plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | jwmneu/PO_CR-master | regressorsepSM3D.m | .m | PO_CR-master/regressorsepSM3D.m | 13,989 | utf_8 | b2fa5209355aafbf7df5acb10c53bd8d | function [] = regressorsep3D()
% chosed shape model of KTorresani = 10, energy = 0.8, KNonrigid = 5
clear;
global VERSIONCHECK;
VERSIONCHECK = 'SM_3D_1';
Kpi = 10;
T = 1;
ridge_param = 0;
smallsize = 0;
use_5_lms = 1;
outputDir = 'Results/';
% Setup_createDir(outputDir, 0);
datasetDir = '../dataset/';
t... |
github | jwmneu/PO_CR-master | regressorsepSM.m | .m | PO_CR-master/regressorsepSM.m | 26,435 | utf_8 | 33c38d53b42bc148eb38e225489d4c3a | function [] = regressorsep()
% clear;
global VERSIONCHECK;
VERSIONCHECK = 'SM_1';
Kpi = 10;
T = 1;
ridge_param = 0;
smallsize = 0;
use_5_lms = 1;
outputDir = 'Results/';
Setup_createDir(outputDir, 0);
datasetDir = '../dataset/';
testsetDir = '../test_data/';
gtParamDir = 'TR_params/';
CLMDir = './';
... |
github | jwmneu/PO_CR-master | regressorsep.m | .m | PO_CR-master/regressorsep.m | 22,580 | utf_8 | 4064a5151faf5fdacdbc6e6968b0a2e6 | function [] = regressorsep()
% clear;
global VERSIONCHECK;
VERSIONCHECK = 'SM_1';
Kpi = 10;
T = 1;
ridge_param = 0;
smallsize = 0;
use_5_lms = 1;
addpath('functions/');
outputDir = 'Results/';
Setup_createDir(outputDir, 0);
datasetDir = '../dataset/';
testsetDir = '../test_data/';
CLMDir = './';
num_... |
github | jwmneu/PO_CR-master | Perturbation_Initialization.m | .m | PO_CR-master/functions/Perturbation_Initialization.m | 5,253 | utf_8 | 0c9ba11cf629aef71a1f0f07b0f94e9b | function [] = Perturbation_Initialization(dataset, choice_perturb, perturb_param)
% this function should run in foler PO_CR_code_v1
% this function is used to initialize p_mat by perturbation and extract SIFT features from initialized p_mat.
% output : p_mat : size( n * Kpi, K); delta_p : size(n * Kpi, K);
% feat... |
github | jwmneu/PO_CR-master | my_SIFT_features.m | .m | PO_CR-master/functions/my_SIFT_features.m | 1,111 | utf_8 | b0b29497ea49fb2a3aef9b93d8c0bce7 | %% extract SIFT features from one image given the image and landmarks
function [d] = my_SIFT_features(input_image, lm, SIFT_scale, face_size, myShape_s0, makeplot, plotDir, gg)
num_of_pts = size(lm, 1);
lm_s0 = reshape(myShape_s0, [], 2);
norm_face_size = (max(lm_s0(:,1)) - min(lm_s0(:,1)) + max(lm_s0(:,2)) - m... |
github | jwmneu/PO_CR-master | SM_Compute_Shape_Params.m | .m | PO_CR-master/functions/SM_Compute_Shape_Params.m | 2,410 | utf_8 | 0ad05fbae4805d6ff9c9187fdd27c0ad | function [myShape_testing] = SM_Compute_Shape_Params()
% this function computes shape parameters for testing images, using myShape model
% clear;
global VERSIONCHECK;
VERSIONCHECK = 'SM_1';
gtParamDir = 'TR_testing_params/';
% initialize parameters
num_of_pts = 68;
n1 = 330;
n2 = 223;
n = n1 + n2;
... |
github | jwmneu/PO_CR-master | SM_shape_model.m | .m | PO_CR-master/functions/SM_shape_model.m | 5,052 | utf_8 | 93fbda90e843d19f89307b4eab66752c | function [myShape] = SM_shape_model()
% this function is no longer used after shifting to 3D shape model
%% initialization
modelDir = 'matfiles/';
shape = load([modelDir 'shape_model.mat']);
shape = shape.shape;
datasetDir = '../dataset/';
testsetDir = '../test_data/';
CLMDir = './';
folder1 = [datasetDir 'hel... |
github | jwmneu/PO_CR-master | readAllImages.m | .m | PO_CR-master/functions/readAllImages.m | 752 | utf_8 | 3c596a6e51c1f257b8353cff9e9bf4ce | function [TR_images] = readAllImages()
[images_helen] = readAllImagesFromHelen();
[images_lfpw] = readAllImagesFromLFPW();
TR_images = {};
TR_images = cat(1, TR_images, images_helen');
TR_images = cat(1, TR_images, images_lfpw');
end
function [images] = readAllImagesFromHelen()
datasetDir = [pwd '/../dataset/']... |
github | jwmneu/PO_CR-master | SIFT_features.m | .m | PO_CR-master/functions/SIFT_features.m | 1,102 | utf_8 | 2f4a23dfe06500ab2e7df148b2791952 | %% extract SIFT features from one image given the image and landmarks
function [d] = SIFT_features(input_image, lm, SIFT_scale, k, face_size, myShape_s0, makeplot, plotDir, gg)
num_of_pts = size(lm, 1);
lm_s0 = reshape(myShape_s0, [], 2);
norm_face_size = (max(lm_s0(:,1)) - min(lm_s0(:,1)) + max(lm_s0(:,2)) - m... |
github | jwmneu/PO_CR-master | Collect_testing_images.m | .m | PO_CR-master/functions/Collect_testing_images.m | 5,029 | utf_8 | 8c3df01db8c18955c96bbea1a950ec43 | function [TR_testing_images, TR_testing_face_size, TR_testing_gt_landmarks, TR_testing_myShape_p, TR_testing_detections] = Collect_testing_images(nHelen, nLFPW)
% inputs: nHelen: number of images of Helen dataset, nLFPW: number of images of LFPW dataset
% 330, 223
disp(['Collecting testing images. ' num2str(nHelen... |
github | jwmneu/PO_CR-master | Extract_Features.m | .m | PO_CR-master/functions/Extract_Features.m | 2,724 | utf_8 | afdc8a10ec143dacef2ccb99382ee022 | function [] = Extract_Features(t, SIFT_scale, choice_perturb)
global VERSIONCHECK;
VERSIONCHECK = 'LM_3D_1';
iter = t;
cd('vlfeat-0.9.20/toolbox');
vl_setup
cd('../../');
plotDir = 'SIFT_Plots/';
if(exist(plotDir, 'dir') == 0)
mkdir(plotDir);
end
if nargin == 1
SIFT_scale = 2;
choice_perturb = 'a... |
github | jwmneu/PO_CR-master | SIFT_features_few.m | .m | PO_CR-master/functions/SIFT_features_few.m | 1,039 | utf_8 | f58050aac937aff91c717cac57b19a10 | %% extract SIFT features from one image given the image and landmarks
function [d] = SIFT_features_few(input_image, lm, SIFT_scale, num_of_lm)
load shape_model;
