function conf = proposal_config(varargin)

% conf = proposal_config(varargin)

% --------------------------------------------------------

% Faster R-CNN

% Copyright (c) 2015, Shaoqing Ren

% Licensed under The MIT License [see LICENSE for details]

% --------------------------------------------------------

    ip = inputParser ;  

    %% training

    ip.addParamValue('use_gpu',         gpuDeviceCount > 0, ...

                                                        @islogical);

    % whether drop the anchors that has edges outside of the image boundary

    ip.addParamValue('drop_boxes_runoff_image', ...

                                        true,           @islogical);

    % Image scales -- the short edge of input image

    ip.addParamValue('scales',          600,            @ismatrix);

    % Max pixel size of a scaled input image

    ip.addParamValue('max_size',        1000,           @isscalar);

    % Images per batch, only supports ims_per_batch = 1 currently

    ip.addParamValue('ims_per_batch',   1,              @isscalar);

    % Minibatch size

    ip.addParamValue('batch_size',      256,            @isscalar);

    % Fraction of minibatch that is foreground labeled (class > 0)

    ip.addParamValue('fg_fraction',     0.5,           @isscalar);

    % weight of background samples, when weight of foreground samples is

    % 1.0

    ip.addParamValue('bg_weight',       1.0,            @isscalar);

    % Overlap threshold for a ROI to be considered foreground (if >= fg_thresh)

    ip.addParamValue('fg_thresh',       0.7,            @isscalar);

    % Overlap threshold for a ROI to be considered background (class = 0 if

    % overlap in [bg_thresh_lo, bg_thresh_hi))

    ip.addParamValue('bg_thresh_hi',    0.3,            @isscalar);

    ip.addParamValue('bg_thresh_lo',    0,              @isscalar);

    % mean image, in RGB order

    ip.addParamValue('image_means',     128,            @ismatrix);

    % Use horizontally-flipped images during training ?

    ip.addParamValue('use_flipped',     true,           @islogical);

    % Stride in input image pixels at ROI pooling level (network specific)

    % 16 is true for {Alex,Caffe}Net, VGG_CNN_M_1024, and VGG16

    ip.addParamValue('feat_stride',     16,             @isscalar);

    % train proposal target only to labled ground-truths or also include

    % other proposal results (selective search, etc.)

    ip.addParamValue('target_only_gt',  true,           @islogical);

    % random seed

    ip.addParamValue('rng_seed',        6,              @isscalar);

    %% testing

    ip.addParamValue('test_scales',     600,            @isscalar);

    ip.addParamValue('test_max_size',   1000,           @isscalar);

    ip.addParamValue('test_nms',        0.3,            @isscalar);

    ip.addParamValue('test_binary',     false,          @islogical);

    ip.addParamValue('test_min_box_size',16,            @isscalar);

    ip.addParamValue('test_drop_boxes_runoff_image', ...

                                        false,          @islogical);

    ip.parse(varargin{:});

    conf = ip.Results;

    assert(conf.ims_per_batch == 1, 'currently rpn only supports ims_per_batch == 1');

    % if image_means is a file, load it...

    if ischar(conf.image_means)

        s = load(conf.image_means);

        s_fieldnames = fieldnames(s);

        assert(length(s_fieldnames) == 1);

        conf.image_means = s.(s_fieldnames{1});

    end

end

Check the validity of required and optional function inputs.

Create a custom function with required and optional inputs in the file findArea.m.

function a = findArea(width,varargin)

   p = inputParser;

   defaultHeight = 1;

   defaultUnits = 'inches';

   defaultShape = 'rectangle';

   expectedShapes = {'square','rectangle','parallelogram'};

   addRequired(p,'width',@isnumeric);

   addOptional(p,'height',defaultHeight,@isnumeric);

   addParameter(p,'units',defaultUnits);

   addParameter(p,'shape',defaultShape,...

                 @(x) any(validatestring(x,expectedShapes)));

   parse(p,width,varargin{:});

   a = p.Results.width .* p.Results.height;

The input parser checks whether width and height are numeric, and whether the shape matches a string in cell array expectedShapes. @ indicates a function handle, and the syntax @(x) creates an anonymous function with input x.

