基于matlab RGB和BP神经网络的人民币识别系统含GUI界面

uperrua · 发表于 2021-5-31 10:31

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一、简介
课题介绍
本设计为基于MATLAB的人民币识别系统。带有一个GUI界面。先利用radon进行倾斜校正，根据不同纸币，选择不同维度的参数识别纸币金额，有通过RGB分量识别100元；

通过面额图像的宽度识别1元、5元；通过构建矩形结构体识别10元；通过RGB分量识别 20元与 50元。

二、源代码

function varargout = main(varargin)
% MAIN MATLAB code for main.fig
% MAIN, by itself, creates a new MAIN or raises the existing
% singleton*.
%
% H = MAIN returns the handle to a new MAIN or the handle to
% the existing singleton*.
%
% MAIN('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in MAIN.M with the given input arguments.
%
% MAIN('Property','Value',...) creates a new MAIN or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before main_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to main_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help main
% Last Modified by GUIDE v2.5 29-May-2020 00:04:07
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @main_OpeningFcn, ...
'gui_OutputFcn', @main_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_maiNFCn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before main is made visible.
function main_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to main (see VARARGIN)
% Choose default command line output for main
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes main wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = main_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
%% 图像读取
[filename, pathname] = uigetfile({'*.jpg;*.tif;*.png;*.gif','All Image Files';...
'*.*','All Files' });
l = imread([ pathname,filename]);
axes(handles.axes1)
imshow(l);
title('原始图像')
l1=rgb2gray(l); %将真彩色图像转换为灰度图像
bw1=edge(l1,'sobel', 'both'); %采用sobel算子进行边缘检测
handles.bw1=bw1;
theta=0:179; %定义theta角度范围
r=radon(bw1,theta); %对图像进行Radon变换
%%%%%检测Radon变换矩阵中的峰值所对应的列坐标%%%%
[m,n]=size(r);
c=1;
for i=1:m
for j=1:n
if r(1,1)<r(i,j)
r(1,1)=r(i,j);
c=j;
end
end
end
rot=90-c;
pic=imrotate(l,rot,'crop'); %对图片进行旋转矫正
handles.pic=pic;
pic_gray=rgb2gray(pic); %转换为灰度图像
handles.pic_gray=pic_gray;
pic_a=imadjust(pic_gray,[0,0.001],[1,0]); %明暗反转
pic_b=1.3*pic_gray+0.7*pic_a;
pic_c=imadjust(pic_b,[0.5,1],[0,1]); %明暗反转
handles.pic_c=pic_c;
pic_b_edge=edge(pic_c,'sobel'); %采用sobel算子进行边缘检测
handles.pic_b_edge=pic_b_edge;
se=[1;1;1]; %线型结构元素
pic_imerode=imerode(pic_b_edge,se); %腐蚀图像
handles.pic_imerode=pic_imerode;
se=strel('rectangle',[60,60]); %矩形结构元素
pic_imclose=imclose(pic_imerode,se); %图像聚类、填充图像
handles.pic_imclose=pic_imclose;
pic_bwareaopen=bwareaopen(pic_imclose,10000); %去除聚团灰度值小于10000的部分
%%%%%求纸币行起始位置和终止位置%%%%%
[y,x]=size(pic_bwareaopen);
I6=double(pic_bwareaopen);
Y1=zeros(y,1);
for i=1:y
for j=1:x
if(I6(i,j,1)==1)
Y1(i,1)= Y1(i,1)+1;
end
end
end
[temp MaxY]=max(Y1);
%%
%%%%%%求纸币列起始位置和终止位置%%%%%
PY1=MaxY;
while ((Y1(PY1,1)>=50)&&(PY1>1))
PY1=PY1-1;
end
PY2=MaxY;
while ((Y1(PY2,1)>=50)&&(PY2<y))
PY2=PY2+1;
end
IY=pic(PY1：PY2,:,：);
X1=zeros(1,x);
for j=1:x
for i=PY1：PY2
if(I6(i,j,1)==1)
X1(1,j)= X1(1,j)+1;
end
end
end
%%
%%提取并画出背景中的RMB图像%%
PX1=1;
while ((X1(1,PX1)<3)&&(PX1<x))
PX1=PX1+1;
end
PX2=x;
while ((X1(1,PX2)<3)&&(PX2> PX1))
PX2=PX2-1;
end
dw=pic(PY1：PY2,PX1：PX2,：);
dw_gray=rgb2gray(dw);
dw_gray=imadjust(dw_gray,[0,1],[1,0]);
dw_bw=im2bw(dw_gray);
handles.dw_bw=dw_bw;
%%
%%分割提取RMB数值图像%%
[m,n]=size(dw_bw);
m1=round(m/3);
m2=round(2*m/3);
n1=round(n/6);
n2=round(n/3);
n3=round(2*n/3);
n4=round(5*n/6);
sum1=sum(sum(dw_bw(m1:m2,n1:n2)));
sum2=sum(sum(dw_bw(m1:m2,n3:n4)));
if sum1>sum2
dw=imrotate(dw,180,'crop');
end
%%
%%图像处理%%
x=dw;
x1=imresize(x,[236,500]);%'缩放图像
关闭
z=imcrop(x1,[270,150,160,65]);%对图像进行剪切，选取有效区域
, d* [$ T8 @+ M) {# C& a2 }
%%
I=imcrop(x1,[130,60,130,65]); %对图像进行剪切，选取有效区域
handles.I=I;
I1=rgb2gray(I); %转换为灰度图像
I2=medfilt2(I1); %滤波默认窗口
I3=imadjust(I2,[0.3,0.5],[0,1],1); %明暗反转
I4=im2bw(I3);
handles.I4=I4;
se=strel('rectangle',[3,3]); %构造结构函数，以长方形构造一个se# n. T% ?% d' S* ?2 m- {( ^6 k

三、运行结果

regngfpcb · 发表于 2021-5-31 11:26

基于matlab RGB和BP神经网络的人民币识别系统含GUI界面

yin123 · 发表于 2021-5-31 13:58

基于matlab RGB和BP神经网络的人民币识别系统含GUI界面

beachsun · 发表于 2021-5-31 13:59

基于matlab RGB和BP神经网络的人民币识别系统含GUI界面

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