load myShape;
load myAppearance;
load fd_stat;
% num_of_pts = 68; % num of landmarks in the annotations
% face_size = (max(lm(:,1))... |
github | jwmneu/PO_CR-master | Collect_wild_imgs.m | .m | PO_CR-master/functions/Collect_wild_imgs.m | 5,733 | utf_8 | 5a0ab8d56cebb5f0daff93897852283e | function [images, detections, labels] = Collect_wild_imgs(root_test_data)
use_afw = true;
use_lfpw = true;
use_helen = true;
use_ibug = true;
use_68 = true;
images = [];
labels = [];
detections = [];
if(use_afw)
[img, det, lbl] ... |
github | jwmneu/PO_CR-master | face_det_stat.m | .m | PO_CR-master/functions/face_det_stat.m | 2,318 | utf_8 | 920f4d93c113f228574c6aea81381747 | % function that comptues statistics of face detection initialization
function [] = face_det_stat()
saveDir = 'matfiles/';
% [TR_images, TR_face_sizes, TR_gt_landmarks, TR_myShape_pRigid, TR_myShape_pNonRigid, ~] = Collect_training_images(n1, n2) ;
gtParamDir = 'TR_params/';
load([gtParamDir 'TR_face_size.mat... |
github | jwmneu/PO_CR-master | getShapeFrom3DParam.m | .m | PO_CR-master/functions/getShapeFrom3DParam.m | 968 | utf_8 | 4d6fe49cada93881b48146e7398ad24c | function [shape2D] = getShapeFrom3DParam(M, V, p)
% get 2D landmarks from 3D shape parameters
% M - mean shape vector
% V - eigenvectors
% p - parameters of non-rigid shape
% R - rotation matrix
% T - translation vector (tx, ty)
% get 3D landmarks by nonrigid parameters
shape3D = getShape3D... |
github | jwmneu/PO_CR-master | Split_training_validation_set.m | .m | PO_CR-master/functions/Split_training_validation_set.m | 6,214 | utf_8 | dbe8d6d23060e4ae7f78581ac7e672e5 | function [] = Split_training_validation_set(n, SIFT_scale, Kpi, saveDir)
% this function should be run in folder PO_CR_code_v1
% It collect ground truth parameters from TR_params and TR_3D_params folder, split
% into training and validation set, and save result to saveDir = '../Learning_Inputs/'.
if nargin == 0
... |
github | jwmneu/PO_CR-master | Collect_training_images.m | .m | PO_CR-master/functions/Collect_training_images.m | 4,760 | utf_8 | c8678c0e463b3878eef432d19197b8e1 | function [TR_images, TR_face_size, TR_gt_landmarks, TR_myShape_p, TR_detections] = Collect_training_images(nHelen, nLFPW)
% inputs: nHelen: number of images of Helen dataset, nLFPW: number of images of LFPW dataset
disp(['Collecting training images. ' num2str(nHelen) ' from Helen and ' num2str(nLFPW) ' from LFPW'])... |
github | jwmneu/PO_CR-master | myCalcReferenceUpdate.m | .m | PO_CR-master/functions/myCalcReferenceUpdate.m | 1,946 | utf_8 | 5f3855253a91272e01bb5a6d81cc2caa | function [updated_p] = myCalcReferenceUpdate(params_delta, current_p) % current_global is rigid parameters
% input: params_delta: row vector, current_p: row vector
% output : updated_p : row vector
% Same goes for scaling, translation and nonrigid parameters
K = size(params_delta, 2);
updated_p = zeros(1, K... |
github | jwmneu/PO_CR-master | Extract_Perturbed_SIFT_Features.m | .m | PO_CR-master/functions/Extract_Perturbed_SIFT_Features.m | 6,165 | utf_8 | 29d2bfd68f4926b7ce33e7e8f7d9f666 | function [] = Extract_Perturbed_SIFT_Features()
% this function should run in foler PO_CR_code_v1
clear;
Kpi = 10;
s = rng;
% load models
cd([pwd '/vlfeat-0.9.20/toolbox']);
vl_setup
cd '../../'
OutputDir = '../PerturbationInitialization/';
% OutputDir = 'debug/';
modelDir = 'matfiles/';
shapemodel = load(... |
github | jwmneu/PO_CR-master | LM_3D_Extract_Perturbed_SIFT_Features.m | .m | PO_CR-master/functions/LM_3D_Extract_Perturbed_SIFT_Features.m | 8,730 | utf_8 | 9af85ec7ef31f57183bf90d1cc149dc8 | function [] = LM_3D_Extract_Perturbed_SIFT_Features(dataset, KTorresani, energy, KNonrigid, t, choice, perturbed_param, indexes_train, indexes_validate, list_train, list_validate, n_train, n_validate)
% this function should run in foler PO_CR_code_v1
% this function is used to initialize p_mat by perturbation and ext... |
github | jwmneu/PO_CR-master | Collect_training_images_3D.m | .m | PO_CR-master/functions/Collect_training_images_3D.m | 5,008 | utf_8 | f8dfebed3c8238a3ddc03a42271a7a84 | function [TR_images, TR_face_size, TR_gt_landmarks, TR_myShape_3D_p, TR_detections] = Collect_training_images_3D(nHelen, nLFPW)
% inputs: nHelen: number of images of Helen dataset, nLFPW: number of images of LFPW dataset
if nargin == 0
nHelen = 2000;
nLFPW = 811;
end
disp(['Collecting training images. ' n... |
github | jwmneu/PO_CR-master | Reconstruct_Torresani.m | .m | PO_CR-master/3D_Shape_Model/Reconstruct_Torresani.m | 1,279 | utf_8 | adfd18b50221d2a85d30ec0c9871f991 | % run this script in folder PO_CR_code_v1
% this script change 2D landmarks to 3D landmarks using Torresani's algorithm
function [] = Reconstruct_Torresani(KTorresani)
shapeModelDir = '3D_Shape_Model/';
addpath(shapeModelDir);
addpath([shapeModelDir '/nrsfm-em/']);
% load big model of 68 landmarks, dataset from H... |
github | jwmneu/PO_CR-master | writePDM.m | .m | PO_CR-master/3D_Shape_Model/writePDM.m | 1,290 | utf_8 | cef353b62a8524853a3381b82d1a717c | function writePDM( V, E, M, outputFile, Vmorph, Emorph )
%WRITEPDM Summary of this function goes here
% Detailed explanation goes here
shapeModelDir = '3D_Shape_Model/';
fId = fopen([shapeModelDir outputFile],'w');
% number of elements
% Comment
fprintf(fId, '# The mean values of the compon... |
github | jwmneu/PO_CR-master | getShapeOrtho.m | .m | PO_CR-master/3D_Shape_Model/getShapeOrtho.m | 479 | utf_8 | 26d95fb10fd17c63ec08e5352a7caeb8 |
