halcondotnet

Represents an instance of a bar code reader.

Represents a generic instance of a handle.

Represents an uninitialized handle instance

Relinquish ownership of managed handle

Caller must ensure that handle is destroyed at some time after this instance is no longer used.

Returns true if the handle has been initialized.

A handle will be uninitialized when creating it with a no-argument constructor or after calling Dispose();

Releases the resources used by this handle object

Invalidates the handle but keeps the HALCON handle alive, which only makes sense if the handle is used externally and cleared later, e.g. by an HOperatorSet based module or another language interface.

Provides a simple string representation of the handle id as hex number, which is mainly useful for debug outputs (to see if two handles are identical)

Cast to IntPtr returns HALCON ID of handle resources

Caller must ensure that input object is kept alive

Returns the HALCON ID for the handle

Caller must ensure that input handle is kept alive

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Clear the content of a handle. Instance represents: Handle to clear.

Deserialize a serialized item. Modified instance represents: Handle containing the deserialized item.

Handle containing the serialized item to be deserialized.

Serialize the content of a handle. Instance represents: Handle that should be serialized.

Handle containing the serialized item.

Test if a tuple is serializable. Instance represents: Tuple to check for serializability.

Boolean value indicating if the input can be serialized.

Check if a handle is valid. Instance represents: The handle to check for validity.

The validity of the handle, 1 or 0.

Return the semantic type of a tuple. Instance represents: Input tuple.

Semantic type of the input tuple as a string.

Read a bar code model from a file and create a new model. Modified instance represents: Handle of the bar code model.

Name of the bar code model file. Default: "bar_code_model.bcm"

Create a model of a bar code reader. Modified instance represents: Handle for using and accessing the bar code model.

Names of the generic parameters that can be adjusted for the bar code model. Default: [] Values of the generic parameters that can be adjusted for the bar code model. Default: []

Create a model of a bar code reader. Modified instance represents: Handle for using and accessing the bar code model.

Names of the generic parameters that can be adjusted for the bar code model. Default: [] Values of the generic parameters that can be adjusted for the bar code model. Default: []

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Deserialize a bar code model. Modified instance represents: Handle of the bar code model.

Handle of the serialized item.

Serialize a bar code model. Instance represents: Handle of the bar code model.

Handle of the serialized item.

Read a bar code model from a file and create a new model. Modified instance represents: Handle of the bar code model.

Name of the bar code model file. Default: "bar_code_model.bcm"

Write a bar code model to a file. Instance represents: Handle of the bar code model.

Name of the bar code model file. Default: "bar_code_model.bcm"

Access iconic objects that were created during the search or decoding of bar code symbols. Instance represents: Handle of the bar code model.

Indicating the bar code results respectively candidates for which the data is required. Default: "all" Name of the iconic object to return. Default: "candidate_regions" Objects that are created as intermediate results during the detection or evaluation of bar codes.

Access iconic objects that were created during the search or decoding of bar code symbols. Instance represents: Handle of the bar code model.

Get the alphanumerical results that were accumulated during the decoding of bar code symbols. Instance represents: Handle of the bar code model.

Indicating the bar code results respectively candidates for which the data is required. Default: "all" Names of the resulting data to return. Default: "decoded_types" List with the results.

Get the alphanumerical results that were accumulated during the decoding of bar code symbols. Instance represents: Handle of the bar code model.

Indicating the bar code results respectively candidates for which the data is required. Default: "all" Names of the resulting data to return. Default: "decoded_types" List with the results.

Decode bar code symbols within a rectangle. Instance represents: Handle of the bar code model.

Input image. Type of the searched bar code. Default: "EAN-13" Row index of the center. Default: 50.0 Column index of the center. Default: 100.0 Orientation of rectangle in radians. Default: 0.0 Half of the length of the rectangle along the reading direction of the bar code. Default: 200.0 Half of the length of the rectangle perpendicular to the reading direction of the bar code. Default: 100.0 Data strings of all successfully decoded bar codes.

Decode bar code symbols within a rectangle. Instance represents: Handle of the bar code model.

Detect and read bar code symbols in an image. Instance represents: Handle of the bar code model.

