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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 |
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github | dustin-cook/Opensees-master | fn_plot_ida.m | .m | Opensees-master/+ida/fn_plot_ida.m | 19,458 | utf_8 | 6b4c3bc0d4f53c391639ac9a92d41014 | function [ ] = fn_plot_ida(analysis, model, gm_set_table, ida_results, SSF_ew, main_dir)
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
%% Initial Setup
% Import packages
import plotting_tools.*
% Defined fixed parames
if analysis.run_z_motion
params = {'b','e','cp'};
else
pa... |
github | dustin-cook/Opensees-master | fn_collect_ida_data.m | .m | Opensees-master/+ida/fn_collect_ida_data.m | 16,452 | utf_8 | b447072a963f905edc4204b5c38695f5 | function [ ] = fn_collect_ida_data(analysis, model, gm_set_table, ida_results, main_dir, write_dir)
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
%% Initial Setup
% Import packages
import plotting_tools.*
% Defined fixed parames
% params = {'b','e','io','ls','cp','euro_th_NC','euro_t... |
github | dustin-cook/Opensees-master | fn_LR_classification.m | .m | Opensees-master/+ida/fn_LR_classification.m | 4,345 | utf_8 | 8d9543972142be96cfc4d77f90939f0a | function [ ] = fn_LR_classification(analysis,model,gm_set_table)
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
%% Initial Setup
% Load Fragility Data
read_dir = ['outputs' '/' model.name{1} '/' analysis.proceedure '_' analysis.id '/' 'IDA' '/' 'Fragility Data'];
ida_table = readtable... |
github | dustin-cook/Opensees-master | fn_create_fragilities.m | .m | Opensees-master/+ida/fn_create_fragilities.m | 20,396 | utf_8 | 4a3ced77909b5d5079c9ae7ba7388c7a | function [ ] = fn_create_fragilities(analysis, write_dir)
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
%% Initial Setup
% Import packages
import plotting_tools.*
% Defined fixed parames
% params = {'b','e','b_e','io','ls','cp','euro_th_NC','euro_th_SD','euro_th_DL'};
% mechs = { 'co... |
github | dustin-cook/Opensees-master | fn_plot_element_scatter.m | .m | Opensees-master/+plotting_tools/fn_plot_element_scatter.m | 17,661 | utf_8 | a1b10c01814b77664a7808663903e84d | function [ ] = fn_plot_element_scatter( element, ele_type, story, hinge, write_dir )
% Description: Fn to create scatter plots of hinge acceptance results
% Created By: Dustin Cook
% Date Created: 3/11/2019
% Inputs:
% Outputs:
% Assumptions:
%% Initial Setup
% Define Plot Dir
plot_dir = [write_dir filesep 'Scatt... |
github | dustin-cook/Opensees-master | fn_plot_edp_profiles.m | .m | Opensees-master/+plotting_tools/fn_plot_edp_profiles.m | 6,536 | utf_8 | a3f4545e519f61100c5c3676eadb551c | function [ ] = fn_plot_edp_profiles( plot_dir, ground_motion, story, target_disp_in, record_edp )
% Description: Fn to plot edp profiles of the analysis
% Created By: Dustin Cook
% Date Created: 1/7/2019
% Inputs:
% Outputs:
% Assumptions:
%% Initial Setup
% Plot Directory
edp_plot_dir = [plot_dir filesep 'EDP Pro... |
github | dustin-cook/Opensees-master | fn_plot_elevation.m | .m | Opensees-master/+plotting_tools/fn_plot_elevation.m | 6,476 | utf_8 | 9b84969410cca7b0f170e082d2dc078f | function [ ] = fn_plot_elevation( hinge_or_joint, element, node, elev_title, plot_dir, direction, x_start, x_end, z_start, z_end )
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
if strcmp(direction,'x')
type_filter = ~strcmp(element.type,'wall');
elseif strcmp(direction,'z')
ty... |
github | vedaldi/mcnSSD-master | compile_mcnSSD.m | .m | mcnSSD-master/compile_mcnSSD.m | 3,094 | utf_8 | a129708c9a7b2fc9ab21ce9c6ee29a84 | function compile_mcnSSD(varargin)
% COMPILE_MCNSSD compile the C++/CUDA components of the SSD Detector
%
% Copyright (C) 2017 Samuel Albanie
% Licensed under The MIT License [see LICENSE.md for details]
tokens = {vl_rootnn, 'matlab', 'mex', '.build', 'last_compile_opts.mat'} ;
last_args_path = fullfile(tokens{:})... |
github | vedaldi/mcnSSD-master | ssd_evaluation.m | .m | mcnSSD-master/core/ssd_evaluation.m | 12,198 | utf_8 | 13583628f1b3f5c051fe580181a6d2db | function ssd_evaluation(expDir, net, opts)
% ----------------------------------------------------------------
% Prepare imdb
% ----------------------------------------------------------------
if exist(opts.dataOpts.imdbPath, 'file')
imdb = load(opts.dataOpts.imd... |
github | vedaldi/mcnSSD-master | ssd_train.m | .m | mcnSSD-master/core/ssd_train.m | 2,022 | utf_8 | 6b556426c87f82c6525207c0a22271c8 | function ssd_train(expDir, opts, varargin)
% ----------------------------------------------------------------
% Prepare imdb
% ----------------------------------------------------------------
imdbPath = opts.dataOpts.imdbPath ;
if exist(imdbPath, 'file')
imdb =... |
github | vedaldi/mcnSSD-master | ssd_zoo.m | .m | mcnSSD-master/core/ssd_zoo.m | 1,688 | utf_8 | 2c7de7034bb3301178fbac60f4f65fcf | function net = ssd_zoo(modelName)
caffeModels = {
'ssd-pascal-vggvd-300', ...
'ssd-pascal-vggvd-512', ...
'ssd-pascal-plus-vggvd-300', ...
'ssd-pascal-vggvd-ft-300', ...
'ssd-pascal-plus-vggvd-512', ...
'ssd-pascal-plus-vggvd-ft-300', ...
'ssd-pascal-plus-vggvd-ft-512', ...
'ssd-pascal-vggvd-ft-512', .... |
github | vedaldi/mcnSSD-master | ssd_init.m | .m | mcnSSD-master/core/ssd_init.m | 22,779 | utf_8 | 81641239af58a92c7173e6e7a85d2f6e | function net = ssd_init(opts)
% SSD_INIT Initialize a Single Shot Multibox Detector Network
% NET = SSD_INIT randomly initializes an SSD network architecture
% for reproducibility, fix the seed
rng(0, 'twister') ;
% load trunk model
net = ssd_zoo('vgg-vd-16-reduced') ;
% -------------------------------------------... |
github | vedaldi/mcnSSD-master | patchSampler.m | .m | mcnSSD-master/matlab/utils/patchSampler.m | 1,636 | utf_8 | f8e0fbe5b2a84939aa6a355c41404674 | function [patch, targets, labels] = patchSampler(targets, labels, opts)
% TODO: docs
strategies = {'jacc_0.1', ...
'jacc_0.3', ...
'jacc_0.5', ...
'jacc_0.7', ...
'jacc_0.9', ...