function [shape2D] = getShapeOrtho(M, V, p, R, T, a)
% M - mean shape vector
% V - eigenvectors
% p - parameters of non-rigid shape
% R - rotation matrix
% T - translation vector (tx, ty)
shape3D = getShape3D(M, V, p);
shape2D = a * R(1:2,:)*shape3D' + repmat(T, 1, numel(M)/3);
shape2D... |
github | jwmneu/PO_CR-master | writeMatrix.m | .m | PO_CR-master/3D_Shape_Model/PDM_helpers/writeMatrix.m | 428 | utf_8 | 3a2c87a966a8dc0f296d992d85f7d445 | % for easier readibility write them row by row
function writeMatrix(fileID, M, type)
fprintf(fileID, '%d\r\n', size(M,1));
fprintf(fileID, '%d\r\n', size(M,2));
fprintf(fileID, '%d\r\n', type);
for i=1:size(M,1)
if(type == 4 || type == 0)
fprintf(fileID, '%d ', M(i,:));
... |
github | jwmneu/PO_CR-master | fit_PDM_ortho_proj_to_2D.m | .m | PO_CR-master/3D_Shape_Model/PDM_helpers/fit_PDM_ortho_proj_to_2D.m | 9,871 | utf_8 | 7accc2b0fee769eecc059448c7e69b26 | function [ a, R, T, T3D, params, error, shapeOrtho ] = fit_PDM_ortho_proj_to_2D( M, E, V, shape2D, f, cx, cy)
%FITPDMTO2DSHAPE Summary of this function goes here
% Detailed explanation goes here
params = zeros(size(E));
hidden = false;
% if some of the points are unavailable modify M, V, and shape2D (c... |
github | jwmneu/PO_CR-master | findG.m | .m | PO_CR-master/3D_Shape_Model/nrsfm-em/findG.m | 1,188 | utf_8 | c81e58ce3f1a6f9066f2ecb4cb4dac67 | function G = findG(Rhat)
[F,D] = size(Rhat); F = F/2;
% Build matrix Q such that Q * v = [1,...,1,0,...,0] where v is a six
% element vector containg all six distinct elements of the Matrix C
%clear Q
for f = 1:F,
g = f + F;
h = g + F;
Q(f,:) = zt2(Rhat(f,:), Rhat(f,:));
Q(g,:) = zt2(Rhat(g,:), Rhat(g,:));
... |
github | jwmneu/PO_CR-master | vl_compile.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/vl_compile.m | 5,060 | utf_8 | 978f5189bb9b2a16db3368891f79aaa6 | function vl_compile(compiler)
% VL_COMPILE Compile VLFeat MEX files
% VL_COMPILE() uses MEX() to compile VLFeat MEX files. This command
% works only under Windows and is used to re-build problematic
% binaries. The preferred method of compiling VLFeat on both UNIX
% and Windows is through the provided Makefile... |
github | jwmneu/PO_CR-master | vl_noprefix.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/vl_noprefix.m | 1,875 | utf_8 | 97d8755f0ba139ac1304bc423d3d86d3 | function vl_noprefix
% VL_NOPREFIX Create a prefix-less version of VLFeat commands
% VL_NOPREFIX() creats prefix-less stubs for VLFeat functions
% (e.g. SIFT for VL_SIFT). This function is seldom used as the stubs
% are included in the VLFeat binary distribution anyways. Moreover,
% on UNIX platforms, the stub... |
github | jwmneu/PO_CR-master | vl_pegasos.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/misc/vl_pegasos.m | 2,837 | utf_8 | d5e0915c439ece94eb5597a07090b67d | % VL_PEGASOS [deprecated]
% VL_PEGASOS is deprecated. Please use VL_SVMTRAIN() instead.
function [w b info] = vl_pegasos(X,Y,LAMBDA, varargin)
% Verbose not supported
if (sum(strcmpi('Verbose',varargin)))
varargin(find(strcmpi('Verbose',varargin),1))=[];
fprintf('Option VERBOSE is no longer supported.\n');
en... |
github | jwmneu/PO_CR-master | vl_svmpegasos.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/misc/vl_svmpegasos.m | 1,178 | utf_8 | 009c2a2b87a375d529ed1a4dbe3af59f | % VL_SVMPEGASOS [deprecated]
% VL_SVMPEGASOS is deprecated. Please use VL_SVMTRAIN() instead.
function [w b info] = vl_svmpegasos(DATA,LAMBDA, varargin)
% Verbose not supported
if (sum(strcmpi('Verbose',varargin)))
varargin(find(strcmpi('Verbose',varargin),1))=[];
fprintf('Option VERBOSE is no longer suppor... |
github | jwmneu/PO_CR-master | vl_override.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/misc/vl_override.m | 4,654 | utf_8 | e233d2ecaeb68f56034a976060c594c5 | function config = vl_override(config,update,varargin)
% VL_OVERRIDE Override structure subset
% CONFIG = VL_OVERRIDE(CONFIG, UPDATE) copies recursively the fileds
% of the structure UPDATE to the corresponding fields of the
% struture CONFIG.
%
% Usually CONFIG is interpreted as a list of paramters with their
... |
github | jwmneu/PO_CR-master | vl_quickvis.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/quickshift/vl_quickvis.m | 3,696 | utf_8 | 27f199dad4c5b9c192a5dd3abc59f9da | function [Iedge dists map gaps] = vl_quickvis(I, ratio, kernelsize, maxdist, maxcuts)
% VL_QUICKVIS Create an edge image from a Quickshift segmentation.
% IEDGE = VL_QUICKVIS(I, RATIO, KERNELSIZE, MAXDIST, MAXCUTS) creates an edge
% stability image from a Quickshift segmentation. RATIO controls the tradeoff
% bet... |
github | jwmneu/PO_CR-master | vl_demo_aib.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/demo/vl_demo_aib.m | 2,928 | utf_8 | 590c6db09451ea608d87bfd094662cac | function vl_demo_aib
% VL_DEMO_AIB Test Agglomerative Information Bottleneck (AIB)
D = 4 ;
K = 20 ;
randn('state',0) ;
rand('state',0) ;
X1 = randn(2,300) ; X1(1,:) = X1(1,:) + 2 ;
X2 = randn(2,300) ; X2(1,:) = X2(1,:) - 2 ;
X3 = randn(2,300) ; X3(2,:) = X3(2,:) + 2 ;
figure(1) ; clf ; hold on ;
vl_plotframe(X... |
github | jwmneu/PO_CR-master | vl_demo_alldist.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/demo/vl_demo_alldist.m | 5,460 | utf_8 | 6d008a64d93445b9d7199b55d58db7eb | function vl_demo_alldist
%
numRepetitions = 3 ;
numDimensions = 1000 ;
numSamplesRange = [300] ;
settingsRange = {{'alldist2', 'double', 'l2', }, ...
{'alldist', 'double', 'l2', 'nosimd'}, ...
{'alldist', 'double', 'l2' }, ...