Input image. If the image has a reduced domain, the barcode search is reduced to that domain. This usually reduces the runtime of the operator. However, if the barcode is not fully inside the domain, the barcode cannot be decoded correctly. Type of the searched bar code. Default: "auto" Data strings of all successfully decoded bar codes. Regions of the successfully decoded bar code symbols.

Detect and read bar code symbols in an image. Instance represents: Handle of the bar code model.

Get the names of the parameters that can be used in set_bar_code* and get_bar_code* operators for a given bar code model Instance represents: Handle of the bar code model.

Properties of the parameters. Default: "trained_general" Names of the generic parameters.

Get parameters that are used by the bar code reader when processing a specific bar code type. Instance represents: Handle of the bar code model.

Names of the bar code types for which parameters should be queried. Default: "EAN-13" Names of the generic parameters that are to be queried for the bar code model. Default: "check_char" Values of the generic parameters.

Get parameters that are used by the bar code reader when processing a specific bar code type. Instance represents: Handle of the bar code model.

Get one or several parameters that describe the bar code model. Instance represents: Handle of the bar code model.

Names of the generic parameters that are to be queried for the bar code model. Default: "element_size_min" Values of the generic parameters.

Get one or several parameters that describe the bar code model. Instance represents: Handle of the bar code model.

Names of the generic parameters that are to be queried for the bar code model. Default: "element_size_min" Values of the generic parameters.

Set selected parameters of the bar code model for selected bar code types Instance represents: Handle of the bar code model.

Names of the bar code types for which parameters should be set. Default: "EAN-13" Names of the generic parameters that shall be adjusted for finding and decoding bar codes. Default: "check_char" Values of the generic parameters that are adjusted for finding and decoding bar codes. Default: "absent"

Set selected parameters of the bar code model for selected bar code types Instance represents: Handle of the bar code model.

Set selected parameters of the bar code model. Instance represents: Handle of the bar code model.

Names of the generic parameters that shall be adjusted for finding and decoding bar codes. Default: "element_size_min" Values of the generic parameters that are adjusted for finding and decoding bar codes. Default: 8

Set selected parameters of the bar code model. Instance represents: Handle of the bar code model.

Delete a bar code model and free the allocated memory

Handle of the bar code model.

Delete a bar code model and free the allocated memory Instance represents: Handle of the bar code model.

Create a model of a bar code reader. Modified instance represents: Handle for using and accessing the bar code model.

Names of the generic parameters that can be adjusted for the bar code model. Default: [] Values of the generic parameters that can be adjusted for the bar code model. Default: []

Create a model of a bar code reader. Modified instance represents: Handle for using and accessing the bar code model.

Names of the generic parameters that can be adjusted for the bar code model. Default: [] Values of the generic parameters that can be adjusted for the bar code model. Default: []

Represents an instance of a barrier synchronization object.

Create a barrier synchronization object. Modified instance represents: Barrier synchronization object.

Barrier attribute. Default: [] Barrier attribute value. Default: [] Barrier team size. Default: 1

Create a barrier synchronization object. Modified instance represents: Barrier synchronization object.

Barrier attribute. Default: [] Barrier attribute value. Default: [] Barrier team size. Default: 1

Destroy a barrier synchronization object. Instance represents: Barrier synchronization object.

Wait on the release of a barrier synchronization object. Instance represents: Barrier synchronization object.

Create a barrier synchronization object. Modified instance represents: Barrier synchronization object.

Barrier attribute. Default: [] Barrier attribute value. Default: [] Barrier team size. Default: 1

Create a barrier synchronization object. Modified instance represents: Barrier synchronization object.

Barrier attribute. Default: [] Barrier attribute value. Default: [] Barrier team size. Default: 1

Represents an instance of the data structure used to inspect beads.

Create a model to inspect beads or adhesive in images. Modified instance represents: Handle for using and accessing the bead inspection model.

XLD contour specifying the expected bead's shape and position. Optimal bead thickness. Default: 50 Tolerance of bead's thickness with respect to TargetThickness. Default: 15 Tolerance of the bead's center position. Default: 15 The bead's polarity. Default: "light" Names of the generic parameters that can be adjusted for the bead inspection model. Default: [] Values of the generic parameters that can be adjusted for the bead inspection model. Default: []

Create a model to inspect beads or adhesive in images. Modified instance represents: Handle for using and accessing the bead inspection model.

