find_aniso_shape_model — Find the best matches of an anisotropically scaled shape model
in an image.
find_aniso_shape_model(Image : : ModelID, AngleStart, AngleExtent, ScaleRMin, ScaleRMax, ScaleCMin, ScaleCMax, MinScore, NumMatches, MaxOverlap, SubPixel, NumLevels, Greediness : Row, Column, Angle, ScaleR, ScaleC, Score)
The operator find_aniso_shape_model finds the best
NumMatches instances of the anisotropically scaled
shape model ModelID in the input image Image. The
model must have been created previously by calling
create_aniso_shape_model or read_shape_model.
The position, rotation, and scale in the row and column direction of
the found instances of the model are returned in Row,
Column, Angle, ScaleR, and
ScaleC. Additionally, the score of each found instance is returned in
Score.
The domain of the image Image determines the search space
for the reference point of the model, i.e., for the center of
gravity of the domain (region) of the image that was used to create
the shape model with create_aniso_shape_model. A different
origin set with set_shape_model_origin is not taken into
account. The model is searched within those points of the domain of
the image, in which the model lies completely within the image.
This means that the model will not be found if it extends beyond the
borders of the image, even if it would achieve a score greater than
MinScore (see below). Note that, if for a certain pyramid
level the model touches the image border, it might not be found
even if it lies completely within the original image.
As a rule of thumb, the model might not be found if its distance to
an image border falls below .
This behavior can be changed with
set_system('border_shape_models','true') for all models or
with set_shape_model_param(ModelID, 'border_shape_models','true')
for a specific model, which will cause
models that extend beyond the image border to be found if they
achieve a score greater than MinScore. Here, points lying
outside the image are regarded as being occluded, i.e., they lower
the score. It should be noted that the runtime of the search will
increase in this mode. Note further, that in rare cases, which occur
typically only for artificial images, the model might not be found
also if for certain pyramid levels the model touches the border
of the reduced domain. Then, it may help to enlarge the reduced
domain by using,
e.g., dilation_circle.
The parameters AngleStart and AngleExtent
determine the range of rotations for which the model is searched.
The parameters ScaleRMin, ScaleRMax,
ScaleCMin, and ScaleCMax determine the range of
scales in the row and column directions for which the model is
searched. If necessary, both ranges are clipped to the range given
when the model was created with create_aniso_shape_model.
In particular, this means that the angle ranges of the model and the
search must overlap.
If in ModelID a model is passed that was created by
using create_shape_model or create_scaled_shape_model
then the model is searched with an isotropic scaling of 1.0
or with an isotropic scaling within the range from ScaleRMin
to ScaleRMax, respectively. In this case, for ScaleR
and ScaleC identical values are returned.
Note that in some cases instances with a rotation or scale that is
slightly outside the specified range are found. This may happen if
the specified range is smaller than the range given during the
creation of the model. AngleStart and AngleExtent
as well as ScaleRMin/ScaleCMin and
ScaleRMax/ScaleCMax are checked only
at the highest pyramid level. Matches that are found on the highest
pyramid level are refined to the lowest pyramid level. For performance
reasons, however, during the refinement it is no longer checked
whether the matches are still within the specified ranges.
The parameter MinScore determines what score a potential
match must at least have to be regarded as an instance of the model
in the image. The larger MinScore is chosen, the faster
the search is. If the model can be expected never to be occluded in
the images, MinScore may be set as high as 0.8 or even 0.9.
If the matches are not tracked to the lowest pyramid level (see
below) it might happen that instances with a score slightly below
MinScore are found.
In case that the shape model has been extended by clutter parameters with
set_shape_model_clutter and thus 'use_clutter' is enabled,
MinScore expects a second value which determines what clutter
value a potential match must at most have to be regarded as an instance
of the model in the image. The runtime using clutter parameters will be at
least as high as the runtime without clutter parameters and
NumMatches set to 0. Changing this second value does not
influence the runtime.