{'alldist2', 's... |
github | jwmneu/PO_CR-master | vl_demo_ikmeans.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/demo/vl_demo_ikmeans.m | 774 | utf_8 | 17ff0bb7259d390fb4f91ea937ba7de0 | function vl_demo_ikmeans()
% VL_DEMO_IKMEANS
numData = 10000 ;
dimension = 2 ;
data = uint8(255*rand(dimension,numData)) ;
numClusters = 3^3 ;
[centers, assignments] = vl_ikmeans(data, numClusters);
figure(1) ; clf ; axis off ;
plotClusters(data, centers, assignments) ;
vl_demo_print('ikmeans_2d',0.6);
[tree, assig... |
github | jwmneu/PO_CR-master | vl_demo_svm.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/demo/vl_demo_svm.m | 1,235 | utf_8 | 7cf6b3504e4fc2cbd10ff3fec6e331a7 | % VL_DEMO_SVM Demo: SVM: 2D linear learning
function vl_demo_svm
y=[];X=[];
% Load training data X and their labels y
load('vl_demo_svm_data.mat')
Xp = X(:,y==1);
Xn = X(:,y==-1);
figure
plot(Xn(1,:),Xn(2,:),'*r')
hold on
plot(Xp(1,:),Xp(2,:),'*b')
axis equal ;
vl_demo_print('svm_training') ;
% Parameters
lambda =... |
github | jwmneu/PO_CR-master | vl_demo_kdtree_sift.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/demo/vl_demo_kdtree_sift.m | 6,832 | utf_8 | e676f80ac330a351f0110533c6ebba89 | function vl_demo_kdtree_sift
% VL_DEMO_KDTREE_SIFT
% Demonstrates the use of a kd-tree forest to match SIFT
% features. If FLANN is present, this function runs a comparison
% against it.
% AUTORIGHS
rand('state',0) ;
randn('state',0);
do_median = 0 ;
do_mean = 1 ;
% try to setup flann
if ~exist('flann_search'... |
github | jwmneu/PO_CR-master | vl_impattern.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/imop/vl_impattern.m | 6,876 | utf_8 | 1716a4d107f0186be3d11c647bc628ce | function im = vl_impattern(varargin)
% VL_IMPATTERN Generate an image from a stock pattern
% IM=VLPATTERN(NAME) returns an instance of the specified
% pattern. These stock patterns are useful for testing algoirthms.
%
% All generated patterns are returned as an image of class
% DOUBLE. Both gray-scale and colou... |
github | jwmneu/PO_CR-master | vl_tpsu.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/imop/vl_tpsu.m | 1,755 | utf_8 | 09f36e1a707c069b375eb2817d0e5f13 | function [U,dU,delta]=vl_tpsu(X,Y)
% VL_TPSU Compute the U matrix of a thin-plate spline transformation
% U=VL_TPSU(X,Y) returns the matrix
%
% [ U(|X(:,1) - Y(:,1)|) ... U(|X(:,1) - Y(:,N)|) ]
% [ ]
% [ U(|X(:,M) - Y(:,1)|) ... U(|X(:,M) - Y(:,N)|) ]
%
% where X... |
github | jwmneu/PO_CR-master | vl_xyz2lab.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/imop/vl_xyz2lab.m | 1,570 | utf_8 | 09f95a6f9ae19c22486ec1157357f0e3 | function J=vl_xyz2lab(I,il)
% VL_XYZ2LAB Convert XYZ color space to LAB
% J = VL_XYZ2LAB(I) converts the image from XYZ format to LAB format.
%
% VL_XYZ2LAB(I,IL) uses one of the illuminants A, B, C, E, D50, D55,
% D65, D75, D93. The default illuminatn is E.
%
% See also: VL_XYZ2LUV(), VL_HELP().
% Copyright ... |
github | jwmneu/PO_CR-master | vl_test_gmm.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_gmm.m | 1,332 | utf_8 | 76782cae6c98781c6c38d4cbf5549d94 | function results = vl_test_gmm(varargin)
% VL_TEST_GMM
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
vl_test_init ;
end
function s = setup()
randn('st... |
github | jwmneu/PO_CR-master | vl_test_twister.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_twister.m | 1,251 | utf_8 | 2bfb5a30cbd6df6ac80c66b73f8646da | function results = vl_test_twister(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_illegal_args()
vl_assert_exception(@() vl_twister(-1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister(1, -1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister([1, -1]), 'vl:invalidArgument') ;
function te... |
github | jwmneu/PO_CR-master | vl_test_kdtree.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_kdtree.m | 2,449 | utf_8 | 9d7ad2b435a88c22084b38e5eb5f9eb9 | function results = vl_test_kdtree(varargin)
% VL_TEST_KDTREE
vl_test_init ;
function s = setup()
randn('state',0) ;
s.X = single(randn(10, 1000)) ;
s.Q = single(randn(10, 10)) ;
function test_nearest(s)
for tmethod = {'median', 'mean'}
for type = {@single, @double}
conv = type{1} ;
tmethod = char(tmethod) ;... |
github | jwmneu/PO_CR-master | vl_test_imwbackward.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_imwbackward.m | 514 | utf_8 | 33baa0784c8f6f785a2951d7f1b49199 | function results = vl_test_imwbackward(varargin)
% VL_TEST_IMWBACKWARD
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
function test_identity(s)
xr = 1:size(s.I,2) ;
yr = 1:size(s.I,1) ;
[x,y] = meshgrid(xr,yr) ;
vl_assert_almost_equal(s.I, vl_imwbackward(xr,yr,s.I,... |
github | jwmneu/PO_CR-master | vl_test_alphanum.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_alphanum.m | 1,624 | utf_8 | 2da2b768c2d0f86d699b8f31614aa424 | function results = vl_test_alphanum(varargin)
% VL_TEST_ALPHANUM
vl_test_init ;
function s = setup()
s.strings = ...
{'1000X Radonius Maximus','10X Radonius','200X Radonius','20X Radonius','20X Radonius Prime','30X Radonius','40X Radonius','Allegia 50 Clasteron','Allegia 500 Clasteron','Allegia 50B Clasteron','Al... |
github | jwmneu/PO_CR-master | vl_test_printsize.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_printsize.m | 1,447 | utf_8 | 0f0b6437c648b7a2e1310900262bd765 | function results = vl_test_printsize(varargin)
% VL_TEST_PRINTSIZE
vl_test_init ;
function s = setup()
s.fig = figure(1) ;
s.usletter = [8.5, 11] ; % inches
s.a4 = [8.26772, 11.6929] ;
clf(s.fig) ; plot(1:10) ;
function teardown(s)
close(s.fig) ;
function test_basic(s)
for sigma = [1 0.5 0.2]
vl_printsize(s.fig, s... |
github | jwmneu/PO_CR-master | vl_test_cummax.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_cummax.m | 838 | utf_8 | 5e98ee1681d4823f32ecc4feaa218611 | function results = vl_test_cummax(varargin)
% VL_TEST_CUMMAX
vl_test_init ;
function test_basic()
vl_assert_almost_equal(...
vl_cummax(1), 1) ;
vl_assert_almost_equal(...
vl_cummax([1 2 3 4], 2), [1 2 3 4]) ;
function test_multidim()
a = [1 2 3 4 3 2 1] ;
b = [1 2 3 4 4 4 4] ;
for k=1:6
dims = ones(1,6) ;
dim... |
github | jwmneu/PO_CR-master | vl_test_imintegral.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_imintegral.m | 1,429 | utf_8 | 4750f04ab0ac9fc4f55df2c8583e5498 | function results = vl_test_imintegral(varargin)
% VL_TEST_IMINTEGRAL
vl_test_init ;
function state = setup()
state.I = ones(5,6) ;
state.correct = [ 1 2 3 4 5 6 ;
2 4 6 8 10 12 ;
3 6 9 12 15 18 ;
4 8 12 ... |
github | jwmneu/PO_CR-master | vl_test_sift.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_sift.m | 1,318 | utf_8 | 806c61f9db9f2ebb1d649c9bfcf3dc0a | function results = vl_test_sift(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
s.I = im2single(imread(fullfile(vl_root,'data','box.pgm'))) ;
[s.ubc.f, s.ubc.d] = ...
vl_ubcread(fullfile(vl_root,'data','box.sift')) ;
function test_ubc_descriptor(s)
err = [] ;
[f, d] = vl_sift(s.I,...
