`find_shape_models`

— Find the best matches of multiple shape models.

**find_shape_models**(*Image* : : *ModelIDs*, *AngleStart*, *AngleExtent*, *MinScore*, *NumMatches*, *MaxOverlap*, *SubPixel*, *NumLevels*, *Greediness* : *Row*, *Column*, *Angle*, *Score*, *Model*)

The operator `find_shape_models`

finds the best
* NumMatches* instances of the shape models that are passed in
the tuple

`ModelIDs`

`Image`

`create_shape_model`

or `read_shape_model`

.
In contrast to `find_shape_model`

, multiple models can
be searched in the same image in one call.
The position and rotation of the found instances of the model is
returned in * Row*,

`Column`

`Angle`

`Score`

`Model`

Compared to `find_shape_model`

, the
semantics of all input parameters have changed to some extent. All input
parameters must either contain one element, in which case the
parameter is used for all models, or must contain the same number of
elements as * ModelIDs*, in which case each parameter element
refers to the corresponding element in

`ModelIDs`

`NumLevels`

`ModelIDs`

`find_shape_models`

with multiple
values for `ModelIDs`

`NumMatches`

`MaxOverlap`

`find_shape_model`

with the respective parameters.
However, a single call to `find_shape_models`

is considerably
more efficient.
**The image and its domain:**-
The domain of the

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`Image`

`create_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(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`MinScore`

`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. 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.,`MinScore`

`dilation_circle`

.As usual, the domain of the input

is used to restrict the search space for the reference point of the models`Image`

. Consistent with the above semantics, the input`ModelIDs`

can therefore contain a single image object or an image object tuple containing multiple image objects. If`Image`

contains a single image object, its domain is used as the region of interest for all models in`Image`

. If`ModelIDs`

contains multiple image objects, each domain is used as the region of interest for the corresponding model in`Image`

. In this case, the images have to be identical except for their domains, i.e.,`ModelIDs`

cannot be constructed in an arbitrary manner using`Image`

`concat_obj`

, but must be created from the same image using`add_channels`

or equivalent calls. If this is not the case, an error message is returned. **AngleStart and AngleExtent:**-
The parameters

and`AngleStart`

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`AngleExtent`

`create_shape_model`

. In particular, this means that the angle ranges of the model and the search must overlap.Note that in some cases instances with a rotation 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.

and`AngleStart`

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 range.`AngleExtent`

**MinScore:**-
The parameter

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.`MinScore`

If a single value is passed in

, this value is applied to found instances of all models. If, on the other hand,`MinScore`

contains multiple values, the values are applied separately for the respective model.`MinScore`

In case that the shape models have been extended by clutter parameters with

`set_shape_model_clutter`

and thus*'use_clutter'*is enabled,expects for each minimum score an additional 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`MinScore`

set to`NumMatches`

*0*. Changing this second value does not influence the runtime. Note that the different shape models must have the same value for*'use_clutter'*.If the maximum clutter is specified separately for each model, which is needed if also the minimum score is set for each model individually,

must contain twice the number of elements as ModelIDs. In this case, the minimum score and the maximum clutter must be specified interleaved in`MinScore`

. If, for example, two models are specified in ModelIDs, the minimum score is 0.9 for the first model and 0.8 for the second model, and the maximum clutter is 0.1 for the first model and 0.2 for the second model,`MinScore`

=`MinScore`

*[0.9,0.1,0.8,0.2]*must be selected. **NumMatches:**-
The maximum number of instances to be found can be determined with

. If more than`NumMatches`

instances with a score greater than`NumMatches`

are found in the image, only the best`MinScore`

instances are returned. If fewer than`NumMatches`

are found, only that number is returned, i.e., the parameter`NumMatches`

takes precedence over`MinScore`

. If all model instances exceeding`NumMatches`

in the image should be found,`MinScore`

must be set to`NumMatches`

*0*.When tracking the matches through the image pyramid, on each level, some less promising matches are rejected based on

. 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`NumMatches`

*1*might be different from the match with the highest score returned when settingto`NumMatches`

*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

, and then select the match with the highest score.`NumMatches`

In case that the shape models have been extended by clutter parameters using

`set_shape_model_clutter`

,also considers the second value passed in`NumMatches`

: If more than`MinScore`

instances with a score greater than the first entry of`NumMatches`

and a clutter value smaller than the second entry of`MinScore`

are found in the image, only the best`MinScore`

instances with respect to clutter are returned. Still,`NumMatches`

takes precedence over`MinScore`

and`NumMatches`

must be set to`NumMatches`

*0*if all model instances fulfilling the conditions imposed byshould 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`MinScore`

set to`NumMatches`

*0*.If

contains one element,`NumMatches`

`find_shape_models`

returns the bestinstances of the model irrespective of the type of the model. If, for example, two models are passed in`NumMatches`

and`ModelIDs`

= 2 is selected, it can happen that two instances of the first model and no instances of the second model, one instance of the first model and one instance of the second model, or no instances of the first model and two instances of the second model are returned. If, on the other hand,`NumMatches`

contains multiple values, the number of instances returned of the different models corresponds to the number specified in the respective entry in`NumMatches`