The maximum number of instances to be found can be determined with
NumMatches. If more than NumMatches instances
with a score greater than MinScore are found in the image,
only the best NumMatches instances are returned. If fewer
than NumMatches are found, only that number is returned,
i.e., the parameter MinScore takes precedence over
NumMatches. If all model instances exceeding
MinScore in the image should be found, NumMatches
must be set to 0.
When tracking the matches through the image pyramid, on each level,
some less promising matches are rejected based on NumMatches. Thus,
it is possible that some matches are rejected that would have had a higher
score on the lowest pyramid level. Due to this, for example, the found
match for NumMatches set to 1 might be
different from the match with the highest score returned when setting
NumMatches to 0 or > 1.
If multiple objects with a similar score are expected, but only the one with
the highest score should be returned, it might be preferable to raise
NumMatches, and then select the match with the highest score.
In case that the shape model has been extended by clutter parameters
using set_shape_model_clutter, NumMatches also
considers the second value passed in MinScore: If more than
NumMatches instances with a score greater than the first
entry of MinScore and a clutter value smaller than the second
entry of MinScore are found in the image,
only the best NumMatches instances with respect to clutter
are returned. Still,
MinScore takes precedence over NumMatches and
NumMatches must be set to 0 if all model instances
fulfilling the conditions imposed by MinScore should be
found. Please note that using clutter parameters, when tracking the matches
through the image pyramid, no matches are rejected. Thus the runtime
using clutter parameters will be at least as high as the runtime without clutter
parameters and NumMatches set to 0.
If the model exhibits symmetries it may happen that multiple
instances with similar positions but different rotations are found
in the image. The parameter MaxOverlap determines by what
fraction (i.e., a number between 0 and 1) two instances may at most
overlap in order to consider them as different instances, and hence
to be returned separately. If two instances overlap each other by
more than MaxOverlap only the best instance is returned.
The calculation of the overlap is based on the smallest enclosing
rectangle of arbitrary orientation (see smallest_rectangle2)
of the found instances. If MaxOverlap=0, the found
instances may not overlap at all, while for MaxOverlap=1
all instances are returned.
The parameter SubPixel determines whether the instances
should be extracted with subpixel accuracy. If SubPixel is
set to 'none' (or 'false' for backwards
compatibility) the model's pose is only determined with pixel
accuracy and the angle and scale resolution that was specified with
create_aniso_shape_model. If SubPixel is set to
'interpolation' (or 'true') the position as well
as the rotation and scale are determined with subpixel accuracy. In
this mode, the model's pose is interpolated from the score function.
This mode costs almost no computation time and achieves an accuracy
that is high enough for most applications. In some applications,
however, the accuracy requirements are extremely high. In these
cases, the model's pose can be determined through a least-squares
adjustment, i.e., by minimizing the distances of the model points to
their corresponding image points. In contrast to
'interpolation', this mode requires additional computation
time. The different modes for least-squares adjustment
('least_squares', 'least_squares_high', and
'least_squares_very_high') can be used to determine the
accuracy with which the minimum distance is being searched. The
higher the accuracy is chosen, the longer the subpixel extraction
will take, however. Usually, SubPixel should be set to
'interpolation'. If least-squares adjustment is desired,
'least_squares' should be chosen because this results in
the best trade-off between runtime and accuracy.
Objects that are slightly deformed with respect to the model, in
some cases cannot be found or are found but only with a low
accuracy. For such objects it is possible to additionally pass a
maximal allowable object deformation in the parameter
SubPixel. The deformation must be specified in pixels. This
can be done by passing the optional parameter value
'max_deformation ' followed by an integer value between
0 and 32 (in the same string), which specifies the
maximum deformation. For example, if the shape of the object may be
deformed by up to 2 pixels with respect to the shape that is stored
in the model, the value 'max_deformation 2' must be passed
in SubPixel in addition to the above described mode for the
subpixel extraction, i.e., for example ['least_squares',
'max_deformation 2']. Passing the value 'max_deformation
0' corresponds to a search without allowing deformations, i.e., the
behavior is the same as if no 'max_deformation ' is passed.
