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 | RainerKuemmerle/csm-master | transform.m | .m | csm-master/misc/matlab/utils/transform.m | 111 | utf_8 | 7c4dd17b08b70e02c32d6539c6c6f34e |
function point = transform(point, dx)
% rotate then translate point
point = dx(1:2,1) + rot(dx(3)) * point;
|
github | RainerKuemmerle/csm-master | params_required.m | .m | csm-master/misc/matlab/utils/params_required.m | 134 | utf_8 | 4f867ee4f0344f566e5136387f62cef2 |
function params_required(p, field)
if not(isfield(p, field))
error('icp:bad_paramater',sprintf('I need field %s.', field));
end
|
github | RainerKuemmerle/csm-master | computeSurfaceNormals_sound.m | .m | csm-master/misc/matlab/orientation/computeSurfaceNormals_sound.m | 2,260 | utf_8 | 6f801919e308b4926f530d0660263858 | % params.sigma
% params.curv
% params.max_points
% params.threshold
% params.min_dist
function ld = computeSurfaceNormals_sound(ld)
params.max_points=10;
params.threshold=2;
params.min_dist = 0.24;
params.curv = 0;
params.sigma = 0.01;
curv = params.curv;
var = params.sigma ^ 2;
MAX = params.max_points;
MIN_... |
github | RainerKuemmerle/csm-master | ld_compute_orientation.m | .m | csm-master/misc/matlab/orientation/ld_compute_orientation.m | 2,643 | utf_8 | 07b9d3d30105bac8c50459d63e878005 | % params.sigma
% params.curv
% params.max_points
% params.threshold
% params.min_dist
function ld = ld_compute_orientation(ld)
params.max_points=10;
params.threshold=2;
params.min_dist = 0.24;
params.curv = 0;
params.sigma = 0.01;
curv = params.curv;
var = params.sigma ^ 2;
MAX = params.max_points;
MIN_DIST ... |
github | RainerKuemmerle/csm-master | regression.m | .m | csm-master/misc/matlab/orientation/regression.m | 712 | utf_8 | cb930de21c4ba2092b721d73317be6d5 |
function [theta, rho, sumOfSqError] = regression(points)
n = size(points,2);
if n<2
error('I need at least 2 points');
end
mu = mean(points,2);
s_x2 = 0;
s_y2 = 0;
s_xy = 0;
for i=1:n
s_x2 = s_x2 + (points(1,i)-mu(1))*(points(1,i)-mu(1));
s_y2 = s_y2 + (points(2,i)-mu(2))*(points(2,i)-mu(2));
s_... |
github | RainerKuemmerle/csm-master | test_est_deriv_log.m | .m | csm-master/misc/matlab/orientation/test_est_deriv_log.m | 1,764 | utf_8 | d53cb62b9364158b6dea768a12bf7a62 | function sd = test_est_deriv_log(k)
sigma=0.02;
if true
S=load('logs/bighouse_half.mat');
log = S.log_bighouse_half;
sd = log{k}
sd.readings = sd.readings + sigma * randn(1,sd.nrays);
sd.points = [sd.readings .* cos(sd.theta); sd.readings .* sin(sd.theta)];
end
%sd = straight(150, pi*0.5, 5, sigma);
sd = ci... |
github | RainerKuemmerle/csm-master | computeSurfaceNormals.m | .m | csm-master/misc/matlab/orientation/computeSurfaceNormals.m | 1,063 | utf_8 | 2af0d22a72596ab882008378b92463e7 | function ld = computeSurfaceNormals(ld, maxDist)
n = size(ld.points,2);
for i=1:n
% consider all points in a ball of radius maxDist
imin=i; stop=0;
while stop == 0 & imin>1
if norm(ld.points(:,i)-ld.points(:,imin-1)) < maxDist
imin = imin -1;
else
stop=1;
end
end
imax=i; stop=0;
while stop==0 &... |
github | RainerKuemmerle/csm-master | test_lag.m | .m | csm-master/misc/matlab/unsorted/test_lag.m | 3,665 | utf_8 | c1c5b826960106b806e7411c8a446aad | function test_lag
t_true = randn(2,1)*4;
theta_true = deg2rad(rand*90-45);
theta_true = deg2rad(170);
pose_true = [t_true; theta_true];
x_true = [t_true; cos(theta_true); sin(theta_true)];
N=400
points1 = rand(2,N);
%points1 = [-1 -1; -1 1; 1 1; 1 -1]';
points2 = rot(theta_true) * points1 + repmat(t_true,... |
github | RainerKuemmerle/csm-master | simpleLowPassFilter.m | .m | csm-master/misc/matlab/unsorted/simpleLowPassFilter.m | 182 | utf_8 | ba4d0e0796f5574e7d8f82f57bffbc82 | % Simple low-pass filter with a gaussian mask
function b = simpleLowPassFilter(a, sigma)
amp=2*sigma; filter=exp(-((-amp:amp).^2)/sigma^2);
b = normalize(convn(a,filter,'same'));
|
github | RainerKuemmerle/csm-master | test_yasmine2.m | .m | csm-master/misc/matlab/unsorted/test_yasmine2.m | 1,349 | utf_8 | d83e461a04710e71f12dc402c352784a | function res = test_yasmine2(k)
%x_true = [1;-2;deg2rad(-10.5)];
%x_true = [0.2;0;deg2rad(5.5)];
%ts = ts_square(8, [1;-4;0], x_true);
%ts = ts_flower(4, 1, 10, [0;0;0], x_true);
x_true = [1;0;0];
ts = ts_flower(4, .5, 10, [0;0;0], x_true);
sigma=0.005;
odometry_cov = diag( [0.2 0.2 deg2rad(5)].^2 );
ts.la... |
github | RainerKuemmerle/csm-master | test_yasmine.m | .m | csm-master/misc/matlab/unsorted/test_yasmine.m | 700 | utf_8 | 26a088946a494b54f150a20c561b1c39 | function res = test_yasmine(k)
sigma=0.00001;
S=load('logs/bighouse_half.mat');
log = S.log_bighouse_half;
params.laser_ref = log{k};
params.laser_ref = add_noise(params.laser_ref, sigma);
params.laser_sens = log{k};
params.laser_sens = add_noise(params.laser_sens, sigma);
params.maxAngularCorrectionDeg = 15;... |
github | RainerKuemmerle/csm-master | simpleLowPassFilter2.m | .m | csm-master/misc/matlab/unsorted/simpleLowPassFilter2.m | 280 | utf_8 | fd5f3b6243cb36d47306c65e53e95bcd | % Simple low-pass filter with a gaussian mask (bidimensional version)
function b = simpleLowPassFilter2(a, sigma)
amp=2*sigma+1;
mask=zeros(amp,amp);
for i=1:amp
for j=1:amp
mask(i,j) = exp(-((i-sigma-1)^2+(j-sigma-1)^2)/sigma^2);
end
end
b = conv2(a,mask,'same');
|
github | RainerKuemmerle/csm-master | simpleLowPassFilterCircular.m | .m | csm-master/misc/matlab/unsorted/simpleLowPassFilterCircular.m | 250 | utf_8 | fb9af65e6b59b971305e91ca2d51ac15 | % Simple low-pass filter with a gaussian mask, for a circular buffer
function b = simpleLowPassFilterCircular(a, sigma)
extended=[a a a];
extendedb=simpleLowPassFilter(extended,sigma);
b = extendedb(size(a,2)+1:end-size(a,2));
b = normalize(b);
|
github | RainerKuemmerle/csm-master | test_yasmine3.m | .m | csm-master/misc/matlab/unsorted/test_yasmine3.m | 1,081 | utf_8 | e341dbe5d31a47593e1ae622478c4785 | function res = test_yasmine3(k)
