Python实现带GUI界面的手写数字识别

1.效果图

有点low，轻喷

点击选择图片会优先从当前目录查找

2.数据集

这部分我是对mnist数据集进行处理保存

对应代码：

import tensorflow as tf
import matplotlib.pyplot as plt
import cv2
from pil import image
import numpy as np
from scipy import misc
(x_train_all,y_train_all),(x_test,y_test) = tf.keras.datasets.mnist.load_data()
x_valid,x_train = x_train_all[:5000],x_train_all[5000:]
y_valid,y_train = y_train_all[:5000],y_train_all[5000:]
print(x_valid.shape,y_valid.shape)
print(x_train.shape,y_train.shape)
print(x_test.shape,y_test.shape)
#读取单张图片
def show_single_img(img_arr,len=100,path='/users/zhangcaihui/desktop/case/jpg/'):
    for i in range(len):#我这种写法会进行覆盖，只能保存10张照片，想保存更多的数据自己看着改
        new_im = image.fromarray(img_arr[i])  # 调用image库，数组归一化
        #new_im.show()
        #plt.imshow(img_arr)  # 显示新图片
        label=y_train[i]
        new_im.save(path+str(label)+'.jpg')  # 保存图片到本地

#显示多张图片
def show_imgs(n_rows,n_cols,x_data,y_data):
    assert len(x_data) == len(y_data)
    assert n_rows * n_cols < len(x_data)
    plt.figure(figsize=(n_cols*1.4,n_rows*1.6))
    for row in range(n_rows):
        for col in range(n_cols):
            index = n_cols * row + col
            plt.subplot(n_rows,n_cols,index+1)
            plt.imshow(x_data[index],cmap="binary",interpolation="nearest")
            plt.axis("off")
    plt.show()
#show_imgs(2,2,x_train,y_train)
show_single_img(x_train)

3.关于模型

我保存了了之前训练好的模型，用来加载预测

关于tensorflow下训练神经网络模型：手把手教你，mnist手写数字识别

训练好的模型model.save(path)即可

4.关于gui设计

1）排版

#ui_openimage.py
# -*- coding: utf-8 -*-
# from pyqt5 import qtcore, qtgui, qtwidgets
# from pyqt5.qtcore import qt
import sys,time
from pyqt5 import qtgui, qtcore, qtwidgets
from pyqt5.qtwidgets import *
from pyqt5.qtcore import *
from pyqt5.qtgui import *

class ui_form(object):
    def setupui(self, form):
        form.setobjectname("form")
        form.resize(1144, 750)
        self.label_1 = qtwidgets.qlabel(form)
        self.label_1.setgeometry(qtcore.qrect(170, 130, 351, 251))
        self.label_1.setobjectname("label_1")
        self.label_2 = qtwidgets.qlabel(form)
        self.label_2.setgeometry(qtcore.qrect(680, 140, 351, 251))
        self.label_2.setobjectname("label_2")
        self.btn_image = qtwidgets.qpushbutton(form)
        self.btn_image.setgeometry(qtcore.qrect(270, 560, 93, 28))
        self.btn_image.setobjectname("btn_image")
        self.btn_recognition = qtwidgets.qpushbutton(form)
        self.btn_recognition.setgeometry(qtcore.qrect(680,560,93,28))
        self.btn_recognition.setobjectname("bnt_recognition")
        #显示时间按钮
        self.bnt_timeshow = qtwidgets.qpushbutton(form)
        self.bnt_timeshow.setgeometry(qtcore.qrect(900,0,200,50))
        self.bnt_timeshow.setobjectname("bnt_timeshow")
        self.retranslateui(form)
        self.btn_image.clicked.connect(self.slot_open_image)
        self.btn_recognition.clicked.connect(self.slot_output_digital)
        self.bnt_timeshow.clicked.connect(self.buttonclicked)
        self.center()
        qtcore.qmetaobject.connectslotsbyname(form)

    def retranslateui(self, form): #设置文本填充label、button
        _translate = qtcore.qcoreapplication.translate
        form.setwindowtitle(_translate("form", "数字识别系统"))
        self.label_1.settext(_translate("form", "点击下方按钮"))
        self.label_1.setstylesheet('font:50px;')
        self.label_2.settext(_translate("form", "0~9"))
        self.label_2.setstylesheet('font:50px;')
        self.btn_image.settext(_translate("form", "选择图片"))
        self.btn_recognition.settext(_translate("from","识别结果"))
        self.bnt_timeshow.settext(_translate("form","当前时间"))

