create_aniso_shape_model_xld
— Prepare an anisotropically scaled shape model for matching
from XLD contours.
create_aniso_shape_model_xld(Contours : : NumLevels, AngleStart, AngleExtent, AngleStep, ScaleRMin, ScaleRMax, ScaleRStep, ScaleCMin, ScaleCMax, ScaleCStep, Optimization, Metric, MinContrast : ModelID)
The operator create_aniso_shape_model_xld
creates an anisotropically
scaled shape model used for matching from the XLD contours passed in
Contours
. The XLD contours represent the gray value edges of the
object to be searched for. In contrast to the operator
create_aniso_shape_model
, which creates a shape model from a
template image, the operator create_aniso_shape_model_xld
creates
the shape model from XLD contours, i.e., without the use of a template
image.
The output parameter ModelID
is a handle for this model, which is
used in subsequent calls to find_aniso_shape_model
. The center of
gravity of the smallest surrounding rectangle of the Contours
that
is parallel to the coordinate axes is used as the origin (reference point)
of the model. A different origin can be set with
set_shape_model_origin
. The model is generated for multiple image
pyramid levels and is stored in memory. If a complete pregeneration of the
model is selected (see below), the model is generated at multiple rotations
and anisotropic scales (i.e., independent scales in the row and
column direction) on each level.
The number of pyramid levels is determined with the parameter
NumLevels
. It should be chosen as large as possible
because by this the time necessary to find the object is
significantly reduced. On the other hand, NumLevels
must
be chosen such that the model is still recognizable and contains a
sufficient number of points (at least four) on the highest pyramid
level. If not enough model points are
generated, the number of pyramid levels is reduced internally until
enough model points are found on the highest pyramid level. If this
procedure would lead to a model with no pyramid levels, i.e., if the
number of model points is already too small on the lowest pyramid
level, create_aniso_shape_model_xld
returns with an error
message.
If NumLevels
is set to 'auto' ,
create_aniso_shape_model_xld
determines the number of pyramid
levels automatically. The computed number of pyramid
levels can be queried using get_shape_model_params
. In rare
cases, it might happen that create_aniso_shape_model_xld
determines a value for the number of pyramid levels that is too
large or too small. If the number of pyramid levels is chosen too
large, the model may not be recognized in the image or it may be
necessary to select very low parameters for MinScore or Greediness
in find_aniso_shape_model
in order to find the model. If
the number of pyramid levels is chosen too small, the time required
to find the model in find_aniso_shape_model
may increase.
In these cases, the number of pyramid levels should be selected manually.
The parameters AngleStart
and AngleExtent
determine the range of possible rotations, in which the object can
occur in the image. Note that the object can only be found in this
range of angles by find_aniso_shape_model
. The parameter
AngleStep
determines the step length within the selected
range of angles. Hence, if subpixel accuracy is not specified in
find_aniso_shape_model
, this parameter specifies the
accuracy that is achievable for the angles in
find_aniso_shape_model
. AngleStep
should be chosen
based on the size of the object. Smaller models do not have many
different discrete rotations in the image, and hence
AngleStep
should be chosen larger for smaller models. If
AngleExtent
is not an integer multiple of
AngleStep
, AngleStep
is modified accordingly.
To ensure that for model instances without rotation angle values of
exactly 0.0 are returned by find_aniso_shape_model
,
the range of possible
rotations is modified as follows: If there is no positive integer
value n such that AngleStart
plus n times
AngleStep
is exactly 0.0, AngleStart
is decreased
by up to AngleStep
and AngleExtent
is increased by
AngleStep
.
The parameters ScaleRMin
, ScaleRMax
,
ScaleCMin
, and ScaleCMax
determine the range of
possible anisotropic scales of the object in the row and column
direction. A scale of 1 in both scale factors corresponds to the
original size of the model. The parameters ScaleRStep
and
ScaleCStep
determine the step length within the selected
range of scales. Hence, if subpixel accuracy is not specified in
find_aniso_shape_model
, these parameters specify the
accuracy that is achievable for the scales in
find_aniso_shape_model
. Like AngleStep
,
ScaleRStep
and ScaleCStep
should be chosen based
on the size of the object. If the respective range of scales is not
an integer multiple of ScaleRStep
and ScaleCStep
,
ScaleRStep
and ScaleCStep
are modified
accordingly.
