m基於深度學習網路的手勢識別系統matlab模擬,包含GUI介面

我爱C编程發表於2024-03-17
深度學習MatlabGUI

1.演算法模擬效果

matlab2022a模擬結果如下：

2.演算法涉及理論知識概要

隨著人工智慧和機器學習技術的飛速發展，手勢識別技術在人機互動、虛擬現實、智慧家居等領域的應用越來越廣泛。基於深度學習網路的手勢識別系統憑藉其強大的特徵提取和分類能力，成為了研究熱點。

手勢識別系統利用深度學習技術對從影像或影片中提取的手勢特徵進行自動學習與分類。主要步驟包括資料預處理、特徵提取、模型訓練與手勢識別。

資料預處理

輸入資料通常為包含手勢的灰度或彩色影像序列。

對影像進行標準化（歸一化）、裁剪、大小調整等操作。

特徵提取

在深度學習框架下，特徵提取和分類是透過卷積神經網路（CNN）實現的。CNN能夠透過多層結構自適應地提取影像中的空間和時間特徵。

手勢識別

經過多層卷積和池化後，最後一層通常是全連線層，用於輸出各個類別的機率分佈。取機率最高的類別作為預測結果。

手勢識別是指透過計算機視覺技術，對影像或影片中的人手姿態進行自動檢測和識別。手勢識別系統通常包括手勢檢測、手勢跟蹤和手勢分類三個主要步驟。其中，手勢檢測負責從複雜的背景中分離出手勢區域；手勢跟蹤則對檢測到的手勢進行連續幀間的跟蹤，以獲取手勢的動態資訊；手勢分類則根據提取的手勢特徵對其進行分類識別。

3.MATLAB核心程式

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模擬
%微信公眾號：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'},'選擇一個圖片','F:\test');
str=[pathname filename];
% 判斷檔案是否為空，也可以不用這個操作！直接讀入圖片也可以的
% 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);

　　

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