【图像处理】基于matlab GUI Hough变换+PDE图像去雨【含Matlab源码 811期】
作者：互联网
一、简介
霍夫变换(Hough Transform)是图像处理中的一种特征提取技术，它通过一种投票算法检测具有特定形状的物体。该过程在一个参数空间中通过计算累计结果的局部最大值得到一个符合该特定形状的集合作为霍夫变换结果。霍夫变换于1962年由Paul Hough 首次提出[53]，后于1972年由Richard Duda和Peter Hart推广使用[54]，经典霍夫变换用来检测图像中的直线，后来霍夫变换扩展到任意形状物体的识别，多为圆和椭圆。
霍夫变换运用两个坐标空间之间的变换将在一个空间中具有相同形状的曲线或直线映射到另一个坐标空间的一个点上形成峰值，从而把检测任意形状的问题转化为统计峰值问题，上一节中已经介绍了车道的直线特征，本节中介绍hough变换检测直线的原理和检测结果。
我们知道，一条直线在直角坐标系下可以用y=kx+b表示, 霍夫变换的主要思想是将该方程的参数和变量交换，即用x,y作为已知量k,b作为变量坐标，所以直角坐标系下的直线y=kx+b在参数空间表示为点(k,b)，而一个点(x1,y1)在直角坐标系下表示为一条直线y1=x1·k+b，其中(k,b)是该直线上的任意点。为了计算方便，我们将参数空间的坐标表示为极坐标下的γ和θ。因为同一条直线上的点对应的(γ,θ)是相同的，因此可以先将图片进行边缘检测，然后对图像上每一个非零像素点，在参数坐标下变换为一条直线，那么在直角坐标下属于同一条直线的点便在参数空间形成多条直线并内交于一点。因此可用该原理进行直线检测。
如图 所示，对于原图内任一点(x,y)都可以在参数空间形成一条直线，以图中一条直线为例有参数(γ,θ)=(69.641,30°)，所有属于同一条直线上的点会在参数空间交于一点，该点即为对应直线的参数。由该图中所有直线所得到的(γ,θ)在参数空间中得到一系列对应曲线见图 霍夫统计变换结果。
二、源代码
function varargout = GUI_part(varargin)
% GUI_PART MATLAB code for GUI_part.fig
% GUI_PART, by itself, creates a new GUI_PART or raises the existing
% singleton*.
%
% H = GUI_PART returns the handle to a new GUI_PART or the handle to
% the existing singleton*.
%
% GUI_PART('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in GUI_PART.M with the given input arguments.
%
% GUI_PART('Property','Value',...) creates a new GUI_PART or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before GUI_part_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to GUI_part_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 GUI_part
% Last Modified by GUIDE v2.5 16-Apr-2014 18:09:28
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @GUI_part_OpeningFcn, ...
'gui_OutputFcn', @GUI_part_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 GUI_part is made visible.
function GUI_part_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 GUI_part (see VARARGIN)
% Choose default command line output for GUI_part
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes GUI_part wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = GUI_part_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 UI_Load.
function UI_Load_Callback(hObject, eventdata, handles)
% hObject handle to UI_Load (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
clc;
global IMG;
[filename, pathname] = uigetfile({'*.tif'; '*.jpg'; '*.bmp';'*.*'},'File Selector');
if isequal(filename,0)
msgbox(sprintf('Please select image :)'),'No Image Selected','warn');
return;
end
IMG =imread(filename);
axes(handles.UI_origin);
imshow(IMG);
% --- Executes on button press in UI_process.
function UI_process_Callback(hObject, eventdata, handles)
% hObject handle to UI_process (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global IMG;
%generate detected image and tag image
[Img, Img_tag] = GeneratorCore(IMG); %use Hough transform to detect rain and tag area
axes(handles.UI_hough);
imshow(Img);
axes(handles.UI_mask);
imshow(Img_tag);
mu = 20;
[xlen,ylen, ~] =size(Img_tag);
IMG2= imresize(IMG,[max(xlen,ylen),max(xlen,ylen)]);
for i = 1:3
IMGt = IMG2(:,:,i)
Origin_resize = double(imresize(IMGt,[max(size(IMGt)),max(size(IMGt))]));
[x,y] = size(Origin_resize);
Structure_img(:,:,i) = uint8(reshape(SB_ATV(Origin_resize, mu), x, y)); %use PDE to get structure information
Texture_img(:,:,i) = uint8(Origin_resize-double(Structure_img(:,:,i)));
end
% figure;subplot(1,2,1);
axes(handles.UI_PDE_low);
imshow(Structure_img);
% title('Structure Image');
% Texture_img = Origin_resize-Structure_img;
% subplot(1,2,2);
axes(handles.UI_PDE_high);
imshow(Texture_img);
% title('Texture Image');
mask = imresize(Img_tag,[max(xlen,ylen),max(xlen,ylen)]);
%Use texture patch to repair image
for i = 1:3
New_i(:,:,i)= Texture_core(double(Structure_img(:,:,i)),double(mask));
New_i(:,:,i) = medfilt2(New_i(:,:,i),[5,5]);
end
% figure;
axes(handles.UI_final);
imshow(uint8(New_i),[0,255]);
% --- Executes on button press in UI_hough_step.
function UI_hough_step_Callback(hObject, eventdata, handles)
% hObject handle to UI_hough_step (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of UI_hough_step
global IMG;
if( get(hObject,'Value') == 1)
[Img, Img_tag] = GeneratorCore(IMG); %use Hough transform to detect rain and tag area
figure;
subplot(1,2,1);
imshow(Img);
subplot(1,2,2);
imshow(Img_tag);
end
% --- Executes on button press in radiobutton2.
function radiobutton2_Callback(hObject, eventdata, handles)
% hObject handle to radiobutton2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
unction B=inpaint_nans(A,method)
% INPAINT_NANS: in-paints over nans in an array
% usage: B=INPAINT_NANS(A) % default method
% usage: B=INPAINT_NANS(A,method) % specify method used
%
% Solves approximation to one of several pdes to
% interpolate and extrapolate holes in an array
%
% arguments (input):
% A - nxm array with some NaNs to be filled in
%
% method - (OPTIONAL) scalar numeric flag - specifies
% which approach (or physical metaphor to use
% for the interpolation.) All methods are capable
% of extrapolation, some are better than others.
