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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 |
|---|---|---|---|---|---|---|---|---|
github | layumi/2015_Face_Detection-master | f48net_c.m | .m | 2015_Face_Detection-master/f48net_c.m | 2,104 | utf_8 | 375f888e903269c98c8277feb9450d80 | function net = f48net_c()
opts.useBnorm = true ;
net.layers = {} ;
net.layers{end+1} = struct('type', 'conv', ...
'weights', {{0.01*randn(5,5,3,64, 'single'), zeros(1, 64, 'single')}}, ...
'stride', 1, ...
'pad', 0) ;
net.layers{end+1} = struct('type', 'pool', ...
'method', 'max', ...
'pool', [3 3... |
github | layumi/2015_Face_Detection-master | cascadeface_12c.m | .m | 2015_Face_Detection-master/cascadeface_12c.m | 2,345 | utf_8 | c7e5100f103c6be18b3d6ba02cbd0390 | function cascadeface_12c(varargin)
setup;
% -------------------------------------------------------------------------
% Part 4.1: prepare the data
% -------------------------------------------------------------------------
% Load character dataset
imdb = load('../../aflw/positive_c.mat') ;
imdb = imdb.imdb;
imdb.meta.... |
github | layumi/2015_Face_Detection-master | f12net_c.m | .m | 2015_Face_Detection-master/f12net_c.m | 1,762 | utf_8 | 1be995c1e17799168617d7f192323ef7 | function net = f12net_c()
lr = [.1 2] ;
opts.useBnorm = true ;
net.layers = {} ;
net.layers{end+1} = struct('type', 'conv', ...
'weights', {{0.01*randn(3,3,3,16, 'single'), zeros(1, 16, 'single')}}, ...
'stride', 1, ...
'learningRate', lr, ...
'pad', 2) ;
net.layers{end+1} = struct('type', 'pool', ..... |
github | layumi/2015_Face_Detection-master | cascadeface_12net.m | .m | 2015_Face_Detection-master/cascadeface_12net.m | 2,332 | utf_8 | b1c2a3b8a2b6c4e33899d7997deabbe3 | function cascadeface_12net(varargin)
% -------------------------------------------------------------------------
% Part 4.1: prepare the data
% -------------------------------------------------------------------------
% Load character dataset
imdb = load('../../aflw/imdb12_v2_extended.mat') ;
imdb = imdb.imdb;
imdb.me... |
github | layumi/2015_Face_Detection-master | cascadeface_48net.m | .m | 2015_Face_Detection-master/cascadeface_48net.m | 2,601 | utf_8 | a63382e5fb528ba3778ab2932a879913 | function cascadeface_48net(varargin)
% -------------------------------------------------------------------------
% Part 4.1: prepare the data
% -------------------------------------------------------------------------
% Load character dataset
imdb = load('../../aflw/matlab/imdb48_v1.mat') ;
imdb = imdb.imdb;
imdb.meta... |
github | layumi/2015_Face_Detection-master | f24net_normB.m | .m | 2015_Face_Detection-master/f24net_normB.m | 1,596 | utf_8 | 8e2ee3a41fb92da9a24e4918514c167a | function net = f24net()
opts.useBnorm = true ;
net.layers = {} ;
net.layers{end+1} = struct('type', 'conv', ...
'weights', {{0.01*randn(5,5,3,64, 'single'), zeros(1, 64, 'single')}}, ...
'stride', 1, ...
'pad', 0) ;
net.layers{end+1} = struct('type', 'pool', ...
'method', 'max', ...
'pool', [3 3],... |
github | layumi/2015_Face_Detection-master | patch_nn.m | .m | 2015_Face_Detection-master/patch_nn.m | 253 | utf_8 | 7719f1b84bc7ec9a0866786a45c9fd17 | % --------------------------------------------------------------------
function array2 = patch_nn(net,patch)
% --------------------------------------------------------------------
res12 = vl_simplenn(net,patch) ;
array2 = reshape(res12(end).x,2,[]); |
github | layumi/2015_Face_Detection-master | f24net_c.m | .m | 2015_Face_Detection-master/f24net_c.m | 1,631 | utf_8 | 9852ad8f8de1a2cf48fced1c8d754c11 | function net = f24net_c()
opts.useBnorm = true ;
net.layers = {} ;
net.layers{end+1} = struct('type', 'conv', ...
'weights', {{0.01*randn(5,5,3,32, 'single'), zeros(1, 32, 'single')}}, ...
'stride', 1, ...
'pad', 0) ;
net.layers{end+1} = struct('type', 'pool', ...
'method', 'max', ...
'pool', [3 3... |
github | layumi/2015_Face_Detection-master | cascadeface_24net_normB.m | .m | 2015_Face_Detection-master/cascadeface_24net_normB.m | 2,421 | utf_8 | 9a719719f91c6b78d9b4a422b6d11ab1 | function cascadeface_24net_normB(varargin)
% -------------------------------------------------------------------------
% Part 4.1: prepare the data
% -------------------------------------------------------------------------
% Load character dataset
imdb = load('../../aflw/matlab/imdb24_v2.mat') ;
imdb = imdb.imdb;
imd... |
github | layumi/2015_Face_Detection-master | cascadeface_24c.m | .m | 2015_Face_Detection-master/cascadeface_24c.m | 2,321 | utf_8 | 523ec695a26d03d9e0b62d66a0021cce | function cascadeface_24c(varargin)
setup;
% -------------------------------------------------------------------------
% Part 4.1: prepare the data
% -------------------------------------------------------------------------
% Load character dataset
imdb = load('../../aflw/positive_c.mat') ;
imdb = imdb.imdb;
imdb.meta.... |
github | layumi/2015_Face_Detection-master | cascadeface_24net.m | .m | 2015_Face_Detection-master/cascadeface_24net.m | 2,464 | utf_8 | 45c4a7cd0083d1c42bc4a7a857db29ed | function cascadeface_24net(varargin)
% -------------------------------------------------------------------------
% Part 4.1: prepare the data
% -------------------------------------------------------------------------
% Load character dataset
imdb = load('../../aflw/matlab/imdb24_v2.mat') ;
imdb = imdb.imdb;
imdb.meta... |
github | layumi/2015_Face_Detection-master | cascadeface_48c.m | .m | 2015_Face_Detection-master/cascadeface_48c.m | 2,328 | utf_8 | a6c31fa253ea74afe1717fbfcd9c15c4 | function cascadeface_48c(varargin)
setup;
% -------------------------------------------------------------------------
% Part 4.1: prepare the data
% -------------------------------------------------------------------------
% Load character dataset
imdb = load('../../aflw/positive_c.mat') ;
imdb = imdb.imdb;
imdb.meta.... |
github | layumi/2015_Face_Detection-master | cascadeface_48net2.m | .m | 2015_Face_Detection-master/cascadeface_48net2.m | 2,627 | utf_8 | b56d64b301d01a2e4f39b35d285353f9 | function cascadeface_48net(varargin)
% -------------------------------------------------------------------------
% Part 4.1: prepare the data
% -------------------------------------------------------------------------
% Load character dataset
imdb = load('../../aflw/matlab/imdb48_v1.mat') ;
imdb = imdb.imdb;
imdb.meta... |
github | layumi/2015_Face_Detection-master | f24net_imagenet.m | .m | 2015_Face_Detection-master/f24net_imagenet.m | 2,682 | utf_8 | 0e9b813e9218a5d0a170743fee5f6a00 | function net = f24net_imagenet(varargin)
% CNN_IMAGENET_INIT Baseline CNN model
opts.scale = 1 ;
opts.initBias = 0.1 ;
opts.weightDecay = 1 ;
opts = vl_argparse(opts, varargin) ;
net.layers = {} ;
% Define layers
net.layers = {} ;
% Block 1
net = add_block(net, opts, 1, 5, 5, 3, 64, 1, 0) ;
net.layers{end+1} = str... |
github | layumi/2015_Face_Detection-master | cascadeface_aflw_24net.m | .m | 2015_Face_Detection-master/cascadeface_aflw_24net.m | 2,956 | utf_8 | 9d06c324f8d511cddaaf94f902a6c6cf | function cascadeface_aflw_24net(varargin)
setup;
% -------------------------------------------------------------------------
% Part 4.1: prepare the data
% -------------------------------------------------------------------------
% Load character dataset
imdb = load('C:/Users/zhedong/Desktop/face-database/train24net... |
github | layumi/2015_Face_Detection-master | f48net_c_2.m | .m | 2015_Face_Detection-master/f48net_c_2.m | 2,059 | utf_8 | e417ef43c692588f5ea3f6e1f0ec9625 | function net = f48net_c()
opts.useBnorm = true ;
net.layers = {} ;
net.layers{end+1} = struct('type', 'conv', ...