'rand_patch'} ;
targetsWH = bboxCoder(targets, 'MinMax', 'MinWH') ;
... |
github | vedaldi/mcnSSD-master | findBestEpoch.m | .m | mcnSSD-master/matlab/utils/findBestEpoch.m | 3,047 | utf_8 | 013d4183ef2103cc519f6bbec63ee734 | function bestEpoch = findBestEpoch(expDir, varargin)
%FINDBESTEPOCH finds the best epoch of training
% FINDBESTEPOCH(EXPDIR) evaluates the checkpoints
% (the `net-epoch-%d.mat` files created during
% training) in EXPDIR
%
% FINDBESTEPOCH(..., 'option', value, ...) accepts the following
% options:
%
% `prio... |
github | vedaldi/mcnSSD-master | printPascalResults.m | .m | mcnSSD-master/matlab/utils/printPascalResults.m | 5,238 | utf_8 | 84ec5f37d3be267ee3307feedeb0b60f | function printPascalResults(cacheDir, varargin)
%PRINTPASCALRESULTS prints out results as formatted tables
% PRINTRESULTS(CACHEDIR) searches the cache directory of
% pascal VOC evaluations and prints out a formatted summary
% CACHEDIR is a string specifying the absolute path of the
% directory holding the cac... |
github | vedaldi/mcnSSD-master | pruneCheckpoints.m | .m | mcnSSD-master/matlab/utils/pruneCheckpoints.m | 1,808 | utf_8 | decdd56329b5b620f8bbdb243d1a945e | function varargout = pruneCheckpoints(expDir, varargin)
%PRUNECHECKPOINTS removes unnecessary checkpoint files
% PRUNECHECKPOINTS(EXPDIR) evaluates the checkpoints
% (the `net-epoch-%d.mat` files created during
% training) in EXPDIR and removes all checkpoints except:
%
% 1. The checkpoint with the lowest v... |
github | vedaldi/mcnSSD-master | matchPriors.m | .m | mcnSSD-master/matlab/utils/matchPriors.m | 2,838 | utf_8 | b2eb3ece2444e7a7f991742c046e05f5 | function [matches, overlaps, ignored] = matchPriors(gtBoxes, pBoxes, varargin)
% MATCHPRIORS
%
% TODO: docs
%
opts.overlapThreshold = 0.5 ;
opts.ignoreXBoundaryBoxes = false ;
opts = vl_argparse(opts, varargin, 'nonrecursive') ;
if opts.ignoreXBoundaryBoxes
boundaryBoxes = pBoxes(:,1) < 0 ...
| ... |
github | vedaldi/mcnSSD-master | confirmConfig.m | .m | mcnSSD-master/matlab/utils/confirmConfig.m | 1,953 | utf_8 | e0d793b88b3c3802c4ff89e950d28208 | function confirmConfig(expDir, opts)
%DOCS: todo - pretty obvs
if ~opts.confirmConfig
return ;
end
[~,expName] = fileparts(expDir) ;
fprintf('Experiment name: %s\n', expName) ;
fprintf('------------------------------------\n') ;
fprintf('Training set: %s\n', opts.dataOpts.trainData) ;
fprintf('Testing set: %s\n',... |
github | vedaldi/mcnSSD-master | nnmultiboxcoder.m | .m | mcnSSD-master/matlab/xtest/suite/nnmultiboxcoder.m | 2,571 | utf_8 | e4acc4d7263d1d0466acd069824ff7b0 | classdef nnmultiboxcoder < nntest
methods (Test)
function basic(test)
batchSize = 5 ;
numPriors = 7 ;
numLocPreds = numPriors * 4 ;
numConfPreds = numPriors * 21 ;
labelRange = [1 21] ;
for i = 1:batchSize
numBoxes = randi(10, 1) ;
xmin = ra... |
github | vedaldi/mcnSSD-master | ssd_pascal_train.m | .m | mcnSSD-master/pascal/ssd_pascal_train.m | 5,582 | utf_8 | 6f40b91f660e510dcc290777ac0f3023 | function ssd_pascal_train(varargin)
opts.gpus = [1] ;
opts.continue = true ;
opts.confirmConfig = true ;
opts.pruneCheckpoints = true ;
opts.architecture = 300 ;
opts.use_vl_imreadjpeg = false ;
opts = vl_argparse(opts, varargin) ;
% ---------------------------
% configure training options
% ------------------------... |
github | vedaldi/mcnSSD-master | ssd_pascal_evaluation.m | .m | mcnSSD-master/pascal/ssd_pascal_evaluation.m | 5,925 | utf_8 | 4fb44fc4c94fd54bbcc87268d62ba3ff | function ssd_pascal_evaluation(varargin)
%SSD_PASCAL_EVALUATION evaluate SSD detector on pascal VOC
opts.net = [] ;
opts.gpus = 2 ;
opts.evalVersion = 'fast' ;
opts.modelName = 'ssd-pascal-vggvd-300' ;
% configure batch opts
opts.batchOpts.batchSize = 8 ;
opts.batchOpts.numThreads = 4 ;
opts.batchOpts.use_vl_imreadjp... |
github | vedaldi/mcnSSD-master | vocSetup.m | .m | mcnSSD-master/pascal/vocSetup.m | 10,820 | utf_8 | b83de8160d7f7d8d2127d3722f17144c | function imdb = vocSetup(varargin)
opts.edition = '12' ;
opts.dataDir = fullfile(vl_rootnn, 'data','datasets', 'voc07') ;
opts.archiveDir = fullfile(vl_rootnn, 'data', 'archives') ;
opts.includeDetection = false ;
opts.includeDevkit = false ;
opts.includeSegmentation = false ;
opts.includeTest = true ;
opts = vl_argpa... |
github | vedaldi/mcnSSD-master | getPascalImdb.m | .m | mcnSSD-master/pascal/getPascalImdb.m | 5,062 | utf_8 | 6766fbe01fbab02e0b5b7b794636822c | function imdb = getPascalImdb(opts, varargin)
% LOADIMDB loads Pascal VOC image database
%
% Inspiration ancestry for code:
% A.Vedaldi -> R.Girshick -> S.Albanie
opts.excludeDifficult = false ;
opts = vl_argparse(opts, varargin) ;
% Although the 2012 data can be used during training, only
% the 2007 test data is ... |
github | vedaldi/mcnSSD-master | eval_voc.m | .m | mcnSSD-master/pascal/helpers/eval_voc.m | 3,611 | utf_8 | bec81a2c9c611348c2df08b282fec8df | function res = eval_voc(cls, imageIds, bboxes, scores, VOCopts, varargin)
% EVAL_VOC evaluate detections on Pascal VOC
% RES = EVAL_VOC(CLS, IMAGEIDS, BBOXES, SCORES, VOCOPTS) evalutes a set of
% detections specified by BBOXES and SCORES for the given class, CLS.
