【图像配准】基于粒子群改进的sift图像配准matlab源码

2021/10/2 17:10:09

编程Tag： 图像源码 image ... %% Pyramid dog 配准 gaussian

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1 基于粒子群改进的sift图像配准

模型参考这里。

2 部分代码

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close all;

clear all;


%% image path

file_image='';


%% read two images

[filename,pathname]=uigetfile({'*.*','All Files(*.*)'},'Select reference image',...

file_image);

image_1=imread(strcat(pathname,filename));

[filename,pathname]=uigetfile({'*.*','All Files(*.*)'},'Select the image to be registered',...

file_image);

image_2=imread(strcat(pathname,filename));


%% Display the reference image and the image to be registered

figure;

subplot(1,2,1);

imshow(image_1);

title('Reference image');

subplot(1,2,2);

imshow(image_2);

title('Image to be registered');


%% make file for save images

if (exist('save_image','dir')==0)%如果文件夹不存在

mkdir('save_image');

end


t1=clock;%Start time

%% Convert input image format

[~,~,num1]=size(image_1);

[~,~,num2]=size(image_2);

if(num1==3)

image_11=rgb2gray(image_1);

else

image_11=image_1;

end

if(num2==3)

image_22=rgb2gray(image_2);

else

image_22=image_2;

end


%Converted to floating point data between 0-1

image_11=im2double(image_11);

image_22=im2double(image_22);


%% Define the constants used

sigma=1.6;%Bottom Gauss Pyramid scale

dog_center_layer=3;%Defines the DOG Pyramid intermediate layer, the default is 3

contrast_threshold_1=0.04;%Contrast threshold of reference image

contrast_threshold_2=0.04;%Contrast threshold of the image to be registered

edge_threshold=10;%Edge threshold

is_double_size=false;%Whether the image size is enlarged,the default is 'false',to get more points, set it to 'true'

change_form='similarity';%Select geometric transformation type,it can be 'similarity','affine'


%% The number of groups in Gauss Pyramid

nOctaves_1=num_octaves(image_11,is_double_size);

nOctaves_2=num_octaves(image_22,is_double_size);


%% Generation of the first layer of the Gauss scale image

image_11=create_initial_image(image_11,is_double_size,sigma);

image_22=create_initial_image(image_22,is_double_size,sigma);


%% Generating Gauss Pyramid of reference image

tic;

[gaussian_pyramid_1,gaussian_gradient_1,gaussian_angle_1]=...

build_gaussian_pyramid(image_11,nOctaves_1,dog_center_layer,sigma);

disp(['Reference image generation Gauss Pyramid spent time is：',num2str(toc),'s']);


%% Generating DOG Pyramid of reference image

tic;

dog_pyramid_1=build_dog_pyramid(gaussian_pyramid_1,nOctaves_1,dog_center_layer);

disp(['Reference image generation DOG Pyramid spent time is：',num2str(toc),'s']);

clear gaussian_pyramid_1;


%% Search for extreme points in the DOG Pyramid of the reference image

tic;

[key_point_array_1]=find_scale_space_extream...

(...

dog_pyramid_1,...

nOctaves_1,...

dog_center_layer,...

contrast_threshold_1,...

sigma,...

edge_threshold,...

gaussian_gradient_1,...

gaussian_angle_1...

);

disp(['The extreme points of the reference image detection spend time is：',num2str(toc),'s']);

clear dog_pyramid_1;


%% The feature point descriptor generation,Reference image

tic;

[descriptors_1,locs_1]=calc_descriptors(gaussian_gradient_1,gaussian_angle_1,.....

key_point_array_1,is_double_size);

disp(['Reference image feature point descriptor generation spend time is：',num2str(toc),'s']);

clear gaussian_gradient_1;

clear gaussian_angle_1;



%% Generating Gauss Pyramid of the image to be registered

tic;

[gaussian_pyramid_2,gaussian_gradient_2,gaussian_angle_2]=...

build_gaussian_pyramid(image_22,nOctaves_2,dog_center_layer,sigma);

disp(['The image to be registered generation Gauss Pyramid spent time is：',num2str(toc),'s']);


%% Generating DOG Pyramid of the image to be registered

tic;

dog_pyramid_2=build_dog_pyramid(gaussian_pyramid_2,nOctaves_2,dog_center_layer);

disp(['The image to be registered generation DOG Pyramid spent time is：：',num2str(toc),'s']);

clear gaussian_pyramid_2;


%% Search for extreme points int the DOG Pyramid of the image to be registered

tic;

[key_point_array_2]=find_scale_space_extream...

(...

dog_pyramid_2,...

nOctaves_2,...

dog_center_layer,...

contrast_threshold_2,...

sigma,...

edge_threshold,...

gaussian_gradient_2,...

gaussian_angle_2...

);

disp(['The extreme points of the image to be registered detection spend time is：',num2str(toc),'s']);

clear dog_pyramid_2;


%% The feature point descriptor generation,the image to be registered

tic;

[descriptors_2,locs_2]=calc_descriptors(gaussian_gradient_2,gaussian_angle_2,...

key_point_array_2,is_double_size);

disp(['The image to be registered feature point descriptor generation spend time is：',num2str(toc),'s']);

clear gaussian_gradient_2;

clear gaussian_angle_2;


%% Calculation of geometric transformation parameters

tic;

[solution,~,cor1,cor2]=...

match(image_2, image_1,descriptors_2,locs_2,descriptors_1,locs_1,change_form);

disp(['Feature point matching spend time is：',num2str(toc),'s']);


tform=maketform('projective',solution');

[M,N,P]=size(image_1);

ff=imtransform(image_2,tform, 'XData',[1 N], 'YData',[1 M]);

button=figure;

subplot(1,2,1);

imshow(image_1);

title('Reference image');

subplot(1,2,2);

imshow(ff);

title('Image after registration');

str1=['.\save_image\','Results after registration','.jpg'];

saveas(button,str1,'jpg');

t2=clock;

disp(['Total spending time is：',num2str(etime(t2,t1)),'s']);


%% Display the detected feature points on the image

[button1,button2]=showpoint_detected(image_1,image_2,locs_1,locs_2);

str1=['.\save_image\','Reference image detection point','.jpg'];

saveas(button1,str1,'jpg');

str1=['.\save_image\','Points detected in the image to be registered','.jpg'];

saveas(button2,str1,'jpg');


%% Image fusion

image_fusion(image_1,image_2,solution);