... |
github | jwmneu/PO_CR-master | vl_test_binsum.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_binsum.m | 1,377 | utf_8 | f07f0f29ba6afe0111c967ab0b353a9d | function results = vl_test_binsum(varargin)
% VL_TEST_BINSUM
vl_test_init ;
function test_three_args()
vl_assert_almost_equal(...
vl_binsum([0 0], 1, 2), [0 1]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, 1), [0 7]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, [1 2 2 2 2 2 2 2]), [0 0]) ;
function te... |
github | jwmneu/PO_CR-master | vl_test_lbp.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_lbp.m | 892 | utf_8 | a79c0ce0c85e25c0b1657f3a0b499538 | function results = vl_test_lbp(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_unfiorm_lbps(s)
% enumerate the 56 uniform lbps
q = 0 ;
for i=0:7
for j=1:7
I = zeros(3) ;
p = mod(s.pixels - i + 8, 8) + 1 ;
I(p <= j) = 1 ;
f = vl_lbp(single(I), 3) ;
q = q + 1 ;
vl_assert_equal(find(f... |
github | jwmneu/PO_CR-master | vl_test_colsubset.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_colsubset.m | 828 | utf_8 | be0c080007445b36333b863326fb0f15 | function results = vl_test_colsubset(varargin)
% VL_TEST_COLSUBSET
vl_test_init ;
function s = setup()
s.x = [5 2 3 6 4 7 1 9 8 0] ;
function test_beginning(s)
vl_assert_equal(1:5, vl_colsubset(1:10, 5, 'beginning')) ;
vl_assert_equal(1:5, vl_colsubset(1:10, .5, 'beginning')) ;
function test_ending(s)
vl_assert_equa... |
github | jwmneu/PO_CR-master | vl_test_alldist.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_alldist.m | 2,373 | utf_8 | 9ea1a36c97fe715dfa2b8693876808ff | function results = vl_test_alldist(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
function s = setup()
vl_twister('state', 0) ;
s.X = 3.1 * vl_twister(10,10) ;
s.Y = 4.7 * vl_twister(10,7) ;
function test_null_args(s)
vl_assert_equal(...
vl_alldist(zeros(15,12), zeros(15,0), 'kl2'), ...
zeros(12,0)) ;
vl_assert_equa... |
github | jwmneu/PO_CR-master | vl_test_ihashsum.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_ihashsum.m | 581 | utf_8 | edc283062469af62056b0782b171f5fc | function results = vl_test_ihashsum(varargin)
% VL_TEST_IHASHSUM
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(round(16*rand(2,100))) ;
sel = find(all(s.data==0)) ;
s.data(1,sel)=1 ;
function test_hash(s)
D = size(s.data,1) ;
K = 5 ;
h = zeros(1,K,'uint32') ;
id = zeros(D,K,'uint8');
next = zer... |
github | jwmneu/PO_CR-master | vl_test_grad.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_grad.m | 434 | utf_8 | 4d03eb33a6a4f68659f868da95930ffb | function results = vl_test_grad(varargin)
% VL_TEST_GRAD
vl_test_init ;
function s = setup()
s.I = rand(150,253) ;
s.I_small = rand(2,2) ;
function test_equiv(s)
vl_assert_equal(gradient(s.I), vl_grad(s.I)) ;
function test_equiv_small(s)
vl_assert_equal(gradient(s.I_small), vl_grad(s.I_small)) ;
function test_equiv... |
github | jwmneu/PO_CR-master | vl_test_whistc.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_whistc.m | 1,384 | utf_8 | 81c446d35c82957659840ab2a579ec2c | function results = vl_test_whistc(varargin)
% VL_TEST_WHISTC
vl_test_init ;
function test_acc()
x = ones(1, 10) ;
e = 1 ;
o = 1:10 ;
vl_assert_equal(vl_whistc(x, o, e), 55) ;
function test_basic()
x = 1:10 ;
e = 1:10 ;
o = ones(1, 10) ;
vl_assert_equal(histc(x, e), vl_whistc(x, o, e)) ;
x = linspace(-1,11,100) ;
o =... |
github | jwmneu/PO_CR-master | vl_test_roc.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_roc.m | 1,019 | utf_8 | 9b2ae71c9dc3eda0fc54c65d55054d0c | function results = vl_test_roc(varargin)
% VL_TEST_ROC
vl_test_init ;
function s = setup()
s.scores0 = [5 4 3 2 1] ;
s.scores1 = [5 3 4 2 1] ;
s.labels = [1 1 -1 -1 -1] ;
function test_perfect_tptn(s)
[tpr,tnr] = vl_roc(s.labels,s.scores0) ;
vl_assert_almost_equal(tpr, [0 1 2 2 2 2] / 2) ;
vl_assert_almost_equal(tnr,... |
github | jwmneu/PO_CR-master | vl_test_dsift.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_dsift.m | 2,048 | utf_8 | fbbfb16d5a21936c1862d9551f657ccc | function results = vl_test_dsift(varargin)
% VL_TEST_DSIFT
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = rgb2gray(single(I)) ;
function test_fast_slow(s)
binSize = 4 ; % bin size in pixels
magnif = 3 ; % bin size / keypoint scale
scale = binSize... |
github | jwmneu/PO_CR-master | vl_test_alldist2.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_alldist2.m | 2,284 | utf_8 | 89a787e3d83516653ae8d99c808b9d67 | function results = vl_test_alldist2(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
% TODO: test integer classes
function s = setup()
vl_twister('state', 0) ;
s.X = 3.1 * vl_twister(10,10) ;
s.Y = 4.7 * vl_twister(10,7) ;
function test_null_args(s)
vl_assert_equal(...
vl_alldist2(zeros(15,12), zeros(15,0), 'kl2'), ...