To ensure that for model instances that are not scaled scale values of
exactly 1.0 are returned by find_aniso_shape_model
,
the range of possible scales is modified as follows: If there are no
positive integer values n and m such that ScaleRMin
plus n
times ScaleRStep
is exactly 1.0 and ScaleCMin
plus
m times ScaleCStep
is exactly 1.0, ScaleRMin
and
ScaleCMin
are decreased by up to ScaleRStep
and
ScaleCStep
, respectively, and ScaleRMax
and
ScaleCMax
are increased such that the range of possible
scales is increased by ScaleRStep
and ScaleCStep
,
respectively.
Note that the transformations are treated internally such that the scalings are applied first, followed by the rotation. Therefore, the model should usually be aligned such that it appears horizontally or vertically in the model image.
For particularly large models, it may be useful to reduce the number
of model points by setting Optimization
to a value
different from 'none' . If Optimization
=
'none' , all model points are stored. In all other cases,
the number of points is reduced according to the value of
Optimization
. If the number of points is reduced, it may
be necessary in find_aniso_shape_model
to set the parameter
Greediness
to a smaller value, e.g., 0.7 or 0.8. For small
models, the reduction of the number of model points does not result
in a speed-up of the search because in this case usually
significantly more potential instances of the model must be
examined.
If Optimization
is set to 'auto' ,
create_aniso_shape_model_xld
automatically determines the
reduction of the number of model points.
The parameter Metric
determines the conditions under which
the model is recognized in the image.
If Metric
= 'use_polarity' , the object in the image and
the model must have the same contrast. If, for example, the model is a
bright object on a dark background, the object is found only if it is also
brighter than the background.
If Metric
= 'ignore_global_polarity' , the object is
found in the image also if the contrast reverses globally. In the above
example, the object hence is also found if it is darker than the background.
The runtime of find_aniso_shape_model
will increase slightly in
this case.
Note that the metrics ('use_polarity' and
'ignore_global_polarity' ) can only be selected if all
Contours
provide the attribute 'edge_direction' , which
defines the polarity of the edges. This attribute is available for contours
created, e.g., with edges_sub_pix
with the parameter
Method
set to, e.g., 'canny' . Otherwise, these two
metrics can be selected with the operator set_shape_model_metric
,
which determines the polarity of the edges from an image.
If Metric
= 'ignore_local_polarity' ,
the model is found even if the contrast changes locally. This mode
can, for example, be useful if the object consists of a part with
medium gray value, within which either darker or brighter
sub-objects lie. Since in this case the runtime of
find_aniso_shape_model
increases significantly, it is
usually better to create several models that reflect the possible
contrast variations of the object with
create_aniso_shape_model_xld
, and to match them simultaneously
with find_aniso_shape_models
.
The above three metrics can only be applied to single-channel images. If a multichannel image is used as the model image or as the search image, only the first channel will be used (and no error message will be returned).
If Metric
= 'ignore_color_polarity' , the model is
found even if the color contrast changes locally. This is, for
example, the case if parts of the object can change their color,
e.g., from red to green. In particular, this mode is useful if it
is not known in advance in which channels the object is visible. In
this mode, the runtime of find_aniso_shape_model
can also
increase significantly. The metric 'ignore_color_polarity'
can be used for images with an arbitrary number of channels. If it
is used for single-channel images it has the same effect as
'ignore_local_polarity' . It should be noted that for
Metric
= 'ignore_color_polarity' the
channels do not need to contain a spectral
subdivision of the light (like in an RGB image). The channels can,
for example, also contain images of the same object that were
obtained by illuminating the object from different directions.
Note that the first two metrics ('use_polarity' and
'ignore_global_polarity' ) can only be selected if all
Contours
provide the attribute 'edge_direction' , which
defines the polarity of the edges. For more information about
contour attributes like 'edge_direction' see
get_contour_attrib_xld
.
