// 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_ABSTRACT_H__
#ifdef DLIB_SCAN_IMaGE_PYRAMID_ABSTRACT_H__
#include "../matrix.h"
#include "../geometry.h"
#include "../image_processing.h"
#include "../array2d.h"
#include <vector>
namespace dlib
{
// ----------------------------------------------------------------------------------------
template <
typename Pyramid_type,
typename Feature_extractor_type
>
class scan_image_pyramid : noncopyable
{
/*!
REQUIREMENTS ON Pyramid_type
- must be one of the pyramid_down objects defined in
dlib/image_transforms/image_pyramid_abstract.h or an object with
a compatible interface
REQUIREMENTS ON Feature_extractor_type
- must be an object with an interface compatible with the hashed_feature_image
object defined in dlib/image_keypoint/hashed_feature_image_abstract.h or
with the nearest_neighbor_feature_image object defined in
dlib/image_keypoint/nearest_neighbor_feature_image_abstract.h
INITIAL VALUE
- get_num_detection_templates() == 0
- is_loaded_with_image() == false
- get_max_detections_per_template() == 10000
- get_max_pyramid_levels() == 1000
- get_min_pyramid_layer_width() == 20
- get_min_pyramid_layer_height() == 20
WHAT THIS OBJECT REPRESENTS
This object is a tool for running a sliding window classifier over
an image pyramid. This object can also be understood as a general
tool for implementing the spatial pyramid models described in the paper:
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing
Natural Scene Categories by Svetlana Lazebnik, Cordelia Schmid,
and Jean Ponce
The sliding window classifiers used by this object have three parts:
1. The underlying feature extraction provided by Feature_extractor_type
objects, which associate a vector with each location in an image.
2. A detection template. This is a rectangle which defines the shape of a
sliding window (the object_box), as well as a set of rectangles which
envelop it. This set of enveloping rectangles defines the spatial
structure of the overall feature extraction within a sliding window.
In particular, each location of a sliding window has a feature vector
associated with it. This feature vector is defined as follows:
- Let N denote the number of enveloping rectangles.
- Let M denote the dimensionality of the vectors output by Feature_extractor_type
objects.
- Let F(i) == the M dimensional vector which is the sum of all vectors
given by our Feature_extractor_type object inside the ith enveloping
rectangle.
- Then the feature vector for a sliding window is an M*N dimensional vector
[F(1) F(2) F(3) ... F(N)] (i.e. it is a concatenation of the N vectors).
This feature vector can be thought of as a collection of N "bags of features",
each bag coming from a spatial location determined by one of the enveloping
rectangles.
3. A weight vector and a threshold value. The dot product between the weight
vector and the feature vector for a sliding window location gives the score
of the window. If this score is greater than the threshold value then the
window location is output as a detection.
Finally, the sliding window classifiers described above are applied to every level
of an image pyramid.
THREAD SAFETY
Concurrent access to an instance of this object is not safe and should be protected
by a mutex lock except for the case where you are copying the configuration
(via copy_configuration()) of a scan_image_pyramid object to many other threads.
In this case, it is safe to copy the configuration of a shared object so long
as no other operations are performed on it.
!*/
public:
typedef matrix<double,0,1> feature_vector_type;
typedef Pyramid_type pyramid_type;
typedef Feature_extractor_type feature_extractor_type;
scan_image_pyramid (
);
/*!
ensures
- this object is properly initialized
!*/
template <
typename image_type
>
void load (
const image_type& img
);
/*!
requires
- image_type must be a type with the following properties:
- image_type is default constructable.
- image_type is swappable by the global swap() function.
- image_type logically represents some kind of image and therefore
has .nr() and .nc() member functions. .nr() should return the
number of rows while .nc() returns the number of columns.
- image_type objects can be loaded into Feature_extractor_type
objects via Feature_extractor_type::load().
- image_type objects can be used with Pyramid_type. That is,
if pyr is an object of type Pyramid_type while img1 and img2
are objects of image_type, then pyr(img1,img2) should be
a valid expression which downsamples img1 into img2.
ensures
- #is_loaded_with_image() == true
- This object is ready to run sliding window classifiers over img. Call
detect() to do this.