% systematic experiments
x_true = [1;0;deg2rad(-10.5)];
ts0 = ts_square(8, [1;-2;deg2rad(10)], x_true );
% ts0 = ts_flower(8, 2, 10, [0;0;0], x_true);
sigma=0.005;
odometry_cov = diag( [0.2 0.2 deg2rad(5)].^2 );
odometry_cov = diag( [0.5 0.5 deg2rad(5)].^2 );
N = 50;
for i=1... |
github | RainerKuemmerle/csm-master | normalize.m | .m | csm-master/misc/matlab/unsorted/normalize.m | 146 | utf_8 | 58697d1fdb5bef9adcf105d921b38e31 | % Normalizes a vector in the [0,1] range
function b = normalize(a)
minimum = min(a);
maximum = max(a);
b = (a - minimum) / (maximum-minimum);
|
github | RainerKuemmerle/csm-master | test_boh.m | .m | csm-master/misc/matlab/unsorted/test_boh.m | 260 | utf_8 | 91ff5da74bc4dcc7ded57ebdc6c0a9e9 | function f = test_boh
th=0:0.001:2*pi;
G = [.5 4; 0 .5]; rand(2,2);
k = 100*rand(2,1);
k =[-1;0];
for i=1:size(th,2)
f(i) = vers(th(i))' * G * vers(th(i)) + k'*vers(th(i));
end
fi=figure;
plot(rad2deg(th),f);
function v = vers(t)
v=[cos(t);sin(t)];
|
github | RainerKuemmerle/csm-master | icp2.m | .m | csm-master/misc/matlab/point-to-line/icp2.m | 5,629 | utf_8 | a72fd8e45caf70012809b2e0ed9f3050 | % Dependences of this script:
% Requires: params_required, params_set_default, icp_get_correspondences
% Requires: ld_plot, icp_covariance, exact_minimization, transform
% Requires: icp_possible_interval
function res = icp2(params)
% Note: it is assumed that params.laser_ref has a radial uniform scan
% params.laser_r... |
github | RainerKuemmerle/csm-master | general_minimization.m | .m | csm-master/misc/matlab/point-to-line/general_minimization.m | 2,800 | utf_8 | 576362b9f96bda81ee4271d7443e08d8 |
function res = general_minimization(corr)
% Input is:
% corr{k}.C
% corr{k}.p
% corr{k}.q
%% First we put the problem in a quadratic+constraint form.
M = zeros(4,4);
g = zeros(4,1);
for k=1:size(corr,2)
M_k = [eye(2) [corr{k}.p (rot(pi/2)*corr{k}.p)]];
M = M + M_k'* corr{k}.C *M_k;
g = g + (- ... |
github | RainerKuemmerle/csm-master | ray_tracing_polygon.m | .m | csm-master/misc/matlab/ray_tracing/ray_tracing_polygon.m | 2,197 | utf_8 | 1d31126a05d2198186898b1fd96e9c92 | % polylist = { [p0;p1], [p1;p2] }
function ld = ray_tracing_polygon(seglist, pose, nrays, fov, max_reading)
t = pose(1:2);
ld.nrays = nrays;
for i=1:nrays
theta = -fov/2 + fov * (i-1)/(nrays-1);
ld.theta(i) = theta;
[valid, reading, alpha] = try_segment_list(seglist, pose(1:2), pose(3)+theta, max_reading);
... |
github | RainerKuemmerle/csm-master | countour_sine.m | .m | csm-master/misc/matlab/ray_tracing/countour_sine.m | 579 | utf_8 | dafc63cd53c42718a519304af8e2ca8c | function [p, alpha] = countour_sine(params, tau)
% params = {rho, amplitude, periods_in_circle}
rho = params{1};
amplitude = params{2};
periods = params{3};
theta = 2 * pi * tau;
p = sine(theta,rho,amplitude, periods);
epsilon = 0.001;
p1 = sine(theta-epsilon,rho,amplitude, periods);
p2 = sine(theta+eps... |
github | RainerKuemmerle/csm-master | ld_plot.m | .m | csm-master/misc/matlab/vis/ld_plot.m | 3,432 | utf_8 | d5e951046a1190736db0b0103f9d2e79 |
function res = ld_plot(ld, params)
% plotLaserData(ld, params)
% Draws on current figure
%
% params.plotNormals = false;
% params.color = 'r.';
% params.rototranslated (= true); if true, the scan is drawn
% rototranslated at ld.estimate, else is drawn at 0;
% params.rototranstated_odometry = false;
%
% ld... |
github | RainerKuemmerle/csm-master | plot_yasmine.m | .m | csm-master/misc/matlab/vis/plot_yasmine.m | 551 | utf_8 | e02893e1e03265100a80459434ff39b1 | function plot_yasmine(f, mmin, mmax)
length = 0.5;
hold on
% number of correspondences
Ktot = size(f.result.s, 2);
drawn=0;
for k=1:Ktot
% if f.result.s{k}.m > mmax
% continue
% end
drawn = drawn + 1;
T = f.result.s{k}.T;
alpha = f.result.s{k}.alpha;
p1 = T + vers(alpha-pi/2) * length... |
github | RainerKuemmerle/csm-master | plotGPM1.m | .m | csm-master/misc/matlab/vis/plotGPM1.m | 426 | utf_8 | aa35dc035649a02d1b81f8238fac7575 | function plotGPM1(result)
hold on
n = size(result.T, 2);
for i=1:n
if result.weight(i) < 0.8
continue
end
T = result.T(:, i);
phi = result.phi(i);
gamma = result.alpha(i);
length = 0.5;
p1 = T + vers(gamma-pi/2) * length;
p2 = T + vers(gamma+pi/2) * length;
plot([p1(1) p2(1)],[p1(2)... |
github | RainerKuemmerle/csm-master | plotVectors.m | .m | csm-master/misc/matlab/vis/plotVectors.m | 334 | utf_8 | 4eef705a834d20f1036b9889eab47cbc |
function handle = plotVectors(ref, points, color)
% TODO: add check on dimensions
t = ref(1:2);
points2 = rot(ref(3)) * points;
%size(points)
%t
%repmat(t,1,size(points,2))
%size(repmat(t,1,size(points,2)))
%size(points2)
points2 = points2 + repmat(t,1,size(points,2));
handle = plot(points2(1,:),points2(... |
github | RainerKuemmerle/csm-master | plotGauss2Db.m | .m | csm-master/misc/matlab/vis/plotGauss2Db.m | 809 | utf_8 | 297ae5444e2a6189391fecb726edc9a7 | % Non disegna il punto
function res = plotGauss2Db(mu,sigma,color)
if (size(mu,2) ~= 1)
mu = mu';
if (size(mu,2) ~= 1)
error('mu deve essere un vettore');
end
end
if (size(mu,2) ~= 1)
error('mu deve essere un vettore bidimensionale');
end
if (size(sigma,1) ~= size(sigma,2))
error('sigma dev... |
github | RainerKuemmerle/csm-master | plot_xyt.m | .m | csm-master/misc/matlab/vis/plot_xyt.m | 2,870 | utf_8 | 504d5989b2eeaf4fc4361d9c5392cebf |
function fs = plot_xyt(m)
p.datasets = m.datasets;
p.covariances = m.covariances;
p.scale = diag([1000 1000 180/pi]);
p.format = m.format;
p.legend = m.legend;
p.use_legend = true;
p.select = [1 0 0; 0 1 0];;
p.equal = true;
p.xlabel = 'x (mm)';
p.ylabel = 'y (mm)';
p.title = sprintf('... |
github | RainerKuemmerle/csm-master | test_gpm.m | .m | csm-master/misc/matlab/gpm/test_gpm.m | 7,502 | utf_8 | bf350cea6ce1d6365c7e81c8f668be4d | function res = gpm(params)
