learn_detector.cc File Reference

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Detailed Description

Main file for the learn_detector executable.

learn_detector [--var value] ... [--exec config_file] ...

learn_detector reads configuration data from learn_detector.cfg in the current directory and acceps standard GVars3 command line arguments for setting variables and running other configuration files.

The tree is serialized by the function tree_element::print(). This tree can be extracted from the output with the following command:

awk 'a&&!NF{exit}a;/Final tree/{a=1}' filename

This file contains a direct implementation of section V of the accompanying paper, in the function learn_detector. For more information, refer to the section on optimization.

Configuration.

The default parameters for learn_detector are in learn_detector.cfg, which are the parameters described in to paper. They are:

//Training data
//Make this point to the directory containing the repeatability data
repeatability_dataset.directory=/home/edrosten/data/repeatability/box
repeatability_dataset.size=3
repeatability_dataset.format=cam

//Distince determining whether a point is repeated
fuzz=5

//Iteration parameters
Temperature.expo.scale=100
Temperature.expo.alpha=30
iterations=100000

//Threshold to use
FAST_threshold=35

//Cost function parameters
repeatability_scale=1
num_cost = 3500
max_nodes=10000

//Random tree parameters
initial_tree_depth=2
offsets.min_radius=2.0
offsets.max_radius=4.2


//Change this one for different sequences
random_seed=84175664

// Debugging
debug.print_old_tree=0
debug.print_new_tree=0
debug.verify_detections=1
debug.verify_scores=1

gvarlist
echo
echo Log starts
echo ==========
echo

Variables can be overridden using the --varname value commandline syntax. For details on how the data loading and so on operated, refer to run_learn_detector.

Definition in file learn_detector.cc.

#include <iostream>
#include <fstream>
#include <climits>
#include <float.h>
#include <cstring>
#include <cerrno>
#include <cmath>
#include <vector>
#include <utility>
#include <algorithm>
#include <cvd/image_io.h>
#include <cvd/random.h>
#include <cvd/vector_image_ref.h>
#include <tag/tuple.h>
#include <tag/stdpp.h>
#include <tag/fn.h>
#include <tag/printf.h>
#include <TooN/TooN.h>
#include "gvars_vector.h"
#include "faster_tree.h"
#include "faster_bytecode.h"
#include "offsets.h"
#include "utility.h"
#include "load_data.h"

Go to the source code of this file.

Functions
double	sq (double d)
vector< int >	range (int num)
vector< ImageRef >	generate_disc (int radius)
Image< bool >	paint_circles (const vector< ImageRef > &corners, const vector< ImageRef > &circle, ImageRef size)
float	compute_repeatability (const vector< vector< Image< array< float, 2 > > > > &warps, const vector< vector< ImageRef > > &corners, int r, ImageRef size)
tree_element *	random_tree (int d, bool is_eq_branch=1)
double	compute_temperature (int i, int imax)
tree_element *	learn_detector (const vector< Image< byte > > &images, const vector< vector< Image< array< float, 2 > > > > &warps)
void	run_learn_detector (int argc, char **argv)
int	main (int argc, char **argv)

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