Matlab 进阶学习记录

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 
 

%% 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);