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esvm_demo_train_synthetic.m
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esvm_demo_train_synthetic.m
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% DEMO: Training Exemplar-SVMs from synthetic data
% This function can generate a nice HTML page by calling:
% publish('esvm_demo_train_synthetic.m','html')
%
% Copyright (C) 2011-12 by Tomasz Malisiewicz
% All rights reserved.
%
% This file is part of the Exemplar-SVM library and is made
% available under the terms of the MIT license (see COPYING file).
% Project homepage: https://github.com/quantombone/exemplarsvm
%
% In this demo, I create a random dataset of circular patterns on a
% random background of noise with extra noise sprinkled on top.
% Because the circles are synthetically generated, we have access
% to ground-truth locations of those circles and these are used to
% define the positive bounding boxes. The learned Exemplar-SVMs
% plus the calibration M-matrix are first learned, then applied to
% a testing set of images along with the top detections.
function [models,M] = esvm_demo_train_synthetic
addpath(genpath(pwd))
%% Create a synthetic dataset of circles on a random background
% The resulting images are also corrupted with noise to provide a
% more difficult case. Negative images are random noise images
% without any circular pattern.
Npos = 100;
Nneg = 50;
[pos_set,neg_set] = esvm_generate_dataset(Npos,Nneg);
models_name = 'circle';
%% Set exemplar-initialization parameters
params = esvm_get_default_params;
params.init_params.sbin = 4;
params.init_params.MAXDIM = 6;
params.model_type = 'exemplar';
%enable display so that nice visualizations pop up during learning
params.dataset_params.display = 1;
%if localdir is not set, we do not dump files
%params.dataset_params.localdir = '/nfs/baikal/tmalisie/synthetic/';
%%Initialize exemplar stream
stream_params.stream_set_name = 'trainval';
stream_params.stream_max_ex = 10;
stream_params.must_have_seg = 0;
stream_params.must_have_seg_string = '';
stream_params.model_type = 'exemplar'; %must be scene or exemplar
%assign pos_set as variable, because we need it for visualization
stream_params.pos_set = pos_set;
stream_params.cls = models_name;
%% Get the positive stream
e_stream_set = esvm_get_pascal_stream(stream_params, ...
params.dataset_params);
% break it up into a set of held out negatives, and the ones used
% for mining
val_neg_set = neg_set((Nneg/2+1):end);
neg_set = neg_set(1:((Nneg/2)));
%% Initialize Exemplars
% Each exemplar will have a figure, where on the first image is
% the exemplar's image, along with the exemplar bounding box and
% HOG grid overlayed. The second image shows the HOG mask along
% with its offset to the ground-truth bounding box. The third
% image shows the initial HOG features used to define the exemplar.
initial_models = esvm_initialize_exemplars(e_stream_set, params, ...
models_name);
%% Set exemplar-svm training parameters
train_params = params;
train_params.detect_max_scale = 1.0;
train_params.train_max_mined_images = 50;
train_params.detect_exemplar_nms_os_threshold = 1.0;
train_params.detect_max_windows_per_exemplar = 100;
%% Perform Exemplar-SVM training
% Because display is turned on, we will show the result of each
% exemplar's training iteration. Each iteration shows a
% diagnostic first column then the remaining rows are the top
% negative support vectors used to define the exemplar's decision
% boundary. The diagnostic row shows: exemplar, w's positive
% part, w's negative part, and four mean support vector images,
% where the means are computed with the first 1:N/4, 1:N/2, .. ,
% 1:N support vectors.
[models] = esvm_train_exemplars(initial_models, ...
neg_set, train_params);
%% Create validation set from positives and extra negatives
val_params = params;
val_params.detect_exemplar_nms_os_threshold = 0.5;
val_params.gt_function = @esvm_load_gt_function;
val_set = cat(1, pos_set(:), val_neg_set(:));
val_set_name = 'valset';
%% Apply trained exemplars on validation set
val_grid = esvm_detect_imageset(val_set, models, val_params, val_set_name);
%% Perform Platt calibration and M-matrix estimation
% The display will include the fit sigmoid, positive exemplar, as
% well as the top detections.
M = esvm_perform_calibration(val_grid, val_set, models, val_params);
%% Define test-set
Ntest = 50;
test_set = esvm_generate_dataset(Ntest);
test_params = params;
test_params.detect_exemplar_nms_os_threshold = 0.5;
test_set_name = 'testset';
%% Apply on test set
test_grid = esvm_detect_imageset(test_set, models, test_params, test_set_name);
%% Apply calibration matrix to test-set results
test_struct = esvm_pool_exemplar_dets(test_grid, models, M, test_params);
%% Show top detections
% Each resulting figure will show the source exemplar/weights (left
% column)from the top detection as well as the detection box in the
% resulting image (top right) and the exemplar inpainting (bottom
% right).
maxk = 20;
allbbs = esvm_show_top_dets(test_struct, test_grid, test_set, models, ...
params, maxk, test_set_name);