m基于深度学习网络的手势识别系统matlab仿真,包含GUI界面

1.算法仿真效果

matlab2022a仿真结果如下：

2.算法涉及理论知识概要

        随着人工智能和机器学习技术的飞速发展，手势识别技术在人机交互、虚拟现实、智能家居等领域的应用越来越广泛。基于深度学习网络的手势识别系统凭借其强大的特征提取和分类能力，成为了研究热点。

       手势识别系统利用深度学习技术对从图像或视频中提取的手势特征进行自动学习与分类。主要步骤包括数据预处理、特征提取、模型训练与手势识别。

数据预处理

输入数据通常为包含手势的灰度或彩色图像序列。

对图像进行标准化（归一化）、裁剪、大小调整等操作。

特征提取

        在深度学习框架下，特征提取和分类是通过卷积神经网络（CNN）实现的。CNN能够通过多层结构自适应地提取图像中的空间和时间特征。

手势识别

       经过多层卷积和池化后，最后一层通常是全连接层，用于输出各个类别的概率分布。取概率最高的类别作为预测结果。

       手势识别是指通过计算机视觉技术，对图像或视频中的人手姿态进行自动检测和识别。手势识别系统通常包括手势检测、手势跟踪和手势分类三个主要步骤。其中，手势检测负责从复杂的背景中分离出手势区域；手势跟踪则对检测到的手势进行连续帧间的跟踪，以获取手势的动态信息；手势分类则根据提取的手势特征对其进行分类识别。

3.MATLAB核心程序

function varargout = tops(varargin)
% TOPS MATLAB code for tops.fig
%      TOPS, by itself, creates a new TOPS or raises the existing
%      singleton*.
%
%      H = TOPS returns the handle to a new TOPS or the handle to
%      the existing singleton*.
%
%      TOPS('CALLBACK',hObject,eventData,handles,...) calls the local
%      function named CALLBACK in TOPS.M with the given input arguments.
%
%      TOPS('Property','Value',...) creates a new TOPS or raises the
%      existing singleton*.  Starting from the left, property value pairs are
%      applied to the GUI before tops_OpeningFcn gets called.  An
%      unrecognized property name or invalid value makes property application
%      stop.  All inputs are passed to tops_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 tops
 
% Last Modified by GUIDE v2.5 02-Sep-2023 16:01:53
 
%FPGA/MATLAB/simulink仿真
%微信公众号：matworld
 
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @tops_OpeningFcn, ...
                   'gui_OutputFcn',  @tops_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 tops is made visible.
function tops_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 tops (see VARARGIN)
 
% Choose default command line output for tops
handles.output = hObject;
 
% Update handles structure
guidata(hObject, handles);
 
% UIWAIT makes tops wait for user response (see UIRESUME)
% uiwait(handles.figure1);
 
 
% --- Outputs from this function are returned to the command line.
function varargout = tops_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)
global im;
global Predicted_Label;
cla (handles.axes1,'reset')
 
axes(handles.axes1);
set(handles.edit2,'string',num2str(0));
load gnet.mat
 
[filename,pathname]=uigetfile({'*.bmp;*.jpg;*.png;*.jpeg;*.tif'},'选择一个图片','F:\test');
str=[pathname filename];
% 判断文件是否为空，也可以不用这个操作！直接读入图片也可以的
% im = imread(str);
% imshow(im)
if isequal(filename,0)||isequal(pathname,0)
    warndlg('please select a picture first!','warning');
    return;
else
    im = imread(str);
    imshow(im);
end
II(:,:,1) = imresize(im(:,:,1),[224,224]);
II(:,:,2) = imresize(im(:,:,2),[224,224]);
II(:,:,3) = imresize(im(:,:,3),[224,224]);
[Predicted_Label, Probability] = classify(net, II);