Call the function with inputs that do not match the scheme. For example, specify a nonnumeric value for the width input:

findArea('text')

Error using findArea (line 14)

The value of 'width' is invalid. It must satisfy the function: isnumeric.

Specify an unsupported value for shape:

findArea(4,'shape','circle')

Error using findArea (line 14)

The value of 'shape' is invalid. Expected input to match one of these strings:

square, rectangle, parallelogram

The input, ''circle'', did not match any of the valid strings.

    assert(mod(conf.batch_size, num_images) == 0, ...

        sprintf('num_images %d must divide BATCH_SIZE %d', num_images, conf.batch_size));

　　重新安排矩阵的x,y,z , 在二维中就相当于把x,y 对换，在三维中相当于可以把三个坐标的位置互换。

比如A =

A(:,:,1)=repmat(1,3,3);

A(:,:,2)=repmat(2,3,3);

A(:,:,3)=repmat(3,3,3);

disp(A);

A(:,:,1) =

     1     1     1

     1     1     1

     1     1     1

A(:,:,2) =

     2     2     2

     2     2     2

     2     2     2

A(:,:,3) =

     3     3     3

     3     3     3

     3     3     3

At = permute(A,[3,2,1]);

disp(At);

At(:,:,1) =

     1     1     1

     2     2     2

     3     3     3

At(:,:,2) =

     1     1     1

     2     2     2

     3     3     3

At(:,:,3) =

     1     1     1

     2     2     2

     3     3     3

permute(A,[3,2,1])

select = importdata('/home/wangxiao/Documents/Sun-80-dataset/VGG_16/iter_1/SUN80_50%_selected_without_HD.txt');

Unlabel = importdata('/home/wangxiao/Documents/Sun-80-dataset/iter_1/Sun_100_UnLabel_Train_0.5_.txt');

fid = fopen('/home/wangxiao/Documents/Sun-80-dataset/VGG_16/iter_1/SUN80_50%_Unselected_data.txt', 'a') ;

selected_list = [] ;

unselected_list = [] ; 

for i = 1:size(Unlabel.data, 1)

    disp(['deal with: ', num2str(i) , '/' , num2str(size(Unlabel.data, 1))]) ;

    unlabel_name = Unlabel.textdata{i, 1};  % Unlabel image name

    unlabel_label = Unlabel.data(i, 1) ;    % Unlabel image label

    count = 0;

    for j = 1:size(select.textdata, 1)

        select_name = select.textdata{j, 1};  % selected image name 

        if strcmp(unlabel_name, select_name) % if have selected, jump it.

            selected_list = [selected_list; unlabel_name];

            % break;

        else

            count = count + 1;

        end

        if count == size(select.textdata, 1)

            fprintf(fid, '%s ', num2str(unlabel_name));

            fprintf(fid, '%s \n', num2str(unlabel_label));

        end

    end 

end 

 clc; close all; clear all;

 image = imread('/home/wangxiao/Documents/mnist_dataset/mnist_0_.png');

 image = im2double(image);

 image = double(image);

 width = size(image, ); height = size(image, );

 synthetic = zeros([, ]);

 for i = :size(image, )

 for j = :size(image, )

 synthetic(i, j) = image(i, j);

 synthetic(i, j) = image(i, j);

 synthetic(i, j) = image(i, j);

 end

 end

 synthetic = im2uint8(synthetic);

 imshow(synthetic);

 figure; imshow(image);

 %%

 synthetic2 = zeros([, , ]);

 for i = :size(image, )

 for j = :size(image, )

 synthetic2(i, j, ) = image(i, j, );

 synthetic2(i, j, ) = image(i, j, );

 synthetic2(i, j, ) = image(i, j, );

 end

 end

 synthetic2 = im2uint8(synthetic2);

 imshow(synthetic2);

 %% change the frame to videos to save.