!*/
bool is_loaded_with_image (
) const;
/*!
ensures
- returns true if this object has been loaded with an image to process
and false otherwise.
!*/
void copy_configuration(
const feature_extractor_type& fe
);
/*!
ensures
- Let BASE_FE denote the feature_extractor_type object used
internally for local feature extraction. Then this function
performs BASE_FE.copy_configuration(fe)
(i.e. this function allows you to configure the parameters of the
underlying feature extractor used by a scan_image_pyramid object)
!*/
void copy_configuration (
const scan_image_pyramid& item
);
/*!
ensures
- copies all the state information of item into *this, except for state
information populated by load(). More precisely, given two scan_image_pyramid
objects S1 and S2, the following sequence of instructions should always
result in both of them having the exact same state.
S2.copy_configuration(S1);
S1.load(img);
S2.load(img);
!*/
void add_detection_template (
const rectangle& object_box,
const std::vector<rectangle>& feature_extraction_regions
);
/*!
requires
- center(object_box) == point(0,0),
- if (get_num_detection_templates() > 0) then
- get_num_components_per_detection_template() == feature_extraction_regions.size()
(i.e. if you already have detection templates in this object, then
any new detection template must declare a consistent number of
feature extraction regions)
ensures
- Adds another detection template to this object. In particular, object_box
defines the size and shape of a sliding window while feature_extraction_regions
defines the locations for feature extraction as discussed in the WHAT THIS
OBJECT REPRESENTS section above. Note also that the locations of the feature
extraction regions are relative to the object_box.
- #get_num_detection_templates() == get_num_detection_templates() + 1
- The order of rectangles in feature_extraction_regions matters. Recall that
each rectangle gets its own set of features. So given two different templates,
their ith rectangles will both share the same part of the weight vector (w)
supplied to detect(). So there should be some reasonable correspondence
between the rectangle ordering in different detection templates. For,
example, different detection templates should place corresponding
feature extraction regions in roughly the same part of the object_box.
!*/
unsigned long get_num_detection_templates (
) const;
/*!
ensures
- returns the number of detection templates in this object
!*/
unsigned long get_num_components_per_detection_template (
) const;
/*!
requires
- get_num_detection_templates() > 0
ensures
- A detection template is a rectangle which defines the shape of a
sliding window (the object_box), as well as a set of rectangles which
envelop it. This function returns the number of enveloping rectangles
in the detection templates used by this object.
!*/
long get_num_dimensions (
) const;
/*!
requires
- get_num_detection_templates() > 0
ensures
- returns the number of dimensions in the feature vector for a sliding window
location. This value is the dimensionality of the underlying feature vectors
produced by Feature_extractor_type times get_num_components_per_detection_template().
!*/
unsigned long get_max_pyramid_levels (
) const;
/*!
ensures
- returns the maximum number of image pyramid levels this object will use.
Note that #get_max_pyramid_levels() == 1 indicates that no image pyramid
will be used at all. That is, only the original image will be processed
and no lower scale versions will be created.
!*/
void set_max_pyramid_levels (
unsigned long max_levels
);
/*!
requires
- max_levels > 0
ensures
- #get_max_pyramid_levels() == max_levels
!*/
void set_min_pyramid_layer_size (
unsigned long width,
unsigned long height
);
/*!
requires
- width > 0
- height > 0
ensures
- #get_min_pyramid_layer_width() == width
- #get_min_pyramid_layer_height() == height
!*/
inline unsigned long get_min_pyramid_layer_width (
) const;
/*!
ensures
- returns the smallest allowable width of an image in the image pyramid.
All pyramids will always include the original input image, however, no
pyramid levels will be created which have a width smaller than the
value returned by this function.
!*/
inline unsigned long get_min_pyramid_layer_height (
) const;
/*!
ensures
- returns the smallest allowable height of an image in the image pyramid.