Otherwise, these two metrics can be selected with the operator
set_shape_model_metric
, which determines
the polarity of the edges from an image.
With MinContrast
, it can be determined which contrast the
object edges must at least have in the recognition performed by
find_aniso_shape_model
. In other words, this parameter
separates the object from the noise in the image. Therefore, a good
choice is the range of gray value changes caused by the noise in the
image. If, for example, the gray values fluctuate within a range of
10 gray levels, MinContrast
should be set to 10. If
multichannel images are used for the model and the search images,
and if the parameter Metric
is set to
'ignore_color_polarity' (see above) the noise in one
channel must be multiplied by the square root of the number of
channels to determine MinContrast
. If, for example, the
gray values fluctuate within a range of 10 gray levels in a single
channel and the image is a three-channel image MinContrast
should be set to 17. If the model should be recognized
in very low contrast images, MinContrast
must be set to a
correspondingly small value. If the model should be recognized even
if it is severely occluded, MinContrast
should be slightly
larger than the range of gray value fluctuations created by noise in
order to ensure that the position and rotation of the model are
extracted robustly and accurately by
find_aniso_shape_model
.
Optionally, a second value can be passed in Optimization
.
This value determines whether the model is pregenerated completely
or not. To do so, the second value of Optimization
must be
set to either 'pregeneration' or
'no_pregeneration' . If the second value is not used (i.e.,
if only one value is passed), the mode that is set with
set_system('pregenerate_shape_models',...)
is used. With
the default value ('pregenerate_shape_models' =
'false' ), the model is not pregenerated completely. The
complete pregeneration of the model normally leads to slightly lower
runtimes because the model does not need to be transformed at
runtime. However, in this case, the memory requirements and the
time required to create the model are significantly higher. It
should also be noted that it cannot be expected that the two modes
return exactly identical results because transforming the model at
runtime necessarily leads to different internal data for the
transformed models than pregenerating the transformed models. For
example, if the model is not pregenerated completely,
find_aniso_shape_model
typically returns slightly lower
scores, which may require setting a slightly lower value for
MinScore than for a completely pregenerated model. Furthermore, the
poses obtained by interpolation may differ slightly in the two
modes. If maximum accuracy is desired, the pose of the model should
be determined by least-squares adjustment.
If a complete pregeneration of the model is selected,
the model is pregenerated for the selected angle and scale range
and stored in memory. The memory required to store the model is
proportional to the number of angle steps, the number of scale
steps, and the number of points in the model. Hence, if
AngleStep
, ScaleRStep
, or ScaleCStep
are
too small or AngleExtent
or the range of scales are too
big, it may happen that the model no longer fits into the (virtual)
memory. In this case, AngleStep
, ScaleRStep
, or
ScaleCStep
must be enlarged or AngleExtent
or the
range of scales must be reduced. In any case, it is desirable that
the model completely fits into the main memory, because this avoids
paging by the operating system, and hence the time to find the
object will be much smaller. Since angles can be determined with
subpixel resolution by find_aniso_shape_model
,
AngleStep
>= 1° and
ScaleRStep
, ScaleCStep
>= 0.02 can be
selected for models of a diameter smaller than about 200 pixels.
If AngleStep
=
'auto' or ScaleRStep
, ScaleCStep
=
'auto'
is selected, create_aniso_shape_model_xld
automatically
determines a suitable angle or scale step length, respectively,
based on the size of the model. The automatically computed angle
and scale step lengths can be queried using
get_shape_model_params
.
If a complete pregeneration of the model is not selected, the model
is only created in a reference pose on each pyramid level. In this
case, the model must be transformed to the different angles and
scales at runtime in find_aniso_shape_model
. Because of
this, the recognition of the model might require slightly more time.
Note that pregenerated shape models are tailored to a specific image size. For runtime reasons using images of different sizes during the search with the same model in parallel is not supported. In this case, copies of the same model must be used, otherwise the program may crash!
The XLD contours passed in Contours
should have been scaled
to approximately the average size of the object in the search images.
This means that the products and
should be
approximately equal to 1.