'weights', {{0.01*randn(5,5,3,64, 'single'), zeros(1, 64, 'single')}}, ...
'stride', 1, ...
'pad', 1) ;
net.layers{end+1} = struct('type', 'pool', ...
'method', 'max', ...
'pool', [3 3... |
github | layumi/2015_Face_Detection-master | f48net_v2.m | .m | 2015_Face_Detection-master/f48net_v2.m | 2,313 | utf_8 | 06a0ffa1046591965d0c6fa9bb7e6293 | function net = f48net_v2()
opts.useBnorm = true ;
net.layers = {} ;
net.layers{end+1} = struct('type', 'conv', ...
'weights', {{0.01*randn(5,5,3,64, 'single'), zeros(1, 64, 'single')}}, ...
'stride', 1, ...
'pad', 0) ;
net.layers{end+1} = struct('type', 'pool', ...
'method', 'max', ...
'pool', [3 ... |
github | layumi/2015_Face_Detection-master | cnn_cifar.m | .m | 2015_Face_Detection-master/examples_12/cnn_cifar.m | 4,529 | utf_8 | e4063362a0a852098aab32182613a0b9 | function [net, info] = cnn_cifar(varargin)
% CNN_CIFAR Demonstrates MatConvNet on CIFAR-10
% The demo includes two standard model: LeNet and Network in
% Network (NIN). Use the 'modelType' option to choose one.
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.modelT... |
github | layumi/2015_Face_Detection-master | cnn_mnist_init.m | .m | 2015_Face_Detection-master/examples_12/cnn_mnist_init.m | 2,359 | utf_8 | f320ebc0228ef986a9b3d1f79e6b93b5 | function net = cnn_mnist_init(varargin)
% CNN_MNIST_LENET Initialize a CNN similar for MNIST
opts.useBnorm = true ;
opts = vl_argparse(opts, varargin) ;
f=1/100 ;
net.layers = {} ;
net.layers{end+1} = struct('type', 'conv', ...
'weights', {{f*randn(5,5,1,20, 'single'), zeros(1, 20, 'single')... |
github | layumi/2015_Face_Detection-master | cnn_imagenet_init_bnorm.m | .m | 2015_Face_Detection-master/examples_12/cnn_imagenet_init_bnorm.m | 2,821 | utf_8 | d50304a797673e101606fed94e114ed9 | function net = cnn_imagenet_init_bnorm(varargin)
% CNN_IMAGENET_INIT_BNORM Baseline CNN model with batch normalization
opts.scale = 1 ;
opts.initBias = 0.1 ;
opts.weightDecay = 1 ;
opts = vl_argparse(opts, varargin) ;
% Define input
net.normalization.imageSize = [227, 227, 3] ;
net.normalization.interpolation = 'bic... |
github | layumi/2015_Face_Detection-master | cnn_imagenet_init.m | .m | 2015_Face_Detection-master/examples_12/cnn_imagenet_init.m | 2,942 | utf_8 | 19125e6eefe0760fedafc54a0599cf64 | function net = cnn_imagenet_init(varargin)
% CNN_IMAGENET_INIT Baseline CNN model
opts.scale = 1 ;
opts.initBias = 0.1 ;
opts.weightDecay = 1 ;
opts = vl_argparse(opts, varargin) ;
net.layers = {} ;
% Define input
net.normalization.imageSize = [227, 227, 3] ;
net.normalization.interpolation = 'bicubic' ;
net.normal... |
github | layumi/2015_Face_Detection-master | cnn_imagenet.m | .m | 2015_Face_Detection-master/examples_12/cnn_imagenet.m | 4,755 | utf_8 | b3febbfe185546c63f36c48c38a275a3 | function cnn_imagenet(varargin)
% CNN_IMAGENET Demonstrates training a CNN on ImageNet
% The demo uses a model similar to AlexNet or the Caffe reference
% model. It can train it in the dropout and batch normalization
% variants using the 'modelType' option.
run(fullfile(fileparts(mfilename('fullpath')), ...
... |
github | layumi/2015_Face_Detection-master | cnn_mnist.m | .m | 2015_Face_Detection-master/examples_12/cnn_mnist.m | 3,318 | utf_8 | ec38784eef80636720cdb6d38a023814 | function [net, info] = cnn_mnist(varargin)
% CNN_MNIST Demonstrated MatConNet on MNIST
run(fullfile(fileparts(mfilename('fullpath')),...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.expDir = fullfile('data','mnist-baseline') ;
[opts, varargin] = vl_argparse(opts, varargin) ;
opts.dataDir = fullfile('data','mnist') ;
o... |
github | layumi/2015_Face_Detection-master | cnn_train.m | .m | 2015_Face_Detection-master/examples_12/cnn_train.m | 13,874 | utf_8 | 0dc4a6af99246de00950fd4db12cb1d9 | function [net, info] = cnn_train(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic
% gradient descent with momentum to train a CNN. It can be used
% with different datasets and tasks by providing a suitable
% getBatch functio... |
github | layumi/2015_Face_Detection-master | cnn_imagenet_mgpu.m | .m | 2015_Face_Detection-master/examples_12/cnn_imagenet_mgpu.m | 9,705 | utf_8 | 8297e5ae85c01972f432a68fd13ac492 | function cnn_imagenet_mgpu(varargin)
% CNN_IMAGENET Demonstrates training a CNN on ImageNet
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data','ram','ILSVRC2012') ;
opts.expDir = fullfile('data','imagenet12-baseline-mgpu') ;
[opts, varargin] = vl_a... |
github | layumi/2015_Face_Detection-master | cnn_imagenet_evaluate.m | .m | 2015_Face_Detection-master/examples_12/cnn_imagenet_evaluate.m | 2,960 | utf_8 | 93a11af0121659362a46b7e80afe492b | function info = cnn_imagenet_evaluate(varargin)
% CNN_IMAGENET_EVALUATE Evauate MatConvNet models on ImageNet
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data', 'ILSVRC2012') ;
opts.expDir = fullfile('data', 'imagenet12-eval-vgg-f') ;
opts.imdbPat... |
github | layumi/2015_Face_Detection-master | cnn_cifar.m | .m | 2015_Face_Detection-master/examples/cnn_cifar.m | 4,529 | utf_8 | e4063362a0a852098aab32182613a0b9 | function [net, info] = cnn_cifar(varargin)
% CNN_CIFAR Demonstrates MatConvNet on CIFAR-10
% The demo includes two standard model: LeNet and Network in
% Network (NIN). Use the 'modelType' option to choose one.