. If, for example,`NumMatches`

= [1,1] is selected, one instance of the first model and one instance of the second model is returned.`NumMatches`

**MaxOverlap:**-
If the model exhibits symmetries it may happen that multiple instances with similar positions but different rotations are found in the image. The parameter

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`MaxOverlap`

`smallest_rectangle2`

) of the found instances. If=0, the found instances may not overlap at all, while for`MaxOverlap`

=1 all instances are returned.`MaxOverlap`

If a single value is passed in

, the overlap is computed for all found instances of the different models, irrespective of the model type, i.e., instances of the same or of different models that overlap too much are eliminated. If, on the other hand, multiple values are passed in`MaxOverlap`

, the overlap is only computed for found instances of the model that have the same model type, i.e., only instances of the same model that overlap too much are eliminated. In this mode, models of different types may overlap completely.`MaxOverlap`

**SubPixel:**-
The parameter

determines whether the instances should be extracted with subpixel accuracy. If`SubPixel`

is set to`SubPixel`

*'none'*(or*'false'*for backwards compatibility) the model's pose is only determined with pixel accuracy and the angle resolution that was specified with`create_shape_model`

. Ifis set to`SubPixel`

*'interpolation'*(or*'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 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,should be set to`SubPixel`

*'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

. The deformation must be specified in pixels. This can be done by passing the optional parameter value`SubPixel`

*'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 inin addition to the above described mode for the subpixel extraction, i.e., for example`SubPixel`

*['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'*andare 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`AngleExtent`

*'max_deformation'*orto 0, or adjust the model such that symmetries are reduced. When specifying a deformation higher than`AngleExtent`

*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.If the subpixel extraction and/or the maximum object deformation is specified separately for each model, for each model passed in

exactly one value for the subpixel extraction must be passed in`ModelIDs`

. After each value for the subpixel extraction optionally a second value can be passed, which describes the maximum object deformation of the corresponding mode. If for a certain model no value for the maximum object deformation is passed, the model is searched without taking deformations into account. For example, if two models are passed in`SubPixel`

and for the first model the subpixel extraction is set to`ModelIDs`

*'interpolation'*and no object deformations are allowed and for the second model the subpixel extraction is set to*'least_squares'*and a maximum object deformation of*3*pixels is allowed, then the tuple*['interpolation', 'least_squares', 'max_deformation 3']*must be passed in. Alternatively, the equivalent tuple`SubPixel`

*['interpolation', 'max_deformation 0', 'least_squares', 'max_deformation 3']*may be passed. **NumLevels:**-
The number of pyramid levels used during the search is determined with

. If necessary, the number of levels is clipped to the range given when the shape model was created with`NumLevels`

`create_shape_model`

. Ifis set to`NumLevels`

*0*, the number of pyramid levels specified in`create_shape_model`

is used.In certain cases, the number of pyramid levels that was determined automatically with, for example,

`create_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 settingto a very low value to find all matches, it may be better to query the value of`MinScore`

with`NumLevels`

`get_shape_model_params`

and then use a slightly lower value in`find_shape_models`

. This approach is often better regarding the speed and robustness of the matching.Optionally,

can contain a second value that determines the lowest pyramid level to which the found matches are tracked. Hence, a value of`NumLevels`

*[4,2]*formeans 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,`NumLevels`

should be set to at least`SubPixel`

*'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.If the lowest pyramid level is specified separately for each model,

must contain twice the number of elements as`NumLevels`

. In this case, the number of pyramid levels and the lowest pyramid level must be specified interleaved in`ModelIDs`

. If, for example, two models are specified in`NumLevels`

, the number of pyramid levels is 5 for the first model and 4 for the second model, and the lowest pyramid level is 2 for the first model and 1 for the second model,`ModelIDs`

=`NumLevels`

*[5,2,4,1]*must be selected. If exactly two models are specified in, a special case occurs. If in this case the lowest pyramid level is to be specified, the number of pyramid levels and the lowest pyramid level must be specified explicitly for both models, even if they are identical, because specifying two values in`ModelIDs`

is interpreted as the explicit specification of the number of pyramid levels for the two models.`NumLevels`

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

. If, e.g.,`NumLevels`

is set to`NumLevels`

*[5,2,4,-1]*, the lowest pyramid level for the first model is set to 2. If no instance of the first model can be found on this pyramid level, no result will be returned for this model. For the second shape model, the lowest pyramid level is set to*-1*. Therefore, an instance of the shape model is searched on the pyramid level 1. If no instance of the second 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.If a model was adapted with

