create_scaled_shape_model_xld — Prepare an isotropically scaled shape model for matching from XLD contours.
create_scaled_shape_model_xld(Contours : : NumLevels, AngleStart, AngleExtent, AngleStep, ScaleMin, ScaleMax, ScaleStep, Optimization, Metric, MinContrast : ModelID)
The operator create_scaled_shape_model_xld creates an isotropically
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_scaled_shape_model, which creates a shape model from a
template image, the operator create_scaled_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_scaled_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 scales on each level. The model can
be extended by clutter parameters with set_shape_model_clutter.
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_scaled_shape_model_xld
returns with an error message.
If NumLevels is set to 'auto',
create_scaled_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_scaled_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_scaled_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_scaled_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
during the search. Note that the object can only be found in this range of
angles by find_scaled_shape_model. The parameter AngleStep
determines the step length within the selected range of angles. Hence, if
subpixel accuracy is not specified in find_scaled_shape_model, this
parameter specifies the accuracy that is achievable for the angles in
find_scaled_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_scaled_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 ScaleMin and ScaleMax determine the
range of possible scales (sizes) of the object. A scale of 1
corresponds to the original size of the model. The parameter
ScaleStep determines the step length within the selected
range of scales. Hence, if subpixel accuracy is not specified in
find_scaled_shape_model, this parameter specifies the
accuracy that is achievable for the scales in
find_scaled_shape_model. Like AngleStep,
ScaleStep should be chosen based on the size of the object.
If the range of scales is not an integer multiple of
ScaleStep, ScaleStep is modified accordingly.
To ensure that for model instances that are not scaled scale values of
exactly 1.0 are returned by find_scaled_shape_model,
the range of possible
scales is modified as follows: If there is no positive integer value
n such that ScaleMin plus n times ScaleStep is
exactly 1.0, ScaleMin is decreased by up to
ScaleStep and ScaleMax is increased such that the
range of possible scales is increased by ScaleStep.
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_scaled_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_scaled_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_scaled_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_scaled_shape_model increases significantly, it is
usually better to create several models that reflect the possible
contrast variations of the object with
create_scaled_shape_model_xld, and to match them simultaneously
with find_scaled_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_scaled_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_scaled_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_scaled_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_scaled_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 or ScaleStep 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, either AngleStep or ScaleStep 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_scaled_shape_model, AngleStep >= 1° and ScaleStep >= 0.02 can be selected for models of a
diameter smaller than about 200 pixels.
If AngleStep =
'auto' or
ScaleStep =
'auto' is selected, create_scaled_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_scaled_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 product should be
approximately equal to 1.
Note that, in contrast to the operator
create_scaled_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_scaled_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
ScaleMin (input_control) number → (real)
Minimum scale of the pattern.
Default value: 0.9
Suggested values: 0.5, 0.6, 0.7, 0.8, 0.9, 1.0
Restriction: ScaleMin > 0
ScaleMax (input_control) number → (real)
Maximum scale of the pattern.
Default value: 1.1
Suggested values: 1.0, 1.1, 1.2, 1.3, 1.4, 1.5
Restriction: ScaleMax >= ScaleMin
ScaleStep (input_control) number → (real / string)
Scale step length (resolution).
Default value: 'auto'
Suggested values: 'auto', 0.01, 0.02, 0.05, 0.1, 0.15, 0.2
Restriction: ScaleStep > 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_scaled_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_scaled_shape_model,
find_scaled_shape_models,
get_shape_model_params,
clear_shape_model,
write_shape_model,
set_shape_model_origin,
set_shape_model_param,
set_shape_model_metric,
set_shape_model_clutter
create_shape_model_xld,
create_aniso_shape_model_xld
Matching