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.modelT... |
github | layumi/2015_Face_Detection-master | cnn_mnist_init.m | .m | 2015_Face_Detection-master/examples/cnn_mnist_init.m | 2,385 | utf_8 | e4895ca0e40a022561d7a80114797bdb | function net = cnn_mnist_init(varargin)
% CNN_MNIST_LENET Initialize a CNN similar for MNIST
opts.useBnorm = true ;
opts = vl_argparse(opts, varargin) ;
rng('default');
rng(0) ;
f=1/100 ;
net.layers = {} ;
net.layers{end+1} = struct('type', 'conv', ...
'weights', {{f*randn(5,5,1,20, 'single... |
github | layumi/2015_Face_Detection-master | cnn_imagenet_init_bnorm.m | .m | 2015_Face_Detection-master/examples/cnn_imagenet_init_bnorm.m | 2,821 | utf_8 | d50304a797673e101606fed94e114ed9 | function net = cnn_imagenet_init_bnorm(varargin)
% CNN_IMAGENET_INIT_BNORM Baseline CNN model with batch normalization
opts.scale = 1 ;
opts.initBias = 0.1 ;
opts.weightDecay = 1 ;
opts = vl_argparse(opts, varargin) ;
% Define input
net.normalization.imageSize = [227, 227, 3] ;
net.normalization.interpolation = 'bic... |
github | layumi/2015_Face_Detection-master | cnn_imagenet_init.m | .m | 2015_Face_Detection-master/examples/cnn_imagenet_init.m | 13,358 | utf_8 | 029a55b46ee627afa3272d449917af3c | function net = cnn_imagenet_init(varargin)
% CNN_IMAGENET_INIT Initialize a standard CNN for ImageNet
opts.scale = 1 ;
opts.initBias = 0.1 ;
opts.weightDecay = 1 ;
%opts.weightInitMethod = 'xavierimproved' ;
opts.weightInitMethod = 'gaussian' ;
opts.model = 'alexnet' ;
opts.batchNormalization = false ;
opts = vl_argp... |
github | layumi/2015_Face_Detection-master | cnn_imagenet.m | .m | 2015_Face_Detection-master/examples/cnn_imagenet.m | 4,845 | utf_8 | 977ffcdc699d894c410a46c4024cef34 | function cnn_imagenet(varargin)
% CNN_IMAGENET Demonstrates training a CNN on ImageNet
% This demo demonstrates training the AlexNet, VGG-F, VGG-S, VGG-M,
% VGG-VD-16, and VGG-VD-19 architectures on ImageNet data.
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataD... |
github | layumi/2015_Face_Detection-master | cnn_mnist.m | .m | 2015_Face_Detection-master/examples/cnn_mnist.m | 3,314 | utf_8 | dcd8a9724a02c534f5157bdc1a521eaa | function [net, info] = cnn_mnist(varargin)
% CNN_MNIST Demonstrated MatConNet on MNIST
run(fullfile(fileparts(mfilename('fullpath')),...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.expDir = fullfile('data','mnist-baseline') ;
[opts, varargin] = vl_argparse(opts, varargin) ;
opts.dataDir = fullfile('data','mnist') ;
o... |
github | layumi/2015_Face_Detection-master | cnn_train.m | .m | 2015_Face_Detection-master/examples/cnn_train.m | 14,403 | utf_8 | fcc2369c67d94400cd15eca628af970c | function [net, info] = cnn_train(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic
% gradient descent with momentum to train a CNN. It can be used
% with different datasets and tasks by providing a suitable
% getBatch functio... |
github | layumi/2015_Face_Detection-master | cnn_imagenet_mgpu.m | .m | 2015_Face_Detection-master/examples/cnn_imagenet_mgpu.m | 9,705 | utf_8 | 8297e5ae85c01972f432a68fd13ac492 | function cnn_imagenet_mgpu(varargin)
% CNN_IMAGENET Demonstrates training a CNN on ImageNet
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data','ram','ILSVRC2012') ;
opts.expDir = fullfile('data','imagenet12-baseline-mgpu') ;
[opts, varargin] = vl_a... |
github | layumi/2015_Face_Detection-master | cnn_imagenet_evaluate.m | .m | 2015_Face_Detection-master/examples/cnn_imagenet_evaluate.m | 2,960 | utf_8 | 93a11af0121659362a46b7e80afe492b | function info = cnn_imagenet_evaluate(varargin)
% CNN_IMAGENET_EVALUATE Evauate MatConvNet models on ImageNet
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data', 'ILSVRC2012') ;
opts.expDir = fullfile('data', 'imagenet12-eval-vgg-f') ;
opts.imdbPat... |
github | layumi/2015_Face_Detection-master | vl_compilenn.m | .m | 2015_Face_Detection-master/matlab_12/vl_compilenn.m | 24,344 | utf_8 | fdc476314ba2cd5c918dbc3a8b627eb2 | function vl_compilenn( varargin )
% VL_COMPILENN Compile the MatConvNet toolbox
% The `vl_compilenn()` function compiles the MEX files in the
% MatConvNet toolbox. See below for the requirements for compiling
% CPU and GPU code, respectively.
%
% `vl_compilenn('OPTION', ARG, ...)` accepts the following opt... |
github | layumi/2015_Face_Detection-master | vl_simplenn_display.m | .m | 2015_Face_Detection-master/matlab_12/simplenn/vl_simplenn_display.m | 10,451 | utf_8 | 0056ef92cb611577ba1907653e32dd21 | function info = vl_simplenn_display(net, varargin)
% VL_SIMPLENN_DISPLAY Simple CNN statistics
% VL_SIMPLENN_DISPLAY(NET) prints statistics about the network NET.
%
% INFO=VL_SIMPLENN_DISPLAY(NET) returns instead a structure INFO
% with several statistics for each layer of the network NET.