% params.laser_ref The #first scan $\frac{pi}{4}$#
% params.laser_sens
% params.maxAngularCorrectionDeg
% params.maxLinearCorrection
% params.sigma
params_required(params, 'laser_sens');
params_required(params, 'laser_ref');
params = params_set_default(params, 'maxAngularCorrectionDeg',... |
github | RainerKuemmerle/csm-master | gpm_plot_res.m | .m | csm-master/misc/matlab/gpm/gpm_plot_res.m | 577 | utf_8 | 07128912a3a55bd1f9e9d0b6ebc98219 | function gpm_plot_res(res, mmin, mmax)
side = 2;
length = sqrt(2)*side;
hold on
% number of correspondences
Ktot = size(res.corr, 2);
drawn=0;
for k=1:Ktot
drawn = drawn + 1;
T = res.corr{k}.T;
alpha = res.corr{k}.alpha;
p1 = T + vers(alpha-pi/2) * length * 0.5;
p2 = T + vers(alpha+pi/... |
github | RainerKuemmerle/csm-master | gpmSample.m | .m | csm-master/misc/matlab/gpm/gpmSample.m | 749 | utf_8 | c8c1b703ea12c93857f37a9fef215507 | function res = gpmSample(gpmres,n)
m = size(gpmres.T, 2);
prepared = prepareSampling(1:m, gpmres.weight);
fprintf('Prepared\n');
i=1;
while i<n
i1 = sampleIndex(prepared);
i2 = sampleIndex(prepared);
alpha1 = gpmres.alpha(i1);
alpha2 = gpmres.alpha(i2);
rho1 = vers(alpha1)' * gpmres.T(:,i1);
rho2 = vers(alph... |
github | RainerKuemmerle/csm-master | yasmine2.m | .m | csm-master/misc/matlab/gpm/yasmine2.m | 5,481 | utf_8 | 4ee39b82b2f3eb8bd32b1af9fcdb3ec3 | % params.sigma
% params.laser_sens % sensor (indexed by i)
% params.laser_ref % map (indexed by j) laser_sens * (x,y,theta) = laser_ref
% params.maxAngularCorreectionDeg
% params.maxLinearCorreection
function res = yasmine2(params)
% readings for which a valid alpha was computed
valid1 = find(params.l... |
github | RainerKuemmerle/csm-master | yasmine.m | .m | csm-master/misc/matlab/gpm/yasmine.m | 4,184 | utf_8 | 5d797238afb553a0ecfd91dcfd1385a1 | function res = yasmine(params)
% params.sigma
% params.laser_sens % sensor (indexed by i)
% params.laser_ref % map (indexed by j) laser_sens * (x,y,theta) = laser_ref
% params.maxAngularCorrectionDeg
% params.maxLinearCorrection
% readings for which a valid alpha was computed
valid1 = find(params.laser... |
github | RainerKuemmerle/csm-master | gpm.m | .m | csm-master/misc/matlab/gpm/gpm.m | 7,528 | utf_8 | 51f279ee68a3f3b982009b8042d3e738 | function res = gpm(params)
% params.laser_ref The #first scan $\frac{pi}{4}$#
% params.laser_sens
% params.maxAngularCorrectionDeg
% params.maxLinearCorrection
% params.sigma
params_required(params, 'laser_sens');
params_required(params, 'laser_ref');
params = params_set_default(params, 'maxAngularCorrectionDeg',... |
github | RainerKuemmerle/csm-master | reading.m | .m | csm-master/misc/matlab/carmen/reading.m | 1,449 | utf_8 | 8faf5be586ad8bd19902eed7937b9bc3 | % I got this code from Matlab file exchange
% http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=7933&objectType=file
% Author: John McArthur (University of Southern California)
% his email: johnnyfisma@hotmail.com
% http://www.mathworks.com/matlabcentral/fileexchange/loadAuthor.do?objectType=auth... |
github | RainerKuemmerle/csm-master | readFileInCells.m | .m | csm-master/misc/matlab/carmen/readFileInCells.m | 1,449 | utf_8 | 8faf5be586ad8bd19902eed7937b9bc3 | % I got this code from Matlab file exchange
% http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=7933&objectType=file
% Author: John McArthur (University of Southern California)
% his email: johnnyfisma@hotmail.com
% http://www.mathworks.com/matlabcentral/fileexchange/loadAuthor.do?objectType=auth... |
github | RainerKuemmerle/csm-master | icp_covariance.m | .m | csm-master/misc/matlab/icp/icp_covariance.m | 4,949 | utf_8 | def49c0a67b41fa4369dd22c2d28104e |
function res = icp_covariance(params, current_estimate, P, valids, jindexes)
% res = icp_covariance(params, current_estimate, P, valids, jindexes)
%
% Compute the covariance of the ICP estimate.
%
% params.laser_sens laser data structure describing sensor scan
% params.laser_ref laser data structure describi... |
github | RainerKuemmerle/csm-master | exact_minimization.m | .m | csm-master/misc/matlab/icp/exact_minimization.m | 886 | utf_8 | db1c7c2f05bbaa677fc759bacb23c256 | function [pose, L, Y] = exact_minimization(points1, points2)
[L,Y] = create_system(points1,points2);
B = L'*Y;
x1 = B(1:2);
x2 = B(3:4);
A = (L'*L);
lB = A(1:2,3:4);
bt=lB'*lB;
b=bt(1,1);
% closed form solution
n = A(1,1);
c = A(3,3);
pol = [ (b* x1'*x1+n^2 * x2'*x2 -2*n*x1'*lB*x2) 0 (-1)];
r = roo... |
github | RainerKuemmerle/csm-master | closest_point_on_segment.m | .m | csm-master/misc/matlab/icp/closest_point_on_segment.m | 1,203 | utf_8 | 52aaad8975058eacb5eb89e67a67711d |
function res = closest_point_on_segment(A,B,p)
% closest_point_on_segment(A,B,p)
% find closest point to p on segment A-B
projection = projection_on_line_seg(A,B,p);
% fprintf('Closest(%s,%s;%s)\n', pv(A), pv(B), pv(p));
% fprintf(' projection: \n', pv(projection));
% fprintf('A: %s B: %s p: %s proj: %s\n',pv(A... |
github | RainerKuemmerle/csm-master | icp_get_correspondences.m | .m | csm-master/misc/matlab/icp/icp_get_correspondences.m | 1,889 | utf_8 | 9a9d5af59d2b6b41c6cc4b4efb1ba2d9 |
function [P,valid,jindexes] = icp_get_correspondences(params,current_estimate)
debug = 0;
for i=1:params.laser_sens.nrays
p_i = params.laser_sens.points(:,i);
p_i_w = transform(p_i, current_estimate);
[from, to] = icp_possible_interval(p_i_w, params.laser_ref,...
params.maxAngularCorrectionDeg, params.maxL... |
github | RainerKuemmerle/csm-master | icp_possible_interval.m | .m | csm-master/misc/matlab/icp/icp_possible_interval.m | 674 | utf_8 | c2046464cdc6b851dd531d50048c5d06 |
function [from, to] = icp_possible_interval(P, ld, maxAngularCorrectionDeg, maxLinearCorrection)
debug = 0;
delta = abs(deg2rad(maxAngularCorrectionDeg)) + ...