% IMAGEIDS is a cell array containing the ids of th... |
github | liweiwang1993/lip-motion-csi-master | get_scaled_csi.m | .m | lip-motion-csi-master/get_scaled_csi.m | 1,842 | utf_8 | 25f6ee30c68e10fbfaaeff35624ab758 | %GET_SCALED_CSI Converts a CSI struct to a channel matrix H.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = get_scaled_csi(csi_st)
% Pull out CSI
csi = csi_st.csi;
% Calculate the scale factor between normalized CSI and RSSI (mW)
csi_sq = csi .* conj(csi);
csi_pwr =... |
github | liweiwang1993/lip-motion-csi-master | PCACleanCSI.m | .m | lip-motion-csi-master/PCACleanCSI.m | 591 | utf_8 | ecabbb827a2fae88ce726a3665df522c | %%PCA the filter csi
%%input:filter the csi data
%%output:PCA csi data
function maincomponents=PCACleanCSI(filtercsi,num)
if (nargin<2)
num=3;%%select the component num
end
warning('off');
startc=2;%%start component
endc=startc+num-1;%%end component
[length,sender,receiver,~]=size(filterc... |
github | liweiwang1993/lip-motion-csi-master | GetRawCSI.m | .m | lip-motion-csi-master/GetRawCSI.m | 614 | utf_8 | b3dd515295a2a5a5e187f627fe080de3 | %%get the raw CSI(not clean)
%file='csi-lip-6-23-train//6-23-all-train//6-23-all-1.dat';
%%input:filename,length:the collect csi data filename,csi length you need
%%output:raw csi data---4-D complex data
function csi=GetRawCSI(file,sender,receiver)
csi_trace=read_bf_file(file);%%read_csi
[length,~]=size(csi_tra... |
github | liweiwang1993/lip-motion-csi-master | FilterCSI.m | .m | lip-motion-csi-master/FilterCSI.m | 921 | utf_8 | 4ca0a62b1a326e6ce7518cafb5cb4b91 | %%filter the noise from the csi signal
%%input:remove the multiple path csi signal
%%output:filter the csi
function filtercsi=FilterCSI(removedCSIInformation)
load SpeakHd2.mat;%%butterwords bandpass filter 0-5hz
[length,sender,receiver,channel]=size(removedCSIInformation);
amptitude=zeros(length,sender,receiver,channe... |
github | liweiwang1993/lip-motion-csi-master | DWTCSI.m | .m | lip-motion-csi-master/DWTCSI.m | 861 | utf_8 | 044d9eb3345b98e5366894ef05adc1ad | %%DWT the maincomponents
%%input:maincomponents
%%output:Dwtcomponents
function Dwtcomponents=DWTCSI(maincomponents,dwtnum)
if (nargin<2)
dwtnum=3;%%select the component num
end
warning('off');
[~,sender,receiver,num]=size(maincomponents);
[C,L] = wavedec(maincomponents(:,1,1,1),dwtnum,'db4');%%DWT3
... |
github | liweiwang1993/lip-motion-csi-master | read_bf_file.m | .m | lip-motion-csi-master/read_bf_file.m | 2,577 | utf_8 | 3046107c2e85bb02155fda059099b086 | %READ_BF_FILE Reads in a file of beamforming feedback logs.
% This version uses the *C* version of read_bfee, compiled with
% MATLAB's MEX utility.
%
% (c) 2008-2011 Daniel Halperin <dhalperi@cs.washington.edu>
%
function ret = read_bf_file(filename)
%% Input check
error(nargchk(1,1,nargin));
%% Open file
f = fope... |
github | liweiwang1993/lip-motion-csi-master | RemoveMultiplePath.m | .m | lip-motion-csi-master/RemoveMultiplePath.m | 1,206 | utf_8 | 19f1188b685b7ab3599866547c3973a7 | %%remove the multiple path distortion to clean the csi
%%input:raw csi---4-D complex data
%%output:remove the multiple path csi---4-D complex data
function removedCSIInformation=RemoveMultiplePath(csi)
warning('off');
[length,sender,receiver,channel] = size(csi);
removedCSIInformation=zeros(length,sender,receiver,chann... |
github | timlautk/BCoAPG-plus-master | scad_prox.m | .m | BCoAPG-plus-master/Tools/scad_prox.m | 966 | utf_8 | 9b021761670680d4cb732bd12f754068 | function p = scad_prox(u,gamma,lambda)
p = zeros(size(u));
p1 = sign(u).*min(lambda,max(0,abs(u)-lambda));
p2 = sign(u).*min(gamma*lambda,max(lambda,(abs(u)*(gamma-1)-gamma*lambda)/(gamma-2)));
p3 = sign(u).*max(gamma*lambda,abs(u));
p((h(p1) < h(p2)) & (h(p1) < h(p3))) = p1((... |
github | tskarhed/Space-Engineer-Notes-master | bikewheel.m | .m | Space-Engineer-Notes-master/MATLAB/bikewheel.m | 3,926 | utf_8 | 86ccbea3e5ae2d5385edc4e1174d5852 | function bikewheel(radius, v, angle)
%Takes the radius of a circle (bikewheel) which rotates at a velocity v and
%calculates the trajectory for particles at angle
%Make a single variable so we don't have to retype
center = [radius radius];
%Draw the circle
drawCircle(radius, center);
hold on;
%points = randi(100, 1,... |
github | paulmmacey/lmgs-master | cspm_lmgs.m | .m | lmgs-master/cspm_lmgs.m | 17,957 | utf_8 | 8ee7e17d952207b9823eedc9620c412b | function cspm_lmgs(Pin,overwrite,prefix,display,GM)
% CSPM_LMGS - Detrend fMRI image series using LMGS method.