 clc; close all; clear all;

 path = '/home/wangxiao/Downloads/files/Visual_Tracking/MDNet-CVPR2016/saved_tracking_results_MDNet_OTB100/Biker/';

 files = dir([path, '*.png']);

 count = ; 

 for i=:size(files, )

     xxx =  strtok(files(i).name, 'M');

     name = xxx(:end-);

     image = imread([path, files(i).name]);

     index = sprintf('%04d', str2double(name));

     newName = [ index,'.jpg'];

 %    a = sprintf('%04d',i);

     imwrite(image, [path, newName]);

 end

 disp('==>> deal with image done !')

 jpgFiles = dir([path, '*.jpg']);

 videoName = '/home/wangxiao/Videos/Biker_MDNet_OTB100.avi';

 fps = ; %帧率

 startFrame = ; %从哪一帧开始

 endFrame = size(jpgFiles, ); %哪一帧结束

 %生成视频的参数设定

 aviobj=VideoWriter(videoName);  %创建一个avi视频文件对象，开始时其为空

 aviobj.FrameRate=fps;

 open(aviobj);%Open file for writing video data

 for i=startFrame:endFrame

     frames = imread([path, jpgFiles(i).name]);

     frames = im2frame(frames);

     writeVideo(aviobj, frames);

 end

 close(aviobj); 

 disp('==>> saved the video !')

 . 普通保存在当前文件夹下

 　　save matPath.mat A B;   % A B都是生成的数据矩阵

 　　需要注意这种方式只能将数据保存在当前文件夹下的第一个参数文件中，下面这样写并不能将数据保存到你想要的文件夹中的。

 　　saldir = './result/';

 　　savePath = [saldir imnames(len).name(:end-) '_KSD'];

 　　save savePath A;  

 　　上面程序也只能实现在当前文件夹下生成savePath.mat文件，然后数据保存到该文件中。并不能保存到需要的文件夹中。正确的写法是下面的方式。

 . 保留数据到其他文件夹下

 　　saldir = './result/';

 　　savePath = [saldir imnames(len).name(:end-) '_KSD' '.mat'];

 　　save(savePath,'A');  % 保存到其他文件夹的写法

     这里也需要注意，保存的数据矩阵，即save函数的第二个参数不可以忘记单引号。

clc;close all;clear all;

Img=imread('/home/wangxiao/Documents/files/Visual_Tracking/MDNet-CVPR2016/MDNet-master/attentionMap/Basketball/0001.png');

if ndims(Img)==

    I=rgb2gray(Img);

else

    I=Img;

end

I=im2bw(I,graythresh(I));

[m,n]=size(I);

imshow(I);title('binary image');

txt=get(gca,'Title');

set(txt,'fontsize',);

L=bwlabel(I);

stats=regionprops(L,'all');

set(gcf,'color','w');

set(gca,'units','pixels','Visible','off');

q=get(gca,'position');

q()=;%设置左边距离值为零

q()=;%设置右边距离值为零

set(gca,'position',q);

for i=:length(stats)

    hold on;

    rectangle('position',stats(i).BoundingBox,'edgecolor','y','linewidth',);

    temp = stats(i).Centroid;

    plot(temp(),temp(),'r.');

    drawnow;

end

frame=getframe(gcf,[,,n,m]);

im=frame2im(frame);

imwrite(im,'a.jpg','jpg');%可以修改保存的格式

import numpy as np

import scipy.ndimage as ndi

from skimage import measure,color

import matplotlib.pyplot as plt

#编写一个函数来生成原始二值图像

def microstructure(l=256):

    n = 5

    x, y = np.ogrid[0:l, 0:l]  #生成网络

    mask = np.zeros((l, l))

    generator = np.random.RandomState(1)  #随机数种子

    points = l * generator.rand(2, n**2)

    mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1

    mask = ndi.gaussian_filter(mask, sigma=l/(4.*n)) #高斯滤波

    return mask > mask.mean()

data = microstructure(l=128)*1 #生成测试图片

labels=measure.label(data,connectivity=2)  #8连通区域标记

dst=color.label2rgb(labels)  #根据不同的标记显示不同的颜色

print('regions number:',labels.max()+1)  #显示连通区域块数(从0开始标记)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))

ax1.imshow(data, plt.cm.gray, interpolation='nearest')

ax1.axis('off')

ax2.imshow(dst,interpolation='nearest')

ax2.axis('off')

fig.tight_layout()

plt.show()