abs(atan(maxLinearCorrection/norm(P)));
n = ld.nrays;
fov = abs(ld.theta(n)-ld.theta(1));
angleRes = fov/n;
start_theta = atan2(P(2),P(1));... |
github | RainerKuemmerle/csm-master | icp.m | .m | csm-master/misc/matlab/icp/icp.m | 3,949 | utf_8 | 4e244ebad147a7618b905090fcc74678 | % Dependences of this script:
% Requires: params_required, params_set_default, icp_get_correspondences
% Requires: ld_plot, icp_covariance, exact_minimization, transform
% Requires: icp_possible_interval
function res = icp(params)
% Note: it is assumed that params.laser_ref has a radial uniform scan
% params.laser_re... |
github | RainerKuemmerle/csm-master | derivn.m | .m | csm-master/misc/matlab/icpcov/derivn.m | 250 | utf_8 | 4b60d67a9483aa7ce5977a8983e0b9ae | %% Numerical derivation with step \verb|epsilon|
function res = deriv(fh, x, epsilon)
% deriv(fh, x, epsilon)
% fh: function handle
% x: point to derive
% epsilon: interval
f1 = fh(x+epsilon/2);
f0 = fh(x-epsilon/2);
res= (f1-f0)/epsilon;
|
github | RainerKuemmerle/csm-master | test_unstructured.m | .m | csm-master/misc/matlab/cramer_rao/test_unstructured.m | 3,198 | utf_8 | 9fab76136b8283fc53fe8a8d7f18b04c | function test_unstructured(res)
% analyzes result of the test_unstructured
rho = 5;
amp = 0.2;
N = 30;
nrays = 181;
fov = pi;
ld2 = ld_sine(rho, amp, N, [0;0;0], nrays, fov);
crb = compute_bounds(ld2);
eig(crb.I0)
% C of the f function in ld_sine
C = amp^2 * N^2 / (2 * rho^2);
D = amp^2 * N^4 /... |
github | RainerKuemmerle/csm-master | plot_bounds.m | .m | csm-master/misc/matlab/cramer_rao/plot_bounds.m | 1,467 | utf_8 | 2a889c6272ae23901ba6705fe62c07c8 | function f = plot_bound(res)
prefix = 'tbs'
s = size(res.etmin,2);
if false
res.eth(1,1) = 0;
res.eth(size(res.etmin,1)-3,size(res.etmin,2)-3) = 0;
res.eth(1,size(res.etmin,2)-10) = 0;
end
f= figure
subplot(1,5,1)
etmax = good_direction(res.etmax);
writeToFile(etmax,strcat(prefix, '_max.png'));
imagesc(etmax)
AXIS... |
github | RainerKuemmerle/csm-master | compute_bounds.m | .m | csm-master/misc/matlab/cramer_rao/compute_bounds.m | 824 | utf_8 | c26a223a47660e8c6a4b0105e4291911 | function bounds = compute_bounds(ld)
% bounds = compute_bounds(ld)
% ld: laserdata
% return bounds.I0
% bounds.C0 if I0 is invertible
I0 = zeros(3,3);
for i=1:ld.nrays
r = ld.readings(i);
alpha_i = ld.true_alpha_abs(i);
if isnan(alpha_i) | isnan(r)
continue;
end
phi_i = ld.theta(i);
theta = ld... |
github | usnistgov/REFPROP-wrappers-master | refpropm.m | .m | REFPROP-wrappers-master/wrappers/MATLAB/legacy/refpropm.m | 34,585 | utf_8 | c2bf972de3370aa8acbfc97c6347e43b | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% refpropm Thermophysical properties of pure substances and mixtures.
% Calling sequence for pure substances:
% result=refpropm(prop_req, spec1, value1, spec2, value2, substance1)
%
% Calling predefined mixtures:
% result=r... |
github | AllenXL/Image_Video_Processing-Matlab-master | readKLTFeatureList.m | .m | Image_Video_Processing-Matlab-master/KLT Tracking/readKLTFeatureList.m | 1,581 | utf_8 | 5f9bceaa51b141e4f821908263d5c081 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PROJECT:
% KLT Tracker
%
% BY:
% Parthipan Siva
% Assignment for SD 770-7: Topics in Particle Filtering
% Systems Design Engineering
% University of Waterloo
%
% DATE/Version:
% Jan. 2007 - V 1.0
%
% Description: read... |
github | AllenXL/Image_Video_Processing-Matlab-master | kltTrack.m | .m | Image_Video_Processing-Matlab-master/KLT Tracking/kltTrack.m | 15,062 | utf_8 | 412fe8a1894f177d57d2f96bfe89fdf7 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PROJECT:
% KLT Tracker
%
% BY:
% Parthipan Siva
% Assignment for SD 770-7: Topics in Particle Filtering
% Systems Design Engineering
% University of Waterloo
%
% DATE/Version:
% Jan. 2007 - V 1.0
%
% Description: kltT... |
github | AllenXL/Image_Video_Processing-Matlab-master | kltTrackSIMPLE.m | .m | Image_Video_Processing-Matlab-master/KLT Tracking/kltTrackSIMPLE.m | 6,942 | utf_8 | dc843e48c9ef7bb4fc8255e7b4723b68 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PROJECT:
% KLT Tracker
%
% BY:
% Parthipan Siva
% Assignment for SD 770-7: Topics in Particle Filtering
% Systems Design Engineering
% University of Waterloo
%
% DATE/Version:
% Jan. 2007 - V 1.0
%
% Description: kltT... |
github | AllenXL/Image_Video_Processing-Matlab-master | drawEllipse.m | .m | Image_Video_Processing-Matlab-master/KLT Tracking/drawEllipse.m | 1,651 | utf_8 | 2ad4ceccbbc2adce995ba39c12e21019 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PROJECT:
% KLT Tracker
%
% BY:
% Parthipan Siva
% Assignment for SD 770-7: Topics in Particle Filtering
% Systems Design Engineering
% University of Waterloo
%
% DATE/Version:
% Jan. 2007 - V 1.0
%
% Description: draw... |
github | AllenXL/Image_Video_Processing-Matlab-master | InsidePolygon.m | .m | Image_Video_Processing-Matlab-master/KLT Tracking/InsidePolygon.m | 1,776 | utf_8 | d621c96da29d719fba7765400455ed0a | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PROJECT:
% KLT Tracker
%
% BY:
% Parthipan Siva
% Assignment for SD 770-7: Topics in Particle Filtering
% Systems Design Engineering
% University of Waterloo
%
% DATE/Version:
% Jan. 2007 - V 1.0
%
% Description: Insi... |
github | AllenXL/Image_Video_Processing-Matlab-master | extractball.m | .m | Image_Video_Processing-Matlab-master/Tracking_Kalman/extractball.m | 1,447 | utf_8 | 8882c9b7955a77e43d021c28e62e087b | % extracts the center (cc,cr) and radius of the largest blob
function [cc,cr,radius,flag]=extractball(Imwork,Imback,index)%,fig1,fig2,fig3,fig15,index)
cc = 0;
cr = 0;
radius = 0;
flag = 0;
[MR,MC,Dim] = size(Imback);
% subtract background & select pixels with a big difference
fore = zeros(MR,MC); ... |
github | AllenXL/Image_Video_Processing-Matlab-master | emgm.m | .m | Image_Video_Processing-Matlab-master/GMM&EM程序汇总/GMMwithEM/emgm/emgm.m | 3,012 | utf_8 | 3e33f09eae2378fb4c43bac397dcedbf | function [label, model, llh] = emgm(X, init)
% Perform EM algorithm for fitting the Gaussian mixture model.
% X: d x n data matrix
% init: k (1 x 1) or label (1 x n, 1<=label(i)<=k) or center (d x k)
% Written by Michael Chen (sth4nth@gmail.com).