Get the value of a parameter in a specific bead inspection model. Instance represents: Handle of the bead inspection model.

Name of the model parameter that is queried. Default: "target_thickness" Value of the queried model parameter.

Get the value of a parameter in a specific bead inspection model. Instance represents: Handle of the bead inspection model.

Name of the model parameter that is queried. Default: "target_thickness" Value of the queried model parameter.

Set parameters of the bead inspection model. Instance represents: Handle of the bead inspection model.

Name of the model parameter that shall be adjusted for the specified bead inspection model. Default: "target_thickness" Value of the model parameter that shall be adjusted for the specified bead inspection model. Default: 40

Set parameters of the bead inspection model. Instance represents: Handle of the bead inspection model.

Inspect beads in an image, as defined by the bead inspection model. Instance represents: Handle of the bead inspection model to be used.

Image to apply bead inspection on. The detected right contour of the beads. Detected error segments Types of detected errors. The detected left contour of the beads.

Delete the bead inspection model and free the allocated memory. Instance represents: Handle of the bead inspection model.

Create a model to inspect beads or adhesive in images. Modified instance represents: Handle for using and accessing the bead inspection model.

Represents an instance of a background estimator.

Generate and initialize a data set for the background estimation. Modified instance represents: ID of the BgEsti data set.

initialization image. 1. system matrix parameter. Default: 0.7 2. system matrix parameter. Default: 0.7 Gain type. Default: "fixed" Kalman gain / foreground adaptation time. Default: 0.002 Kalman gain / background adaptation time. Default: 0.02 Threshold adaptation. Default: "on" Foreground/background threshold. Default: 7.0 Number of statistic data sets. Default: 10 Confidence constant. Default: 3.25 Constant for decay time. Default: 15.0

Delete the background estimation data set. Instance represents: ID of the BgEsti data set.

Return the estimated background image. Instance represents: ID of the BgEsti data set.

Estimated background image of the current data set.

Change the estimated background image. Instance represents: ID of the BgEsti data set.

Current image. Region describing areas to change.

Estimate the background and return the foreground region. Instance represents: ID of the BgEsti data set.

Current image. Region of the detected foreground.

Return the parameters of the data set. Instance represents: ID of the BgEsti data set.

2. system matrix parameter. Gain type. Kalman gain / foreground adaptation time. Kalman gain / background adaptation time. Threshold adaptation. Foreground / background threshold. Number of statistic data sets. Confidence constant. Constant for decay time. 1. system matrix parameter.

Change the parameters of the data set. Instance represents: ID of the BgEsti data set.

1. system matrix parameter. Default: 0.7 2. system matrix parameter. Default: 0.7 Gain type. Default: "fixed" Kalman gain / foreground adaptation time. Default: 0.002 Kalman gain / background adaptation time. Default: 0.02 Threshold adaptation. Default: "on" Foreground/background threshold. Default: 7.0 Number of statistic data sets. Default: 10 Confidence constant. Default: 3.25 Constant for decay time. Default: 15.0

Generate and initialize a data set for the background estimation. Modified instance represents: ID of the BgEsti data set.

Represents an instance of a camera calibration model.

Restore a calibration data model from a file. Modified instance represents: Handle of a calibration data model.

The path and file name of the model file.

Create a HALCON calibration data model. Modified instance represents: Handle of the created calibration data model.

Type of the calibration setup. Default: "calibration_object" Number of cameras in the calibration setup. Default: 1 Number of calibration objects. Default: 1

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Free the memory of a calibration data model. Instance represents: Handle of a calibration data model.

Deserialize a serialized calibration data model. Modified instance represents: Handle of a calibration data model.

Handle of the serialized item.

Serialize a calibration data model. Instance represents: Handle of a calibration data model.

Handle of the serialized item.

Restore a calibration data model from a file. Modified instance represents: Handle of a calibration data model.

The path and file name of the model file.

Store a calibration data model into a file. Instance represents: Handle of a calibration data model.

The file name of the model to be saved.

Perform a hand-eye calibration. Instance represents: Handle of a calibration data model.

Average residual error of the optimization.

Determine all camera parameters by a simultaneous minimization process. Instance represents: Handle of a calibration data model.

