1 简介
本文提出了一种基于计算机视觉的复杂环境下果蔬识别方法,包括以下步骤:首先,获取待识别果蔬图像;其次,将获取到的果蔬图像进行预处理,预处理后的图像被分割为果蔬区域和背景区域;提取预处理后的果蔬图像特征,其中提取的图像特征为颜色特征和纹理特征;然后,采用自适应加权方法对果蔬特征进行融合;最后,采用最近邻分类算法对果蔬进行识别.本发明相比已有的果蔬识别系统,算法复杂度低,识别率高,具有很强的使用性,可以有效的应用于日常生活中.
2 部分代码
function varargout = untitled(varargin)
% UNTITLED MATLAB code for untitled.fig
% UNTITLED, by itself, creates a new UNTITLED or raises the existing
% singleton*.
%
% H = UNTITLED returns the handle to a new UNTITLED or the handle to
% the existing singleton*.
%
% UNTITLED('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in UNTITLED.M with the given input arguments.
%
% UNTITLED('Property','Value',...) creates a new UNTITLED or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before untitled_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to untitled_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 untitled
% Last Modified by GUIDE v2.5 15-Jan-2017 01:03:42
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @untitled_OpeningFcn, ...
'gui_OutputFcn', @untitled_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 untitled is made visible.
function untitled_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 untitled (see VARARGIN)
% Choose default command line output for untitled
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes untitled wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = untitled_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 pushbutton3.
function pushbutton3_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton3 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global I FilledLabel HSV MeanHue Ecllipseratio Label num Premeter Area
for i = 1 : num
Premeter(i) = 0;
end
[row,col] = size(Label);
for i = 1 : row
for j = 1 : col
if(Label(i,j) > 0)
Premeter(Label(i,j)) = Premeter(Label(i,j)) + 1; %计算标记后的各块图形边界中像素的个数的总数
end
end
end
%计算各个图形单元的面积
FilledLabel=imfill(Label,'holes'); %填充打过标记的边界线中间围成的图形区域
for i = 1 : num
Area(i) = 0;
end
[row,col] = size(FilledLabel);
for i = 1 : row
for j = 1 : col
if(FilledLabel(i,j) > 0)
Area(FilledLabel(i,j)) = Area(FilledLabel(i,j)) + 1; %通过统计像素点个数的方式来求各形状的面积
end
end
end
%计算各个图形单元的圆度
for i = 1 : num
Ecllipseratio(i) = 4*pi*Area(i)/Premeter(i)^2;
end
%计算各个图像的颜色（色度）
HSV=rgb2hsv(I); %转换为HSV，为后面的颜色元素的提取做准备
[row,col] = size(FilledLabel); %统计填充后的图形中各块图形所含像素的个数的多少
MeanHue = zeros(1,num);
for i = 1 : num
Hue = zeros(Area(i),1);
nPoint = 0;
for j = 1 : row
for k = 1 : col
if(FilledLabel(j,k) == i)
nPoint = nPoint + 1;
Hue(nPoint,1) = HSV(j,k,1);
end
end
end
Hue(:,i) = sort(Hue(:,1));
for j = floor(nPoint*0.1) : floor(nPoint*0.9)
MeanHue(i) = MeanHue(i) + Hue(j,1);
end
MeanHue(i) = MeanHue(i) / (0.8*nPoint); %计算出平均的色度值
end
% --- Executes on button press in pushbutton4.
function pushbutton4_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton4 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global I Area FilledLabel HSV MeanHue Ecllipseratio Label num Premeter
HSV=rgb2hsv(I); %转换为HSV，为后面的颜色元素的提取做准备
[row,col] = size(FilledLabel); %统计填充后的图形中各块图形所含像素的个数的多少
MeanHue = zeros(1,num);
for i = 1 : num
Hue = zeros(Area(i),1);
nPoint = 0;
for j = 1 : row
for k = 1 : col
if(FilledLabel(j,k) == i)
nPoint = nPoint + 1;
Hue(nPoint,1) = HSV(j,k,1);
end
end
end
Hue(:,i) = sort(Hue(:,1));
for j = floor(nPoint*0.1) : floor(nPoint*0.9)
MeanHue(i) = MeanHue(i) + Hue(j,1);
end
MeanHue(i) = MeanHue(i) / (0.8*nPoint); %计算出平均的色度值
end
%识别黄瓜
%构建黄瓜的分类器，在二维特征空间对各个图像进行类别区分
huanggua=0;
for i=1:num
if((Ecllipseratio(i)<0.7))%分类器识别黄瓜的准则：判断各个图形中平均圆率值小于0.7的为黄瓜
huanggua=i;
end
end
end
%变换生成最终的结果图像，图像中显示的结果即对应分类器中指定的类别
juzimatrix = hsv2rgb(juziHSV); %转换为RGB彩图，彩图中已经滤去了其余水果，只剩下橘子
subplot(2,2,4),imshow(juzimatrix);
3 仿真结果
4 参考文献
[1]陶华伟, 赵力, 高瑞军, 黄永盛, 奚吉, & 虞玲等. 一种基于计算机视觉的复杂环境下果蔬识别方法.
部分理论引用网络文献，若有侵权联系博主删除。
5 MATLAB代码与数据下载地址
见博客主页