%
% The function... |
github | layumi/2015_Face_Detection-master | vl_test_economic_relu.m | .m | 2015_Face_Detection-master/matlab_12/xtest/vl_test_economic_relu.m | 790 | utf_8 | 35a3dbe98b9a2f080ee5f911630ab6f3 | % VL_TEST_ECONOMIC_RELU
function vl_test_economic_relu()
x = randn(11,12,8,'single');
w = randn(5,6,8,9,'single');
b = randn(1,9,'single') ;
net.layers{1} = struct('type', 'conv', ...
'filters', w, ...
'biases', b, ...
'stride', 1, ...
... |
github | layumi/2015_Face_Detection-master | vl_compile.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/vl_compile.m | 5,060 | utf_8 | 978f5189bb9b2a16db3368891f79aaa6 | function vl_compile(compiler)
% VL_COMPILE Compile VLFeat MEX files
% VL_COMPILE() uses MEX() to compile VLFeat MEX files. This command
% works only under Windows and is used to re-build problematic
% binaries. The preferred method of compiling VLFeat on both UNIX
% and Windows is through the provided Makefile... |
github | layumi/2015_Face_Detection-master | vl_noprefix.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/vl_noprefix.m | 1,875 | utf_8 | 97d8755f0ba139ac1304bc423d3d86d3 | function vl_noprefix
% VL_NOPREFIX Create a prefix-less version of VLFeat commands
% VL_NOPREFIX() creats prefix-less stubs for VLFeat functions
% (e.g. SIFT for VL_SIFT). This function is seldom used as the stubs
% are included in the VLFeat binary distribution anyways. Moreover,
% on UNIX platforms, the stub... |
github | layumi/2015_Face_Detection-master | vl_pegasos.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/misc/vl_pegasos.m | 2,837 | utf_8 | d5e0915c439ece94eb5597a07090b67d | % VL_PEGASOS [deprecated]
% VL_PEGASOS is deprecated. Please use VL_SVMTRAIN() instead.
function [w b info] = vl_pegasos(X,Y,LAMBDA, varargin)
% Verbose not supported
if (sum(strcmpi('Verbose',varargin)))
varargin(find(strcmpi('Verbose',varargin),1))=[];
fprintf('Option VERBOSE is no longer supported.\n');
en... |
github | layumi/2015_Face_Detection-master | vl_svmpegasos.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/misc/vl_svmpegasos.m | 1,178 | utf_8 | 009c2a2b87a375d529ed1a4dbe3af59f | % VL_SVMPEGASOS [deprecated]
% VL_SVMPEGASOS is deprecated. Please use VL_SVMTRAIN() instead.
function [w b info] = vl_svmpegasos(DATA,LAMBDA, varargin)
% Verbose not supported
if (sum(strcmpi('Verbose',varargin)))
varargin(find(strcmpi('Verbose',varargin),1))=[];
fprintf('Option VERBOSE is no longer suppor... |
github | layumi/2015_Face_Detection-master | vl_override.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/misc/vl_override.m | 4,654 | utf_8 | e233d2ecaeb68f56034a976060c594c5 | function config = vl_override(config,update,varargin)
% VL_OVERRIDE Override structure subset
% CONFIG = VL_OVERRIDE(CONFIG, UPDATE) copies recursively the fileds
% of the structure UPDATE to the corresponding fields of the
% struture CONFIG.
%
% Usually CONFIG is interpreted as a list of paramters with their
... |
github | layumi/2015_Face_Detection-master | vl_quickvis.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/quickshift/vl_quickvis.m | 3,696 | utf_8 | 27f199dad4c5b9c192a5dd3abc59f9da | function [Iedge dists map gaps] = vl_quickvis(I, ratio, kernelsize, maxdist, maxcuts)
% VL_QUICKVIS Create an edge image from a Quickshift segmentation.
% IEDGE = VL_QUICKVIS(I, RATIO, KERNELSIZE, MAXDIST, MAXCUTS) creates an edge
% stability image from a Quickshift segmentation. RATIO controls the tradeoff
% bet... |
github | layumi/2015_Face_Detection-master | vl_demo_aib.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/demo/vl_demo_aib.m | 2,928 | utf_8 | 590c6db09451ea608d87bfd094662cac | function vl_demo_aib
% VL_DEMO_AIB Test Agglomerative Information Bottleneck (AIB)
D = 4 ;
K = 20 ;
randn('state',0) ;
rand('state',0) ;
X1 = randn(2,300) ; X1(1,:) = X1(1,:) + 2 ;
X2 = randn(2,300) ; X2(1,:) = X2(1,:) - 2 ;
X3 = randn(2,300) ; X3(2,:) = X3(2,:) + 2 ;
figure(1) ; clf ; hold on ;
vl_plotframe(X... |
github | layumi/2015_Face_Detection-master | vl_demo_alldist.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/demo/vl_demo_alldist.m | 5,460 | utf_8 | 6d008a64d93445b9d7199b55d58db7eb | function vl_demo_alldist
%
numRepetitions = 3 ;
numDimensions = 1000 ;
numSamplesRange = [300] ;
settingsRange = {{'alldist2', 'double', 'l2', }, ...
{'alldist', 'double', 'l2', 'nosimd'}, ...
{'alldist', 'double', 'l2' }, ...
{'alldist2', 's... |
github | layumi/2015_Face_Detection-master | vl_demo_ikmeans.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/demo/vl_demo_ikmeans.m | 774 | utf_8 | 17ff0bb7259d390fb4f91ea937ba7de0 | function vl_demo_ikmeans()
% VL_DEMO_IKMEANS
numData = 10000 ;
dimension = 2 ;
data = uint8(255*rand(dimension,numData)) ;
numClusters = 3^3 ;
[centers, assignments] = vl_ikmeans(data, numClusters);
figure(1) ; clf ; axis off ;
plotClusters(data, centers, assignments) ;
vl_demo_print('ikmeans_2d',0.6);
[tree, assig... |
github | layumi/2015_Face_Detection-master | vl_demo_svm.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/demo/vl_demo_svm.m | 1,235 | utf_8 | 7cf6b3504e4fc2cbd10ff3fec6e331a7 | % VL_DEMO_SVM Demo: SVM: 2D linear learning
function vl_demo_svm
y=[];X=[];
% Load training data X and their labels y
load('vl_demo_svm_data.mat')
Xp = X(:,y==1);
Xn = X(:,y==-1);
figure
plot(Xn(1,:),Xn(2,:),'*r')
hold on
plot(Xp(1,:),Xp(2,:),'*b')
axis equal ;
vl_demo_print('svm_training') ;
% Parameters
lambda =... |
github | layumi/2015_Face_Detection-master | vl_demo_kdtree_sift.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/demo/vl_demo_kdtree_sift.m | 6,832 | utf_8 | e676f80ac330a351f0110533c6ebba89 | function vl_demo_kdtree_sift
% VL_DEMO_KDTREE_SIFT
% Demonstrates the use of a kd-tree forest to match SIFT
% features. If FLANN is present, this function runs a comparison
% against it.
% AUTORIGHS
rand('state',0) ;
randn('state',0);
do_median = 0 ;
do_mean = 1 ;
% try to setup flann
if ~exist('flann_search'... |
github | layumi/2015_Face_Detection-master | vl_impattern.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/imop/vl_impattern.m | 6,876 | utf_8 | 1716a4d107f0186be3d11c647bc628ce | function im = vl_impattern(varargin)
% VL_IMPATTERN Generate an image from a stock pattern
% IM=VLPATTERN(NAME) returns an instance of the specified
% pattern. These stock patterns are useful for testing algoirthms.