%% Input 2 videos and divide it into frames

clc; clear all; close all;

infraredvideo = 'C:\Users\王逍\Desktop\跟踪数据集\videos\';

savePath = 'C:\Users\王逍\Desktop\跟踪数据集\frames\';

% devide the infrared video into infrared images

videoList1=dir(fullfile(infraredvideo,'*.mp4'));

video_num=length(videoList1);

for j=1:video_num

    infraredOutPath = [savePath, videoList1(j).name, '\'];

    mkdir(infraredOutPath);

    frames = VideoReader([strcat(infraredvideo,videoList1(j).name)]);

    numFrames =frames.NumberOfFrames;

    for k = 1 : numFrames

        disp(['==>> processing video ',num2str(k),' frames, please waiting....']);

        frame = read(frames,k);

        frame = imresize(frame, [480, 640]);

        % figure(1); imshow(frame);

        imwrite(frame, [infraredOutPath, sprintf('%08d.png',k)]);

    end

end

clear all; clc  

addpath('/home/wangxiao/jsonlab-1.5/jsonlab-1.5');

fname='results.json';

jsonData=loadjson(fname);

%%

clc; close all; clear all;

path = '\MotorRolling\';

files = dir([path, '*.png']);

savePath = '\fakeColor_MotorRolling\'; 

ori_img_path = '\Benchmark\MotorRolling\img\';

ori_img = imread([ori_img_path, '0001.jpg']);

oriFiles = dir([ori_img_path, '*.jpg']);

height = size(ori_img, );

width = size(ori_img, );

for img_idex =:length(files)

    im = imread([path, files(img_idex).name]);

    originalImage = imread([ori_img_path, oriFiles(img_idex).name]);

%     for idx =:size(im, )

%         for jdx = :size(im, )

%                if im(idx, jdx) <= ;

%                    im(idx, jdx) = ;

%                end

%         end

%     end 

    I=double(im);

    [m,n]=size(I);

    L=;

    for i=:m

        for j=:n

            if I(i,j)<=L/

                R(i,j)=;

                G(i,j)=*I(i,j);

                B(i,j)=L;

            else if I(i,j)<=L/

                    R(i,j)=;

                    G(i,j)=L;

                    B(i,j)=-*I(i,j)+*L;

                else if I(i,j)<=*L/

                        R(i,j)=*I(i,j)-*L;

                        G(i,j)=L;

                        B(i,j)=;

                    else

                        R(i,j)=L;

                        G(i,j)=-*I(i,j)+*L;

                        B(i,j)=;

                    end

                end

            end

        end

    end

    for i=:m

        for j=:n

            rgbim(i,j,)=R(i,j);

            rgbim(i,j,)=G(i,j);

            rgbim(i,j,)=B(i,j);

        end

    end

    rgbim=rgbim/;

%     figure;

%     subplot(,,);

%     imshow(gray);

%     subplot(,,);

    fake_color_img = imresize(rgbim, [height, width]);

    imshow(fake_color_img);

    imwrite(fake_color_img, [savePath, oriFiles(img_idex).name]);

    img_idex

end 

%%

clc; close all; clear all;

videoPath='\demo_videos\demo_Skater2\';

salVideo = '\Benchmark\Skater2\img\';

resultsPath = '\saved_figure_tracking_results\Skater2\v\';

file=dir([videoPath,'*.jpg']);

file_=dir([salVideo,'*.jpg']);

resultsFiles = dir([resultsPath, '*.jpg']);

writerObject = VideoWriter('OUR_Skater2.avi');

writerObject.FrameRate =25; %writerObject.Quality = 50;

open( writerObject );