%% initialization
fprintf('EM for Gaussian mixture: running ... \n');... |
github | AllenXL/Image_Video_Processing-Matlab-master | plotSVMroc.m | .m | Image_Video_Processing-Matlab-master/My-Matlab-Toolbox/plotSVMroc.m | 1,396 | utf_8 | c0ed05124207ca1492f9071550ebb31c |
function AUC=plotSVMroc(true_labels,dec_values,classnumber)
% plotSVMroc
% by faruto
% Email:farutoliyang@gmail.com
% 2010.01.11
%%
if nargin == 2
classnumber = 2;
end
%%
[X,Y,THRE,AUC,OPTROCPT,SUBY,SUBYNAMES] = ...
perfcurve(true_labels,dec_values(:,1),'1');
%AUC=1-AUC;
true_labels= [true_labels, 1 - true_labels]... |
github | AllenXL/Image_Video_Processing-Matlab-master | manifold_demo.m | .m | Image_Video_Processing-Matlab-master/My-Matlab-Toolbox/manifold_demo.m | 65,365 | utf_8 | 2ef0fe6a199ff0f0e790936aa252f6a9 | function varargout = mani(varargin)
% mani: MANIfold learning demonstration GUI
% by Todd Wittman, Department of Mathematics, University of Minnesota
% E-mail wittman@math.ucla.edu with comments & questions.
% MANI Website: http://www.math.ucla.edu/~wittman/mani/index.html
% Last Modified by GUIDE v2.5 10-Apr-2... |
github | AllenXL/Image_Video_Processing-Matlab-master | nmf.m | .m | Image_Video_Processing-Matlab-master/My-Matlab-Toolbox/spams-matlab-v2.3-svn2013-06-20/spams-matlab/build/nmf.m | 3,099 | utf_8 | 04975453432077d74521a3a5fe37cf74 | %
% Usage: [U [,V]]=nmf(X,param);
%
% Name: nmf
%
% Description: mexTrainDL is an efficient implementation of the
% non-negative matrix factorization technique presented in
%
% "Online Learning for Matrix Factorization and Sparse Coding"
% by Julien Mairal, Francis Bach, Jean Ponce and Guillermo Sapiro
... |
github | AllenXL/Image_Video_Processing-Matlab-master | TriTClassifier.m | .m | Image_Video_Processing-Matlab-master/Classifiers-byTuEnMei/TriTClassifier.m | 4,109 | utf_8 | a9d466254615e9a5db6633b51da79fa9 | function lab=TriTClassifier(X_L, labels, Xtest, groundtruth)
%put labeled samples first
% labels(i)>=0 if x_i in X_L is labled
% groundtruth: true label of X_L
if nargin<4
groundtruth=[];
end
[Dim,N]=size(X_L);
idx_label=labels>=0;
NL=sum(idx_label);
CL=unique(labels(idx_label));
CN=length(CL);
lab=labels(1:sum(idx... |
github | AllenXL/Image_Video_Processing-Matlab-master | MRClassifier.m | .m | Image_Video_Processing-Matlab-master/Classifiers-byTuEnMei/MRClassifier.m | 6,584 | utf_8 | b0804ab1d64f5f6744002f5d1f9b1f65 | function [labsvm,labrls]=MRClassifier(X_L,lab_T, X_U,SIGMA,gamma_A,gamma_I,DEGREE,NN)
% Manifold Regularization Classifier
%labsvm is the result of LapSVM and labrls is the result of LapRLS
% sigma: Gaussian kernel width, for graph construction and kernel in SVM
% gamma_A and gamma_I: smoothness regularizer and intri... |
github | AllenXL/Image_Video_Processing-Matlab-master | LGCClassifier.m | .m | Image_Video_Processing-Matlab-master/Classifiers-byTuEnMei/LGCClassifier.m | 3,255 | utf_8 | 8905a95b0466cb3731053a28508f9913 | function [lab, F]=LGC(X_T,lab_T, X_U,sigma,standard,ratio, const)
% lab: the class label of the first samples in X
% standard=0: standard matrix invert implementation; standard=1: iteration
% implemented; standard==2: personalized
% ratio: for personalized on, the proportion of propagation rate less than
% 0.99.
% cons... |
github | AllenXL/Image_Video_Processing-Matlab-master | ParticleEx5.m | .m | Image_Video_Processing-Matlab-master/Partical Filter/ParticleEx5.m | 7,777 | utf_8 | 6f90e117438c8131c41d290343edded2 | function [StdErr, EKFErr] = ParticleEx5
% EKF Particle filter example.
% Track a body falling through the atmosphere.
% This system is taken from [Jul00], which was based on [Ath68].
% Compare the particle filter with the EKF particle filter.
global rho0 g k dt
rho0 = 2; % lb-sec^2/ft^4
g = 32.2; % ft/sec^2
k = 2e4;... |
github | AllenXL/Image_Video_Processing-Matlab-master | ParticleEx4.m | .m | Image_Video_Processing-Matlab-master/Partical Filter/ParticleEx4.m | 7,467 | utf_8 | 2809a444b5b9f301ea08fd22ac79bc82 | function [StdRMSErr, AuxRMSErr] = ParticleEx4
% Particle filter example.
% Track a body falling through the atmosphere.
% This system is taken from [Jul00], which was based on [Ath68].
% Compare the particle filter with the auxiliary particle filter.
global rho0 g k dt
rho0 = 2; % lb-sec^2/ft^4
g = 32.2; % ft/sec^2
... |
github | AllenXL/Image_Video_Processing-Matlab-master | dirSynch.m | .m | Image_Video_Processing-Matlab-master/Dollar's Code/piotr_toolbox_V3.02/toolbox/matlab/dirSynch.m | 4,631 | utf_8 | 2223662cc84d1249ca9988061e3a09b9 | function dirSynch( root1, root2, showOnly, flag, ignDate )
% Synchronize two directory trees (or show differences between them).
%
% If a file or directory 'name' is found in both tree1 and tree2:
% 1) if 'name' is a file in both the pair is considered the same if they
% have identical size and identical datestamp... |
github | AllenXL/Image_Video_Processing-Matlab-master | plotRoc.m | .m | Image_Video_Processing-Matlab-master/Dollar's Code/piotr_toolbox_V3.02/toolbox/matlab/plotRoc.m | 5,272 | utf_8 | 6ee93b5093189ea3acf21b7ee71a6aa7 | function [h,miss,stds] = plotRoc( D, varargin )
% Function for display of rocs (receiver operator characteristic curves).
%
% Display roc curves. Consistent usage ensures uniform look for rocs. The
% input D should have n rows, each of which is of the form:
% D = [falsePosRate truePosRate]
% D is generated, for exampl... |
github | AllenXL/Image_Video_Processing-Matlab-master | simpleCache.m | .m | Image_Video_Processing-Matlab-master/Dollar's Code/piotr_toolbox_V3.02/toolbox/matlab/simpleCache.m | 4,159 | utf_8 | 3ee36c5476544f93ae5bf5d39c45726e | function varargout = simpleCache( op, cache, varargin )
% A simple cache that can be used to store results of computations.
%
% Can save and retrieve arbitrary values using a vector (includnig char
% vectors) as a key. Especially useful if a function must perform heavy
% computation but is often called with the same in... |
github | AllenXL/Image_Video_Processing-Matlab-master | tpsInterpolate.m | .m | Image_Video_Processing-Matlab-master/Dollar's Code/piotr_toolbox_V3.02/toolbox/matlab/tpsInterpolate.m | 1,707 | utf_8 | cd63faf887af2215d7433b56deac6871 | function [xsR,ysR] = tpsInterpolate( warp, xs, ys, show )
% Apply warp (obtained by tpsGetWarp) to a set of new points.