%
% All generated patterns are returned as an image of class
% DOUBLE. Both gray-scale and colou... |
github | layumi/2015_Face_Detection-master | vl_tpsu.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/imop/vl_tpsu.m | 1,755 | utf_8 | 09f36e1a707c069b375eb2817d0e5f13 | function [U,dU,delta]=vl_tpsu(X,Y)
% VL_TPSU Compute the U matrix of a thin-plate spline transformation
% U=VL_TPSU(X,Y) returns the matrix
%
% [ U(|X(:,1) - Y(:,1)|) ... U(|X(:,1) - Y(:,N)|) ]
% [ ]
% [ U(|X(:,M) - Y(:,1)|) ... U(|X(:,M) - Y(:,N)|) ]
%
% where X... |
github | layumi/2015_Face_Detection-master | vl_xyz2lab.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/imop/vl_xyz2lab.m | 1,570 | utf_8 | 09f95a6f9ae19c22486ec1157357f0e3 | function J=vl_xyz2lab(I,il)
% VL_XYZ2LAB Convert XYZ color space to LAB
% J = VL_XYZ2LAB(I) converts the image from XYZ format to LAB format.
%
% VL_XYZ2LAB(I,IL) uses one of the illuminants A, B, C, E, D50, D55,
% D65, D75, D93. The default illuminatn is E.
%
% See also: VL_XYZ2LUV(), VL_HELP().
% Copyright ... |
github | layumi/2015_Face_Detection-master | vl_test_gmm.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_gmm.m | 1,332 | utf_8 | 76782cae6c98781c6c38d4cbf5549d94 | function results = vl_test_gmm(varargin)
% VL_TEST_GMM
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
vl_test_init ;
end
function s = setup()
randn('st... |
github | layumi/2015_Face_Detection-master | vl_test_twister.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_twister.m | 1,251 | utf_8 | 2bfb5a30cbd6df6ac80c66b73f8646da | function results = vl_test_twister(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_illegal_args()
vl_assert_exception(@() vl_twister(-1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister(1, -1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister([1, -1]), 'vl:invalidArgument') ;
function te... |
github | layumi/2015_Face_Detection-master | vl_test_kdtree.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_kdtree.m | 2,449 | utf_8 | 9d7ad2b435a88c22084b38e5eb5f9eb9 | function results = vl_test_kdtree(varargin)
% VL_TEST_KDTREE
vl_test_init ;
function s = setup()
randn('state',0) ;
s.X = single(randn(10, 1000)) ;
s.Q = single(randn(10, 10)) ;
function test_nearest(s)
for tmethod = {'median', 'mean'}
for type = {@single, @double}
conv = type{1} ;
tmethod = char(tmethod) ;... |
github | layumi/2015_Face_Detection-master | vl_test_imwbackward.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_imwbackward.m | 514 | utf_8 | 33baa0784c8f6f785a2951d7f1b49199 | function results = vl_test_imwbackward(varargin)
% VL_TEST_IMWBACKWARD
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
function test_identity(s)
xr = 1:size(s.I,2) ;
yr = 1:size(s.I,1) ;
[x,y] = meshgrid(xr,yr) ;
vl_assert_almost_equal(s.I, vl_imwbackward(xr,yr,s.I,... |
github | layumi/2015_Face_Detection-master | vl_test_alphanum.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_alphanum.m | 1,624 | utf_8 | 2da2b768c2d0f86d699b8f31614aa424 | function results = vl_test_alphanum(varargin)
% VL_TEST_ALPHANUM
vl_test_init ;
function s = setup()
s.strings = ...
{'1000X Radonius Maximus','10X Radonius','200X Radonius','20X Radonius','20X Radonius Prime','30X Radonius','40X Radonius','Allegia 50 Clasteron','Allegia 500 Clasteron','Allegia 50B Clasteron','Al... |
github | layumi/2015_Face_Detection-master | vl_test_printsize.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_printsize.m | 1,447 | utf_8 | 0f0b6437c648b7a2e1310900262bd765 | function results = vl_test_printsize(varargin)
% VL_TEST_PRINTSIZE
vl_test_init ;
function s = setup()
s.fig = figure(1) ;
s.usletter = [8.5, 11] ; % inches
s.a4 = [8.26772, 11.6929] ;
clf(s.fig) ; plot(1:10) ;
function teardown(s)
close(s.fig) ;
function test_basic(s)
for sigma = [1 0.5 0.2]
vl_printsize(s.fig, s... |
github | layumi/2015_Face_Detection-master | vl_test_cummax.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_cummax.m | 838 | utf_8 | 5e98ee1681d4823f32ecc4feaa218611 | function results = vl_test_cummax(varargin)
% VL_TEST_CUMMAX
vl_test_init ;
function test_basic()
vl_assert_almost_equal(...
vl_cummax(1), 1) ;
vl_assert_almost_equal(...
vl_cummax([1 2 3 4], 2), [1 2 3 4]) ;
function test_multidim()
a = [1 2 3 4 3 2 1] ;
b = [1 2 3 4 4 4 4] ;
for k=1:6
dims = ones(1,6) ;
dim... |
github | layumi/2015_Face_Detection-master | vl_test_imintegral.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_imintegral.m | 1,429 | utf_8 | 4750f04ab0ac9fc4f55df2c8583e5498 | function results = vl_test_imintegral(varargin)
% VL_TEST_IMINTEGRAL
vl_test_init ;
function state = setup()
state.I = ones(5,6) ;
state.correct = [ 1 2 3 4 5 6 ;
2 4 6 8 10 12 ;
3 6 9 12 15 18 ;
4 8 12 ... |
github | layumi/2015_Face_Detection-master | vl_test_sift.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_sift.m | 1,318 | utf_8 | 806c61f9db9f2ebb1d649c9bfcf3dc0a | function results = vl_test_sift(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
s.I = im2single(imread(fullfile(vl_root,'data','box.pgm'))) ;
[s.ubc.f, s.ubc.d] = ...
vl_ubcread(fullfile(vl_root,'data','box.sift')) ;
function test_ubc_descriptor(s)
err = [] ;
[f, d] = vl_sift(s.I,...