% CSPM_LMGS(P, OVERWRITE, PREFIX, DISPLAY, GRANDMEAN )
% P is array of images returned by spm, e.g. (SPM2):
% P = spm_get(Inf, '.img',{'Please select images for detrending'}... |
github | lanl-ansi/OPFRecourse.jl-master | nesta_case30_ieee.m | .m | OPFRecourse.jl-master/test/data/nesta_case30_ieee.m | 17,949 | utf_8 | a80e55940e636ed49d716d79ca744eb9 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% %%%%%
%%%% NICTA Energy System Test Case Archive (NESTA) - v0.5.0 %%%%%
%%%% Optimal Power Flow - Typical Operation %%%%%
%%%% ... |
github | dzwallkilled/IEforAR-master | extract_feature_JLd.m | .m | IEforAR-master/extract_feature_JLd.m | 4,296 | utf_8 | 1b604e72cee456b300ee63e67b4c3df3 | function [features] = extract_feature_JLd(skeleton_input)
index = get_JLd_index01;
% index = get_JLd_index02;
% index = get_JLd_index03;
pts1 = skeleton_input(index(:,1),:,:,:);
pts2 = skeleton_input(index(:,2),:,:,:);
pts3 = skeleton_input(index(:,3),:,:,:);
features = squeeze((sum(cross(pts2-pts1,pts3-pts1,2).^2,2).... |
github | dzwallkilled/IEforAR-master | extract_feature_LLa.m | .m | IEforAR-master/extract_feature_LLa.m | 1,282 | utf_8 | 93762664bd183badcd6223f6f25ef4ff |
function [features] = extract_feature_LLa(skeleton_input)
index = get_LLa_index01;
lines1 = skeleton_input(index(:,2),:,:,:) - skeleton_input(index(:,1),:,:,:);
lines2 = skeleton_input(index(:,4),:,:,:) - skeleton_input(index(:,3),:,:,:);
features = squeeze(acos(dot(lines1,lines2,2) ...\
./(sum(lines1.^2,2... |
github | dzwallkilled/IEforAR-master | transform_method9.m | .m | IEforAR-master/transform_method9.m | 609 | utf_8 | 3eb5f96647c624ee797e178a73b86bed |
function image_output = transform_method9(features,img_size)
global cb;%claimed in main file
features = imresize(features,img_size,'bilinear');
[dim,frame,~] = size(features);
color_index = ones(dim,frame);
for f = 1:frame
min_value = min(features(:,1:f,1),[],2);
range = max(features(:,1:f,1)-min_value,[],2);... |
github | dzwallkilled/IEforAR-master | extract_feature_JJd.m | .m | IEforAR-master/extract_feature_JJd.m | 2,236 | utf_8 | 7b73db695bde272f8e98750651ab2b7f | function features = extract_feature_JJd(skeleton_input)
% index = get_JJd_index01;
% index = get_JJd_index02;
% index = get_JJd_index03;
index = get_JJd_index04;
joints = skeleton_input;
vec = joints(index(:,2),:,:,:) - joints(index(:,1),:,:,:);
features = squeeze(sum(vec.^2,2).^(1/2));
end
function index = get_JJ... |
github | dzwallkilled/IEforAR-master | transform_method1.m | .m | IEforAR-master/transform_method1.m | 310 | utf_8 | 28e6ffff361be7a8228a8859d45731b1 | %linear transform
%specifically for JJo which has 3 dimensional features
function image_output = transform_method1(features,img_size)
features = imresize(features,img_size,'bilinear');
min_value = min(features,[],2);
range = max(features,[],2) - min_value;
image_output = (features- min_value)./range;
end |
github | dzwallkilled/IEforAR-master | extract_feature_JJd01.m | .m | IEforAR-master/extract_feature_JJd01.m | 1,170 | utf_8 | 74b608dd2b792e512c333299c397f1d6 | function features = extract_feature_JJd01(skeleton_input)
[joint_num,cor_dim,frame,body_num] = size(skeleton_input);
index = get_JJd_index01;
% index = get_JJd_index02;
joints = zeros(2*joint_num,cor_dim,frame);
joints(1:joint_num,:,:) = skeleton_input(:,:,:,1);
if body_num == 1
joints(joint_num+1:end,:,:) = skel... |
github | dzwallkilled/IEforAR-master | create_list.m | .m | IEforAR-master/create_list.m | 2,545 | utf_8 | 720b47076443cfef8e3e82163b3d8c6e | % train and test list for two evaluations
% cross subject and cross view
function [cross_sub_V2Tr1Te0_index, cross_view_V2Tr1Te0_index] = create_list(skeleton_file_list, target_folder)
mkdir(target_folder);
labels = str2num(skeleton_file_list(:,18:20));
%% cross subject training and test list
performer_list = str2num(... |
github | dzwallkilled/IEforAR-master | transform_method8.m | .m | IEforAR-master/transform_method8.m | 601 | utf_8 | 2685988e6ba5c23acfff76e96361e8fd |
%temporal information included
%active function
function image_output = transform_method8(features)
global cb;%claimed in main file
[dim,frame,~] = size(features);
main_feature = features(:,:,1);
min_value = min(main_feature(:));
range = max(main_feature(:)) - min_value;
% f = (main_feature - min_value)./range;
alpha... |
github | dzwallkilled/IEforAR-master | preprocess_skeleton_data.m | .m | IEforAR-master/preprocess_skeleton_data.m | 4,026 | utf_8 | b39d4b54fd0cdca8144d8c56fddd71e5 | function [skeleton_output, body_num] = preprocess_skeleton_data(skeleton_input)
%align the skeleton data according to body_ID
%meanwhile normalizing the joint xyz corrdinates based on chain distances
[skeleton_output, body_cnt] = enhance_skeleton(skeleton_input);
% visualize_skeleton(skeleton_output);
%translate (actu... |
github | dzwallkilled/IEforAR-master | extract_feature_JLd01.m | .m | IEforAR-master/extract_feature_JLd01.m | 4,847 | utf_8 | 3601954b68d43129dca09c1e14fa4d45 |
%use this for computation efficiency speed = 0.079215 seconds
function [features] = extract_feature_JLd01(skeleton_input)
[joint_num,cor_dim,frame,body_num] = size(skeleton_input);
% index = get_JLd_index01;
% index = get_JLd_index02;
index = get_JLd_index03;
dim = length(index);
joints = zeros(2*joint_num,cor_dim,f... |
github | dzwallkilled/IEforAR-master | translate_skeleton.m | .m | IEforAR-master/translate_skeleton.m | 743 | utf_8 | 3469b7f8aab3e71f30b0ccffd165df34 | %method following 'On Geometric Features for Skeleton-Based Action Recognition using Multilayer
%LSTM Networks'
%including traslating and rotation
function skeleton_output = translate_skeleton(skeleton_input)
[~,~,frame,body_num] = size(skeleton_input);
skeleton_output = zeros(25,3,frame,body_num);
for f = 1:frame
... |
github | dzwallkilled/IEforAR-master | transform_method3.m | .m | IEforAR-master/transform_method3.m | 571 | utf_8 | bbff146b7c9ed4a9bb65f5c9158f7c78 | %specifically for Features like JLd and LLa which have many dimensions,
%which could be mapped into three channels of RGB
function image_output = transform_method3(features,img_size)
dim = size(features,1);
features = imresize(features,[dim img_size(2)],'bilinear');
main_feature = features(:,:,1);
min_value = min(main_... |
github | dzwallkilled/IEforAR-master | transform_method4.m | .m | IEforAR-master/transform_method4.m | 616 | utf_8 | ed4ae61059983b0b84ff2075fcc0c96b |
function image_output = transform_method4(features)
global cb;%claimed in main file
[dim,frame,body_num] = size(features);
new_features = zeros(2*dim,frame);
new_features(1:2:end,:) = features(:,:,1);
if body_num == 1
new_features(2:2:end,:) = features(:,:,1);
else
new_features(2:2:end,:) = features(:,:,2);
... |
github | dzwallkilled/IEforAR-master | extract_feature_JJo01.m | .m | IEforAR-master/extract_feature_JJo01.m | 1,209 | utf_8 | 8ba3bd991d61abc8edafc86241e51575 | function features = extract_feature_JJo01(skeleton_input)
