Python如何实现带GUI界面的手写数字识别

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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 tfimport matplotlib.pyplot as pltimport cv2from PIL import Imageimport numpy as npfrom 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 Qtimport sys,timefrom PyQt5 import QtGui, QtCore, QtWidgetsfrom 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):#窗口放置中央        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.pyimport randomfrom PyQt5.QtWidgets import QFileDialogfrom PyQt5.QtGui import QPixmapfrom ui_openimage import Ui_Formimport sysfrom PyQt5 import QtWidgets, QtGuifrom PyQt5.QtWidgets import QMainWindow, QTextEdit, QAction, QApplicationimport os,sysfrom PyQt5.QtCore import Qtimport tensorflowfrom tensorflow.keras.models import load_modelfrom tensorflow.keras.datasets import mnistfrom tensorflow.keras import modelsfrom tensorflow.keras import layersfrom tensorflow.keras.utils import to_categoricalimport tensorflow.keras.preprocessing.image as imageimport matplotlib.pyplot as pltimport numpy as npimport cv2import warningswarnings.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.h6'        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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