%
% USAGE
% [xsR,ysR] = tpsInterpolate( warp, xs, ys, [show] )
%
% INPUTS
% warp - [see tpsGetWarp] bookstein warping parameters
% xs, ys - points to apply warp to
% show - [1] will disp... |
github | AllenXL/Image_Video_Processing-Matlab-master | checkNumArgs.m | .m | Image_Video_Processing-Matlab-master/Dollar's Code/piotr_toolbox_V3.02/toolbox/matlab/checkNumArgs.m | 3,857 | utf_8 | 9d88a888a42acf2c5373eaf21d79eae2 | function [ x, er ] = checkNumArgs( x, siz, intFlag, signFlag )
% Helper utility for checking numeric vector arguments.
%
% Runs a number of tests on the numeric array x. Tests to see if x has all
% integer values, all positive values, and so on, depending on the values
% for intFlag and signFlag. Also tests to see if ... |
github | AllenXL/Image_Video_Processing-Matlab-master | fevalDistr.m | .m | Image_Video_Processing-Matlab-master/Dollar's Code/piotr_toolbox_V3.02/toolbox/matlab/fevalDistr.m | 12,495 | utf_8 | 71e24d6774571bc7b25533c6ed6dcb85 | function [out,res] = fevalDistr( funNm, jobs, varargin )
% Wrapper for embarrassingly parallel function evaluation.
%
% Runs "r=feval(funNm,jobs{i}{:})" for each job in a parallel manner. jobs
% should be a cell array of length nJob and each job should be a cell array
% of parameters to pass to funNm. funNm must be a f... |
github | AllenXL/Image_Video_Processing-Matlab-master | getError.m | .m | Image_Video_Processing-Matlab-master/Adaboost/getError.m | 757 | utf_8 | 2ecaef7d9f84e7e4b1196cd85ff15a06 | %%
% file: getError.m
% This function calculates the error returned by the current run of the weak learner.
%%
function [errorTrain,errorTest]=getError(boost,train,train_label,test,test_label)
disp('run getError');
d=size(boost);
num=size(train);
prediction=zeros(num(1),1);
% geting the train error
... |
github | AllenXL/Image_Video_Processing-Matlab-master | runAdaBoosting.m | .m | Image_Video_Processing-Matlab-master/Adaboost/runAdaBoosting.m | 1,964 | utf_8 | 72dcc64fbeaebd45f988a49c483ab7c1 | %%
% File Name: runAdaBoosing
% This is the "main" it runs the ada boost algorithm and for different sizes of
% training sets. It provides a graph of the errors according to the size of the
% training set.
%%
%!Important: run this commands in the Matlab "Command Window" before you run
%open usps.mat;
%load usps.mat;
... |
github | AllenXL/Image_Video_Processing-Matlab-master | adaBoost.m | .m | Image_Video_Processing-Matlab-master/Adaboost/adaBoost.m | 907 | utf_8 | f4a71735c4021f055b34fb21bca3d260 | %%
% File Name: AdaBoost
% This is the implementation of the ada boost algorithm.
% Parameters - very easy to guess by name...
% Return values: i - hypothesis-index vector.
% t - threshhols vector
% beta - weighted beta.
%%
function boosted=adaBoost(train,train_label,cycles)
disp('run... |
github | AllenXL/Image_Video_Processing-Matlab-master | weakLearner.m | .m | Image_Video_Processing-Matlab-master/Adaboost/weakLearner.m | 301 | utf_8 | db7cb5c65c397014240bfbd47e6850d2 |
function [i,t] = weakLearner(distribution,train,label)
%disp('run weakLearner');
for tt=1:(16*256-1)
error(tt)=distribution*abs(label-(train(:,floor(tt/16)+1)>=16*(mod(tt,16)+1)));
end
[val,tt]=max(abs(error-0.5));
i=floor(tt/16)+1;
t=16*(mod(tt,16)+1); |
github | AllenXL/Image_Video_Processing-Matlab-master | affine_warp.m | .m | Image_Video_Processing-Matlab-master/OpenSURF_version1c/WarpFunctions/affine_warp.m | 9,477 | utf_8 | 33332e6239393e5b9bbb52f45a0094d8 | function Iout=affine_warp(Iin,M,mode)
% Affine transformation function (Rotation, Translation, Resize)
% This function transforms a volume with a 3x3 transformation matrix
%
% Iout=affine_warp(Iin,Minv,mode)
%
% inputs,
% Iin: The input image
% Minv: The (inverse) 3x3 transformation matrix
% mode: If 0: linear i... |
github | AllenXL/Image_Video_Processing-Matlab-master | FastHessian_interpolateExtremum.m | .m | Image_Video_Processing-Matlab-master/OpenSURF_version1c/SubFunctions/FastHessian_interpolateExtremum.m | 2,415 | utf_8 | 677ef42cd95702442a465243e8c67322 | function [ipts, np]=FastHessian_interpolateExtremum(r, c, t, m, b, ipts, np)
% This function FastHessian_interpolateExtremum will ..
%
% [ipts,np] = FastHessian_interpolateExtremum( r,c,t,m,b,ipts,np )
%
% inputs,
% r :
% c :
% t :
% m :
% b :
% ipts :
% np :
%
% outputs,
% ipts... |
github | AllenXL/Image_Video_Processing-Matlab-master | SurfDescriptor_GetDescriptor.m | .m | Image_Video_Processing-Matlab-master/OpenSURF_version1c/SubFunctions/SurfDescriptor_GetDescriptor.m | 3,518 | utf_8 | 8edffda0072130e6ee27d2a2fec98926 | function descriptor=SurfDescriptor_GetDescriptor(ip, bUpright, bExtended, img, verbose)
% This function SurfDescriptor_GetDescriptor will ..
%
% [descriptor] = SurfDescriptor_GetDescriptor( ip,bUpright,bExtended,img )
%
% inputs,
% ip : Interest Point (x,y,scale, orientation)
% bUpright : If true not rotation ... |
github | AllenXL/Image_Video_Processing-Matlab-master | FastHessian_isExtremum.m | .m | Image_Video_Processing-Matlab-master/OpenSURF_version1c/SubFunctions/FastHessian_isExtremum.m | 1,633 | utf_8 | 17a80bb81cf34442fcba86aad00c54ff | function an=FastHessian_isExtremum(r, c, t, m, b,FastHessianData)
% This function FastHessian_isExtremum will ..
%
% [an] = FastHessian_isExtremum( r,c,t,m,b,FastHessianData )
%
% inputs,
% r :
% c :
% t :
% m :
% b :
% FastHessianData :
%
% outputs,
% an :
%
% Function is written... |
github | AllenXL/Image_Video_Processing-Matlab-master | gaussian.m | .m | Image_Video_Processing-Matlab-master/DrawGuassin/gaussian.m | 2,579 | utf_8 | 2ad6860809ebdd47a14ed116fe70d7b1 | function out = gaussian(data, gParam);
% gaussian: Multi-dimensional Gaussian propability density function
% Usage: out = gaussian(data, gParam)
% data: d x n data matrix, representing n data vector of dimension d
% gParam.mu: d x 1 vector
% gParam.sigma: covariance matrix of 3 possible sizes
% 1 x 1: scalar times... |
github | AllenXL/Image_Video_Processing-Matlab-master | People_Counter.m | .m | Image_Video_Processing-Matlab-master/PeopleCounter/People_Counter.m | 71,840 | utf_8 | e56ea563f05a414dadfa6934082661c0 | function varargout = People_Counter(varargin)
% PEOPLE_COUNTER M-file for People_Counter.fig
% PEOPLE_COUNTER, by itself, creates a new PEOPLE_COUNTER or raises the existing
% singleton*.
%
% H = PEOPLE_COUNTER returns the handle to a new PEOPLE_COUNTER or the handle to
% the existing singleton*.