... |
github | jwmneu/PO_CR-master | vl_test_fisher.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_fisher.m | 2,097 | utf_8 | c9afd9ab635bd412cbf8be3c2d235f6b | function results = vl_test_fisher(varargin)
% VL_TEST_FISHER
vl_test_init ;
function s = setup()
randn('state',0) ;
dimension = 5 ;
numData = 21 ;
numComponents = 3 ;
s.x = randn(dimension,numData) ;
s.mu = randn(dimension,numComponents) ;
s.sigma2 = ones(dimension,numComponents) ;
s.prior = ones(1,numComponents) ;
s... |
github | jwmneu/PO_CR-master | vl_test_imsmooth.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_imsmooth.m | 1,837 | utf_8 | 718235242cad61c9804ba5e881c22f59 | function results = vl_test_imsmooth(varargin)
% VL_TEST_IMSMOOTH
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
I = max(min(vl_imdown(I),1),0) ;
s.I = single(I) ;
function test_pad_by_continuity(s)
% Convolving a constant signal padded with continuity does not change... |
github | jwmneu/PO_CR-master | vl_test_svmtrain.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_svmtrain.m | 4,277 | utf_8 | 071b7c66191a22e8236fda16752b27aa | function results = vl_test_svmtrain(varargin)
% VL_TEST_SVMTRAIN
vl_test_init ;
end
function s = setup()
randn('state',0) ;
Np = 10 ;
Nn = 10 ;
xp = diag([1 3])*randn(2, Np) ;
xn = diag([1 3])*randn(2, Nn) ;
xp(1,:) = xp(1,:) + 2 + 1 ;
xn(1,:) = xn(1,:) - 2 + 1 ;
s.x = [xp xn] ;
s.y = [ones(1,Np) ... |
github | jwmneu/PO_CR-master | vl_test_phow.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_phow.m | 549 | utf_8 | f761a3bb218af855986263c67b2da411 | function results = vl_test_phow(varargin)
% VL_TEST_PHOPW
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = single(s.I) ;
function test_gray(s)
[f,d] = vl_phow(s.I, 'color', 'gray') ;
assert(size(d,1) == 128) ;
function test_rgb(s)
[f,d] = vl_phow(s.I, 'color',... |
github | jwmneu/PO_CR-master | vl_test_kmeans.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_kmeans.m | 3,632 | utf_8 | 0e1d6f4f8101c8982a0e743e0980c65a | function results = vl_test_kmeans(varargin)
% VL_TEST_KMEANS
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
vl_test_init ;
function s = setup()
randn('sta... |
github | jwmneu/PO_CR-master | vl_test_hikmeans.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_hikmeans.m | 463 | utf_8 | dc3b493646e66316184e86ff4e6138ab | function results = vl_test_hikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[tree, assign] = vl_hikmeans(s.data,3,100) ;
assign_ = vl_hikmeanspush(tree, s.data) ;
vl_assert_equal(assign,assign_) ;
function test_elka... |
github | jwmneu/PO_CR-master | vl_test_aib.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_aib.m | 1,277 | utf_8 | 78978ae54e7ebe991d136336ba4bf9c6 | function results = vl_test_aib(varargin)
% VL_TEST_AIB
vl_test_init ;
function s = setup()
s = [] ;
function test_basic(s)
Pcx = [.3 .3 0 0
0 0 .2 .2] ;
% This results in the AIB tree
%
% 1 - \
% 5 - \
% 2 - / \
% - 7
% 3 - \ /
% 6 - /
% 4 - /
%
% coded by the map [5 ... |
github | jwmneu/PO_CR-master | vl_test_plotbox.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_plotbox.m | 414 | utf_8 | aa06ce4932a213fb933bbede6072b029 | function results = vl_test_plotbox(varargin)
% VL_TEST_PLOTBOX
vl_test_init ;
function test_basic(s)
figure(1) ; clf ;
vl_plotbox([-1 -1 1 1]') ;
xlim([-2 2]) ;
ylim([-2 2]) ;
close(1) ;
function test_multiple(s)
figure(1) ; clf ;
randn('state', 0) ;
vl_plotbox(randn(4,10)) ;
close(1) ;
function test_style(s)
figure... |
github | jwmneu/PO_CR-master | vl_test_imarray.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_imarray.m | 795 | utf_8 | c5e6a5aa8c2e63e248814f5bd89832a8 | function results = vl_test_imarray(varargin)
% VL_TEST_IMARRAY
vl_test_init ;
function test_movie_rgb(s)
A = rand(23,15,3,4) ;
B = vl_imarray(A,'movie',true) ;
function test_movie_indexed(s)
cmap = get(0,'DefaultFigureColormap') ;
A = uint8(size(cmap,1)*rand(23,15,4)) ;
A = min(A,size(cmap,1)-1) ;
B = vl_imarray(A,'m... |
github | jwmneu/PO_CR-master | vl_test_homkermap.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_homkermap.m | 1,903 | utf_8 | c157052bf4213793a961bde1f73fb307 | function results = vl_test_homkermap(varargin)
% VL_TEST_HOMKERMAP
vl_test_init ;
function check_ker(ker, n, window, period)
args = {n, ker, 'window', window} ;
if nargin > 3
args = {args{:}, 'period', period} ;
end
x = [-1 -.5 0 .5 1] ;
y = linspace(0,2,100) ;
for conv = {@single, @double}
x = feval(conv{1}, x) ;... |
github | jwmneu/PO_CR-master | vl_test_slic.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_slic.m | 200 | utf_8 | 12a6465e3ef5b4bcfd7303cd8a9229d4 | function results = vl_test_slic(varargin)
% VL_TEST_SLIC
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
function test_slic(s)
segmentation = vl_slic(s.im, 10, 0.1) ;
|
github | jwmneu/PO_CR-master | vl_test_ikmeans.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_ikmeans.m | 466 | utf_8 | 1ee2f647ac0035ed0d704a0cd615b040 | function results = vl_test_ikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[centers, assign] = vl_ikmeans(s.data,100) ;
assign_ = vl_ikmeanspush(s.data, centers) ;
vl_assert_equal(assign,assign_) ;
function test_elk... |
github | jwmneu/PO_CR-master | vl_test_mser.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_mser.m | 242 | utf_8 | 1ad33563b0c86542a2978ee94e0f4a39 | function results = vl_test_mser(varargin)
% VL_TEST_MSER
vl_test_init ;
function s = setup()
s.im = im2uint8(rgb2gray(vl_impattern('roofs1'))) ;
function test_mser(s)
[regions,frames] = vl_mser(s.im) ;
mask = vl_erfill(s.im, regions(1)) ;
|
github | jwmneu/PO_CR-master | vl_test_inthist.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_inthist.m | 811 | utf_8 | 459027d0c54d8f197563a02ab66ef45d | function results = vl_test_inthist(varargin)
% VL_TEST_INTHIST
vl_test_init ;
function s = setup()
rand('state',0) ;
s.labels = uint32(8*rand(123, 76, 3)) ;
function test_basic(s)
l = 10 ;
hist = vl_inthist(s.labels, 'numlabels', l) ;
hist_ = inthist_slow(s.labels, l) ;
vl_assert_equal(double(hist),hist_) ;
function... |