... |
github | layumi/2015_Face_Detection-master | vl_test_binsum.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_binsum.m | 1,377 | utf_8 | f07f0f29ba6afe0111c967ab0b353a9d | function results = vl_test_binsum(varargin)
% VL_TEST_BINSUM
vl_test_init ;
function test_three_args()
vl_assert_almost_equal(...
vl_binsum([0 0], 1, 2), [0 1]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, 1), [0 7]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, [1 2 2 2 2 2 2 2]), [0 0]) ;
function te... |
github | layumi/2015_Face_Detection-master | vl_test_lbp.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_lbp.m | 892 | utf_8 | a79c0ce0c85e25c0b1657f3a0b499538 | function results = vl_test_lbp(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_unfiorm_lbps(s)
% enumerate the 56 uniform lbps
q = 0 ;
for i=0:7
for j=1:7
I = zeros(3) ;
p = mod(s.pixels - i + 8, 8) + 1 ;
I(p <= j) = 1 ;
f = vl_lbp(single(I), 3) ;
q = q + 1 ;
vl_assert_equal(find(f... |
github | layumi/2015_Face_Detection-master | vl_test_colsubset.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_colsubset.m | 828 | utf_8 | be0c080007445b36333b863326fb0f15 | function results = vl_test_colsubset(varargin)
% VL_TEST_COLSUBSET
vl_test_init ;
function s = setup()
s.x = [5 2 3 6 4 7 1 9 8 0] ;
function test_beginning(s)
vl_assert_equal(1:5, vl_colsubset(1:10, 5, 'beginning')) ;
vl_assert_equal(1:5, vl_colsubset(1:10, .5, 'beginning')) ;
function test_ending(s)
vl_assert_equa... |
github | layumi/2015_Face_Detection-master | vl_test_alldist.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_alldist.m | 2,373 | utf_8 | 9ea1a36c97fe715dfa2b8693876808ff | function results = vl_test_alldist(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
function s = setup()
vl_twister('state', 0) ;
s.X = 3.1 * vl_twister(10,10) ;
s.Y = 4.7 * vl_twister(10,7) ;
function test_null_args(s)
vl_assert_equal(...
vl_alldist(zeros(15,12), zeros(15,0), 'kl2'), ...
zeros(12,0)) ;
vl_assert_equa... |
github | layumi/2015_Face_Detection-master | vl_test_ihashsum.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_ihashsum.m | 581 | utf_8 | edc283062469af62056b0782b171f5fc | function results = vl_test_ihashsum(varargin)
% VL_TEST_IHASHSUM
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(round(16*rand(2,100))) ;
sel = find(all(s.data==0)) ;
s.data(1,sel)=1 ;
function test_hash(s)
D = size(s.data,1) ;
K = 5 ;
h = zeros(1,K,'uint32') ;
id = zeros(D,K,'uint8');
next = zer... |
github | layumi/2015_Face_Detection-master | vl_test_grad.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_grad.m | 434 | utf_8 | 4d03eb33a6a4f68659f868da95930ffb | function results = vl_test_grad(varargin)
% VL_TEST_GRAD
vl_test_init ;
function s = setup()
s.I = rand(150,253) ;
s.I_small = rand(2,2) ;
function test_equiv(s)
vl_assert_equal(gradient(s.I), vl_grad(s.I)) ;
function test_equiv_small(s)
vl_assert_equal(gradient(s.I_small), vl_grad(s.I_small)) ;
function test_equiv... |
github | layumi/2015_Face_Detection-master | vl_test_whistc.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_whistc.m | 1,384 | utf_8 | 81c446d35c82957659840ab2a579ec2c | function results = vl_test_whistc(varargin)
% VL_TEST_WHISTC
vl_test_init ;
function test_acc()
x = ones(1, 10) ;
e = 1 ;
o = 1:10 ;
vl_assert_equal(vl_whistc(x, o, e), 55) ;
function test_basic()
x = 1:10 ;
e = 1:10 ;
o = ones(1, 10) ;
vl_assert_equal(histc(x, e), vl_whistc(x, o, e)) ;
x = linspace(-1,11,100) ;
o =... |
github | layumi/2015_Face_Detection-master | vl_test_roc.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_roc.m | 1,019 | utf_8 | 9b2ae71c9dc3eda0fc54c65d55054d0c | function results = vl_test_roc(varargin)
% VL_TEST_ROC
vl_test_init ;
function s = setup()
s.scores0 = [5 4 3 2 1] ;
s.scores1 = [5 3 4 2 1] ;
s.labels = [1 1 -1 -1 -1] ;
function test_perfect_tptn(s)
[tpr,tnr] = vl_roc(s.labels,s.scores0) ;
vl_assert_almost_equal(tpr, [0 1 2 2 2 2] / 2) ;
vl_assert_almost_equal(tnr,... |
github | layumi/2015_Face_Detection-master | vl_test_dsift.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_dsift.m | 2,048 | utf_8 | fbbfb16d5a21936c1862d9551f657ccc | function results = vl_test_dsift(varargin)
% VL_TEST_DSIFT
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = rgb2gray(single(I)) ;
function test_fast_slow(s)
binSize = 4 ; % bin size in pixels
magnif = 3 ; % bin size / keypoint scale
scale = binSize... |
github | layumi/2015_Face_Detection-master | vl_test_alldist2.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_alldist2.m | 2,284 | utf_8 | 89a787e3d83516653ae8d99c808b9d67 | function results = vl_test_alldist2(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
% TODO: test integer classes
function s = setup()
vl_twister('state', 0) ;
s.X = 3.1 * vl_twister(10,10) ;
s.Y = 4.7 * vl_twister(10,7) ;
function test_null_args(s)
vl_assert_equal(...
vl_alldist2(zeros(15,12), zeros(15,0), 'kl2'), ...
