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// Copyright (C) 2011 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_SCAN_IMaGE_PYRAMID_TOOLS_ABSTRACT_H__
#ifdef DLIB_SCAN_IMaGE_PYRAMID_TOOLS_ABSTRACT_H__
#include "scan_image_pyramid_abstract.h"
#include "../lsh/projection_hash_abstract.h"
#include "../image_keypoint/hashed_feature_image_abstract.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
class image_hash_construction_failure : public error
{
/*!
WHAT THIS OBJECT REPRESENTS
This is the exception object used by the routines in this file.
!*/
};
// ----------------------------------------------------------------------------------------
template <
typename image_array,
typename pyramid,
typename feature_extractor
>
void setup_hashed_features (
scan_image_pyramid<pyramid, hashed_feature_image<feature_extractor, projection_hash> >& scanner,
const image_array& images,
const feature_extractor& fe,
int bits,
unsigned long num_samples = 200000
);
/*!
requires
- 0 < bits <= 32
- num_samples > 1
- images.size() > 0
- it must be valid to pass images[0] into scanner.load().
(also, image_array must be an implementation of dlib/array/array_kernel_abstract.h)
ensures
- Creates a projection_hash suitable for hashing the feature vectors produced by
fe and then configures scanner to use this hash function.
- The hash function will map vectors into integers in the range [0, pow(2,bits))
- The hash function will be setup so that it hashes a random sample of num_samples
vectors from fe such that each bin ends up with roughly the same number of
elements in it.
throws
- image_hash_construction_failure
This exception is thrown if there is a problem creating the projection_hash.
This should only happen the images are so small they contain less than 2
feature vectors.
!*/
// ----------------------------------------------------------------------------------------
template <
typename image_array,
typename pyramid,
typename feature_extractor
>
void setup_hashed_features (
scan_image_pyramid<pyramid, hashed_feature_image<feature_extractor, projection_hash> >& scanner,
const image_array& images,
int bits,
unsigned long num_samples = 200000
);
/*!
requires
- 0 < bits <= 32
- num_samples > 1
- images.size() > 0
- it must be valid to pass images[0] into scanner.load().
(also, image_array must be an implementation of dlib/array/array_kernel_abstract.h)
ensures
- performs: setup_hashed_features(scanner, images, feature_extractor(), bits, num_samples)
throws
- image_hash_construction_failure
This exception is thrown if there is a problem creating the projection_hash.
This should only happen the images are so small they contain less than 2
feature vectors.
!*/
// ----------------------------------------------------------------------------------------
template <
typename image_scanner_type
>
std::vector<rectangle> determine_object_boxes (
const image_scanner_type& scanner,
const std::vector<rectangle>& rects,
double min_match_score
);
/*!
requires
- 0 < min_match_score <= 1
- image_scanner_type == an implementation of the scan_image_pyramid
object defined in dlib/image_processing/scan_image_pyramid_tools_abstract.h
ensures
- returns a set of object boxes which, when used as detection
templates with the given scanner, can attain at least
min_match_score alignment with every element of rects. Note that
the alignment between two rectangles A and B is defined as
(A.intersect(B).area())/(double)(A+B).area()
!*/
// ----------------------------------------------------------------------------------------
template <
typename image_scanner_type
>
std::vector<rectangle> determine_object_boxes (
const image_scanner_type& scanner,
const std::vector<std::vector<rectangle> >& rects,
double min_match_score
);
/*!
requires
- 0 < min_match_score <= 1
- image_scanner_type == an implementation of the scan_image_pyramid
object defined in dlib/image_processing/scan_image_pyramid_tools_abstract.h
ensures
- copies all rectangles in rects into a std::vector<rectangle> object, call it
R. Then this function returns determine_object_boxes(scanner,R,min_match_score).
That is, it just called the version of determine_object_boxes() defined above
and returns the results.
!*/
// ----------------------------------------------------------------------------------------
template <
typename image_scanner_type
>
void setup_grid_detection_templates (
image_scanner_type& scanner,
const std::vector<std::vector<rectangle> >& rects,
unsigned int cells_x,
unsigned int cells_y,
double min_match_score = 0.75
);
/*!
requires
- cells_x > 0
- cells_y > 0
- 0 < min_match_score <= 1
- image_scanner_type == an implementation of the scan_image_pyramid
object defined in dlib/image_processing/scan_image_pyramid_tools_abstract.h
ensures
- uses determine_object_boxes(scanner,rects,min_match_score) to obtain a set of
object boxes and then adds them to the given scanner object as detection templates.
Also uses create_grid_detection_template(object_box, cells_x, cells_y) to create
each feature extraction region. Therefore, the detection templates will extract
features from a regular grid inside each object box.
!*/
// ----------------------------------------------------------------------------------------
template <
typename image_scanner_type
>
void setup_grid_detection_templates_verbose (
image_scanner_type& scanner,
const std::vector<std::vector<rectangle> >& rects,
unsigned int cells_x,
unsigned int cells_y,
double min_match_score = 0.75
);
/*!
requires
- cells_x > 0
- cells_y > 0
- 0 < min_match_score <= 1
- image_scanner_type == an implementation of the scan_image_pyramid
object defined in dlib/image_processing/scan_image_pyramid_tools_abstract.h
ensures
- this function is identical to setup_grid_detection_templates() except
that it also outputs the selected detection templates to standard out.
!*/
}
#endif // DLIB_SCAN_IMaGE_PYRAMID_TOOLS_ABSTRACT_H__
|