github | jwmneu/PO_CR-master | vl_test_imdisttf.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_imdisttf.m | 1,885 | utf_8 | ae921197988abeb984cbcdf9eaf80e77 | function results = vl_test_imdisttf(varargin)
% VL_TEST_DISTTF
vl_test_init ;
function test_basic()
for conv = {@single, @double}
conv = conv{1} ;
I = conv([0 0 0 ; 0 -2 0 ; 0 0 0]) ;
D = vl_imdisttf(I);
assert(isequal(D, conv(- [0 1 0 ; 1 2 1 ; 0 1 0]))) ;
I(2,2) = -3 ;
[D,map] = vl_imdisttf(I) ;
asse... |
github | jwmneu/PO_CR-master | vl_test_vlad.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_vlad.m | 1,977 | utf_8 | d3797288d6edb1d445b890db3780c8ce | function results = vl_test_vlad(varargin)
% VL_TEST_VLAD
vl_test_init ;
function s = setup()
randn('state',0) ;
s.x = randn(128,256) ;
s.mu = randn(128,16) ;
assignments = rand(16, 256) ;
s.assignments = bsxfun(@times, assignments, 1 ./ sum(assignments,1)) ;
function test_basic (s)
x = [1, 2, 3] ;
mu = [0, 0, 0] ;
a... |
github | jwmneu/PO_CR-master | vl_test_pr.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_pr.m | 3,763 | utf_8 | 4d1da5ccda1a7df2bec35b8f12fdd620 | function results = vl_test_pr(varargin)
% VL_TEST_PR
vl_test_init ;
function s = setup()
s.scores0 = [5 4 3 2 1] ;
s.scores1 = [5 3 4 2 1] ;
s.labels = [1 1 -1 -1 -1] ;
function test_perfect_tptn(s)
[rc,pr] = vl_pr(s.labels,s.scores0) ;
vl_assert_almost_equal(pr, [1 1/1 2/2 2/3 2/4 2/5]) ;
vl_assert_almost_equal(rc, ... |
github | jwmneu/PO_CR-master | vl_test_hog.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_hog.m | 1,555 | utf_8 | eed7b2a116d142040587dc9c4eb7cd2e | function results = vl_test_hog(varargin)
% VL_TEST_HOG
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
[x,y]= meshgrid(linspace(-1,1,128)) ;
s.round = single(x.^2+y.^2);
s.imSmall = s.im(1:128,1:128,:) ;
s.imSmall = s.im ;
s.imSmallFlipped = s.imSmall(:,end:-1:1,:) ;
function test_basic... |
github | jwmneu/PO_CR-master | vl_test_argparse.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_argparse.m | 795 | utf_8 | e72185b27206d0ee1dfdc19fe77a5be6 | function results = vl_test_argparse(varargin)
% VL_TEST_ARGPARSE
vl_test_init ;
function test_basic()
opts.field1 = 1 ;
opts.field2 = 2 ;
opts.field3 = 3 ;
opts_ = opts ;
opts_.field1 = 3 ;
opts_.field2 = 10 ;
opts = vl_argparse(opts, {'field2', 10, 'field1', 3}) ;
assert(isequal(opts, opts_)) ;
opts_.field1 = 9 ;
... |
github | jwmneu/PO_CR-master | vl_test_liop.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_liop.m | 1,023 | utf_8 | a162be369073bed18e61210f44088cf3 | function results = vl_test_liop(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
randn('state',0) ;
s.patch = randn(65,'single') ;
xr = -32:32 ;
[x,y] = meshgrid(xr) ;
s.blob = - single(x.^2+y.^2) ;
function test_basic(s)
d = vl_liop(s.patch) ;
function test_blob(s)
% with a blob, all local intensity ord... |
github | jwmneu/PO_CR-master | vl_test_binsearch.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/xtest/vl_test_binsearch.m | 1,339 | utf_8 | 85dc020adce3f228fe7dfb24cf3acc63 | function results = vl_test_binsearch(varargin)
% VL_TEST_BINSEARCH
vl_test_init ;
function test_inf_bins()
x = [-inf -1 0 1 +inf] ;
vl_assert_equal(vl_binsearch([], x), [0 0 0 0 0]) ;
vl_assert_equal(vl_binsearch([-inf 0], x), [1 1 2 2 2]) ;
vl_assert_equal(vl_binsearch([-inf], x), [1 1 1 1 1]) ;
vl_a... |
github | jwmneu/PO_CR-master | vl_roc.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/plotop/vl_roc.m | 10,113 | utf_8 | 22fd8ff455ee62a96ffd94b9074eafeb | function [tpr,tnr,info] = vl_roc(labels, scores, varargin)
%VL_ROC ROC curve.
% [TPR,TNR] = VL_ROC(LABELS, SCORES) computes the Receiver Operating
% Characteristic (ROC) curve [1]. LABELS is a row vector of ground
% truth labels, greater than zero for a positive sample and smaller
% than zero for a negative o... |
github | jwmneu/PO_CR-master | vl_click.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/plotop/vl_click.m | 2,661 | utf_8 | 6982e869cf80da57fdf68f5ebcd05a86 | function P = vl_click(N,varargin) ;
% VL_CLICK Click a point
% P=VL_CLICK() let the user click a point in the current figure and
% returns its coordinates in P. P is a two dimensiona vectors where
% P(1) is the point X-coordinate and P(2) the point Y-coordinate. The
% user can abort the operation by pressing any k... |
github | jwmneu/PO_CR-master | vl_pr.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/plotop/vl_pr.m | 9,138 | utf_8 | c7fe6832d2b6b9917896810c52a05479 | function [recall, precision, info] = vl_pr(labels, scores, varargin)
%VL_PR Precision-recall curve.
% [RECALL, PRECISION] = VL_PR(LABELS, SCORES) computes the
% precision-recall (PR) curve. LABELS are the ground truth labels,
% greather than zero for a positive sample and smaller than zero for
% a negative on... |
github | jwmneu/PO_CR-master | vl_ubcread.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/sift/vl_ubcread.m | 3,015 | utf_8 | e8ddd3ecd87e76b6c738ba153fef050f | function [f,d] = vl_ubcread(file, varargin)
% SIFTREAD Read Lowe's SIFT implementation data files
% [F,D] = VL_UBCREAD(FILE) reads the frames F and the descriptors D
% from FILE in UBC (Lowe's original implementation of SIFT) format
% and returns F and D as defined by VL_SIFT().
%
% VL_UBCREAD(FILE, 'FORMAT', '... |
github | jwmneu/PO_CR-master | vl_frame2oell.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/sift/vl_frame2oell.m | 2,806 | utf_8 | c93792632f630743485fa4c2cf12d647 | function eframes = vl_frame2oell(frames)
% VL_FRAMES2OELL Convert a geometric frame to an oriented ellipse
% EFRAME = VL_FRAME2OELL(FRAME) converts the generic FRAME to an
% oriented ellipses EFRAME. FRAME and EFRAME can be matrices, with
% one frame per column.
%
% A frame is either a point, a disc, an orien... |
github | jwmneu/PO_CR-master | vl_plotsiftdescriptor.m | .m | PO_CR-master/vlfeat-0.9.20/toolbox/sift/vl_plotsiftdescriptor.m | 5,114 | utf_8 | a4e125a8916653f00143b61cceda2f23 | function h=vl_plotsiftdescriptor(d,f,varargin)
% VL_PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% VL_PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptor D. If D is a
% matrix, it plots one descriptor per column. D has the same format
% used by VL_SIFT().