Note that higher values for the maximum deformation often result in
an increased runtime. Furthermore, the higher the deformation value
is chosen, the higher is the risk of finding wrong model instances.
Both problems mainly arise when searching for small objects or for
objects with fine structures. This is because such kinds of objects
for higher deformations lose their characteristic shape, which is
important for a robust search. Also note that for higher
deformations the accuracy of partially occluded objects might
decrease if clutter is present close to the object. Consequently,
the maximum deformation should be chosen as small as possible and
only as high as necessary.
Approximately rotationally symmetric objects may not be found if
'max_deformation' and AngleExtent are both set to a value
greater than 0. In that case, ambiguities may occur that cannot be resolved,
and the match is rejected as false. If this happens, try to set either
'max_deformation' or AngleExtent to 0, or adjust the model
such that symmetries are reduced. When specifying a deformation higher
than 0 the computation of the score depends on the chosen
value for the subpixel extraction. In most cases, the score of a
match changes if 'least_squares',
'least_squares_high', or 'least_squares_very_high'
(see above) is chosen for the subpixel extraction (in comparison to
'none' or 'interpolation'). Furthermore, if one of
the least-squares adjustments is selected the score might increase
when increasing the maximum deformation because then for the model
points more corresponding image points can be found.
To get a meaningful score value and to avoid erroneous matches, we
recommend to always combine the allowance of a deformation with a
least-squares adjustment.
The number of pyramid levels used during the search is determined
with NumLevels. If necessary, the number of levels is
clipped to the range given when the shape model was created with
create_aniso_shape_model. If NumLevels is set to
0, the number of pyramid levels specified in
create_aniso_shape_model is used.
In certain cases, the number of pyramid levels that was determined
automatically with, for example, create_aniso_shape_model may be
too high. The consequence may be that some matches that may have a high
final score are rejected on the highest pyramid level and thus are not
found. Instead of setting MinScore to a very low value to find all
matches, it may be better to query the value of NumLevels with
get_shape_model_params and then use a slightly lower value in
find_aniso_shape_model. This approach is often better regarding
the speed and robustness of the matching.
Optionally, NumLevels can contain a second value that determines
the lowest pyramid level to which the found matches are tracked. Hence,
a value of [4,2] for NumLevels means that the
matching starts at the fourth pyramid level and tracks the matches
to the second lowest pyramid level (the lowest pyramid level is
denoted by a value of 1). This mechanism can be used to decrease
the runtime of the matching. It should be noted, however, that in
general the accuracy of the extracted pose parameters is lower in
this mode than in the normal mode, in which the matches are tracked
to the lowest pyramid level. Hence, if a high accuracy is desired,
SubPixel should be set to at least
'least_squares'. If the lowest pyramid level to use is
chosen too large, it may happen that the desired accuracy cannot be
achieved, or that wrong instances of the model are found because the
model is not specific enough on the higher pyramid levels to
facilitate a reliable selection of the correct instance of the
model. In this case, the lowest pyramid level to use must be set to
a smaller value.
In input images of poor quality, i.e., in images that are, e.g.,
defocused, deformed, or noisy, often no instances of the shape model
can be found on the lowest pyramid level. The reason for this
behavior is the missing or deformed edge information which is a
result of the poor image quality. Nevertheless, the edge
information may be sufficient on higher pyramid levels. But keep in
mind the above mentioned restrictions on accuracy and robustness if
instances that were found on higher pyramid levels are used. The
selection of the suitable pyramid level, i.e., the lowest pyramid
level on which at least one instance of the shape model can be
found, depends on the model and on the input image. This pyramid
level may vary from image to image. To facilitate the matching on
images of poor quality, the lowest pyramid level on which at least
one instance of the model can be found can be determined
automatically during the matching. To activate this mechanism, i.e.,
to use the so-called 'increased tolerance mode', the
lowest pyramid level must be specified negatively in
NumLevels. If, e.g., NumLevels is set to
[4,-2], the matching starts at the fourth pyramid level and
tracks the matches to the second lowest pyramid level. This means
that an instance of the shape model is searched on the pyramid level
2. If no instance of the model can be found on this pyramid level,
the lowest pyramid level is determined on which at least one
instance of the model can be found. The instances of this pyramid
level will then be returned.