All pyramids will always include the original input image, however, no
pyramid levels will be created which have a height smaller than the
value returned by this function.
!*/
unsigned long get_max_detections_per_template (
) const;
/*!
ensures
- For each image pyramid layer and detection template, this object scans a sliding
window classifier over an image and produces a number of detections. This
function returns a number which defines a hard upper limit on the number of
detections allowed by a single scan. This means that the total number of
possible detections produced by detect() is get_max_detections_per_template()*
get_num_detection_templates()*(number of image pyramid layers).
!*/
void set_max_detections_per_template (
unsigned long max_dets
);
/*!
requires
- max_dets > 0
ensures
- #get_max_detections_per_template() == max_dets
!*/
void detect (
const feature_vector_type& w,
std::vector<std::pair<double, rectangle> >& dets,
const double thresh
) const;
/*!
requires
- w.size() >= get_num_dimensions()
- is_loaded_with_image() == true
- get_num_detection_templates() > 0
ensures
- Scans all the detection templates over all pyramid layers as discussed in the
WHAT THIS OBJECT REPRESENTS section and stores all detections into #dets.
- for all valid i:
- #dets[i].second == The object box which produced this detection. This rectangle gives
the location of the detection. Note that the rectangle will have been converted back into
the original image input space. That is, if this detection was made at a low level in the
image pyramid then the object box will have been automatically mapped up the pyramid layers
to the original image space. Or in other words, if you plot #dets[i].second on top of the
image given to load() it will show up in the right place.
- #dets[i].first == The score for this detection. This value is equal to dot(w, feature vector
for this sliding window location).
- #dets[i].first >= thresh
- #dets will be sorted in descending order. (i.e. #dets[i].first >= #dets[j].first for all i, and j>i)
- Elements of w beyond index get_num_dimensions()-1 are ignored. I.e. only the first
get_num_dimensions() are used.
- Note that no form of non-max suppression is performed. If a window has a score >= thresh
then it is reported in #dets (assuming the limit imposed by get_max_detections_per_template() hasn't
been reached).
!*/
const rectangle get_best_matching_rect (
const rectangle& rect
) const;
/*!
requires
- get_num_detection_templates() > 0
ensures
- Since scan_image_pyramid is a sliding window classifier system, not all possible rectangles
can be represented. Therefore, this function allows you to supply a rectangle and obtain the
nearest possible sliding window rectangle.
!*/
void get_feature_vector (
const rectangle& rects,
feature_vector_type& psi
) const;
/*!
requires
- is_loaded_with_image() == true
- get_num_detection_templates() > 0
- psi.size() >= get_num_dimensions()
ensures
- This function allows you to determine the feature vector used for a sliding window location.
Note that this vector is added to psi.
- if (rect was produced by a call to detect(), i.e. rect contains an element of dets) then
- #psi == psi + the feature vector corresponding to the sliding window location indicated
by rect.
- Let w denote the w vector given to detect(), then if we assigned psi to 0 before calling
get_feature_vector() then we have:
- dot(w,#psi) == the score produced by detect() for rect.
- get_best_matching_rect(rect) == rect
- else
- Since scan_image_pyramid is a sliding window classifier system, not all possible rectangles can
be output by detect(). So in the case where rect could not arise from a call to detect(), this
function will map rect to the nearest possible object box and then add the feature vector for
the mapped rectangle into #psi.
- get_best_matching_rect(rect) == the rectangle rect gets mapped to for feature extraction.
!*/
};
// ----------------------------------------------------------------------------------------
template <
typename Pyramid_type,
typename Feature_extractor_type
>
void serialize (
const scan_image_pyramid<Pyramid_type,Feature_extractor_type>& item,
std::ostream& out
);
/*!
provides serialization support
!*/
template <
typename Pyramid_type,
typename Feature_extractor_type
>
void deserialize (
scan_image_pyramid<Pyramid_type,Feature_extractor_type>& item,
std::istream& in
);
/*!
provides deserialization support
!*/
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_SCAN_IMaGE_PYRAMID_ABSTRACT_H__