create_shape_model — Prepare a shape model for matching.
create_shape_model(Template : : NumLevels, AngleStart, AngleExtent, AngleStep, Optimization, Metric, Contrast, MinContrast : ModelID)
The operator create_shape_model prepares a template, which is passed in the image Template, as a shape model used for matching. The ROI of the model is passed as the domain of Template.
The output parameter ModelID is a handle for this model, which is used in subsequent calls to find_shape_model. The center of gravity of the domain (region) of the model image Template 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 using 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 on each level.
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. This can be checked using the output of inspect_shape_model. 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_shape_model returns with an error message. If NumLevels is set to 'auto' (or 0 for backwards compatibility), create_shape_model determines the number of pyramid levels automatically. The automatically computed number of pyramid levels can be queried using get_shape_model_params. In rare cases, it might happen that create_shape_model 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_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_shape_model may increase. In these cases, the number of pyramid levels should be selected using the output of inspect_shape_model.
The parameters AngleStart and AngleExtent determine the range of possible rotations, in which the model can occur in the image. Note that the model can only be found in this range of angles by find_shape_model. The parameter AngleStep determines the step length within the selected range of angles. Hence, if subpixel accuracy is not specified in find_shape_model, this parameter specifies the accuracy that is achievable for the angles in find_shape_model. AngleStep should be chosen based on the size of the object. Smaller models do not possess 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_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.
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_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_shape_model 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_shape_model will increase slightly in this case. 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_shape_model increases significantly, it is usually better to create several models that reflect the possible contrast variations of the object with create_shape_model, and to match them simultaneously with find_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_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 number of channels in the model creation with create_shape_model and in the search with find_shape_model can be different. This can, for example, be used to create a model from a synthetically generated single-channel image. Furthermore, it should be noted that 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.
The parameter Contrast determines the contrast the model points must have. The contrast is a measure for local gray value differences between the object and the background and between different parts of the object. Contrast should be chosen such that only the significant features of the template are used for the model. Contrast can also contain a tuple with two values. In this case, the model is segmented using a method similar to the hysteresis threshold method used in edges_image. Here, the first element of the tuple determines the lower threshold, while the second element determines the upper threshold. For more information about the hysteresis threshold method, see hysteresis_threshold. Optionally, Contrast can contain a third value as the last element of the tuple. This value determines a threshold for the selection of significant model components based on the size of the components, i.e., components that have fewer points than the minimum size thus specified are suppressed. This threshold for the minimum size is divided by two for each successive pyramid level. If small model components should be suppressed, but hysteresis thresholding should not be performed, nevertheless three values must be specified in Contrast. In this case, the first two values can simply be set to identical values. The effect of this parameter can be checked in advance with inspect_shape_model. If Contrast is set to 'auto', create_shape_model determines the three above described values automatically. Alternatively, only the contrast ('auto_contrast'), the hysteresis thresholds ('auto_contrast_hyst'), or the minimum size ('auto_min_size') can be determined automatically. The remaining values that are not determined automatically can additionally be passed in the form of a tuple. Also various combinations are allowed: If, for example, ['auto_contrast','auto_min_size'] is passed, both the contrast and the minimum size are determined automatically. If ['auto_min_size',20,30] is passed, the minimum size is determined automatically while the hysteresis thresholds are set to 20 and 30, etc. In certain cases, it might happen that the automatic determination of the contrast thresholds is not satisfying. For example, a manual setting of these parameters should be preferred if certain model components should be included or suppressed because of application-specific reasons or if the object contains several different contrasts. Therefore, the contrast thresholds should be automatically determined with determine_shape_model_params and subsequently verified using inspect_shape_model before calling create_shape_model.
With MinContrast, it can be determined which contrast the model must at least have in the recognition performed by find_shape_model. In other words, this parameter separates the model 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. Obviously, MinContrast must be smaller than Contrast. 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_shape_model. If MinContrast is set to 'auto', the minimum contrast is determined automatically based on the noise in the model image. Consequently, an automatic determination only makes sense if the image noise during the recognition is similar to the noise in the model image. Furthermore, in some cases it is advisable to increase the automatically determined value in order to increase the robustness against occlusions (see above). The automatically computed minimum contrast can be queried using get_shape_model_params.
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_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 range and stored in memory. The memory required to store the model is proportional to the number of angle steps and the number of points in the model. Hence, if AngleStep is too small or AngleExtent too big, it may happen that the model no longer fits into the (virtual) memory. In this case, either AngleStep must be enlarged or AngleExtent 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_shape_model, AngleStep >= 1 can be selected for models of a diameter smaller than about 200 pixels. If AngleStep = 'auto' (or 0 for backwards compatibility) is selected, create_shape_model automatically determines a suitable angle step length based on the size of the model. The automatically computed angle step length 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_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!
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.
Input image whose domain will be used to create the model.
Maximum number of pyramid levels.
Default value: 'auto'
List of values: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 'auto'
Smallest rotation of the pattern.
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
Restriction: AngleExtent >= 0
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
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'
Default value: 'use_polarity'
List of values: 'ignore_color_polarity', 'ignore_global_polarity', 'ignore_local_polarity', 'use_polarity'
Threshold or hysteresis thresholds for the contrast of the object in the template image and optionally minimum size of the object parts.
Default value: 'auto'
Suggested values: 'auto', 'auto_contrast', 'auto_contrast_hyst', 'auto_min_size', 10, 20, 30, 40, 60, 80, 100, 120, 140, 160
Minimum contrast of the objects in the search images.
Default value: 'auto'
Suggested values: 'auto', 1, 2, 3, 5, 7, 10, 20, 30, 40
Restriction: MinContrast < Contrast
Handle of the model.
If the parameters are valid, the operator create_shape_model returns the value 2 (H_MSG_TRUE). If necessary an exception is raised. If the parameters NumLevels and Contrast are chosen such that the model contains too few points, the error 8510 is raised.
draw_region, reduce_domain, threshold
find_shape_model, find_shape_models, get_shape_model_params, clear_shape_model, write_shape_model, set_shape_model_origin