[joint_num,cor_dim,frame,body_num] = size(skeleton_input);
% index = get_JJo_index01;
index = get_JJo_index02;
joints = zeros(2*joint_num,cor_dim,frame);
joints(1:joint_num,:,:) = skeleton_input(:,:,:,1);
if body_num == 1
joints(joint_num+1:end,:,:) = skel... |
github | dzwallkilled/IEforAR-master | visualize_skeleton.m | .m | IEforAR-master/visualize_skeleton.m | 628 | utf_8 | b6769c2058c7a054693356b8ee4f479b | function visualize_skeleton(skeleton_input)
[~,~,frame,body_num] = size(skeleton_input);
index = get_chain_index;
figure;
for f=1:frame
for b=1:body_num
figure(b);
for l = 1:18
points = skeleton_input(index(l,:)',:,f,b);
plot3(points(:,1),points(:,2),points(:,3));
... |
github | dzwallkilled/IEforAR-master | transform_method5.m | .m | IEforAR-master/transform_method5.m | 364 | utf_8 | 8cdf1a756b49bf563b8e02ff149e2b56 |
%3 channels for JJd, JLd, LLa
%part of the method is implemented in the begin of this file, i.e. in
% the file-name function
function image_output = transform_method5(features,img_size)
features = imresize(features, img_size,'bilinear');
min_value = min(features,[],2);
range = max(features,[],2) - min_value;
image_o... |
github | dzwallkilled/IEforAR-master | transform_method2.m | .m | IEforAR-master/transform_method2.m | 692 | utf_8 | 456bd13f1e22d19cb8a73bf4007cd0d9 |
%linear transform (modified from method1 which has some problems)
function image_output = transform_method2(features,img_size)
features = imresize(features,img_size);
[dim,frame,body_num] = size(features);
image_output = zeros(dim,frame,3);
min_value = min(features,[],2);
range = max(features,[],2) - min_value;
ima... |
github | dzwallkilled/IEforAR-master | extract_skeleton_feature.m | .m | IEforAR-master/extract_skeleton_feature.m | 413 | utf_8 | 63df637562e07aba87cb7e8975bb33cf | %the codes have been optimised for speed
function features = extract_skeleton_feature(skeleton_input, flags)
%Joint Joint Distances (JJd)
if flags(1) == 1
features.JJd = calculate_JJd(skeleton_input);
end
%Joint Line Distances (JLd)
if flags(2) == 1
features.JLd = calculate_JLd(skeleton_input);
end
%Line Lin... |
github | dzwallkilled/IEforAR-master | extract_feature_JJo.m | .m | IEforAR-master/extract_feature_JJo.m | 411 | utf_8 | eaef8bc7236879fbcd8978dcf9b4d3b0 | function features = extract_feature_JJo(skeleton_input)
index = get_JJo_index01;
joints = skeleton_input;
vec = joints(index(:,2),:,:,1) - joints(index(:,1),:,:,1);
features = vec./(sum(vec.^2,2).^(1/2));
features = permute(features,[1 3 2]);
end
function index = get_JJo_index01
%
index = zeros(300,2);
n = 0;
for ... |
github | dzwallkilled/IEforAR-master | extract_feature_JJv.m | .m | IEforAR-master/extract_feature_JJv.m | 375 | utf_8 | 9161ef7c41e2767f1ea74611a0668b9a | function features = extract_feature_JJv(skeleton_input)
index = get_JJo_index01;
joints = skeleton_input;
features = joints(index(:,2),:,:,1) - joints(index(:,1),:,:,1);
features = permute(features,[1 3 2]);
end
function index = get_JJo_index01
%
index = zeros(300,2);
n = 0;
for i = 1:24
for j = i+1:25
... |
github | dzwallkilled/IEforAR-master | extract_feature_JJd02.m | .m | IEforAR-master/extract_feature_JJd02.m | 1,447 | utf_8 | 128336bd7e04afc3a163f359916bbebe | function features = extract_feature_JJd02(skeleton_input)
[joint_num,cor_dim,frame,body_num] = size(skeleton_input);
% index = get_JJd_index01;
index = get_JJd_index02;
joints = zeros(2*joint_num,cor_dim,frame);
joints(1:joint_num,:,:) = skeleton_input(:,:,:,1);
if body_num == 1
joints(joint_num+1:end,:,:) = skel... |
github | dzwallkilled/IEforAR-master | transform_method7.m | .m | IEforAR-master/transform_method7.m | 1,043 | utf_8 | 0b038c0de81e48878b0718f95acfa8e1 |
%temporal information included
function image_output = transform_method7(features)
[dim,frame,~] = size(features);
image = zeros(dim,frame,3);
feature_main = features(:,:,1);
min_value = min(feature_main,[],2);
range = max(feature_main,[],2) - min_value;
image(:,:,1) = ((feature_main - min_value)./range);
velocity =... |
github | dzwallkilled/IEforAR-master | extract_feature_JJv01.m | .m | IEforAR-master/extract_feature_JJv01.m | 1,185 | utf_8 | 419dac66efe27e6dacc8069fb39be46d | function features = extract_feature_JJv01(skeleton_input)
[joint_num,cor_dim,frame,body_num] = size(skeleton_input);
% index = get_JJo_index01;
index = get_JJo_index02;
joints = zeros(2*joint_num,cor_dim,frame);
joints(1:joint_num,:,:) = skeleton_input(:,:,:,1);
if body_num == 1
joints(joint_num+1:end,:,:) = skel... |
github | pshibby/fepll_public-master | operators.m | .m | fepll_public-master/operators/operators.m | 13,038 | utf_8 | 53fbe972cad218d67f9f7ab37ab962ed | function op = operators(name, M, N, varargin)
% % Function Name: operators
%
%
% Inputs:
% name : operator name
% - vignetting
% - id (denoising)
% - blur (deconvolution)
% - blur+border (deconv with masked borders)
% - m... |
github | pshibby/fepll_public-master | gstree_match.m | .m | fepll_public-master/fepll/gstree_match.m | 2,286 | utf_8 | 7aff4b07361ca7601c40ee3360b81c91 | function labels = gstree_match(y, GStree, sig2, varargin)
% % Function Name: gstree_match
%
%
% Inputs:
% y : matrix containing image patches
% GStree : GMM-tree
% sig2 : noise variance
%
% Outputs:
% labels : index of Gaussian components each patch belongs to
% Citation:
% If you u... |
github | pshibby/fepll_public-master | gstree_sv_threshold.m | .m | fepll_public-master/fepll/gstree_sv_threshold.m | 1,291 | utf_8 | f03453a4b1dd0ab6bcdd432629f255b3 | function [GStree, GS] = gstree_sv_threshold(GStree, GS, p)
% % Function Name: gstee_sv_threshold
%
%
% Inputs:
% GStree : GMM-tree
% GS : GMM model
% p : threshold for truncation
%
% Outputs:
% GStree : GMM-tree after flat-tail approximation
% GS : GMM model after flat-t... |
github | RobinAmsters/GT_mobile_robotics-master | build_map.m | .m | GT_mobile_robotics-master/mobile_robotics/Session 2/build_map.m | 2,200 | utf_8 | 3b8057bef157c70bd61c6987b12a5660 | clear all
close all
clc
%% INTRODUCTION
% To start using a turtlebot 3 following procedure
% needs to be followed:
%
% 1) Turn the TB3 on.
% 2) Set up the putty SSH connection (IP adress is given)
% 3) Type "bringup".
% 4) You are now able to run your MATLAB code
%
% This file file will let the turtlebot ... |
github | RobinAmsters/GT_mobile_robotics-master | protectfig.m | .m | GT_mobile_robotics-master/common/rvctools/common/protectfig.m | 881 | utf_8 | 02ea9bf0328e3371f877b1e9e1b5be6d |
% Copyright (C) 1993-2017, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of the License, o... |
github | RobinAmsters/GT_mobile_robotics-master | Polygon.m | .m | GT_mobile_robotics-master/common/rvctools/common/Polygon.m | 44,407 | utf_8 | 0f8730e021e78210d0e8ae7ff334f5e9 | %POLYGON Polygon class
%
% A general class for manipulating polygons and vectors of polygons.
