find_ncc_model — Find the best matches of an NCC model in an image.
find_ncc_model(Image : : ModelID, AngleStart, AngleExtent, MinScore, NumMatches, MaxOverlap, SubPixel, NumLevels : Row, Column, Angle, Score)
The operator find_ncc_model finds the best
NumMatches instances of the NCC model ModelID in
the input image Image. The model must have been created
previously by calling create_ncc_model or
read_ncc_model.
The position and rotation of the found instances of the model is
returned in Row, Column, and Angle.
Additionally, the score of each found instance is returned in
Score.
It should be noted that the NCC is very sensitive to occlusion and
clutter as well as to nonlinear illumination changes in the image.
If a model should be found in the presence of occlusion, clutter, or
nonlinear illumination changes the search should be performed using
the shape-based matching (see, e.g., create_shape_model).
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 NCC model with create_ncc_model. A different origin
set with set_ncc_model_origin is not taken into account
here. 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 rounding effects can cause
matches to lie up to
pixels outside the image.
The parameters AngleStart and AngleExtent
determine the range of rotations for which the model is searched.
If necessary, the range of rotations is clipped to the range given
when the model was created with create_ncc_model.
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.
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.
If the model exhibits symmetries it may happen that multiple
instances with similar positions but different rotations are found
in the image. If the model has repeating structures it may happen
that multiple instances with identical rotations are found at
similar positions 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 'false', the model's pose is only determined with
pixel accuracy and the angle resolution that was specified with
create_ncc_model. If SubPixel is set to
'true', the position as well as the rotation 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 a high accuracy. Hence,
SubPixel should usually be set to 'true'.
Note that the subpixel accurate determination of the model's pose is
only possible if the found instance lies at least 2 pixels away from
the image border of the lowest used pyramid level. If the instance
lies closer to the image border, its pose is only determined with
pixel accuracy and the angle resolution that was specified with
create_ncc_model, even if SubPixel is set to
'true'.
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 NCC model was created with
create_ncc_model. If NumLevels is set to
0, the number of pyramid levels specified in
create_ncc_model is used.
In certain cases, the number of pyramid levels that was determined
automatically with, for example, create_ncc_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_ncc_model_params and then use a slightly lower value in
find_ncc_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. 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.
The position and rotation of the found instances of the model is
returned in Row, Column, and Angle.
The coordinates Row and Column are related to the
position of the origin of the model in the search image. However,
Row and Column do not exactly correspond to this
position. Instead, find_ncc_model returns slightly modified
values that are optimized for creating a transformation matrix, that
can be used for alignment or visualization of the model. (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 how to calculate the exact
coordinates of the found matches.
By default, the origin is the center of gravity of the domain (region)
of the image that was used to create the NCC model with
create_ncc_model. A different origin can be set with
set_ncc_model_origin.
Additionally, the score of each found instance is returned in
Score. The score is the normalized cross correlation of
the template t(r,c) and the image i(r,c):
Here, n denotes the number of points in the template, R denotes
the domain (ROI) of the template, is the mean gray
value of the template
is the variance of the gray values of the
template
is the mean gray value of the image at
position (r,c) over all points of the template (i.e., the template
points are shifted by (r,c))
and is the variance of the gray
values of the image at position (r,c) over all points of the
template
The NCC measures how well the template and image correspond at a particular point (r,c). It assumes values between -1 and 1. The larger the absolute value of the correlation, the larger the degree of correspondence between the template and image. A value of 1 means that the gray values in the image are a linear transformation of the gray values in the template: i(r+u,c+v) = a * t(u,v) + b where a > 0. Similarly, a value of -1 means that the gray values in the image are a linear transformation of the gray values in the template with a < 0. Hence, in this case the template occurs with a reversed polarity in the image. Because of the above property, the NCC is invariant to linear illumination changes.
The NCC as defined above is used if the NCC model has been created
with Metric = 'use_polarity'. If the model has
been created with Metric =
'ignore_global_polarity', the absolute value of
ncc(r,c) is used as the score.
Using the operator set_ncc_model_param you can specify a
'timeout' for find_ncc_model. If find_ncc_model
reaches this 'timeout', it terminates without results and
returns the error code 9400 (H_ERR_TIMEOUT).
To display the results found by correlation-based matching, we highly
recommend the usage of the procedure
dev_display_ncc_matching_results.
For an explanation of the different 2D coordinate systems used in HALCON, see the introduction of chapter Transformations / 2D Transformations.
find_ncc_model can be partially executed on OpenCL devices that
support the cl_khr_global_int32_base_atomics OpenCL extension. Only the
initial search for the model in the topmost pyramid level is done on the
OpenCL device, while the tracking of matches is done on the host CPU. If the
domain of the image to search in is substantially smaller than the size of
the image, use crop_domain to reduce the amount of data that needs to
be copied from the OpenCL device to the host CPU. Note that
find_ncc_model using OpenCL may run either substantially faster or
slower depending on a wide number of factors, so the only way to tell if
using OpenCL is beneficial is by testing with images from the actual
application.
Furthermore, note that the internally used memory increases with the number of used threads.
Image (input_object) singlechannelimage → object (byte* / uint2*) *allowed for compute devices
Input image in which the model should be found.
ModelID (input_control) ncc_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
MinScore (input_control) real → (real)
Minimum score of the instances of the model to be found.
Default value: 0.8
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 → (string)
Subpixel accuracy.
Default value: 'true'
List of values: 'false', 'true'
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
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.
Score (output_control) real-array → (real)
Score of the found instances of the model.
create_ncc_model (TemplateImage, 'auto', rad(-45), rad(90), 'auto', \
'use_polarity', ModelID)
find_ncc_model (SearchImage, ModelID, rad(-45), rad(90), 0.7, 1, \
0.5, 'true', 0, Row, Column, Angle, Score)
* Create transformation matrix
vector_angle_to_rigid (0, 0, 0, Row, Column, Angle, HomMat2D)
* Calculate true position of the model origin in the search image
affine_trans_pixel (HomMat2D, 0, 0, RowObject, ColumnObject)
* display the results
dev_display_ncc_matching_results (ModelID, 'red', Row, Column, \
Angle, 0)
If the parameter values are correct, the operator
find_ncc_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_ncc_model,
read_ncc_model,
set_ncc_model_origin
find_shape_model,
find_scaled_shape_model,
find_aniso_shape_model,
find_shape_models,
find_scaled_shape_models,
find_aniso_shape_models
Matching