... |
github | layumi/2015_Face_Detection-master | vl_test_fisher.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_fisher.m | 2,097 | utf_8 | c9afd9ab635bd412cbf8be3c2d235f6b | function results = vl_test_fisher(varargin)
% VL_TEST_FISHER
vl_test_init ;
function s = setup()
randn('state',0) ;
dimension = 5 ;
numData = 21 ;
numComponents = 3 ;
s.x = randn(dimension,numData) ;
s.mu = randn(dimension,numComponents) ;
s.sigma2 = ones(dimension,numComponents) ;
s.prior = ones(1,numComponents) ;
s... |
github | layumi/2015_Face_Detection-master | vl_test_imsmooth.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_imsmooth.m | 1,837 | utf_8 | 718235242cad61c9804ba5e881c22f59 | function results = vl_test_imsmooth(varargin)
% VL_TEST_IMSMOOTH
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
I = max(min(vl_imdown(I),1),0) ;
s.I = single(I) ;
function test_pad_by_continuity(s)
% Convolving a constant signal padded with continuity does not change... |
github | layumi/2015_Face_Detection-master | vl_test_svmtrain.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_svmtrain.m | 4,277 | utf_8 | 071b7c66191a22e8236fda16752b27aa | function results = vl_test_svmtrain(varargin)
% VL_TEST_SVMTRAIN
vl_test_init ;
end
function s = setup()
randn('state',0) ;
Np = 10 ;
Nn = 10 ;
xp = diag([1 3])*randn(2, Np) ;
xn = diag([1 3])*randn(2, Nn) ;
xp(1,:) = xp(1,:) + 2 + 1 ;
xn(1,:) = xn(1,:) - 2 + 1 ;
s.x = [xp xn] ;
s.y = [ones(1,Np) ... |
github | layumi/2015_Face_Detection-master | vl_test_phow.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_phow.m | 549 | utf_8 | f761a3bb218af855986263c67b2da411 | function results = vl_test_phow(varargin)
% VL_TEST_PHOPW
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = single(s.I) ;
function test_gray(s)
[f,d] = vl_phow(s.I, 'color', 'gray') ;
assert(size(d,1) == 128) ;
function test_rgb(s)
[f,d] = vl_phow(s.I, 'color',... |
github | layumi/2015_Face_Detection-master | vl_test_kmeans.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_kmeans.m | 3,632 | utf_8 | 0e1d6f4f8101c8982a0e743e0980c65a | function results = vl_test_kmeans(varargin)
% VL_TEST_KMEANS
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
vl_test_init ;
function s = setup()
randn('sta... |
github | layumi/2015_Face_Detection-master | vl_test_hikmeans.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_hikmeans.m | 463 | utf_8 | dc3b493646e66316184e86ff4e6138ab | function results = vl_test_hikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[tree, assign] = vl_hikmeans(s.data,3,100) ;
assign_ = vl_hikmeanspush(tree, s.data) ;
vl_assert_equal(assign,assign_) ;
function test_elka... |
github | layumi/2015_Face_Detection-master | vl_test_aib.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_aib.m | 1,277 | utf_8 | 78978ae54e7ebe991d136336ba4bf9c6 | function results = vl_test_aib(varargin)
% VL_TEST_AIB
vl_test_init ;
function s = setup()
s = [] ;
function test_basic(s)
Pcx = [.3 .3 0 0
0 0 .2 .2] ;
% This results in the AIB tree
%
% 1 - \
% 5 - \
% 2 - / \
% - 7
% 3 - \ /
% 6 - /
% 4 - /
%
% coded by the map [5 ... |
github | layumi/2015_Face_Detection-master | vl_test_plotbox.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_plotbox.m | 414 | utf_8 | aa06ce4932a213fb933bbede6072b029 | function results = vl_test_plotbox(varargin)
% VL_TEST_PLOTBOX
vl_test_init ;
function test_basic(s)
figure(1) ; clf ;
vl_plotbox([-1 -1 1 1]') ;
xlim([-2 2]) ;
ylim([-2 2]) ;
close(1) ;
function test_multiple(s)
figure(1) ; clf ;
randn('state', 0) ;
vl_plotbox(randn(4,10)) ;
close(1) ;
function test_style(s)
figure... |
github | layumi/2015_Face_Detection-master | vl_test_imarray.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_imarray.m | 795 | utf_8 | c5e6a5aa8c2e63e248814f5bd89832a8 | function results = vl_test_imarray(varargin)
% VL_TEST_IMARRAY
vl_test_init ;
function test_movie_rgb(s)
A = rand(23,15,3,4) ;
B = vl_imarray(A,'movie',true) ;
function test_movie_indexed(s)
cmap = get(0,'DefaultFigureColormap') ;
A = uint8(size(cmap,1)*rand(23,15,4)) ;
A = min(A,size(cmap,1)-1) ;
B = vl_imarray(A,'m... |
github | layumi/2015_Face_Detection-master | vl_test_homkermap.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_homkermap.m | 1,903 | utf_8 | c157052bf4213793a961bde1f73fb307 | function results = vl_test_homkermap(varargin)
% VL_TEST_HOMKERMAP
vl_test_init ;
function check_ker(ker, n, window, period)
args = {n, ker, 'window', window} ;
if nargin > 3
args = {args{:}, 'period', period} ;
end
x = [-1 -.5 0 .5 1] ;
y = linspace(0,2,100) ;
for conv = {@single, @double}
x = feval(conv{1}, x) ;... |
github | layumi/2015_Face_Detection-master | vl_test_slic.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_slic.m | 200 | utf_8 | 12a6465e3ef5b4bcfd7303cd8a9229d4 | function results = vl_test_slic(varargin)
% VL_TEST_SLIC
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
function test_slic(s)
segmentation = vl_slic(s.im, 10, 0.1) ;
|
github | layumi/2015_Face_Detection-master | vl_test_ikmeans.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_ikmeans.m | 466 | utf_8 | 1ee2f647ac0035ed0d704a0cd615b040 | function results = vl_test_ikmeans(varargin)
% VL_TEST_IKMEANS
vl_test_init ;
function s = setup()
rand('state',0) ;
s.data = uint8(rand(2,1000) * 255) ;
function test_basic(s)
[centers, assign] = vl_ikmeans(s.data,100) ;
assign_ = vl_ikmeanspush(s.data, centers) ;
vl_assert_equal(assign,assign_) ;
function test_elk... |
github | layumi/2015_Face_Detection-master | vl_test_mser.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_mser.m | 242 | utf_8 | 1ad33563b0c86542a2978ee94e0f4a39 | function results = vl_test_mser(varargin)
% VL_TEST_MSER
vl_test_init ;
function s = setup()
s.im = im2uint8(rgb2gray(vl_impattern('roofs1'))) ;
function test_mser(s)
[regions,frames] = vl_mser(s.im) ;
mask = vl_erfill(s.im, regions(1)) ;
|
github | layumi/2015_Face_Detection-master | vl_test_inthist.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_inthist.m | 811 | utf_8 | 459027d0c54d8f197563a02ab66ef45d | function results = vl_test_inthist(varargin)
% VL_TEST_INTHIST
vl_test_init ;
function s = setup()
rand('state',0) ;
s.labels = uint32(8*rand(123, 76, 3)) ;
function test_basic(s)
l = 10 ;
hist = vl_inthist(s.labels, 'numlabels', l) ;
hist_ = inthist_slow(s.labels, l) ;
vl_assert_equal(double(hist),hist_) ;
function... |
github | layumi/2015_Face_Detection-master | vl_test_imdisttf.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_imdisttf.m | 1,885 | utf_8 | ae921197988abeb984cbcdf9eaf80e77 | function results = vl_test_imdisttf(varargin)
% VL_TEST_DISTTF
vl_test_init ;
function test_basic()
for conv = {@single, @double}
conv = conv{1} ;
I = conv([0 0 0 ; 0 -2 0 ; 0 0 0]) ;
D = vl_imdisttf(I);
assert(isequal(D, conv(- [0 1 0 ; 1 2 1 ; 0 1 0]))) ;
I(2,2) = -3 ;
[D,map] = vl_imdisttf(I) ;
asse... |