`adapt_shape_model_high_noise`

the estimated lowest pyramid level will be used by default. However, the user can override the estimated lowest pyramid level by providing explicitly the lowest pyramid level as described above. **Greediness:**The parameter

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.`Greediness`

**Row, Column and Angle:**-
The position and rotation of the found instances of the model is returned in

,`Row`

, and`Column`

. The coordinates`Angle`

and`Row`

are the coordinates of the origin of the shape model in the search image. By default, the origin is the center of gravity of the domain (region) of the image that was used to create the shape model with`Column`

`create_shape_model`

. A different origin can be set with`set_shape_model_origin`

.Note that the coordinates

and`Row`

do not exactly correspond to the position of the model in the search image. Thus, you cannot directly use them. Instead, the values are optimized for creating the transformation matrix with which you can use the results of the matching for various tasks, e.g., to align ROIs for other processing steps. The example given for`Column`

`find_shape_model`

shows how to create this matrix and use it to display the model at the found position in the search image.Note also that for visualizing the model at the found position, also the procedure dev_display_shape_matching_results can be used.

**Score:**-
The score of each found instance is returned in

. 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.`Score`

In case that the shape models have been extended by clutter parameters using

`set_shape_model_clutter`

, following the above valuesalso 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`

=`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. **Model:**-
The type of the found instances of the models is returned in

. The elements of`Model`

are indices into the tuple`Model`

, i.e., they can contain values from 0 to |`ModelIDs`

|-1. Hence, a value of 0 in an element of`ModelIDs`

corresponds to an instance of the first model in`Model`

.`ModelIDs`

Using the operator `set_shape_model_param`

you can specify a
*'timeout'* for `find_shape_models`

. If the shape models
referenced by * ModelIDs* hold different values for

`find_shape_models`

uses the smallest one.
If `find_shape_models`

reaches this
Please note, that the different models that are given with the parameter
* ModelIDs* should have been created with the same value of

`MinContrast`

. If they were created with different values for
`MinContrast`

, `find_shape_models`

will use the smallest of
these values.
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.

- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Automatically parallelized on internal data level.

This operator supports cancelling timeouts and interrupts.

`Image`

`→`

object (byte / uint2)
Input image in which the models should be found.

`ModelIDs`

`→`

(handle)
Handle of the models.

`AngleStart`

`→`

(real)
Smallest rotation of the models.

Default value: -0.39

Suggested values: -3.14, -1.57, -0.79, -0.39, -0.20, 0.0

`AngleExtent`

`→`

(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`

`→`

(real)
Minimum score of the instances of the models 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`

`→`

(integer)
Number of instances of the models to be found (or 0 for all matches).

Default value: 1

Suggested values: 0, 1, 2, 3, 4, 5, 10, 20

`MaxOverlap`

`→`

(real)
Maximum overlap of the instances of the models 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`

`→`

(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`

`→`

(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`

`→`

(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`

`→`

(real)
Row coordinate of the found instances of the models.

`Column`

`→`

(real)
Column coordinate of the found instances of the models.

`Angle`

`→`

(real)
Rotation angle of the found instances of the models.

`Score`

`→`

(real)
Score of the found instances of the models.

`Model`

`→`

(integer)
Index of the found instances of the models.

read_image (Image, 'pcb_focus/pcb_focus_telecentric_061') gen_rectangle1 (ROI_0, 236, 241, 313, 321) gen_circle (ROI_1, 281, 653, 41) reduce_domain (Image, ROI_0, ImageReduced1) reduce_domain (Image, ROI_1, ImageReduced2) create_shape_model (ImageReduced1, 0, rad(-45), rad(180), 0, \ 'none', 'use_polarity', 30, 10, ModelID1) create_shape_model (ImageReduced2, 0, rad(-45), rad(180), 0, \ 'none', 'use_polarity', 30, 10, ModelID2) ModelIDs:=[ModelID1, ModelID2] find_shape_models (Image, ModelIDs, rad(-45), rad(90), 0.7, [1,1], 0.5, \ 'least_squares', 0, 1, Row, Column, Angle, Score, Model) * Display results dev_display_shape_matching_results (ModelIDs, 'red', Row, Column, Angle, \ 1, 1, Model)

If the parameter values are correct, the operator
`find_shape_models`

returns the value 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.

`add_channels`

,
`create_shape_model`

,
`read_shape_model`

,
`set_shape_model_origin`

,
`set_shape_model_param`

,
`set_shape_model_clutter`

`find_scaled_shape_models`

,
`find_aniso_shape_models`

,
`find_shape_model`

,
`find_scaled_shape_model`

,
`find_aniso_shape_model`

,
`find_ncc_model`

,
`find_ncc_models`

Matching