find_calib_object🔗
Short description🔗
find_calib_object — Find the HALCON calibration plate and set the extracted points and
contours in a calibration data model.
Signature🔗
find_calib_object( image Image, calib_data CalibDataID, number CameraIdx, number CalibObjIdx, number CalibObjPoseIdx, attribute.name GenParamName, attribute.value GenParamValue )
Description🔗
find_calib_object searches in Image for a HALCON
calibration plate corresponding to the description of the calibration object
with the index CalibObjIdx from the calibration data model
CalibDataID. If a calibration plate is found,
find_calib_object extracts the centers and the contours of its marks
and estimates the pose of the plate relative to the observing camera
CameraIdx. All collected observation data is stored in the
calibration data model for the calibration object pose
CalibObjPoseIdx. In order to ensure a successful detection of the
calibration plate, at least one finder pattern has to be visible in the
image. For calibration plates with hexagonally arranged marks this is
a special mark hexagon where either four or six marks contain a hole, while
for calibration plates with rectangularly arranged marks this is the border
of the calibration plate with a triangle in one corner.
Preparation of the input data🔗
Before the operator find_calib_object can be called, a calibration
data model has to be defined performing the following steps:
-
Create a calibration data model with the operator
create_calib_data, specifying the number of cameras in the setup and the number of used calibration objects. -
Specify the camera type and the initial internal camera parameters for all cameras with the operator
set_calib_data_cam_param. Note that only cameras of the same type can be calibrated in a single setup. -
Specify the description of all calibration objects with the operator
set_calib_data_calib_object. Note that for a successful call offind_calib_objecta valid description file of the calibration plate is necessary. This description file has to be set beforehand via the operatorset_calib_data_calib_object. As a consequence, the usage of a user-defined calibration object can only be made by the operatorset_calib_data_observ_points.
Collecting observation data🔗
find_calib_object is used to collect observations in a calibration
data model. Beyond, it stores additional observation
data that cannot be added to the model with
set_calib_data_observ_points and that is dependent on the used
calibration plate. While for calibration plates with rectangularly arranged
marks (see gen_caltab) the rim of the calibration plate is added to
the observations, calibration plates with hexagonal pattern (see
create_caltab) store one of their finder pattern. Additionally and
irrespective of the used calibration plate, the contour of each mark is added
to the calibration model.
Setting additional parameters🔗
Using calibration plates with hexagonally arranged marks, the
following additional parameter can be set via GenParamName and
GenParamValue:
-
'sigma': Smoothing factor for the extraction of the mark contours. For increasing values of 'sigma', the filter width and thereby the amount of smoothing increases (see also
edges_sub_pixfor the influence of the filter width on the Canny filter).Suggested values: 0.5, 0.7, 0.9, 1.0, 1.2, 1.5
Default: 1.0
For calibration plates with rectangularly arranged marks,
find_calib_object essentially encapsulates the sequence of three
operator calls: find_caltab, find_marks_and_pose and
set_calib_data_observ_points. For this kind of calibration plates
the following parameters can be set using GenParamName and
GenParamValue:
-
'alpha': Smoothing factor for the extraction of the mark contours. For increasing values of 'alpha', the filter width and thereby the amount of smoothing decreases (see also
edges_sub_pixfor the influence of the filter width on the Lanser2 filter ).Suggested values: 0.5, 0.7, 0.9, 1.0, 1.2, 1.5
Default: 0.9
-
'gap_tolerance': Tolerance factor for gaps between the marks. If the marks appear closer to each other than expected, you might set 'gap_tolerance' < 1.0 to avoid disturbing patterns outside the calibration plate to be associated with the calibration plate. This can typically happen if the plate is strongly tilted and positioned in front of a background that exposes mark-like patterns. If the distances between single marks vary in a wide range, e.g., if the calibration plate appears with strong perspective distortion in the image, you might set 'gap_tolerance' > 1.0 to enforce the marks grouping (see also
find_caltab).Suggested values: 0.75, 0.9, 1.0, 1.1, 1.2, 1.5
Default: 1.0
-
'max_diam_marks': Maximum expected diameter of the marks (needed internally by
find_marks_and_pose). By default, this value is estimated by the preceding internal call tofind_caltab. However, if the estimation is erroneous for no obvious reason or the internal call tofind_caltabfails or is simply skipped (see 'skip_find_caltab' below), you might have to adjust this value.Suggested values: 50.0, 100.0, 150.0, 200.0, 300.0
-
'skip_find_caltab': Skip the internal call to
find_caltab. If activated, only the domain ofImagereduces the search area for the internal call offind_marks_and_pose. Thus, a user defined calibration plate region can be incorporated by setting 'skip_find_caltab'='false' and reducing theImagedomain to the user region.List of values: 'false', 'true'
Default: 'false'
If using a HALCON calibration plate as calibration object, it is recommended
to use find_calib_object instead of
set_calib_data_observ_points where possible, since the contour
information, which it stores in the calibration data model, enables a more
precise calibration procedure with calibrate_cameras.
After a successful call to find_calib_object, the extracted
points can be queried by get_calib_data_observ_points and the
extracted contours can be accessed by get_calib_data_observ_contours.
Execution information🔗
Execution information
-
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 modifies the state of the following input parameter:
During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.
Parameters🔗
Image (input_object) singlechannelimage → object (byte / uint2)
Input image.
CalibDataID (input_control, state is modified) calib_data → (handle)
Handle of a calibration data model.
CameraIdx (input_control) number → (integer)
Index of the observing camera.
Default: 0
Suggested values: 0, 1, 2
CalibObjIdx (input_control) number → (integer)
Index of the calibration object.
Default: 0
Suggested values: 0, 1, 2
CalibObjPoseIdx (input_control) number → (integer)
Index of the observed calibration object.
Default: 0
Suggested values: 0, 1, 2
Restriction: CalibObjPoseIdx >= 0
GenParamName (input_control) attribute.name-array → (string)
Names of the generic parameters to be set.
Default: []
List of values: 'alpha', 'gap_tolerance', 'max_diam_marks', 'sigma', 'skip_find_caltab'
GenParamValue (input_control) attribute.value-array → (string / real / integer)
Values of the generic parameters to be set.
Default: []
Suggested values: 0.5, 0.9, 1.0, 1.2, 1.5, 2.0, 'true', 'false'
Combinations with other operators🔗
Combinations
Possible predecessors
read_image, find_marks_and_pose, set_calib_data_cam_param, set_calib_data_calib_object
Possible successors
set_calib_data, calibrate_cameras
Alternatives
find_caltab, find_marks_and_pose, set_calib_data_observ_points
Module🔗
Calibration