    # 状态条显示时间模块
    def buttonclicked(self):  # 动态显示时间
        timer = qtimer(self)
        timer.timeout.connect(self.showtime)
        timer.start()
    def showtime(self):
        datetime = qdatetime.currentdatetime()
        time_now = time.strftime('%y-%m-%d %h:%m:%s', time.localtime())
        #self.statusbar().showmessage(time_now)
        #self.bnt_timeshow.setfont(qtgui.qfont().setpointsize(100))
        self.bnt_timeshow.settext(time_now)

    def center(self):#窗口放置*
        screen = qdesktopwidget().screengeometry()
        size = self.geometry()
        self.move((screen.width() - size.width()) / 2,
                    (screen.height() - size.height()) / 2)


    def keypressevent(self, e):
        if e.key() == qt.key_escape:
            self.close()

2）直接运行这个文件（调用1）

#ui_main.py
import random

from pyqt5.qtwidgets import qfiledialog
from pyqt5.qtgui import qpixmap
from ui_openimage import ui_form
import sys
from pyqt5 import qtwidgets, qtgui
from pyqt5.qtwidgets import qmainwindow, qtextedit, qaction, qapplication
import os,sys
from pyqt5.qtcore import qt

import tensorflow
from tensorflow.keras.models import load_model
from tensorflow.keras.datasets import mnist
from tensorflow.keras import models
from tensorflow.keras import layers
from tensorflow.keras.utils import to_categorical
import tensorflow.keras.preprocessing.image as image
import matplotlib.pyplot as plt
import numpy as np
import cv2
import warnings
warnings.filterwarnings("ignore")
class window(qtwidgets.qmainwindow,ui_form):
    def __init__(self):
        super(window, self).__init__()
        self.cwd = os.getcwd()
        self.setupui(self)
        self.labels = self.label_1
        self.img=none
    def slot_open_image(self):
        file, filetype = qfiledialog.getopenfilename(self, '打开多个图片', self.cwd, "*.jpg, *.png, *.jpg, *.jpeg, all files(*)")
        jpg = qtgui.qpixmap(file).scaled(self.labels.width(), self.labels.height())
        self.labels.setpixmap(jpg)
        self.img=file

    def slot_output_digital(self):
    	'''path为之前保存的模型路径'''
        path='/users/zhangcaihui/pycharmprojects/py38_tf/dl_book_keras/save_the_model.h5'
        model= load_model(path)
        #防止不上传数字照片而直接点击识别
        if self.img==none:
            self.label_2.settext('请上传照片！')
            return
        img = image.load_img(self.img, target_size=(28, 28))
        img = img.convert('l')#转灰度图像
        x = image.img_to_array(img)
        #x = abs(255 - x)
        x = np.expand_dims(x, axis=0)
        print(x.shape)
        x = x / 255.0
        prediction = model.predict(x)
        print(prediction)
        output = np.argmax(prediction, axis=1)
        print("手写数字识别为：" + str(output[0]))
        self.label_2.settext(str(output[0]))

if __name__ == "__main__":
  app = qtwidgets.qapplication(sys.argv)
  my = window()
  my.show()
  sys.exit(app.exec_())

5.缺点

界面low

只能识别单个数字

其实可以将多数字图片进行裁剪分割，这就涉及到制作数据集了

6.遗留问题

我自己手写的数据照片处理成28281送入网络预测，识别结果紊乱。

反思：自己写的数据是rgb，且一张几kb，图片预处理后，按28*28读入失真太严重了，谁有好的方法可以联系我！！！

其他的水果识别系统，手势识别系统啊，改改直接套！

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