%
... |
github | AllenXL/Image_Video_Processing-Matlab-master | Process.m | .m | Image_Video_Processing-Matlab-master/DoG/Process.m | 351 | utf_8 | 631e80e4f12c662a1107afa2f83d7a81 | % DoG Process
% -----------------------------------------------------------
% img - Input Image
% sig1, sig2 - Sigma values
function [ out_img ] = Process( img, sig1, sig2, size )
x = size;
ksig1 = fspecial('gaussian', [x x], sig1);
ksig2 = fspecial('gaussian', [x x], sig2);
A = imfilter(img, ksig1);
B = imfilter(img... |
github | AllenXL/Image_Video_Processing-Matlab-master | LucasKanadeRefined.m | .m | Image_Video_Processing-Matlab-master/mcontext/LucasKanadeRefined.m | 2,877 | utf_8 | 1ae58bcb3fc08978f02370ed19ad6515 | function [u,v,cert] = LucasKanadeRefined(uIn, vIn, im1, im2);
% Lucas Kanade Refined computes lucas kanade flow at the current level given previous estimates!
%current implementation is only for a 3x3 window
%[fx, fy, ft] = ComputeDerivatives(im1, im2);
uIn = round(uIn);
vIn = round(vIn);
%uIn = uIn(2:size(uIn,1), 2... |
github | AllenXL/Image_Video_Processing-Matlab-master | extract_boxs.m | .m | Image_Video_Processing-Matlab-master/mcontext/extract_boxs.m | 331 | utf_8 | 4297f490a97f3b45365f7ba962caf645 | %(C) Du Tran dutran2@uiuc.edu 2008
function extract_boxs(dir_in, txt_file, file_ext)
f = fopen(txt_file,'wt');
files = dir([dir_in file_ext]);
for i=1:size(files)
fn = sprintf('%s%s',dir_in,files(i).name);
im = imread(fn);
p = extract_silhouette(im);
fprintf(f,'%4d%4d%4d%4d\n',p(1),p(2),p(3),p(4));
end;... |
github | AllenXL/Image_Video_Processing-Matlab-master | do_all.m | .m | Image_Video_Processing-Matlab-master/mcontext/do_all.m | 929 | utf_8 | b05b0ca869fd4eb71fba5790be81192c | %(C) Du Tran dutran2@uiuc.edu 2008
function do_all(dir_in)
% PARAMETERS
% dir_in is a folder which contains
% 1. a sub-folder whose name 'frm'
% 1.1 the folder 'frm' contains k sub-folders containing images
% - each folder is a video (you have k-video)
% 2. a sub-folder whose name 'bkg'
% 2.1 the folder... |
github | AllenXL/Image_Video_Processing-Matlab-master | get_vid.m | .m | Image_Video_Processing-Matlab-master/mcontext/get_vid.m | 411 | utf_8 | 87c6453979ec665d187b90fee476d008 | %(C) Du Tran dutran2@uiuc.edu 2008
function vlabels = get_vid(l)
cur_id = 1;
n = size(l,2);
vlabels = zeros(1,n,'uint16');
cur_name = parse_name(char(l(1)));
for i=1:n
tmp = parse_name(char(l(i)));
if ~strcmp(tmp,cur_name)
cur_name = tmp;
cur_id = cur_id + 1;
end
vlabels(i) = cur_id;
end... |
github | AllenXL/Image_Video_Processing-Matlab-master | LucasKanade.m | .m | Image_Video_Processing-Matlab-master/mcontext/LucasKanade.m | 1,851 | utf_8 | 2279f25f7039a0a75999e6562fbd901e | function [u, v] = LucasKanade(im1, im2, windowSize);
%LucasKanade lucas kanade algorithm, without pyramids (only 1 level);
%REVISION: NaN vals are replaced by zeros
[fx, fy, ft] = ComputeDerivatives(im1, im2);
u = zeros(size(im1));
v = zeros(size(im2));
halfWindow = floor(windowSize/2);
for i = halfWindow+1:size(f... |
github | AllenXL/Image_Video_Processing-Matlab-master | extract_silhouette.m | .m | Image_Video_Processing-Matlab-master/mcontext/extract_silhouette.m | 430 | utf_8 | 21c8a10b9df4bdba3fc025230a393574 | %(C) Du Tran dutran2@uiuc.edu 2008
function p = extract_silhouette(im)
h = sum(im');
v = sum(im);
[x x0] = max(v);
[c y0] = max(h);
p = zeros(1,4);
p(1) = x0;
p(2) = y0;
while (p(1)>1) && (v(p(1))>0)
p(1) = p(1) - 1;
end
while (p(2)>1) && (h(p(2))>0)
p(2) = p(2) - 1;
end
p(3) = x0;
p(4) = y0;
while (p(3)<size... |
github | AllenXL/Image_Video_Processing-Matlab-master | do_compute_flow.m | .m | Image_Video_Processing-Matlab-master/mcontext/do_compute_flow.m | 1,171 | utf_8 | 341e75433f3252e52e7739459be298e9 | %(C) Du Tran dutran2@uiuc.edu 2008
function do_compute_flow(frm_dir, flow_dir, file_ext)
dir_u = sprintf('%su/',flow_dir);
du_mkdir(dir_u);
dir_v = sprintf('%sv/',flow_dir);
du_mkdir(dir_v);
dirs = dir(frm_dir);
for i=3:size(dirs,1)
cur_frm_dir = sprintf('%s%s/',frm_dir,dirs(i).name);
cur_u_dir = sprintf('%s%s/... |
github | AllenXL/Image_Video_Processing-Matlab-master | do_box_localize.m | .m | Image_Video_Processing-Matlab-master/mcontext/do_box_localize.m | 355 | utf_8 | 843811f9491244dd7c1527b8bb18288e | %(C) Du Tran dutran2@uiuc.edu 2008
function do_box_localize(bkg_dir,box_dir,file_ext)
du_mkdir(box_dir);
video_bkg_dirs = dir(bkg_dir);
for i=3:size(video_bkg_dirs,1)
cur_bkg_dir = sprintf('%s%s/',bkg_dir,video_bkg_dirs(i).name);
box_file = sprintf('%s%s.txt',box_dir,video_bkg_dirs(i).name);
extract_boxs(cu... |
github | AllenXL/Image_Video_Processing-Matlab-master | do_compute_frame_feature.m | .m | Image_Video_Processing-Matlab-master/mcontext/do_compute_frame_feature.m | 1,498 | utf_8 | 49a1d426a4a82631f4a3fd88e000bafd | %(C) Du Tran dutran2@uiuc.edu 2008
function do_compute_frame_feature(frm_dir, bkg_dir, flow_dir, box_dir, file_ext, tmp_dir)
txt_files = dir([box_dir '*.txt']);
scale = 120;
ki = 2;
N = 18;
angel_matrix = compute_angle_matrix([scale/ki scale/ki],N);
num = 0;
f = [];
s = [];
for j=1:size(txt_files,1)
box_file = spr... |
github | AllenXL/Image_Video_Processing-Matlab-master | normalize_vector.m | .m | Image_Video_Processing-Matlab-master/mcontext/normalize_vector.m | 122 | utf_8 | 6e2d25a19eccf2c07a56038235afbd68 | %(C) Du Tran dutran2@uiuc.edu 2008
function f = normalize_vector(a)
n = norm(a);
if n > 0
f = a/n;
else
f = a;
end |
github | AllenXL/Image_Video_Processing-Matlab-master | du_mkdir.m | .m | Image_Video_Processing-Matlab-master/mcontext/du_mkdir.m | 105 | utf_8 | 3e3124a9ff22bfc9ca5aa6f394cc559b | %(C) Du Tran dutran2@uiuc.edu 2008
function du_mkdir(mydir)
if ~exist(mydir,'dir')
mkdir(mydir);
end
|
github | AllenXL/Image_Video_Processing-Matlab-master | kltTrackSIMPLE.m | .m | Image_Video_Processing-Matlab-master/kltTrackSIMPLEmatlab/kltTrackSIMPLE.m | 6,942 | utf_8 | dc843e48c9ef7bb4fc8255e7b4723b68 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PROJECT:
