find_ncc_models — Find the best matches of multiple NCC models.
The operator find_ncc_models finds the best NumMatches instances of the NCC models that are passed in the tuple ModelIDs in the input Image. The models must have been created previously by calling create_ncc_model or read_ncc_model. In contrast to find_ncc_model, multiple models can be searched in the same image in one call.
Compared to find_ncc_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 they must contain the same number of elements as ModelIDs, in which case each parameter element refers to the corresponding element in ModelIDs. (NumLevels may also contain either two or twice the number of elements as ModelIDs). More details can be found below in the sections containing information for the respective parameters. Note that a call to find_ncc_models with multiple values for ModelIDs, NumMatches and MaxOverlap has the same effect as multiple independent calls to find_ncc_model with the respective parameters. However, a single call to find_ncc_models is considerably more efficient.
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. The model has to lie 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).
The input Image can contain a single image object or an image object array containing multiple image objects. If Image contains a single image object, its domain is used as the region of interest for all models in ModelIDs. If Image contains multiple image objects, each domain is used as the region of interest for the corresponding model in ModelIDs. In this case, the images have to be identical except for their domains, i.e., Image cannot be constructed in an arbitrary manner using 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.
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.
Furthermore, it should be noted that in some cases instances with a rotation that is slightly outside the specified range of rotations are found. This may happen if the specified range of rotations is smaller than the range given when the model was created.
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 NumLevels), it might happen that instances with a score slightly below MinScore are found.
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 NumMatches contains one element, find_ncc_models returns the best NumMatches instances of the model irrespective of the type of the model. If, for example, two models are passed in ModelIDs and NumMatches = 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.
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.
If a single value is passed in MaxOverlap, 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.
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.
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_models. 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.
If the lowest pyramid level is specified separately for each model, NumLevels must contain twice the number of elements as ModelIDs. In this case, the number of pyramid levels and the lowest pyramid level must be specified interleaved in NumLevels. If, for example, two models are specified in ModelIDs, 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, NumLevels = [5,2,4,1] must be selected. If exactly two models are specified in ModelIDs, 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 NumLevels is interpreted as the explicit specification of the number of pyramid levels for the two models.
The position and rotation of the found instances of the model is returned in Row, Column, and Angle. The coordinates Row and Column are the coordinates of the origin of the NCC 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 NCC model with create_ncc_model. A different origin can be set with set_ncc_model_origin.
Note that the coordinates Row and Column 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 find_ncc_model shows how to create this matrix and use it to display the model at the found position in the search image and to calculate the exact coordinates.
Note also that for visualizing the model at the found position, also the procedure dev_display_ncc_matching_results can be used.
Additionally, the score of each found instance is returned in Score.
The type of the found instances of the models is returned in Model. The elements of Model are indices into the tuple ModelIDs, i.e., they can contain values from 0 to |ModelIDs|-1. Hence, a value of 0 in an element of Model corresponds to an instance of the first model in ModelIDs.
Using the operator set_ncc_model_param you can specify a 'timeout' for find_ncc_models. If the NCC models referenced by ModelIDs hold different values for 'timeout', find_ncc_models uses the smallest one. If find_ncc_models reaches this 'timeout', it terminates without results and returns the error code 9400 (H_ERR_TIMEOUT).
Note that the internally used memory increases with the number of used threads.
Input image in which the model should be found.
Handle of the models.
Smallest rotation of the models.
Default value: -0.39
Suggested values: -3.14, -1.57, -0.79, -0.39, -0.20, 0.0
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
Minimum score of the instances of the models 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
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
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 accuracy if not equal to 'none'.
Default value: 'true'
List of values: 'false', 'true'
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 coordinate of the found instances of the models.
Column coordinate of the found instances of the models.
Rotation angle of the found instances of the models.
Score of the found instances of the models.
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_ncc_model (ImageReduced1, 'auto', rad(-45), rad(90), 'auto', \ 'use_polarity', ModelID1) create_ncc_model (ImageReduced2, 'auto', rad(-45), rad(90), 'auto', \ 'use_polarity', ModelID2) ModelIDs:=[ModelID1, ModelID2] find_ncc_models (Image, ModelIDs, rad(-45), rad(90), 0.7, [1,1], 0.5, \ 'true', 0, Row, Column, Angle, Score, Model) dev_display_ncc_matching_results (ModelIDs, 'red', Row, Column, \ Angle, Model)
If the parameter values are correct, the operator find_ncc_models 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