Back projection root mean square error (RMSE) of the optimization.

Remove a data set from a calibration data model. Instance represents: Handle of a calibration data model.

Type of the calibration data item. Default: "tool" Index of the affected item. Default: 0

Remove a data set from a calibration data model. Instance represents: Handle of a calibration data model.

Type of the calibration data item. Default: "tool" Index of the affected item. Default: 0

Set data in a calibration data model. Instance represents: Handle of a calibration data model.

Type of calibration data item. Default: "model" Index of the affected item (depending on the selected ItemType). Default: "general" Parameter(s) to set. Default: "reference_camera" New value(s). Default: 0

Set data in a calibration data model. Instance represents: Handle of a calibration data model.

Find the HALCON calibration plate and set the extracted points and contours in a calibration data model. Instance represents: Handle of a calibration data model.

Input image. Index of the observing camera. Default: 0 Index of the calibration object. Default: 0 Index of the observed calibration object. Default: 0 Names of the generic parameters to be set. Default: [] Values of the generic parameters to be set. Default: []

Remove observation data from a calibration data model. Instance represents: Handle of a calibration data model.

Index of the observing camera. Default: 0 Index of the observed calibration object. Default: 0 Index of the observed calibration object pose. Default: 0

Get contour-based observation data from a calibration data model. Instance represents: Handle of a calibration data model.

Name of contour objects to be returned. Default: "marks" Index of the observing camera. Default: 0 Index of the observed calibration object. Default: 0 Index of the observed calibration object pose. Default: 0 Contour-based result(s).

Get observed calibration object poses from a calibration data model. Instance represents: Handle of a calibration data model.

Index of the observing camera. Default: 0 Index of the observed calibration object. Default: 0 Index of the observed calibration object pose. Default: 0 Stored observed calibration object pose relative to the observing camera.

Set observed calibration object poses in a calibration data model. Instance represents: Handle of a calibration data model.

Index of the observing camera. Default: 0 Index of the calibration object. Default: 0 Index of the observed calibration object. Default: 0 Pose of the observed calibration object relative to the observing camera.

Get point-based observation data from a calibration data model. Instance represents: Handle of a calibration data model.

Index of the observing camera. Default: 0 Index of the observed calibration object. Default: 0 Index of the observed calibration object pose. Default: 0 Row coordinates of the detected points. Column coordinates of the detected points. Correspondence of the detected points to the points of the observed calibration object. Roughly estimated pose of the observed calibration object relative to the observing camera.

Set point-based observation data in a calibration data model. Instance represents: Handle of a calibration data model.

Index of the observing camera. Default: 0 Index of the calibration object. Default: 0 Index of the observed calibration object. Default: 0 Row coordinates of the extracted points. Column coordinates of the extracted points. Correspondence of the extracted points to the calibration marks of the observed calibration object. Default: "all" Roughly estimated pose of the observed calibration object relative to the observing camera.

Query information about the relations between cameras, calibration objects, and calibration object poses. Instance represents: Handle of a calibration data model.

Kind of referred object. Default: "camera" Camera index or calibration object index (depending on the selected ItemType). Default: 0 Calibration object numbers. List of calibration object indices or list of camera indices (depending on ItemType).

Query data stored or computed in a calibration data model. Instance represents: Handle of a calibration data model.

Type of calibration data item. Default: "camera" Index of the affected item (depending on the selected ItemType). Default: 0 The name of the inspected data. Default: "params" Requested data.

Query data stored or computed in a calibration data model. Instance represents: Handle of a calibration data model.

Type of calibration data item. Default: "camera" Index of the affected item (depending on the selected ItemType). Default: 0 The name of the inspected data. Default: "params" Requested data.

Define a calibration object in a calibration model. Instance represents: Handle of a calibration data model.

Calibration object index. Default: 0 3D point coordinates or a description file name.

Define a calibration object in a calibration model. Instance represents: Handle of a calibration data model.

Calibration object index. Default: 0 3D point coordinates or a description file name.

Set type and initial parameters of a camera in a calibration data model. Instance represents: Handle of a calibration data model.

Camera index. Default: 0 Type of the camera. Default: [] Initial camera internal parameters.

Set type and initial parameters of a camera in a calibration data model. Instance represents: Handle of a calibration data model.

