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.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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