% There are also speed differences, as well as
% accuracy differences for smooth surfaces.
%
% methods {0,1,2} use a simple plate metaphor.
% method 3 uses a better plate equation,
% but may be much slower and uses
% more memory.
% method 4 uses a spring metaphor.
% method 5 is an 8 neighbor average, with no
% rationale behind it compared to the
% other methods. I do not recommend
% its use.
%
% method == 0 --> (DEFAULT) see method 1, but
% this method does not build as large of a
% linear system in the case of only a few
% NaNs in a large array.
% Extrapolation behavior is linear.
%
% method == 1 --> simple approach, applies del^2
% over the entire array, then drops those parts
% of the array which do not have any contact with
% NaNs. Uses a least squares approach, but it
% does not modify known values.
% In the case of small arrays, this method is
% quite fast as it does very little extra work.
% Extrapolation behavior is linear.
%
% method == 2 --> uses del^2, but solving a direct
% linear system of equations for nan elements.
% This method will be the fastest possible for
% large systems since it uses the sparsest
% possible system of equations. Not a least
% squares approach, so it may be least robust
% to noise on the boundaries of any holes.
% This method will also be least able to
% interpolate accurately for smooth surfaces.
% Extrapolation behavior is linear.
%
% Note: method 2 has problems in 1-d, so this
% method is disabled for vector inputs.
%
% method == 3 --+ See method 0, but uses del^4 for
% the interpolating operator. This may result
% in more accurate interpolations, at some cost
% in speed.
%
% method == 4 --+ Uses a spring metaphor. Assumes
% springs (with a nominal length of zero)
% connect each node with every neighbor
% (horizontally, vertically and diagonally)
% Since each node tries to be like its neighbors,
% extrapolation is as a constant function where
% this is consistent with the neighboring nodes.
%
% method == 5 --+ See method 2, but use an average
% of the 8 nearest neighbors to any element.
% This method is NOT recommended for use.
%
%
% arguments (output):
% B - nxm array with NaNs replaced
%
%
% Example:
% [x,y] = meshgrid(0:.01:1);
% z0 = exp(x+y);
% znan = z0;
% znan(20:50,40:70) = NaN;
% znan(30:90,5:10) = NaN;
% znan(70:75,40:90) = NaN;
%
% z = inpaint_nans(znan);
%
%
% See also: griddata, interp1
%
% Author: John D'Errico
% e-mail address: woodchips@rochester.rr.com
% Release: 2
% Release date: 4/15/06
% I always need to know which elements are NaN,
% and what size the array is for any method
[n,m]=size(A);
A=A(:);
nm=n*m;
k=isnan(A(:));
% list the nodes which are known, and which will
% be interpolated
nan_list=find(k);
known_list=find(~k);
% how many nans overall
nan_count=length(nan_list);
% convert NaN indices to (r,c) form
% nan_list==find(k) are the unrolled (linear) indices
% (row,column) form
[nr,nc]=ind2sub([n,m],nan_list);
% both forms of index in one array:
% column 1 == unrolled index
% column 2 == row index
% column 3 == column index
nan_list=[nan_list,nr,nc];
% supply default method
if (nargin<2) || isempty(method)
method = 0;
elseif ~ismember(method,0:5)
error 'If supplied, method must be one of: {0,1,2,3,4,5}.'
end
% for different methods
switch method
case 0
% The same as method == 1, except only work on those
% elements which are NaN, or at least touch a NaN.
% is it 1-d or 2-d?
if (m == 1) || (n == 1)
% really a 1-d case
work_list = nan_list(:,1);
work_list = unique([work_list;work_list - 1;work_list + 1]);
work_list(work_list <= 1) = [];
work_list(work_list >= nm) = [];
nw = numel(work_list);
u = (1:nw)';
fda = sparse(repmat(u,1,3),bsxfun(@plus,work_list,-1:1), ...
repmat([1 -2 1],nw,1),nw,nm);
else
% a 2-d case
% horizontal and vertical neighbors only
talks_to = [-1 0;0 -1;1 0;0 1];
neighbors_list=identify_neighbors(n,m,nan_list,talks_to);
% list of all nodes we have identified
all_list=[nan_list;neighbors_list];
% generate sparse array with second partials on row
% variable for each element in either list, but only
% for those nodes which have a row index > 1 or < n
L = find((all_list(:,2) > 1) & (all_list(:,2) < n));
nl=length(L);
if nl>0
fda=sparse(repmat(all_list(L,1),1,3), ...
repmat(all_list(L,1),1,3)+repmat([-1 0 1],nl,1), ...
repmat([1 -2 1],nl,1),nm,nm);
else
fda=spalloc(n*m,n*m,size(all_list,1)*5);
end
三、运行结果
四、备注
完整代码或者代写添加QQ 1564658423
标签：Hough,GUI,list,hObject,handles,源码,UI,method 来源： https://www.cnblogs.com/homeofmatlab/p/14735131.html