Note that, in contrast to the operator
create_aniso_shape_model
, it is not possible to specify a
minimum size of the model components. To avoid small model
components in the shape model, short contours can be eliminated
before calling create_aniso_shape_model_xld
with the
operator select_contours_xld
.
This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.
Contours
(input_object) xld_cont(-array) →
object
Input contours that will be used to create the model.
NumLevels
(input_control) integer →
(integer / string)
Maximum number of pyramid levels.
Default value: 'auto'
List of values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 'auto'
AngleStart
(input_control) angle.rad →
(real)
Smallest rotation of the pattern.
Default value: -0.39
Suggested values: -3.14, -1.57, -0.79, -0.39, -0.20, 0.0
AngleExtent
(input_control) angle.rad →
(real)
Extent of the rotation angles.
Default value: 0.79
Suggested values: 6.29, 3.14, 1.57, 0.79, 0.39
Restriction: AngleExtent >= 0
AngleStep
(input_control) angle.rad →
(real / string)
Step length of the angles (resolution).
Default value: 'auto'
Suggested values: 'auto' , 0.0175, 0.0349, 0.0524, 0.0698, 0.0873
Restriction: AngleStep > 0 && AngleStep <= pi / 16
ScaleRMin
(input_control) number →
(real)
Minimum scale of the pattern in the row direction.
Default value: 0.9
Suggested values: 0.5, 0.6, 0.7, 0.8, 0.9, 1.0
Restriction: ScaleRMin > 0
ScaleRMax
(input_control) number →
(real)
Maximum scale of the pattern in the row direction.
Default value: 1.1
Suggested values: 1.0, 1.1, 1.2, 1.3, 1.4, 1.5
Restriction: ScaleRMax >= ScaleRMin
ScaleRStep
(input_control) number →
(real / string)
Scale step length (resolution) in the row direction.
Default value: 'auto'
Suggested values: 'auto' , 0.01, 0.02, 0.05, 0.1, 0.15, 0.2
Restriction: ScaleRStep > 0
ScaleCMin
(input_control) number →
(real)
Minimum scale of the pattern in the column direction.
Default value: 0.9
Suggested values: 0.5, 0.6, 0.7, 0.8, 0.9, 1.0
Restriction: ScaleCMin > 0
ScaleCMax
(input_control) number →
(real)
Maximum scale of the pattern in the column direction.
Default value: 1.1
Suggested values: 1.0, 1.1, 1.2, 1.3, 1.4, 1.5
Restriction: ScaleCMax >= ScaleCMin
ScaleCStep
(input_control) number →
(real / string)
Scale step length (resolution) in the column direction.
Default value: 'auto'
Suggested values: 'auto' , 0.01, 0.02, 0.05, 0.1, 0.15, 0.2
Restriction: ScaleCStep > 0
Optimization
(input_control) string(-array) →
(string)
Kind of optimization and optionally method used for generating the model.
Default value: 'auto'
List of values: 'auto' , 'no_pregeneration' , 'none' , 'point_reduction_high' , 'point_reduction_low' , 'point_reduction_medium' , 'pregeneration'
Metric
(input_control) string →
(string)
Match metric.
Default value: 'ignore_local_polarity'
List of values: 'ignore_color_polarity' , 'ignore_global_polarity' , 'ignore_local_polarity' , 'use_polarity'
MinContrast
(input_control) number →
(integer)
Minimum contrast of the objects in the search images.
Default value: 5
Suggested values: 1, 2, 3, 5, 7, 10, 20, 30, 40
ModelID
(output_control) shape_model →
(handle)
Handle of the model.
If the parameters are valid, the operator
create_aniso_shape_model_xld
returns the value 2 (H_MSG_TRUE). If necessary an
exception is raised. If the parameter NumLevels
is chosen such that
the model contains too few points, the error 8510 is raised.
read_contour_xld_dxf
,
edges_sub_pix
,
select_contours_xld
find_aniso_shape_model
,
find_aniso_shape_models
,
get_shape_model_params
,
clear_shape_model
,
write_shape_model
,
set_shape_model_origin
,
set_shape_model_param
,
set_shape_model_metric
create_shape_model_xld
,
create_scaled_shape_model_xld
Matching