Camera index. Default: 0 Type of the camera. Default: [] Initial camera internal parameters.

Create a HALCON calibration data model. Modified instance represents: Handle of the created calibration data model.

Type of the calibration setup. Default: "calibration_object" Number of cameras in the calibration setup. Default: 1 Number of calibration objects. Default: 1

Represents an instance of a camera setup model.

Restore a camera setup model from a file. Modified instance represents: Handle to the camera setup model.

The path and file name of the model file.

Create a model for a setup of calibrated cameras. Modified instance represents: Handle to the camera setup model.

Number of cameras in the setup. Default: 2

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Create a HALCON stereo model. Instance represents: Handle to the camera setup model.

Reconstruction method. Default: "surface_pairwise" Name of the model parameter to be set. Default: [] Value of the model parameter to be set. Default: [] Handle of the stereo model.

Create a HALCON stereo model. Instance represents: Handle to the camera setup model.

Reconstruction method. Default: "surface_pairwise" Name of the model parameter to be set. Default: [] Value of the model parameter to be set. Default: [] Handle of the stereo model.

Free the memory of a calibration setup model. Instance represents: Handle of the camera setup model.

Serialize a camera setup model. Instance represents: Handle to the camera setup model.

Handle of the serialized item.

Deserialize a serialized camera setup model. Modified instance represents: Handle to the camera setup model.

Handle of the serialized item.

Store a camera setup model into a file. Instance represents: Handle to the camera setup model.

The file name of the model to be saved.

Restore a camera setup model from a file. Modified instance represents: Handle to the camera setup model.

The path and file name of the model file.

Get generic camera setup model parameters. Instance represents: Handle to the camera setup model.

Index of the camera in the setup. Default: 0 Names of the generic parameters to be queried. Values of the generic parameters to be queried.

Get generic camera setup model parameters. Instance represents: Handle to the camera setup model.

Index of the camera in the setup. Default: 0 Names of the generic parameters to be queried. Values of the generic parameters to be queried.

Set generic camera setup model parameters. Instance represents: Handle to the camera setup model.

Unique index of the camera in the setup. Default: 0 Names of the generic parameters to be set. Values of the generic parameters to be set.

Set generic camera setup model parameters. Instance represents: Handle to the camera setup model.

Unique index of the camera in the setup. Default: 0 Names of the generic parameters to be set. Values of the generic parameters to be set.

Define type, parameters, and relative pose of a camera in a camera setup model. Instance represents: Handle to the camera setup model.

Index of the camera in the setup. Type of the camera. Default: [] Internal camera parameters. Pose of the camera relative to the setup's coordinate system.

Define type, parameters, and relative pose of a camera in a camera setup model. Instance represents: Handle to the camera setup model.

Index of the camera in the setup. Type of the camera. Default: [] Internal camera parameters. Pose of the camera relative to the setup's coordinate system.

Create a model for a setup of calibrated cameras. Modified instance represents: Handle to the camera setup model.

Number of cameras in the setup. Default: 2

Represents internal camera parameters.

Provides access to the internally used tuple data

Provides access to the value at the specified index

Create an uninitialized instance.

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Compute the distance values for a rectified stereo image pair using multi-scanline optimization. Instance represents: Internal camera parameters of the rectified camera 1.

Rectified image of camera 1. Rectified image of camera 2. Score of the calculated disparity. Internal camera parameters of the rectified camera 2. Point transformation from the rectified camera 2 to the rectified camera 1. Minimum of the expected disparities. Default: -30 Maximum of the expected disparities. Default: 30 Smoothing of surfaces. Default: 50 Smoothing of edges. Default: 50 Parameter name(s) for the multi-scanline algorithm. Default: [] Parameter value(s) for the multi-scanline algorithm. Default: [] Distance image.

Compute the distance values for a rectified stereo image pair using multi-scanline optimization. Instance represents: Internal camera parameters of the rectified camera 1.

Compute the distance values for a rectified stereo image pair using multigrid methods. Instance represents: Internal camera parameters of the rectified camera 1.