%
% Methods::
% plot Plot polygon
% area Area of polygon
% moments Moments of polygon
% centroid Centroid of polygon
% perimeter Perimter of polygon
% transform Transform polygo... |
github | RobinAmsters/GT_mobile_robotics-master | edgelist.m | .m | GT_mobile_robotics-master/common/rvctools/common/edgelist.m | 4,737 | utf_8 | 3d7876f26f51a03f4c49328237250877 | %EDGELIST Return list of edge pixels for region
%
% EG = EDGELIST(IM, SEED) is a list of edge pixels (2xN) of a region in the
% image IM starting at edge coordinate SEED=[X,Y]. The edgelist has one column per
% edge point coordinate (x,y).
%
% EG = EDGELIST(IM, SEED, DIRECTION) as above, but the direction of edge
% ... |
github | RobinAmsters/GT_mobile_robotics-master | diff2.m | .m | GT_mobile_robotics-master/common/rvctools/common/diff2.m | 1,285 | utf_8 | d2018ea5bfa0dc51016c7c936df1b4d1 | %DIFF2 First-order difference
%
% D = DIFF2(V) is the first-order difference (1xN) of the series data in
% vector V (1xN) and the first element is zero.
%
% D = DIFF2(A) is the first-order difference (MxN) of the series data in
% each row of the matrix A (MxN) and the first element in each row is zero.
%
% Notes::
% ... |
github | RobinAmsters/GT_mobile_robotics-master | rvccheck.m | .m | GT_mobile_robotics-master/common/rvctools/common/rvccheck.m | 2,868 | utf_8 | 0a6c6a7bbb84d1de106fefff5323bd67 | function rvccheck(verbose)
if nargin == 0
verbose = true;
end
% display current versions of MATLAB
year = version('-release');
if verbose
fprintf('You are using:\n - MATLAB release %s\n', year);
end
% check how old it is
today = datevec(now);
age = tod... |
github | RobinAmsters/GT_mobile_robotics-master | filt1d.m | .m | GT_mobile_robotics-master/common/rvctools/common/filt1d.m | 2,048 | utf_8 | bc8104a2b86eb158bb26e4ed77ac7e0f | %FILT1D 1-dimensional rank filter
%
% Y = FILT1D(X, OPTIONS) is the minimum, maximum or median value (1xN) of the
% vector X (1xN) compute over an odd length sliding window.
%
% Options::
% 'max' Compute maximum value over the window (default)
% 'min' Compute minimum value over the window
% 'median' ... |
github | RobinAmsters/GT_mobile_robotics-master | yaxis.m | .m | GT_mobile_robotics-master/common/rvctools/common/yaxis.m | 1,310 | utf_8 | 0db9a465f805810b37b09d08336176c6 | %YAYIS set Y-axis scaling
%
% YAXIS(MAX) set y-axis scaling from 0 to MAX.
%
% YAXIS(MIN, MAX) set y-axis scaling from MIN to MAX.
%
% YAXIS([MIN MAX]) as above.
%
% YAXIS restore automatic scaling for y-axis.
%
% See also YAXIS.
% Copyright (C) 1993-2017, by Peter I. Corke
%
% This file is part of The Robotics Toolb... |
github | RobinAmsters/GT_mobile_robotics-master | colorname.m | .m | GT_mobile_robotics-master/common/rvctools/common/colorname.m | 5,777 | utf_8 | c7a519e639e1ff6b85516095712405d9 | %COLORNAME Map between color names and RGB values
%
% RGB = COLORNAME(NAME) is the RGB-tristimulus value (1x3) corresponding to
% the color specified by the string NAME. If RGB is a cell-array (1xN) of
% names then RGB is a matrix (Nx3) with each row being the corresponding
% tristimulus.
%
% XYZ = COLORNAME(NAME, 'xy... |
github | RobinAmsters/GT_mobile_robotics-master | polydiff.m | .m | GT_mobile_robotics-master/common/rvctools/common/polydiff.m | 1,119 | utf_8 | 3358d9e6a460f1a769fc034f0a00897f | %POLYDIFF Differentiate a polynomial
%
% PD = POLYDIFF(P) is a vector of coefficients of a polynomial (1xN-1) which is the
% derivative of the polynomial P (1xN).
%
% p = [3 2 -1];
% polydiff(p)
% ans =
% 6 2
%
% See also POLYVAL.
% Copyright (C) 1993-2017, by Peter I. Cork... |
github | RobinAmsters/GT_mobile_robotics-master | randinit.m | .m | GT_mobile_robotics-master/common/rvctools/common/randinit.m | 978 | utf_8 | 118aaf915c94e7df6953a8a937ba88f8 | %RANDINIT Reset random number generator
%
% RANDINIT resets the defaul random number stream.
%
% See also RandStream.
% Copyright (C) 1993-2017, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of th... |
github | RobinAmsters/GT_mobile_robotics-master | xaxis.m | .m | GT_mobile_robotics-master/common/rvctools/common/xaxis.m | 1,832 | utf_8 | aeb2d1ed8dcaf8d74d9cfff5546879cf | %XAXIS Set X-axis scaling
%
% XAXIS(MAX) set x-axis scaling from 0 to MAX.
%
% XAXIS(MIN, MAX) set x-axis scaling from MIN to MAX.
%
% XAXIS([MIN MAX]) as above.
%
% XAXIS restore automatic scaling for x-axis.
%
% See also YAXIS.
% Copyright (C) 1993-2017, by Peter I. Corke
%
% This file is part of The Robotics Tool... |
github | RobinAmsters/GT_mobile_robotics-master | dockfigs.m | .m | GT_mobile_robotics-master/common/rvctools/common/dockfigs.m | 1,180 | utf_8 | 55bce19340194193b60ce661b658894a | %DOCKFIGS Control figure docking in the GUI
%
% dockfigs causes all new figures to be docked into the GUI
%
% dockfigs(1) as above.
%
% dockfigs(0) causes all new figures to be undocked from the GUI
% Copyright (C) 1993-2017, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB ... |
github | RobinAmsters/GT_mobile_robotics-master | usefig.m | .m | GT_mobile_robotics-master/common/rvctools/common/usefig.m | 348 | utf_8 | c47f23a7a2dcad028e840e34500f5959 | %USEFIG Named figure windows
%
% usefig('Foo') makes figure 'Foo' the current figure, if it doesn't
% exist create it.
%
% h = usefig('Foo') as above, but returns the figure handle
function H = usefig(name)
h = findobj('Name', name);
if isempty(h),
h = figure;
set(h, 'Name', name);
else
figure(h);
end
if... |
github | RobinAmsters/GT_mobile_robotics-master | circle.m | .m | GT_mobile_robotics-master/common/rvctools/common/circle.m | 2,075 | utf_8 | 6a9dea8240a3f9b5329ef331aef61802 | %CIRCLE Compute points on a circle
%
% CIRCLE(C, R, OPTIONS) plots a circle centred at C (1x2) with radius R on the current
% axes.