The parameter Greediness determines how “greedily” the
search should be carried out. If Greediness=0, a safe
search heuristic is used, which always finds the model if it is
visible in the image and the other parameters are set appropriately.
However, the search will be relatively time
consuming in this case. If Greediness=1, an unsafe
search heuristic is used, which may cause the model not to be found
in rare cases, even though it is visible in the image. For
Greediness=1, the maximum search speed is achieved. In
almost all cases, the shape model will always be found for
Greediness=0.9.
The position, rotation, and scale in the row and column direction of
the found instances of the model are returned in Row,
Column, Angle, ScaleR, and
ScaleC.
The coordinates Row and Column are related to the
position of the origin of the shape model in the search image.
However, Row and Column do not
exactly correspond to this position.
Instead, find_aniso_shape_model returns slightly modified values
that are optimized for creating a transformation matrix, that can be used for
alignment or visualization of the model contours.
(This has to do with the way HALCON transforms iconic objects,
see affine_trans_pixel).
The example below shows how to
create the transformation matrix for alignment and calculate the
exact coordinates of the found matches.
By default, the model
origin is the center of gravity of the domain (region) of the image
that was used to create the shape model with
create_aniso_shape_model. A different origin can be set with
set_shape_model_origin.
The score of each found instance is returned in
Score. The score is a number between 0 and 1, which is an
approximate measure of how much of the model is visible in the
image. If, for example, half of the model is occluded, the score
cannot exceed 0.5.
In case that the shape model has been extended by clutter parameters
using set_shape_model_clutter, following the above values
Score also returns the clutter values of each found instance.
If, for example, half of the clutter region is filled by clutter edges,
the clutter value will equal 0.5.
If, e.g., two instances are found, the score is 0.9 for
the first instance and 0.8 for the second instance, and the clutter value
is 0.2 for the first instance and 0.1 for the second instance,
Score = [0.9,0.8,0.2,0.1] is returned.
Please note that of all shape-based matching results, clutter values are
affected the most when a variation of illumination occurs.
Using the operator set_shape_model_param you can specify a
'timeout' for find_aniso_shape_model.
If find_aniso_shape_model reaches this 'timeout', it
terminates without results and returns the error code 9400 (H_ERR_TIMEOUT).
Depending on the scaling ranges specified by ScaleRMin,
ScaleRMax, ScaleCMin, and ScaleCMax,
find_aniso_shape_model needs a significant
amount of time to free cached transformations if the shape model is not
pregenerated. As this transformations have to be freed after a timeout
occurs, the runtime of find_aniso_shape_model exceeds the value of
the specified 'timeout' by this time.
To display the results found by shape-based matching,
we highly recommend the usage of the procedure
dev_display_shape_matching_results.
For an explanation of the different 2D coordinate systems used in HALCON, see the introduction of chapter Transformations / 2D Transformations.
This operator supports cancelling timeouts and interrupts.
Image (input_object) (multichannel-)image → object (byte / uint2)
Input image in which the model should be found.
ModelID (input_control) shape_model → (handle)
Handle of the model.
AngleStart (input_control) angle.rad → (real)
Smallest rotation of the model.
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, 0.0
Restriction: AngleExtent >= 0
ScaleRMin (input_control) number → (real)
Minimum scale of the model 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 model 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
ScaleCMin (input_control) number → (real)
Minimum scale of the model 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 model 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
MinScore (input_control) real(-array) → (real)
Minimum score of the instances of the model to be found.
