Pull RE

lab01_git/questions.md

@@ -1,24 +1,27 @@
 # Warmup Questions
 
 1.  What is the clone url of this repository?
-    >   answer
+    >   https://github.com/diego0020/lab_vision
 
 2.  What is the output of the ``cal`` command?
 
-        multi
-        line
-        answer
+         -1 Displays a month 
++        -3 Displays previous/actual/Next Month
++        -m Shows monday as first week's day
++        -j Days sorted and numerated from January 1st
++        -y Current year's calendar.
 
 # Homework Questions
 
 1.  What is the ``grep``command?
-    >   answer
+    >   It means "Global regular expression print" it looks for a pattern of text inside a file.
 
 2.  What is a *makefile*?
-    >   answer
+    >   A command that helps to compile programs. It recompiles files that are attached to the file that is being worked on.
 
 4.  What does the ``-prune`` option of ``find`` do? Give an example
-    >   answer
+    >   -prune in find command ignores directories and files that do not contain the item being searched for.
++        i.e. : find / -name Test.txt -prune
 
 5.  Where is the ``grub.cfg``  file
     >   answer
@@ -30,25 +33,25 @@
     >   answer
 
 8.  What does the ``cut`` command do?
-    >   answer
+    >   Extracts/cuts txt characters from a .txt file
 
 9.  What does the ``wget`` command do?
-    >   answer
+    >   It downloads archives/applications from the http listed after wget
 
 9.  What does the ``rsync`` command do?
-    >   answer
+    >   Copies and/or synchronizes files and directories remotely and locally in the system
 
 10.  What does the ``diff`` command do?
-    >   answer
+    >   Looks for differences in two files and/or the previous versions of a file.
 
 10.  What does the ``tail`` command do?
-    >   answer
+    >   Shows the last 10 lines modified in a file.
 
 10.  What does the ``tail -f`` command do?
-    >   answer
+    >   Shows the last 10 lines modified on a file  and keeps track on them
 
 10.  What does the ``link`` command do?
-    >   answer
+    >   Binds together two files in a new file containing information about said files, should one file be modified, it will apply to the other file too.
 
 11.  How many users exist in the course server?
     >   answer
@@ -64,7 +67,7 @@
     Save this script as ``find_duplicates.sh`` in this directory and commit your changes to github
 
 16. What is the meaning of ``#! /bin/bash`` at the start of scripts?
-    >   answer
+    >   "#! /bin/bash Specifies which program to use to run said script, #(Sharp) !(Bin) 
 
 17. How many unique images are in the ``sipi_images`` database?
     >   answer

lab3_hybrid/Image_Description.txt

@@ -0,0 +1,17 @@
+Boot Image
+
+The Boot Image was taken from myself in the very same classroom in which we take the course
+"Computer Vision", the idea of taking a picture of my very own boot was due to the fact that I 
+tried before implementing the code with my own face and a coin, yet I had no good results.
+This boot has been with me for about a year now, and all I can say about it, is that it is a very
+good piece of leatherwork and also it is a reliable foot wear.
+
+Bottle Image
+
+As soon as I took the picture of my boot, I thought about it's shape, I needed to have an item
+with about the same proportions of said piece, and so as I was going home I realised how much
+water I needed to drink due to the flue that I currently have, and so as I took up my bottle and
+drank a little bit of water I realised that the bottle had about the same size and shape of my 
+boot, and so as I arrived home, I took my bottle out and had a picture of it. 
+This bottle is a sturdy one even if it is made of plastic, it has been with me for one and a half
+years.

lab3_hybrid/TorresF_LabHyb.m

@@ -0,0 +1,46 @@
+clc, clear all, close all
+%% Begining, reading the images
+%For the Boot
+Boot = imread('https://www.dropbox.com/s/snulaqkfod3shmu/Boot.jpg?dl=1');
+Boot = rgb2gray(imresize(Boot,[256,256]));
+%For the Bottle
+Bottle = imread('https://www.dropbox.com/s/jp4trcppem35o6m/Bottle.jpg?dl=1');
+Bottle = rgb2gray(imresize(Bottle,[256,256]));
+
+%% Creation of the filters.
+Gss = fspecial('gaussian',[45 45],4);
+Gss2 = fspecial('gaussian',[30,30],8);
+
+%% Filtering of the images
+Low = imfilter(Bottle,Gss2);
+Hi = Boot-imfilter(Boot,Gss);
+
+Hyb = Low-Hi;
+figure
+imshow(Hyb),
+title('Hybrid image between the bottle and the boot');
+
+pause
+
+%% Creation of the Piramid
+
+Vec = [256,200,175,150,125,100,75,50,25];
+Ims = im2uint8(zeros([256,256,3,length(Vec)]));
+for x = 1:length(Vec)
+    Im = imresize(Hyb,[Vec(x) Vec(x)]);
+    Im = imresize(Im,[256 256]);
+    Im = repmat(Im,1,1,3);
+    Ims(:,:,:,x) = Im;
+end
+
+figure
+montage(Ims);
+title('Image Pyramid');
+
+%Storing the Pyramid
+im = getimage(gca);
+imwrite(im,'Pyramid.png');
+
+
+
+