%
% X = CIRCLE(C, R, OPTIONS) is a matrix (2xN) whose columns define the
% coordinates [x,y] of points around the circumferance of a circle
% centred at C (1x2) and of radius R.
%
% C is... |
github | RobinAmsters/GT_mobile_robotics-master | stlRead.m | .m | GT_mobile_robotics-master/common/rvctools/common/stlRead.m | 11,483 | utf_8 | 152f25d793763b55b15b646992987e02 | %STLREAD reads any STL file not depending on its format
%
% [v, f, n, name] = stlRead(fileName) reads the STL format file (ASCII or
% binary) and returns vertices V, faces F, normals N and NAME is the name
% of the STL object (NOT the name of the STL file).
%
% Authors::
% - from MATLAB File Exchange by Pau Mico... |
github | RobinAmsters/GT_mobile_robotics-master | pickregion.m | .m | GT_mobile_robotics-master/common/rvctools/common/pickregion.m | 3,137 | utf_8 | f282eb7d77ff4e8df065d73a0c7d9e37 | %PICKREGION Pick a rectangular region of a figure using mouse
%
% [p1,p2] = PICKREGION() initiates a rubberband box at the current click point
% and animates it so long as the mouse button remains down. Returns the first
% and last coordinates in axis units.
%
% Options::
% 'axis',A The axis to select from (defaul... |
github | RobinAmsters/GT_mobile_robotics-master | mplot.m | .m | GT_mobile_robotics-master/common/rvctools/common/mplot.m | 7,123 | utf_8 | 1b7af413e24c0753dcef8934378925e1 | %MPLOT Plot time-series data
%
% A convenience function for plotting time-series data held in a matrix.
% Each row is a timestep and the first column is time.
%
% MPLOT(Y, OPTIONS) plots the time series data Y(NxM) in multiple
% subplots. The first column is assumed to be time, so M-1 plots are
% produced.
%
% MPLOT(... |
github | RobinAmsters/GT_mobile_robotics-master | runscript.m | .m | GT_mobile_robotics-master/common/rvctools/common/runscript.m | 8,329 | utf_8 | 1a60ae9f2904735bde6dca3af52d4e0a | %RUNSCRIPT Run an M-file in interactive fashion
%
% RUNSCRIPT(SCRIPT, OPTIONS) runs the M-file SCRIPT and pauses after every
% executable line in the file until a key is pressed. Comment lines are shown
% without any delay between lines.
%
% Options::
% 'delay',D Don't wait for keypress, just delay of D seconds (de... |
github | RobinAmsters/GT_mobile_robotics-master | rvcpath.m | .m | GT_mobile_robotics-master/common/rvctools/common/rvcpath.m | 1,137 | utf_8 | f1b242a7ae5a23962546beba760c6ae1 | %RVCPATH Install location of RVC tools
%
% p = RVCPATH is the path of the top level folder for the installed RVC
% tools.
%
% p = RVCPATH(FOLDER) is the full path of the specified FOLDER which is relative to the
% installed RVC tools.
%
% Copyright (C) 1993-2017, by Peter I. Corke
%
% This file is part of The Robotics... |
github | RobinAmsters/GT_mobile_robotics-master | mmlabel.m | .m | GT_mobile_robotics-master/common/rvctools/common/mmlabel.m | 1,458 | utf_8 | 6bb8d41bb64488913bcc3a1ae684d829 | %MMLABEL labels for mplot style graph
%
% mmlabel({lab1 lab2 lab3})
%
% Notes::
% - was previously (rev 9) named mlabel() but changed to avoid clash with the
% Mapping Toolbox.
% Copyright (C) 1993-2017, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you... |
github | RobinAmsters/GT_mobile_robotics-master | plotp.m | .m | GT_mobile_robotics-master/common/rvctools/common/plotp.m | 1,573 | utf_8 | 820ce5525622153db6f6a564377af983 | %PLOTP Plot trajectory
%
% Convenience function to plot points stored columnwise.
%
% PLOTP(P) plots a set of points P, which by Toolbox convention are stored
% one per column. P can be 2xN or 3xN. By default a linestyle of 'bx'
% is used.
%
% PLOTP(P, LS) as above but the line style arguments LS are passed to plot.
... |
github | RobinAmsters/GT_mobile_robotics-master | mtools.m | .m | GT_mobile_robotics-master/common/rvctools/common/mtools.m | 1,847 | utf_8 | bc44bea8453c76d09fb4f2c4fee81dbd | %MTOOLS add simple/useful tools to all windows in figure
%
% Copyright (C) 1993-2017, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the F... |
github | RobinAmsters/GT_mobile_robotics-master | bresenham.m | .m | GT_mobile_robotics-master/common/rvctools/common/bresenham.m | 3,069 | utf_8 | 433dcd2346920722c37d9bbed814dbe8 | %BRESENHAM Generate a line
%
% P = BRESENHAM(X1, Y1, X2, Y2) is a list of integer coordinates (2xN) for
% points lying on the line segment joining the integer coordinates (X1,Y1)
% and (X2,Y2).
%
% P = BRESENHAM(P1, P2) as above but P1=[X1; Y1] and P2=[X2; Y2].
%
% Notes::
% - Endpoint coordinates must be integer value... |
github | RobinAmsters/GT_mobile_robotics-master | gaussfunc.m | .m | GT_mobile_robotics-master/common/rvctools/common/gaussfunc.m | 2,159 | utf_8 | ce83ce3f5a887482ea5ec66c52e5f2d7 | %GAUSSFUNC Gaussian kernel
%
% G = GAUSSFUNC(MEAN, VARIANCE, X) is the value of the normal
% distribution (Gaussian) function with MEAN (1x1) and VARIANCE (1x1), at
% the point X.
%
% G = GAUSSFUNC(MEAN, COVARIANCE, X, Y) is the value of the bivariate
% normal distribution (Gaussian) function with MEAN (1x2) and COVA... |
github | RobinAmsters/GT_mobile_robotics-master | PluckerTest.m | .m | GT_mobile_robotics-master/common/rvctools/common/unit_test/PluckerTest.m | 790 | utf_8 | 2ba1dac6e1882a015dc1371045aa13bd |
function tests = PluckerTest
tests = functiontests(localfunctions);
end
function constructor_test(tc)
end
function methods_test(tc)
% intersection
px = Plucker([0 0 0], [1 0 0]); % x-axis
py = Plucker([0 0 0], [0 1 0]); % y-axis
px1 = Plucker([0 1 0], [1 1 0]); % offset x-axis
verify... |
github | RobinAmsters/GT_mobile_robotics-master | plotXTest.m | .m | GT_mobile_robotics-master/common/rvctools/common/unit_test/plotXTest.m | 10,075 | utf_8 | 5e0c1a61709cdd8015d7777373ef326a | % 2d outline, filled case
% 3d outlien, filled case
% with LS or edgecolor, color options etc.