Default value: 0.5
Suggested values: 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0
Typical range of values: 0
≤
MinScore
≤
1
Minimum increment: 0.01
Recommended increment: 0.05
NumMatches (input_control) integer → (integer)
Number of instances of the model to be found (or 0 for all matches).
Default value: 1
Suggested values: 0, 1, 2, 3, 4, 5, 10, 20
MaxOverlap (input_control) real → (real)
Maximum overlap of the instances of the model to be found.
Default value: 0.5
Suggested values: 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0
Typical range of values: 0
≤
MaxOverlap
≤
1
Minimum increment: 0.01
Recommended increment: 0.05
SubPixel (input_control) string(-array) → (string)
Subpixel accuracy if not equal to 'none'.
Default value: 'least_squares'
Suggested values: 'none', 'interpolation', 'least_squares', 'least_squares_high', 'least_squares_very_high', 'max_deformation 1', 'max_deformation 2', 'max_deformation 3', 'max_deformation 4', 'max_deformation 5', 'max_deformation 6'
NumLevels (input_control) integer(-array) → (integer)
Number of pyramid levels used in the matching
(and lowest pyramid level to use if
|NumLevels| = 2).
Default value: 0
List of values: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
Greediness (input_control) real → (real)
“Greediness” of the search heuristic (0: safe but slow; 1: fast but matches may be missed).
Default value: 0.9
Suggested values: 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0
Typical range of values: 0
≤
Greediness
≤
1
Minimum increment: 0.01
Recommended increment: 0.05
Row (output_control) point.y-array → (real)
Row coordinate of the found instances of the model.
Column (output_control) point.x-array → (real)
Column coordinate of the found instances of the model.
Angle (output_control) angle.rad-array → (real)
Rotation angle of the found instances of the model.
ScaleR (output_control) number-array → (real)
Scale of the found instances of the model in the row direction.
ScaleC (output_control) number-array → (real)
Scale of the found instances of the model in the column direction.
Score (output_control) real-array → (real)
Score of the found instances of the model.
create_aniso_shape_model (ImageReduced, 0, rad(-15), rad(30), 0, \
0.9, 1.1, 0, 0.9, 1.1, 0, 'none', \
'use_polarity', 30, 10, ModelID)
get_shape_model_contours (ModelXLD, ModelID, 1)
find_aniso_shape_model (SearchImage, ModelID, rad(-15), rad(30), \
0.9, 1.1, 0.9, 1.1, 0.5, 1, 0.5, 'interpolation', \
0, 0, Row, Column, Angle, ScaleR, ScaleC, Score)
* Create transformation matrix
hom_mat2d_identity (HomMat2DIdentity)
hom_mat2d_scale (HomMat2DIdentity, ScaleR, ScaleC, 0, 0, HomMat2DScale)
hom_mat2d_rotate (HomMat2DScale, Angle, 0, 0, HomMat2DRotate)
hom_mat2d_translate (HomMat2DRotate, Row, Column, HomMat2DObject)
* Calculate true position of the model origin in the search image
affine_trans_pixel (HomMat2DObject, 0, 0, RowObject, ColObject)
* Display results
dev_display_shape_matching_results (ModelID, 'red', Row, Column, Angle, \
ScaleR, ScaleC, 0)
If the parameter values are correct, the operator
find_aniso_shape_model returns the value 2 (H_MSG_TRUE). If the
input is empty (no input images are available) the behavior can be
set via set_system('no_object_result',<Result>). If
necessary, an exception is raised.
create_aniso_shape_model,
read_shape_model,
set_shape_model_origin,
set_shape_model_clutter
find_shape_model,
find_scaled_shape_model,
find_shape_models,
find_scaled_shape_models,
find_aniso_shape_models,
find_ncc_model,
find_ncc_models
Matching