%
% VL_PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warpe... |
github | jwmneu/PO_CR-master | phow_caltech101.m | .m | PO_CR-master/vlfeat-0.9.20/apps/phow_caltech101.m | 11,594 | utf_8 | 7f4890a2e6844ca56debbfe23cca64f3 | function phow_caltech101()
% PHOW_CALTECH101 Image classification in the Caltech-101 dataset
% This program demonstrates how to use VLFeat to construct an image
% classifier on the Caltech-101 data. The classifier uses PHOW
% features (dense SIFT), spatial histograms of visual words, and a
% Chi2 SVM. To speedu... |
github | jwmneu/PO_CR-master | sift_mosaic.m | .m | PO_CR-master/vlfeat-0.9.20/apps/sift_mosaic.m | 4,621 | utf_8 | 8fa3ad91b401b8f2400fb65944c79712 | function mosaic = sift_mosaic(im1, im2)
% SIFT_MOSAIC Demonstrates matching two images using SIFT and RANSAC
%
% SIFT_MOSAIC demonstrates matching two images based on SIFT
% features and RANSAC and computing their mosaic.
%
% SIFT_MOSAIC by itself runs the algorithm on two standard test
% images. Use SIFT_MOSAI... |
github | jwmneu/PO_CR-master | encodeImage.m | .m | PO_CR-master/vlfeat-0.9.20/apps/recognition/encodeImage.m | 5,278 | utf_8 | 5d9dc6161995b8e10366b5649bf4fda4 | function descrs = encodeImage(encoder, im, varargin)
% ENCODEIMAGE Apply an encoder to an image
% DESCRS = ENCODEIMAGE(ENCODER, IM) applies the ENCODER
% to image IM, returning a corresponding code vector PSI.
%
% IM can be an image, the path to an image, or a cell array of
% the same, to operate on multiple ... |
github | jwmneu/PO_CR-master | experiments.m | .m | PO_CR-master/vlfeat-0.9.20/apps/recognition/experiments.m | 6,905 | utf_8 | 1e4a4911eed4a451b9488b9e6cc9b39c | function experiments()
% EXPERIMENTS Run image classification experiments
% The experimens download a number of benchmark datasets in the
% 'data/' subfolder. Make sure that there are several GBs of
% space available.
%
% By default, experiments run with a lite option turned on. This
% quickly runs all... |
github | jwmneu/PO_CR-master | getDenseSIFT.m | .m | PO_CR-master/vlfeat-0.9.20/apps/recognition/getDenseSIFT.m | 1,679 | utf_8 | 2059c0a2a4e762226d89121408c6e51c | function features = getDenseSIFT(im, varargin)
% GETDENSESIFT Extract dense SIFT features
% FEATURES = GETDENSESIFT(IM) extract dense SIFT features from
% image IM.
% Author: Andrea Vedaldi
% Copyright (C) 2013 Andrea Vedaldi
% All rights reserved.
%
% This file is part of the VLFeat library and is made availab... |
github | mdtdev/EEG_robustCAR-master | eg.m | .m | EEG_robustCAR-master/eg.m | 580 | utf_8 | b2fb7243eceb152a90daad23dd18f37b |
function eg
in_nm = '../../data/MG56_Seizure5.mat';
load( in_nm );
data = sz.ECoG.Data'; % data
[ n_channels n_samples ] = size( data );
n_min_contributing_channels = 8;
dt ... |
github | mdtdev/EEG_robustCAR-master | rCAR.m | .m | EEG_robustCAR-master/rCAR.m | 2,357 | utf_8 | 909b00bfec8abeea014b41116237da19 | % Author: Kyle Q. Lepage
% The author takes no responsibility for anything
% that results from the use of this code for anything.
%
%
% data_in - n_channels x n_times
% dt - sample period in seconds.
%
% n_min_contributing_channels - minimum # of channels to contribute to re... |
github | mdtdev/EEG_robustCAR-master | robustCAR_helper.m | .m | EEG_robustCAR-master/robustCAR_helper.m | 13,805 | utf_8 | e1a440c268a212949810af975156f6a6 | % data_in - n_channels x n_times
%
%
%
% Side Effect: Removes the mean of the data.
%
function [ data_out ref_est nn_ref_est ] = ...
robustCAR3c( data_in, dt, n_min_contributing_channels, nm, taxis )
% ===================================================
% Set the bisquare function cutoff, c
... |
github | danielbicho/PIB-master | createCaptcha.m | .m | PIB-master/Ex3/createCaptcha.m | 1,058 | utf_8 | 9dfbfcef72cb2d403ad24adbed181e07 | function createCaptcha()
for j=1:10
captcha = [];
for i=1:4
c = getChar();
safe_color = getRandomSafeColors();
H = vision.TextInserter(c);
H.Color = safe_color;
H.FontSize = 60;
H.Font = getFamily();
I = ones(75,45,3);
v=0.1;
... |
github | danielbicho/PIB-master | transformRGBToRGBSafe.m | .m | PIB-master/Ex1/transformRGBToRGBSafe.m | 1,077 | utf_8 | ffff2514d2c57155d7678e246529eb50 | function transformRGBToRGBSafe( filename )
I = imread( filename );
safe_colors = getSafeColors();
[M N K] = size( I );
Ip = zeros( M, N, K,'uint8' );
pixel_write = zeros(1,1,K);
for i=1:M
for j=1:N
pixel_read = I(i,j,:);
pixel_read = [pixel_read(1,1,1),pixel_read(1,1,2),pixel_read(1,1... |
github | ujemd/NSS-of-LWIR-and-Vissible-Images-master | norm_sender_normalized.m | .m | NSS-of-LWIR-and-Vissible-Images-master/norm_sender_normalized.m | 4,624 | utf_8 | 60643b2b3fcb00a26e0e949534fd004b | % Y. Zhang and D. M. Chandler, \An algorithm for no-reference image quality
% assessment based on log-derivative statistics of natural scenes," Image
% Quality and System Performance, vol. 8653, p. 86530J, 2013.
% try to fix the range of z field
function [subband, size_band] = norm_sender_normalized(pyro,pind,Nsc... |
github | lionsimbatoolbox/LIONSIMBA-master | startSimulation.m | .m | LIONSIMBA-master/startSimulation.m | 34,684 | utf_8 | 1e1bc4b593dd00cc6edde4afc6a98b94 | function results = startSimulation(t0,tf,initialState,input_density,startParameters)
% startSimulation Starts the simulation of the Li-ion battery.
%
% results = startSimulation(t0,tf,initialStates,input_density,startParameters)
% Starts the simulation of the Li-ion Cell with LIONSIMBA.
%
% Input:
% - t0 :... |
github | lionsimbatoolbox/LIONSIMBA-master | retreiveData.m | .m | LIONSIMBA-master/battery_model_files/P2D_equations/retreiveData.m | 2,306 | utf_8 | e87cf34d3147b5dad91bc3723d2a9333 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This code was written by Marcello Torchio, University of Pavia.
% Please send comments or questions to
% marcello.torchio01@ateneopv.it
%
% Copyright 2017: Marcello Torchio, Lalo Magni, and Davide M. Raimondo, University of Pavia
% Bhush... |
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