PIL import Image, ImageFilter
import tensorflow as tf
#import matplotlib.pyplot as plt
import cv2

def imageprepare():
    """
    This function returns the pixel values.
    The imput is a png file location.
    """
    file_name='pic_data/3.png'#导入自己的图片地址
    #in terminal 'mogrify -format png *.jpg' convert jpg to png
    im = Image.open(file_name).convert('L')
    #im.save("pic_data/sample.png")
    #plt.imshow(im)
    #plt.show()
    tv = list(im.getdata()) #get pixel values

    #normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.
    tva = [ (255-x)*1.0/255.0 for x in tv]
    #print(tva)
    return tva

    # Define the model (same as when creating the model file)
result=imageprepare()
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([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(tf.float32)
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)

init_op = tf.global_variables_initializer()

saver = tf.train.Saver()
with tf.Session() as sess:
    sess.run(init_op)
    saver.restore(sess, "learning_tensorflow/model.ckpt")#这里使用了之前保存的模型参数
    #print ("Model restored.")

    prediction=tf.argmax(y_conv,1)
    predint=prediction.eval(feed_dict={x: [result],keep_prob: 1.0}, session=sess)
    print(h_conv2)

    print('recognize result:')
    print(predint[0])

4.结合API远程调用

    接口代码：

   

# coding=UTF-8
from flask import Flask,jsonify,request,url_for
from utils import QssClient as utl
from utils import TensorClient as tcf
import urllib
import os
app = Flask(__name__)
foo = utl.QssClient()
foo2 = tcf.TensorClient()

@app.route('/')
def api_root():
    return 'Welcome'

@app.route('/articles')
def api_articles():
    return 'List of ' + url_for('api_articles')

@app.route('/articles/<articleid>')
def api_article(articleid):
    return 'You are reading ' + articleid

@app.route('/test1', methods=['GET', 'POST'])
def test1():
    resultCode='0'
    print (request.method)
    if request.method == 'POST':
       dic=request.form.to_dict()
       print(dic['img'])
       foo.baseConvert(dic['img'])
       resultCode=foo2.recognize("../pic_data/1.jpg", "../save_bp/lenet5.pb")
       #resultCode = '0'
    else:
        print(request.args.get('img'))
        resultCode = '0'
    return resultCode
@app.route('/test', methods=['GET', 'POST'])
def test():
    resultCode='0'
    print (request.method)
    if request.method == 'POST':
       dic=request.form.to_dict()
       print(dic['img'])
       foo.baseConvert(dic['img'])
       resultCode=foo2.autoCheckImg()
       #resultCode = '0'
    else:
        print(request.args.get('img'))
        resultCode = '0'
    return resultCode
if __name__ == '__main__':
    app.run(host = '0.0.0.0',port = 6001,debug = True)

工具类：
    qssclient:

   

# coding=UTF-8
import sys
import os,base64
import uuid
import requests
class QssClient(object):
    def __new__(cls, *args, **kw):
        if not hasattr(cls, '_instance'):
            orig = super(QssClient, cls)
            cls._instance = orig.__new__(cls, *args, **kw)
        return cls._instance

    def baseConvert(self,filedata):
        print ("write ok1")
        print filedata
        imgdata = base64.b64decode(filedata)
        file = open('../pic_data/1.jpg', 'wb')
        file.write(imgdata)
        print ("write ok2")
        file.close()

  TensorClient.py:
  