% KLT Tracker
%
% BY:
% Parthipan Siva
% Assignment for SD 770-7: Topics in Particle Filtering
% Systems Design Engineering
% University of Waterloo
%
% DATE/Version:
% Jan. 2007 - V 1.0
%
% Description: kltT... |
github | AllenXL/Image_Video_Processing-Matlab-master | MakeKeypoint.m | .m | Image_Video_Processing-Matlab-master/3DSIFT_CODE_v1/MakeKeypoint.m | 750 | utf_8 | efcc84149a29f3c4d0e0e7c6f64ebd5b | function key = MakeKeypoint(pix, xyScale, tScale, x, y, z)
k.x = x;
k.y = y;
k.z = z;
k.xyScale = xyScale;
k.tScale = tScale;
key = MakeKeypointSample(k, pix);
return;
end
function key = MakeKeypointSample(key, pix)
LoadParams;
MaxIndexVal = 0.2;
changed = 0;
vec = KeySampleVec(key, pix... |
github | AllenXL/Image_Video_Processing-Matlab-master | mesh_refine_tri4.m | .m | Image_Video_Processing-Matlab-master/3DSIFT_CODE_v1/mesh_refine_tri4.m | 3,982 | utf_8 | 00de00ed8dc2abf3b048e6a8f6135120 | function [ FV ] = mesh_refine_tri4(FV)
% mesh_refine_tri4 - creates 4 triangle from each triangle of a mesh
%
% [ FV ] = mesh_refine_tri4( FV )
%
% FV.vertices - mesh vertices (Nx3 matrix)
% FV.faces - faces with indices into 3 rows
% of FV.vertices (Mx3 matrix)
%
% For each face, 3 new vertices... |
github | AllenXL/Image_Video_Processing-Matlab-master | computeColor.m | .m | Image_Video_Processing-Matlab-master/光流/coarse2fine/computeColor.m | 3,142 | utf_8 | a36a650437bc93d4d8ffe079fe712901 | function img = computeColor(u,v)
% computeColor color codes flow field U, V
% According to the c++ source code of Daniel Scharstein
% Contact: schar@middlebury.edu
% Author: Deqing Sun, Department of Computer Science, Brown University
% Contact: dqsun@cs.brown.edu
% $Date: 2007-10-31 21:20:30 (Wed, 31 O... |
github | AllenXL/Image_Video_Processing-Matlab-master | LucasKanade.m | .m | Image_Video_Processing-Matlab-master/光流/HS/LucasKanade.m | 1,851 | utf_8 | 2279f25f7039a0a75999e6562fbd901e | function [u, v] = LucasKanade(im1, im2, windowSize);
%LucasKanade lucas kanade algorithm, without pyramids (only 1 level);
%REVISION: NaN vals are replaced by zeros
[fx, fy, ft] = ComputeDerivatives(im1, im2);
u = zeros(size(im1));
v = zeros(size(im2));
halfWindow = floor(windowSize/2);
for i = halfWindow+1:size(f... |
github | AllenXL/Image_Video_Processing-Matlab-master | LucasKanadeRefined.m | .m | Image_Video_Processing-Matlab-master/光流/HierarchicalLK/LucasKanadeRefined.m | 2,877 | utf_8 | 1ae58bcb3fc08978f02370ed19ad6515 | function [u,v,cert] = LucasKanadeRefined(uIn, vIn, im1, im2);
% Lucas Kanade Refined computes lucas kanade flow at the current level given previous estimates!
%current implementation is only for a 3x3 window
%[fx, fy, ft] = ComputeDerivatives(im1, im2);
uIn = round(uIn);
vIn = round(vIn);
%uIn = uIn(2:size(uIn,1), 2... |
github | AllenXL/Image_Video_Processing-Matlab-master | LucasKanade.m | .m | Image_Video_Processing-Matlab-master/光流/HierarchicalLK/LucasKanade.m | 1,851 | utf_8 | 2279f25f7039a0a75999e6562fbd901e | function [u, v] = LucasKanade(im1, im2, windowSize);
%LucasKanade lucas kanade algorithm, without pyramids (only 1 level);
%REVISION: NaN vals are replaced by zeros
[fx, fy, ft] = ComputeDerivatives(im1, im2);
u = zeros(size(im1));
v = zeros(size(im2));
halfWindow = floor(windowSize/2);
for i = halfWindow+1:size(f... |
github | pwkraft/Miscellaneous-R-Code-master | onefactorRE.m | .m | Miscellaneous-R-Code-master/ModelFitting/onefactorRE.m | 747 | utf_8 | c042f014d7bc0b3bf317c536478c3c0a | % matlab from Statistical Modeling and Computation (2014 p 311). See the
% associated twofactorRE.R file for details.
function sfran_loglike(mu, sigma2_mu, sigma2, y)
[d ni] = size(y);
Sigmai = sigma2*eye(ni) + sigma2_mu*ones(ni,ni);
l = -(ni*d)/2*log(2*pi) - d/2*log(det(Sigmai));
for i=1:d
yi = y(i,:)';
l... |
github | pwkraft/Miscellaneous-R-Code-master | twofactorRE.m | .m | Miscellaneous-R-Code-master/ModelFitting/twofactorRE.m | 868 | utf_8 | f31135f3063fcefc589eaf0237f951c8 | % matlab from Statistical Modeling and Computation (2014 p 314). See the
% associated twofactorRE.R file for details.
function sfran2_loglike(mu, eta_alpha, eta_gamma, eta, y, Xalpha, Xgamma)
sigma2_alpha = exp(eta_alpha);
sigma2_gamma = exp(eta_gamma);
sigma2 = exp(eta);
n = length(y);
Sigma = sigma2*speye... |
github | santhosh-kumar/MultiviewAppearanceTracker-master | make_layout.m | .m | MultiviewAppearanceTracker-master/lib/GraphVizWrapper/make_layout.m | 2,261 | utf_8 | 6c9128a0f5fbeac2b1e8519c510dc10f | function [x, y] = make_layout(adj)
% [x, y] = make_layout(adj) Creates a layout from an adjacency matrix
%
% INPUT: adj - adjacency matrix (source, sink)
% OUTPUT: x, y - Positions of nodes
%
% WARNING: Uses some very simple heuristics, any algorithm would do better
N = size(adj,1);
tps = toposort(adj);
if ~isemp... |
github | santhosh-kumar/MultiviewAppearanceTracker-master | graph_draw.m | .m | MultiviewAppearanceTracker-master/lib/GraphVizWrapper/graph_draw.m | 22,767 | utf_8 | ab0cbccec63f182d0aa9d6a10e54f02e | function [x, y, h] = graph_draw(adj, varargin)
% [x, y, h] = graph_draw(adj, varargin)
%
% INPUTS: ADJ - Adjacency matrix (source, sink)
% 'linestyle' - default '-'
% 'linewidth' - default .5
% 'linecolor' - default Black
% 'fontsize' - fontsize for labels, default 8
% ... |
github | santhosh-kumar/MultiviewAppearanceTracker-master | sample_click.m | .m | MultiviewAppearanceTracker-master/lib/GraphVizWrapper/sample_click.m | 396 | utf_8 | d96c31eb4914a7dcbc23e230085246e6 | text_elms = findall(gca,'Type','text');
for ndx = 1:length(text_elms)
callbk = 'my_call(str2num(get(gcbo,''String'')))';
set(text_elms(ndx), 'ButtonDownFcn', callbk); % assume the node label is a number
end
function varargout = my_call(value)
label = get(gcbo,'String'); % "gcbo" is the handle of th... |
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