| Operators |
find_planar_uncalib_deformable_model — Find the best matches of a planar projective invariant deformable model in an image.
find_planar_uncalib_deformable_model(Image : : ModelID, AngleStart, AngleExtent, ScaleRMin, ScaleRMax, ScaleCMin, ScaleCMax, MinScore, NumMatches, MaxOverlap, NumLevels, Greediness, GenParamName, GenParamValue : HomMat2D, Score)
The operator find_planar_uncalib_deformable_model finds the best NumMatches instances of the perspectively distorted deformable model ModelID in the input image Image. The model must have been created previously by calling create_planar_uncalib_deformable_model or read_deformable_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 deformable model with create_planar_uncalib_deformable_model. A different origin set with set_deformable_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'), 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, AngleExtent, ScaleRMin, ScaleRMax, ScaleCMin and ScaleCMax are used to specify a basic range of up to an anisotropic transformation that is exhaustively searched on the top level of the image pyramid. The parameters AngleStart and AngleExtent determine the range of possible rotations in which the model is exhaustively searched. ScaleRMin, ScaleRMax, ScaleCMin, and ScaleCMax determine the range of possible anisotropic scales that are exhaustively searched in the image. A scale of 1 in both scale factors corresponds to the original size of the model.
The operator find_planar_uncalib_deformable_model may find objects outside this range, e.g., when the object is perspectively distorted. Hence, the range parameters are a kind of suggestion for the search algorithm. Starting from this, certain models in a wider range of transformations can be detected, depending on the used pyramid levels, but also on the model/image content. It is important to note that, e.g., small scale changes can be tolerated without the need to specify a scale range, leading to faster execution times.
Often, it is not necessary to use an anisotropic scaling to find the object on the top level of the pyramid. In these cases, ScaleCMin and ScaleCMax should be set to 1. The search is then performed with isotropic scaling only, which is much faster. If the object should be detected despite severe perspective distortions anisotropic scaling is required. Here, ScaleRMin and ScaleRMax specify the anisotropic scaling in row, ScaleCMin and ScaleCMax in column direction.
Note that the transformations are treated internally such that the scalings are applied first, followed by the rotation. Therefore, the model should usually be aligned such that it appears horizontally or vertically in the model image.
Additionally, the operator find_planar_uncalib_deformable_model processes the parameters 'angle_step', 'scale_r_step' and 'scale_c_step' which can be set with the operator create_planar_uncalib_deformable_model or, as described below, with the generic parameters GenParamName and GenParamValue. In most cases, the values that can be determined automatically by create_planar_uncalib_deformable_model lead to good results.
The parameter 'angle_step' determines the step size within the selected range of angles. 'angle_step' should be chosen based on the size of the object. Smaller models do not have many different discrete rotations in the image, and hence 'angle_step' should be chosen larger for smaller models. If AngleExtent is not an integer multiple of 'angle_step', 'angle_step' is modified accordingly. The parameters 'scale_r_step' and 'scale_c_step' determine the step size within the selected range of scales. Like 'angle_step', 'scale_r_step' and 'scale_c_step' should be chosen based on the size of the object. If the respective range of scales is not an integer multiple of 'scale_r_step' and 'scale_c_step', 'scale_r_step' and 'scale_c_step' are modified accordingly.
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.
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. In rare cases, NumMatches must be set to a higher value than the required number of matches. This is the case if, for instance, a small MinScore is set.
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.
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.
With the generic parameters GenParamName and GenParamValue it is possible to adjust parameters that typically do not have to be set by the user. By default the pose is extracted with high subpixel accuracy ('least_squares_very_high') through a least-squares adjustment, i.e., by minimizing the distances of the model points to their corresponding image points. However, if no high accuracy is required by an application, the subpixel precise extraction can be reduced or switched off as it increases the processing time. Here, 'subpixel' must be passed in GenParamName and 'none', 'least_squares', 'least_squares_high' for GenParamValue. A further use of GenParamName and GenParamValue is to override the discretization steps of the search space 'angle_step', 'scale_r_step' and 'scale_c_step' that have been defined when the model was created in create_planar_uncalib_deformable_model.