Rectified image of camera 1. Rectified image of camera 2. Score of the calculated disparity if CalculateScore is set to 'true'. Internal camera parameters of the rectified camera 2. Point transformation from the rectified camera 2 to the rectified camera 1. Weight of the gray value constancy in the data term. Default: 1.0 Weight of the gradient constancy in the data term. Default: 30.0 Weight of the smoothness term in relation to the data term. Default: 5.0 Initial guess of the disparity. Default: 0.0 Should the quality measure be returned in Score? Default: "false" Parameter name(s) for the multigrid algorithm. Default: "default_parameters" Parameter value(s) for the multigrid algorithm. Default: "fast_accurate" Distance image.

Compute the distance values for a rectified stereo image pair using multigrid methods. Instance represents: Internal camera parameters of the rectified camera 1.

Compute the fundamental matrix from the relative orientation of two cameras. Instance represents: Parameters of the 1. camera.

Relative orientation of the cameras (3D pose). 6x6 covariance matrix of relative pose. Default: [] Parameters of the 2. camera. 9x9 covariance matrix of the fundamental matrix. Computed fundamental matrix.

Compute the relative orientation between two cameras given image point correspondences and known camera parameters and reconstruct 3D space points. Instance represents: Camera parameters of the 1st camera.

Input points in image 1 (row coordinate). Input points in image 1 (column coordinate). Input points in image 2 (row coordinate). Input points in image 2 (column coordinate). Row coordinate variance of the points in image 1. Default: [] Covariance of the points in image 1. Default: [] Column coordinate variance of the points in image 1. Default: [] Row coordinate variance of the points in image 2. Default: [] Covariance of the points in image 2. Default: [] Column coordinate variance of the points in image 2. Default: [] Camera parameters of the 2nd camera. Algorithm for the computation of the relative pose and for special pose types. Default: "normalized_dlt" 6x6 covariance matrix of the relative camera orientation. Root-Mean-Square of the epipolar distance error. X coordinates of the reconstructed 3D points. Y coordinates of the reconstructed 3D points. Z coordinates of the reconstructed 3D points. Covariance matrices of the reconstructed 3D points. Computed relative orientation of the cameras (3D pose).

Compute the relative orientation between two cameras by automatically finding correspondences between image points. Instance represents: Parameters of the 1st camera.

Input image 1. Input image 2. Row coordinates of characteristic points in image 1. Column coordinates of characteristic points in image 1. Row coordinates of characteristic points in image 2. Column coordinates of characteristic points in image 2. Parameters of the 2nd camera. Gray value comparison metric. Default: "ssd" Size of gray value masks. Default: 10 Average row coordinate shift of corresponding points. Default: 0 Average column coordinate shift of corresponding points. Default: 0 Half height of matching search window. Default: 200 Half width of matching search window. Default: 200 Estimate of the relative orientation of the right image with respect to the left image. Default: 0.0 Threshold for gray value matching. Default: 10 Algorithm for the computation of the relative pose and for special pose types. Default: "normalized_dlt" Maximal deviation of a point from its epipolar line. Default: 1 Seed for the random number generator. Default: 0 6x6 covariance matrix of the relative orientation. Root-Mean-Square of the epipolar distance error. Indices of matched input points in image 1. Indices of matched input points in image 2. Computed relative orientation of the cameras (3D pose).

Compute the relative orientation between two cameras by automatically finding correspondences between image points. Instance represents: Parameters of the 1st camera.

Compute the distance values for a rectified stereo image pair using correlation techniques. Instance represents: Internal camera parameters of the rectified camera 1.

Rectified image of camera 1. Rectified image of camera 2. Evaluation of a distance value. Internal camera parameters of the rectified camera 2. Point transformation from the rectified camera 2 to the rectified camera 1. Matching function. Default: "ncc" Width of the correlation window. Default: 11 Height of the correlation window. Default: 11 Variance threshold of textured image regions. Default: 0.0 Minimum of the expected disparities. Default: 0 Maximum of the expected disparities. Default: 30 Number of pyramid levels. Default: 1 Threshold of the correlation function. Default: 0.0 Downstream filters. Default: "none" Distance interpolation. Default: "none" Distance image.

Compute the distance values for a rectified stereo image pair using correlation techniques. Instance represents: Internal camera parameters of the rectified camera 1.

Get a 3D point from the intersection of two lines of sight within a binocular camera system. Instance represents: Internal parameters of the projective camera 1.