Num= length(file);

for k =1 : Num

    trackingResults = imread([resultsPath, resultsFiles(k).name]);

    disp(['processing the ',num2str(k),'/',num2str(Num),' frames, please waiting....']);

    frame_1 = imread([videoPath,file(k).name]);

    frame_222 = imread([salVideo,file_(k).name]);

    frame_2 = zeros(size(frame_222, 1), size(frame_222, 2), 3);

    if size(frame_222, 3) == 1

        frame_2(:,:,1) =  frame_222;

        frame_2(:,:,1) =  frame_222;

        frame_2(:,:,1) =  frame_222;

    end

    frame_2 = uint8(frame_2); 

    frame_3 = frame_1*0.5+frame_2*0.5;

    trackingResults = imresize(trackingResults, [size(frame_2, 1), size(frame_2, 2)]);

    fullframe = [ frame_2, frame_3, trackingResults ];

    writeVideo( writerObject, fullframe );

end

close( writerObject );
          

clear all;close all;

%% video

videoPath='.\2\';

outPath='.\2\';

T=[1.72844 0.0692766 -293

    -0.0692766 1.72844 -94

    0 0 1];

tform = maketform('affine', T');

videoList=dir(fullfile(videoPath,'*.avi'));

video_num=length(videoList);

for j=1:video_num

    frames = VideoReader (strcat(videoPath,videoList(j).name));

    numFrames =frames.NumberOfFrames;

    for k = 1 : numFrames

        disp(['processing the ',num2str(k),' frames, please waiting....']);

        frame = read(frames,k);

        transformFrame = imtransform(frame,tform,'XData',[1 320], 'YData',[1 240]);

        %             frame=imresize(frame,[288,384]);

        imwrite(transformFrame,[outPath,sprintf('%08d.png',k)]);

    end

end

%%

clc; close all; clear all; warning off;

addpath('E:\Matlab2018\jsonlab-1.5\jsonlab-1.5');

oriVideoPath = 'I:\Tracking-Dataset\Video_Labeled_set\';

savePath = 'E:\dataset_processsed_data\';

videoFiles = dir(oriVideoPath);

videoFiles = videoFiles(:end);

for videoIDX =:size(videoFiles, )

    videoName = videoFiles(videoIDX).name;

    videoPath = [oriVideoPath videoName '\'];

    video_imgPath = [videoPath '*.png'];

    video_imgFiles = dir(video_imgPath);

    saveImage_path = [savePath videoName '\imgs\'];

    mkdir(saveImage_path);

    anno_files = dir(videoPath);

    anno_name = anno_files( length(anno_files)).name;

    anno_path = [videoPath anno_name '\'];

    annoFiles = dir(anno_path);

    annoFiles = annoFiles(:end);

    gt_txtFiles = fopen([savePath videoName '\groundtruth.txt'], 'w');

    for imgIDX =:size(video_imgFiles, )

        disp(['==>> video ', num2str(videoIDX), '\', num2str(size(videoFiles, 1)), ' img ', num2str(imgIDX), '\', num2str(size(video_imgFiles, 1))]);

        image = imread([videoPath video_imgFiles(imgIDX).name]);

        imageIndex = sprintf('%05d', imgIDX);

        newImgName = [num2str(imageIndex) '.png'] ;

        imwrite(image, [saveImage_path newImgName]);

        jsonFIle_path = [anno_path  annoFiles(imgIDX).name];

        json2data = loadjson(jsonFIle_path);

        BBox = json2data.outputs.object{, }.bndbox;

        x1 = BBox.xmin;

        y1 = BBox.ymin;

        w = BBox.xmax - BBox.xmin;

        h = BBox.ymax - BBox.ymin;

        fprintf(gt_txtFiles, '%s', num2str(x1));

        fprintf(gt_txtFiles, ',');

        fprintf(gt_txtFiles, '%s', num2str(y1));

        fprintf(gt_txtFiles, ',');

        fprintf(gt_txtFiles, '%s', num2str(w));

        fprintf(gt_txtFiles, ',');

        fprintf(gt_txtFiles, '%s', num2str(h));

        fprintf(gt_txtFiles, '\n');

    end

    fclose(gt_txtFiles);

end