As described in create_planar_uncalib_deformable_model the deformable matching algorithm searches exhaustively a basic set of parameters that are specified with AngleStart,AngleExtent, ScaleRMin,ScaleRMax,ScaleCMin and ScaleCMax. However, to allow a detection even when the object is imaged under perspective distortion, an additional transformation is estimated. This additional transformation transforms the model from the original search range to a bigger perspectively distorted one. By allowing perspective distortions, the risk of false positives is also increased. One possible use of the parameter GenParamName is to help discarding false positives that occur, if for instance a small score was specified in MinScore and the image contains significant clutter with similar shape as the model.
To restrict arbitrary perspective matches from occurring, the values 'angle_change_restriction' and 'aniso_scale_change_restriction' can be used in GenParamName. With 'angle_change_restriction' the maximal tolerated angular distortion can be restricted (from the default value 0.0, where arbitrary distortion is allowed, to where no distortion is allowed). This parameter tests, if the angle of 90 degree at the corners of the axis-aligned rectangle around the model points is changed by more than the corresponding GenParamValue for the found instance of the model. Note that this parameter helps to restrict both affine (a shear mapping) and perspective parts of the transformation. As an example, with 'angle_change_restriction' a square-like model can be prevented to match with a parallelogram or an arbitrary trapezium.
With the parameter 'aniso_scale_change_restriction' the ratio of anisotropic scaling can be restricted (the smaller scale factor divided by the bigger scale factor). The value of this parameter ranges from the default value 0.0, where arbitrary distortion is allowed, to 1.0, where no distortion is allowed. One typical use for this parameter is to restrict for instance a square-like model to deform to a rectangular model.
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 deformable model was created with create_planar_uncalib_deformable_model. If NumLevels is set to 0, the number of pyramid levels specified in create_planar_uncalib_deformable_model is used.
The parameter Greediness determines how “greedily” the search should be carried out. If Greediness=0, a safe search heuristic is used, which 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 deformable model will be found for Greediness=0.9.
The projective transformation (homographies) that encode the position of the found instances of the model are returned in HomMat2D. In case that multiple objects are found, the different homographies are concatenated. A single homography can easily be extracted by tuple_select_range(HomMat2D,Index*9,(Index+1)*9-1, SelectedHomMat2D). The different detection results are sorted in decreasing order of Score. The row and column coordinates are the coordinates of the origin of the deformable model in the search image, which can be found by calling projective_trans_pixel(HomMat2D,0,0,Row,Column). By default, the origin is the center of gravity of the domain (region) of the image that was used to create the deformable model with create_planar_uncalib_deformable_model. A different origin can be set with set_deformable_model_origin. For visualization purposes, the model contours that are extracted by get_deformable_model_contours can be projected to the found location given HomMat2D with projective_trans_contour_xld.
Additionally, 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.
Input image in which the model should be found.
Handle of the model.
Smallest rotation of the model.
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: 0.78
Suggested values: 6.29, 3.14, 1.57, 0.79, 0.39, 0.0
Restriction: AngleExtent >= 0
Minimum scale of the model in row direction.
Default value: 1.0
Suggested values: 0.5, 0.6, 0.7, 0.8, 0.9, 1.0
Restriction: ScaleRMin > 0
Maximum scale of the model in row direction.
Default value: 1.0
Suggested values: 1.0, 1.1, 1.2, 1.3, 1.4, 1.5
Restriction: ScaleRMax >= ScaleRMin
Minimum scale of the model in column direction.
Default value: 1.0
Suggested values: 0.5, 0.6, 0.7, 0.8, 0.9, 1.0
Restriction: ScaleCMin > 0
Maximum scale of the model in column direction.
Default value: 1.0
Suggested values: 1.0, 1.1, 1.2, 1.3, 1.4, 1.5
Restriction: ScaleCMax >= ScaleCMin
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
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
Maximum overlap of the instances of the model to be found.
Default value: 1.0
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
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” 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
The general parameter names.
Default value: []
List of values: [], 'angle_change_restriction', 'angle_step', 'aniso_scale_change_restriction', 'scale_c_step', 'scale_r_step', 'subpixel'
Values of the general parameters.
Default value: []
List of values: [], 'least_squares', 'least_squares_high', 'least_squares_very_high', 'none'
Homographies between model and found instances.
Score of the found instances of the model.
If the parameters are valid, the operator find_planar_uncalib_deformable_model returns the value 2 (H_MSG_TRUE). If necessary an exception is raised.
create_planar_uncalib_deformable_model, read_deformable_model
find_planar_calib_deformable_model
Matching
| Operators |