function tests = plotXTest
tests = functiontests(localfunctions);
close all
end
function teardownOnce(tc)
close all
end
function plotpoint_test(tc)
% simple
points = rand(2,5);
clf; plo... |
github | RobinAmsters/GT_mobile_robotics-master | tboptparseTest.m | .m | GT_mobile_robotics-master/common/rvctools/common/unit_test/tboptparseTest.m | 8,306 | utf_8 | 7663089541e7d9d2c91c64363ab51eb9 | function tests = tboptparseTest()
tests = functiontests(localfunctions);
end
function setupOnce(tc)
opt.foo = false;
opt.bar = true;
opt.blah = [];
opt.stuff = {};
opt.choose = {'this', 'that', 'other'};
opt.select = {'#no', '#yes'};
opt.old = '@foo';
opt.d_3d = false;
tc.TestData.opt = ... |
github | RobinAmsters/GT_mobile_robotics-master | wtrans.m | .m | GT_mobile_robotics-master/common/rvctools/robot/wtrans.m | 1,393 | utf_8 | b7605b6674672f58fa44fff0ff9f444b | %WTRANS Transform a wrench between coordinate frames
%
% WT = WTRANS(T, W) is a wrench (6x1) in the frame represented by the homogeneous
% transform T (4x4) corresponding to the world frame wrench W (6x1).
%
% The wrenches W and WT are 6-vectors of the form [Fx Fy Fz Mx My Mz]'.
%
% See also TR2DELTA, TR2JAC.
% Co... |
github | RobinAmsters/GT_mobile_robotics-master | mtraj.m | .m | GT_mobile_robotics-master/common/rvctools/robot/mtraj.m | 3,306 | utf_8 | 3b82474dbf11dba63f8278f8dc1b72f9 | %MTRAJ Multi-axis trajectory between two points
%
% [Q,QD,QDD] = MTRAJ(TFUNC, Q0, QF, M) is a multi-axis trajectory (MxN) varying
% from configuration Q0 (1xN) to QF (1xN) according to the scalar trajectory function
% TFUNC in M steps. Joint velocity and acceleration can be optionally returned as
% QD (MxN) and QDD (... |
github | RobinAmsters/GT_mobile_robotics-master | lspb.m | .m | GT_mobile_robotics-master/common/rvctools/robot/lspb.m | 5,386 | utf_8 | fbf1346e01e601dd3d554501c4367a67 | %LSPB Linear segment with parabolic blend
%
% [S,SD,SDD] = LSPB(S0, SF, M) is a scalar trajectory (Mx1) that varies
% smoothly from S0 to SF in M steps using a constant velocity segment and
% parabolic blends (a trapezoidal velocity profile). Velocity and
% acceleration can be optionally returned as SD (Mx1) and SDD ... |
github | RobinAmsters/GT_mobile_robotics-master | qplot.m | .m | GT_mobile_robotics-master/common/rvctools/robot/qplot.m | 1,557 | utf_8 | 585f70da4e5c4f31e64f8f5b54973326 | %QPLOT Plot robot joint angles
%
% QPLOT(Q) is a convenience function to plot joint angle trajectories (Mx6) for
% a 6-axis robot, where each row represents one time step.
%
% The first three joints are shown as solid lines, the last three joints (wrist)
% are shown as dashed lines. A legend is also displayed.
%
% QP... |
github | RobinAmsters/GT_mobile_robotics-master | distributeblocks.m | .m | GT_mobile_robotics-master/common/rvctools/robot/distributeblocks.m | 2,689 | utf_8 | 02d506ec479136e1e31aa0bc8d7e55e2 | %DISTRIBUTEBLOCKS Distribute blocks in Simulink block library
%
% distributeBlocks(MODEL) equidistantly distributes blocks in a Simulink
% block library named MODEL.
%
% Notes::
% - The MATLAB functions to create Simulink blocks from symbolic
% expresssions actually place all blocks on top of each other. This... |
github | RobinAmsters/GT_mobile_robotics-master | ccodefunctionstring.m | .m | GT_mobile_robotics-master/common/rvctools/robot/ccodefunctionstring.m | 7,927 | utf_8 | cfe55f9ddbe99a594495d3620bfe4dd3 | %CCODEFUNCTIONSTRING Converts a symbolic expression into a C-code function
%
% [FUNSTR, HDRSTR] = ccodefunctionstring(SYMEXPR, ARGLIST) returns a string
% representing a C-code implementation of a symbolic expression SYMEXPR.
% The C-code implementation has a signature of the form:
%
% void funname(double... |
github | RobinAmsters/GT_mobile_robotics-master | PoseGraph.m | .m | GT_mobile_robotics-master/common/rvctools/robot/PoseGraph.m | 19,934 | utf_8 | fb33eb8e5b89b2304d1d87c7ccfcae17 | %PoseGraph Pose graph
% Copyright (C) 1993-2017, by Peter I. Corke
%
% This file is part of The Robotics Toolbox for MATLAB (RTB).
%
% RTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either versi... |
github | RobinAmsters/GT_mobile_robotics-master | jsingu.m | .m | GT_mobile_robotics-master/common/rvctools/robot/jsingu.m | 1,404 | utf_8 | be339e3f968bb5c9a77a957fdf69e028 | %JSINGU Show the linearly dependent joints in a Jacobian matrix
%
% JSINGU(J) displays the linear dependency of joints in a Jacobian matrix.
% This dependency indicates joint axes that are aligned and causes singularity.
%
% See also SerialLink.jacobn.
% Copyright (C) 1993-2017, by Peter I. Corke
%
% This file is pa... |
github | RobinAmsters/GT_mobile_robotics-master | ReedsShepp.m | .m | GT_mobile_robotics-master/common/rvctools/robot/ReedsShepp.m | 9,770 | utf_8 | 67ddf6852b22805e223af77aad5a75b0 | % Reeds Shepp path planner sample code
%
% based on python code from Python Robotics by Atsushi Sakai(@Atsushi_twi)
%
% Peter 3/18
%
% Finds the shortest path between 2 configurations:
% - robot can move forward or backward
% - the robot turns at zero or maximum curvature
% - there are discontinuities in velocity and s... |
github | RobinAmsters/GT_mobile_robotics-master | purepursuit.m | .m | GT_mobile_robotics-master/common/rvctools/robot/purepursuit.m | 2,081 | utf_8 | 2e538c81d09c6a5cac6d21d0139799dc | %PUREPURSUIT Find pure pursuit goal
%
% P = PUREPURSUIT(CP, R, PATH) is the current pursuit point (2x1) for a robot at
% location CP (2x1) following a PATH (Nx2). The pursuit point is the
% closest point along the path that is a distance >= R from the current
% point CP.
%
% Reference::
% - A review of some pure-pursu... |
github | RobinAmsters/GT_mobile_robotics-master | joy2tr.m | .m | GT_mobile_robotics-master/common/rvctools/robot/joy2tr.m | 2,953 | utf_8 | fb046603cd51e0ad017bba56b43b5b8c | %JOY2TR Update transform from joystick
%
% T = JOY2TR(T, OPTIONS) updates the SE(3) homogeneous transform (4x4)
% according to spatial velocities sourced from a connected joystick device.
%
% Options::
% 'delay',D Pause for D seconds after reading (default 0.1)
% 'scale',S A 2-vector which scales joystick trans... |
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