lab4_segmentation/segment_by_clustering.m

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+function Out = segment_by_clustering(rgb_image,feature_space,...
+               clustering_method,number_of_clusters)
+%      FUNCTION SEGMENT_BY_ClUSTERING
+%      OUT = SEGMENT_BY_CLUSTERING(RGB_IMAGE,FEATURE_SPACE,...
+%      CLUSTERING_METHOD,NUMBER_OF_CLUSTERS)
+%      RGB_IMAGE. Rgb_image is the input image, it represents what you 
+%      would want to segment.
+%      FEATURE_SPACE.Feature_Space sets the space of representation for the
+%      segmentation, it can either be RGB,L*AB,HSV; as well RGB+XY,L*AB+XY
+%      and HSV+XY. XY means that the input will be considering X and Y 
+%      distances in the image, meaning localization.
+%      ClUSTERING_METHOD. Clustering method adjusts the way the clusters
+%      will be selected. It can either be 'K-Means','GMM','Watershed'. If
+%      watershed is selected, the number of clusters selecetd as an input
+%      will need to be a value from the range [0,255].
+%      NUMBER_OF_CLUSTERS. Number of Clusters adjusts the 'K' for each
+%      clustering method that requires it, if no argument for 
+%      Number_of_clusters is input, the default will be 3.
+%      You may want to set the dynamic range of the image in imshow as [].
+%      Then I highly suggest you to use colormap(colorcube) to see the
+%      output.
+%                                 Felipe Torres F. Universidad de los Andes
+
+%%  Initialization
+%Check for input variables
+if(nargin<4)
+   number_of_clusters = 3;
+end         
+if(nargin<3)
+    clustering_method = 'k-means';
+end
+if(nargin<2)
+    feature_space = 'rgb';
+end
+
+%Assigning the image to the right data type (optimal for k-means and GMM)
+rgb_image = im2double(rgb_image);
+[sr,sc,N] = size(rgb_image);
+%% Check for input names of variables.
+%Feature_Space
+if(strcmpi(feature_space,'rgb')==1)
+    Im = rgb_image;
+elseif(strcmpi(feature_space,'lab')==1)
+    Im = rgb2lab(rgb_image);
+elseif(strcmpi(feature_space,'hsv')==1)
+    Im = rgb2hsv(rgb_image);
+elseif(strcmpi(feature_space,'rgb+xy')==1)
+    Im = zeros([sr,sc,N+2]);
+    %Assignation of the distance coordinates.
+    Im(:,:,1:3) = rgb_image;
+    [F,C] = meshgrid(1:sr,1:sc);
+    Im(:,:,4) = C'/sc;Im(:,:,5) = F'/sr;
+elseif(strcmpi(feature_space,'lab+xy')==1)
+    Im = zeros([sr,sc,N+2]);
+    Im(:,:,1:3) = rgb2lab(rgb_image);
+    %Assignation of the distance coordinates.
+    [F,C] = meshgrid(1:sr,1:sc);
+    Im(:,:,4) = C'/sc;Im(:,:,5) = F'/sr;
+elseif(strcmpi(feature_space,'hsv+xy')==1)
+    Im = zeros([sr,sc,N+2]);
+    Im(:,:,1:3) = rgb2hsv(rgb_image);
+    %Assignation of the distance coordinates.
+    [F,C] = meshgrid(1:sr,1:sc);
+    Im(:,:,4) = C'/sc;Im(:,:,5) = F'/sr;
+else        
+    msg = strcat('The feature space must be either: rgb, lab, hsv,',...
+        {' '},'rgb+xy, lab+xy or hsv+xy');
+    error(char(msg));
+end
+
+%Clustering method
+if(strcmpi(clustering_method,'K-MEANS')==1)    
+    %Check for image size.
+    [~,~,z] = size(Im);
+    %Creation of the N-Dimensional array.
+    Vk = zeros(numel(Im(:,:,1)),z);    
+    for x = 1:z
+        chn = Im(:,:,x);
+        Vk(:,x) = chn(:);
+    end 
+    %Application of K-means.
+    [lbl,cent] = kmeans(Vk,number_of_clusters); 
+    [~,I] = size(cent);    
+    %Assigning centroids to values.
+    Vec = cent(lbl,round(I/2));
+    %Reshaping to get the clustered Image.
+    Out = reshape(Vec,[sr,sc]);
+
+elseif(strcmpi(clustering_method,'GMM')==1)
+    [~,~,z] = size(Im);
+    %Creation of the N-Dimensional Vector.
+    Vk = zeros(numel(Im(:,:,1)),z);    
+    for x = 1:z
+        chn = Im(:,:,x);
+        Vk(:,x) = chn(:);
+    end     
+    %Creation of the GMM model.
+    GM = fitgmdist(Vk,number_of_clusters);
+    %Finding the values for each cluster.
+    idx = cluster(GM,Vk);
+    %Reshaping to get the clustered Image.
+    Out = reshape(idx,[sr,sc]);
+
+elseif(strcmpi(clustering_method,'WATERSHED')==1)
+    h = number_of_clusters;
+    Im2 = Im(:,:,1:3);
+    Im2 = im2uint8(rgb2gray(Im2));
+    str = strel('disk',1);
+    dil = imdilate(Im2,str);
+    ero = imerode(Im2,str);  
+    grad = dil-ero;
+    marker = imextendedmin(grad, h);
+    new_grad = imimposemin(grad, marker);
+    ws = watershed(new_grad);
+    Out = (ws);
+else
+    %Error message to be displayed if the clustering methods does not exist
+    %in the parameters.
+    msg2 = strcat('Clustering method must be either K-Means,GMM or'...
+        ,{' '},'Watersed');
+    error(char(msg2))
+end
+end