#coding=utf-8
from PIL import Image, ImageFilter
import tensorflow as tf
import matplotlib as mpl
mpl.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
#import matplotlib.pyplot as plt
import cv2
from skimage import io, transform

class TensorClient(object):
    def __new__(cls, *args, **kw):
        if not hasattr(cls, '_instance'):
            orig = super(TensorClient, cls)
            cls._instance = orig.__new__(cls, *args, **kw)
        return cls._instance
    def imageprepare(self):
        file_name = '../pic_data/1.jpg'  # 导入自己的图片地址27  For 5000次训练,20000次以上可以达到99%
        # file_name = 'pic_data2/0.png'  # 导入自己的图片地址
        # in terminal 'mogrify -format png *.jpg' convert jpg to png
        im = Image.open(file_name).convert('L')
        im.save("../pic_data/sample.png")
        #plt.imshow(im)
        #plt.show()
        tv = list(im.getdata())  # get pixel values
        # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.
        tva = [(255 - x) * 1.0 / 255.0 for x in tv]
        # print(tva)
        return tva

    def weight_variable(self,shape):
        initial = tf.truncated_normal(shape, stddev=0.1)
        return tf.Variable(initial)

    def bias_variable(self,shape):
        initial = tf.constant(0.1, shape=shape)
        return tf.Variable(initial)

    def conv2d(self,x, W):
        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

    def max_pool_2x2(self,x):
        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

   #此方法每次执行时要重起服务，不知为什么
    def autoCheckImg(self):
        result = self.imageprepare()
        x = tf.placeholder(tf.float32, [None, 784])
        #x = tf.placeholder(tf.float32, [1, 784])
        W = tf.Variable(tf.zeros([784, 10]))
        b = tf.Variable(tf.zeros([10]))

        W_conv1 = self.weight_variable([5, 5, 1, 32])
        b_conv1 = self.bias_variable([32])

        x_image = tf.reshape(x, [-1, 28, 28, 1])
        h_conv1 = tf.nn.relu(self.conv2d(x_image, W_conv1) + b_conv1)
        h_pool1 = self.max_pool_2x2(h_conv1)

        W_conv2 = self.weight_variable([5, 5, 32, 64])
        b_conv2 = self.bias_variable([64])

        h_conv2 = tf.nn.relu(self.conv2d(h_pool1, W_conv2) + b_conv2)
        h_pool2 =self. max_pool_2x2(h_conv2)

        W_fc1 = self.weight_variable([7 * 7 * 64, 1024])
        b_fc1 = self.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(tf.float32)
        h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

        W_fc2 = self.weight_variable([1024, 10])
        b_fc2 = self.bias_variable([10])
        y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
        init_op = tf.global_variables_initializer()
        saver = tf.train.Saver()

        #saver = tf.train.import_meta_graph("../learning20000/model.ckpt.meta")

        checkRlt=0;
        with tf.Session() as sess:
#旧方式
            sess.run(init_op)
            saver.restore(sess, "../learning20000/model.ckpt")  # 这里使用了之前保存的模型参数
#另一种方式
            #saver.restore(sess, "../learning20000/model.ckpt")
            #sess.run(tf.get_default_graph().get_tensor_by_name("add:0"))

            prediction = tf.argmax(y_conv, 1)
            predint = prediction.eval(feed_dict={x: [result], keep_prob: 1.0}, session=sess)
            print(h_conv2)
            print('recognize result:')
            print(predint[0])
            checkRlt=predint[0]
        return str(checkRlt)
   #这个方法识别率有问题
    def recognize(self,img_path, pb_file_path):
        with tf.Graph().as_default():
            output_graph_def = tf.GraphDef()

            with open(pb_file_path, "rb") as f:
                output_graph_def.ParseFromString(f.read())
                _ = tf.import_graph_def(output_graph_def, name="")

            with tf.Session() as sess:
                init = tf.global_variables_initializer()
                sess.run(init)

                input_x = sess.graph.get_tensor_by_name("input:0")
                print(input_x)
                keep_prob = sess.graph.get_tensor_by_name("keep_prob:0")
                print(keep_prob)
                out_softmax = sess.graph.get_tensor_by_name("softmax:0")
                print(out_softmax)
                out_label = sess.graph.get_tensor_by_name("output:0")
                print(out_label)

                img = Image.open(img_path).convert('L')
                img = img.resize((28, 28))
                arr = []
                pixelmin = float(img.getpixel((0, 0)))
                pixelmax = float(img.getpixel((0, 0)))
                for i in range(28):
                    for j in range(28):

