带GUI界面的手写数字识别

1.效果图
2.数据集
3.关于模型
4.关于GUI设计
- 1）排版
5.缺点
6.遗留问题

1.效果图

有点low，轻喷
在这里插入图片描述
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 + colplt.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.translateForm.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）```python
#ui_main.py
import randomfrom 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 Qtimport 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_1self.img=Nonedef 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=filedef 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！')returnimg = 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 / 255.0prediction = 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_())