lab6_textons/Assign_THist.m

@@ -0,0 +1,40 @@
+function OutStruct = Assign_THist(Struct,k)
+%Function ASSIGN TEXTON MAP & HISTOGRAM     OUTSTRUCT = ASSIGN_TMAP(STRUCT,
+%                                                       FBANK,K,assign)
+%                                           Function that Assigns to each
+%                                           and every single image of the
+%                                           images contained in structure
+%                                           STRUCT the texton map obtained
+%                                           with the Texton Dictionary.
+%                                           Struct. The ingresed Structure
+%                                           must contain fields: 'name',
+%                                           'image', 'map', 'textons'.
+%                                           Otherwise an error will be
+%                                           K. K used to implement K-Means
+%                                           in the construction of the
+%                                           Dictionary. Default K = 50.
+
+%% Check for input variables
+%Check for K.
+if(nargin<2)
+    k = 50;
+end
+
+%% Running the body of the function
+addpath('lib');
+%Check for the length of the structure
+Lg = length(Struct);
+try
+    for x = 1:Lg        
+        %Recovering the Texton Map 
+        Tmap = Struct(x).map;
+        %Assigning the Histogram
+        Struct(x).Hist = histc(Tmap(:),1:k)/numel(Tmap);
+    end
+catch
+    er = strcat('Input arguments not correct, perhaps the Structure does',...
+        {' '},'have the required field "map"');
+    error(char(er))
+end
+OutStruct = Struct;
+end

lab6_textons/DicoCreation_Test.m

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+clc, clear all, close all
+%% Initiation
+%Addition of the path
+addpath('lib')
+%Route to train directory
+dTrain = fullfile(cd,'test');
+%Creation of structure containing the names of the cathegories
+nomCat = {'bark1','bark2','bark3','wood1','wood2','wood3',...
+    'water','granite','marble','floor1','floor2','pebbles',...
+    'wall','brick1','brick2','glass1','glass2','carpet1',...
+    'carpet2','upholstery','wallpaper','fur','knit','corduory',...
+    'plaid'};
+%Cathegory-wise codes
+indCat = {'T01','T02','T03','T04','T05','T06','T07','T08','T09','T10',...
+'T11','T12','T13','T14','T15','T16','T17','T18','T19','T20','T21','T22',...
+'T23','T24','T25'};
+
+%Creation of Structure containing the suffixes for each image in each
+%cathegory
+for x = 30:40
+    if x <10
+        nomPerm{x} = strcat('_0',num2str(x),'.jpg');
+    else
+        nomPerm{x} = strcat('_',num2str(x),'.jpg');
+    end
+end
+
+%Filter Bank creation
+[fb] = fbCreate;
+%Number of textons in the dictionary
+k = 50;
+
+%Creation of Texton Dictionary Structure
+TXT=struct('name',[],'image',[],'map',[],'textons',[]);
+%% Preparation before passing the for to assign textons for ten images per
+%cathegory
+inds = 1:10:260;
+cats = 1:25;
+%Aleatorization of the system
+vec = 31:40;
+VEC = repmat(vec,1,100);
+%For assigning the textons and creating the texton dictionary per
+%cathegory, each cathegory has 10 sample images
+for x = 1:250
+    for y = 2:length(inds) 
+        if x>=inds(y-1)&&x<inds(y)
+            %Assigning the name
+            TXT(x).name = char(nomCat(cats(y-1))); 
+            nam = char(indCat(cats(y-1)));
+            %Assigning the image cathegory to the correct image code
+            imnom = strcat(nam,char(nomPerm(VEC(x))));
+            im = im2double(imread(fullfile(dTrain,imnom)));
+            TXT(x).image = im;                
+            %Computation of textons
+            [map,textons] = computeTextons(fbRun(fb,im),k);
+            TXT(x).map = map;
+            TXT(x).textons = textons;  
+            disp(strcat('Succesfully created Textons dictionary for image'...
+            ,{' '},imnom));
+        end
+    end               
+end
+save('TextureDico_Test.mat','TXT');
+
+
+
+
+
+
+