                        if pixelmin > float(img.getpixel((j, i))):
                            pixelmin = float(img.getpixel((j, i)))
                        if pixelmax < float(img.getpixel((j, i))):
                            pixelmax = float(img.getpixel((j, i)))
                # print(pixelmin, pixelmax)
                for i in range(28):
                    for j in range(28):
                        pixel = (float(img.getpixel((j, i))) - pixelmin) / (pixelmax - pixelmin)
                        arr.append(pixel)

                # print(arr)
                img_out_softmax = sess.run(out_softmax, feed_dict={input_x: np.reshape(arr, [-1, 784]), keep_prob: 1.0})

                print("img_out_softmax:", img_out_softmax)
                prediction_labels = np.argmax(img_out_softmax, axis=1)
                print("label:", prediction_labels)
                return str(prediction_labels[0])

 

5.测试客户端

    

    关键代码POST请求

    

 public static string ImageHttpPost(string Url, string postDataStr)
        {
            try
            {
                //WriteLog(DateTime.Now + " 影像识别Url：" + Url + " postDataStr：" + postDataStr);
                postDataStr = postDataStr.Replace("+", "%2B");
                HttpWebRequest request = (HttpWebRequest)WebRequest.Create(Url);
                request.Method = "POST";
                request.Timeout = 10000;
                //request.UserAgent = "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.2; .NET CLR 4.0.30319;)";
                request.ContentType = "application/x-www-form-urlencoded";
                request.ContentLength = postDataStr.Length;
                //增加下面两个属性即可  
                //request.KeepAlive = false;
                //request.ProtocolVersion = HttpVersion.Version10;  

                StreamWriter writer = new StreamWriter(request.GetRequestStream(), Encoding.ASCII);
                writer.Write(postDataStr);
                writer.Flush();
                writer.Close();
                writer.Dispose();
                //ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls;
                //ServicePointManager.SecurityProtocol = (SecurityProtocolType)3072;
                ServicePointManager.SecurityProtocol = SecurityProtocolType.Ssl3 | SecurityProtocolType.Tls;
                HttpWebResponse response = (HttpWebResponse)request.GetResponse();
                string encoding = response.ContentEncoding;
                //if (encoding == null || encoding.Length < 1)
                //{
                //    encoding = "UTF-8"; //默认编码
                //}
                Stream myResponseStream = response.GetResponseStream();
                StreamReader myStreamReader = new StreamReader(myResponseStream, Encoding.GetEncoding("utf-8"));
                string retString = myStreamReader.ReadToEnd();
                myStreamReader.Close();
                myResponseStream.Close();
                return retString;
            }
            catch (Exception ex)
            {
                Console.WriteLine(ex);
                return null;
            }
        }

图片生成base64:

  /// <summary>
        /// 图片生成64
        /// </summary>
        /// <param name="Imagefilename"></param>
        /// <returns></returns>
        protected string ImgToBase64String(string Imagefilename)
        {
            try
            {
                //生成base64
                Bitmap bmp = new Bitmap(Imagefilename);

                MemoryStream ms = new MemoryStream();
                bmp.Save(ms, System.Drawing.Imaging.ImageFormat.Jpeg);
                byte[] arr = new byte[ms.Length];
                ms.Position = 0;
                ms.Read(arr, 0, (int)ms.Length);
                ms.Close();
               
                return Convert.ToBase64String(arr);
            }
            catch (Exception ex)
            {
                return null;
            }
        }

  请求API: 

 //MessageBox.Show("保存成功！");
                var base64img = ImgToBase64String(filestring);
                // MessageBox.Show("图片准备成功！");
                //post
                var value = ImageHttpPost("http://192.168.1.168:6001/test", "img=" + base64img);
             
                label3.Text = "识别结束";
                if (value == null)
                {
                    label2.Text = "未识别";
                }
                else
                {
                    label2.Text = value;
                }

   这个是客户端功能是左则手写0~9后点击保存即可调用服务API进行识别

 

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