github | layumi/2015_Face_Detection-master | vl_test_vlad.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_vlad.m | 1,977 | utf_8 | d3797288d6edb1d445b890db3780c8ce | function results = vl_test_vlad(varargin)
% VL_TEST_VLAD
vl_test_init ;
function s = setup()
randn('state',0) ;
s.x = randn(128,256) ;
s.mu = randn(128,16) ;
assignments = rand(16, 256) ;
s.assignments = bsxfun(@times, assignments, 1 ./ sum(assignments,1)) ;
function test_basic (s)
x = [1, 2, 3] ;
mu = [0, 0, 0] ;
a... |
github | layumi/2015_Face_Detection-master | vl_test_pr.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_pr.m | 3,763 | utf_8 | 4d1da5ccda1a7df2bec35b8f12fdd620 | function results = vl_test_pr(varargin)
% VL_TEST_PR
vl_test_init ;
function s = setup()
s.scores0 = [5 4 3 2 1] ;
s.scores1 = [5 3 4 2 1] ;
s.labels = [1 1 -1 -1 -1] ;
function test_perfect_tptn(s)
[rc,pr] = vl_pr(s.labels,s.scores0) ;
vl_assert_almost_equal(pr, [1 1/1 2/2 2/3 2/4 2/5]) ;
vl_assert_almost_equal(rc, ... |
github | layumi/2015_Face_Detection-master | vl_test_hog.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_hog.m | 1,555 | utf_8 | eed7b2a116d142040587dc9c4eb7cd2e | function results = vl_test_hog(varargin)
% VL_TEST_HOG
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
[x,y]= meshgrid(linspace(-1,1,128)) ;
s.round = single(x.^2+y.^2);
s.imSmall = s.im(1:128,1:128,:) ;
s.imSmall = s.im ;
s.imSmallFlipped = s.imSmall(:,end:-1:1,:) ;
function test_basic... |
github | layumi/2015_Face_Detection-master | vl_test_argparse.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_argparse.m | 795 | utf_8 | e72185b27206d0ee1dfdc19fe77a5be6 | function results = vl_test_argparse(varargin)
% VL_TEST_ARGPARSE
vl_test_init ;
function test_basic()
opts.field1 = 1 ;
opts.field2 = 2 ;
opts.field3 = 3 ;
opts_ = opts ;
opts_.field1 = 3 ;
opts_.field2 = 10 ;
opts = vl_argparse(opts, {'field2', 10, 'field1', 3}) ;
assert(isequal(opts, opts_)) ;
opts_.field1 = 9 ;
... |
github | layumi/2015_Face_Detection-master | vl_test_liop.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_liop.m | 1,023 | utf_8 | a162be369073bed18e61210f44088cf3 | function results = vl_test_liop(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
randn('state',0) ;
s.patch = randn(65,'single') ;
xr = -32:32 ;
[x,y] = meshgrid(xr) ;
s.blob = - single(x.^2+y.^2) ;
function test_basic(s)
d = vl_liop(s.patch) ;
function test_blob(s)
% with a blob, all local intensity ord... |
github | layumi/2015_Face_Detection-master | vl_test_binsearch.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/xtest/vl_test_binsearch.m | 1,339 | utf_8 | 85dc020adce3f228fe7dfb24cf3acc63 | function results = vl_test_binsearch(varargin)
% VL_TEST_BINSEARCH
vl_test_init ;
function test_inf_bins()
x = [-inf -1 0 1 +inf] ;
vl_assert_equal(vl_binsearch([], x), [0 0 0 0 0]) ;
vl_assert_equal(vl_binsearch([-inf 0], x), [1 1 2 2 2]) ;
vl_assert_equal(vl_binsearch([-inf], x), [1 1 1 1 1]) ;
vl_a... |
github | layumi/2015_Face_Detection-master | vl_roc.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/plotop/vl_roc.m | 10,113 | utf_8 | 22fd8ff455ee62a96ffd94b9074eafeb | function [tpr,tnr,info] = vl_roc(labels, scores, varargin)
%VL_ROC ROC curve.
% [TPR,TNR] = VL_ROC(LABELS, SCORES) computes the Receiver Operating
% Characteristic (ROC) curve [1]. LABELS is a row vector of ground
% truth labels, greater than zero for a positive sample and smaller
% than zero for a negative o... |
github | layumi/2015_Face_Detection-master | vl_click.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/plotop/vl_click.m | 2,661 | utf_8 | 6982e869cf80da57fdf68f5ebcd05a86 | function P = vl_click(N,varargin) ;
% VL_CLICK Click a point
% P=VL_CLICK() let the user click a point in the current figure and
% returns its coordinates in P. P is a two dimensiona vectors where
% P(1) is the point X-coordinate and P(2) the point Y-coordinate. The
% user can abort the operation by pressing any k... |
github | layumi/2015_Face_Detection-master | vl_pr.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/plotop/vl_pr.m | 9,138 | utf_8 | c7fe6832d2b6b9917896810c52a05479 | function [recall, precision, info] = vl_pr(labels, scores, varargin)
%VL_PR Precision-recall curve.
% [RECALL, PRECISION] = VL_PR(LABELS, SCORES) computes the
% precision-recall (PR) curve. LABELS are the ground truth labels,
% greather than zero for a positive sample and smaller than zero for
% a negative on... |
github | layumi/2015_Face_Detection-master | vl_ubcread.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/sift/vl_ubcread.m | 3,015 | utf_8 | e8ddd3ecd87e76b6c738ba153fef050f | function [f,d] = vl_ubcread(file, varargin)
% SIFTREAD Read Lowe's SIFT implementation data files
% [F,D] = VL_UBCREAD(FILE) reads the frames F and the descriptors D
% from FILE in UBC (Lowe's original implementation of SIFT) format
% and returns F and D as defined by VL_SIFT().
%
% VL_UBCREAD(FILE, 'FORMAT', '... |
github | layumi/2015_Face_Detection-master | vl_frame2oell.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/sift/vl_frame2oell.m | 2,806 | utf_8 | c93792632f630743485fa4c2cf12d647 | function eframes = vl_frame2oell(frames)
% VL_FRAMES2OELL Convert a geometric frame to an oriented ellipse
% EFRAME = VL_FRAME2OELL(FRAME) converts the generic FRAME to an
% oriented ellipses EFRAME. FRAME and EFRAME can be matrices, with
% one frame per column.
%
% A frame is either a point, a disc, an orien... |
github | layumi/2015_Face_Detection-master | vl_plotsiftdescriptor.m | .m | 2015_Face_Detection-master/vlfeat/toolbox/sift/vl_plotsiftdescriptor.m | 5,114 | utf_8 | a4e125a8916653f00143b61cceda2f23 | function h=vl_plotsiftdescriptor(d,f,varargin)
% VL_PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% VL_PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptor D. If D is a
% matrix, it plots one descriptor per column. D has the same format
% used by VL_SIFT().
%
% VL_PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warpe... |
github | layumi/2015_Face_Detection-master | phow_caltech101.m | .m | 2015_Face_Detection-master/vlfeat/apps/phow_caltech101.m | 11,594 | utf_8 | 7f4890a2e6844ca56debbfe23cca64f3 | function phow_caltech101()
% PHOW_CALTECH101 Image classification in the Caltech-101 dataset
% This program demonstrates how to use VLFeat to construct an image
% classifier on the Caltech-101 data. The classifier uses PHOW
% features (dense SIFT), spatial histograms of visual words, and a
% Chi2 SVM. To speedu... |
github | layumi/2015_Face_Detection-master | sift_mosaic.m | .m | 2015_Face_Detection-master/vlfeat/apps/sift_mosaic.m | 4,621 | utf_8 | 8fa3ad91b401b8f2400fb65944c79712 | function mosaic = sift_mosaic(im1, im2)
% SIFT_MOSAIC Demonstrates matching two images using SIFT and RANSAC
%
% SIFT_MOSAIC demonstrates matching two images based on SIFT
% features and RANSAC and computing their mosaic.
%
% SIFT_MOSAIC by itself runs the algorithm on two standard test
% images. Use SIFT_MOSAI... |
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