Internal parameters of the projective camera 2. Point transformation from camera 2 to camera 1. Row coordinate of a point in image 1. Column coordinate of a point in image 1. Row coordinate of the corresponding point in image 2. Column coordinate of the corresponding point in image 2. X coordinate of the 3D point. Y coordinate of the 3D point. Z coordinate of the 3D point. Distance of the 3D point to the lines of sight.

Get a 3D point from the intersection of two lines of sight within a binocular camera system. Instance represents: Internal parameters of the projective camera 1.

Transform a disparity image into 3D points in a rectified stereo system. Instance represents: Internal camera parameters of the rectified camera 1.

Disparity image. Y coordinates of the points in the rectified camera system 1. Z coordinates of the points in the rectified camera system 1. Internal camera parameters of the rectified camera 2. Pose of the rectified camera 2 in relation to the rectified camera 1. X coordinates of the points in the rectified camera system 1.

Transform an image point and its disparity into a 3D point in a rectified stereo system. Instance represents: Rectified internal camera parameters of camera 1.

Rectified internal camera parameters of camera 2. Pose of the rectified camera 2 in relation to the rectified camera 1. Row coordinate of a point in the rectified image 1. Column coordinate of a point in the rectified image 1. Disparity of the images of the world point. X coordinate of the 3D point. Y coordinate of the 3D point. Z coordinate of the 3D point.

Transform an image point and its disparity into a 3D point in a rectified stereo system. Instance represents: Rectified internal camera parameters of camera 1.

Transform a disparity value into a distance value in a rectified binocular stereo system. Instance represents: Rectified internal camera parameters of camera 1.

Rectified internal camera parameters of camera 2. Point transformation from the rectified camera 2 to the rectified camera 1. Disparity between the images of the world point. Distance of a world point to the rectified camera system.

Transform a disparity value into a distance value in a rectified binocular stereo system. Instance represents: Rectified internal camera parameters of camera 1.

Transfrom a distance value into a disparity in a rectified stereo system. Instance represents: Rectified internal camera parameters of camera 1.

Rectified internal camera parameters of camera 2. Point transformation from the rectified camera 2 to the rectified camera 1. Distance of a world point to camera 1. Disparity between the images of the point.

Transfrom a distance value into a disparity in a rectified stereo system. Instance represents: Rectified internal camera parameters of camera 1.

Generate transformation maps that describe the mapping of the images of a binocular camera pair to a common rectified image plane. Instance represents: Internal parameters of camera 1.

Image containing the mapping data of camera 2. Internal parameters of camera 2. Point transformation from camera 2 to camera 1. Subsampling factor. Default: 1.0 Type of rectification. Default: "viewing_direction" Type of mapping. Default: "bilinear" Rectified internal parameters of camera 1. Rectified internal parameters of camera 2. Point transformation from the rectified camera 1 to the original camera 1. Point transformation from the rectified camera 1 to the original camera 1. Point transformation from the rectified camera 2 to the rectified camera 1. Image containing the mapping data of camera 1.

Determine all camera parameters of a binocular stereo system. Instance represents: Initial values for the internal parameters of camera 1.

Ordered Tuple with all X-coordinates of the calibration marks (in meters). Ordered Tuple with all Y-coordinates of the calibration marks (in meters). Ordered Tuple with all Z-coordinates of the calibration marks (in meters). Ordered Tuple with all row-coordinates of the extracted calibration marks of camera 1 (in pixels). Ordered Tuple with all column-coordinates of the extracted calibration marks of camera 1 (in pixels). Ordered Tuple with all row-coordinates of the extracted calibration marks of camera 2 (in pixels). Ordered Tuple with all column-coordinates of the extracted calibration marks of camera 2 (in pixels). Initial values for the internal parameters of camera 2. Ordered tuple with all initial values for the poses of the calibration model in relation to camera 1. Ordered tuple with all initial values for the poses of the calibration model in relation to camera 2. Camera parameters to be estimated. Default: "all" Internal parameters of camera 2. Ordered tuple with all poses of the calibration model in relation to camera 1. Ordered tuple with all poses of the calibration model in relation to camera 2. Pose of camera 2 in relation to camera 1. Average error distances in pixels. Internal parameters of camera 1.

Determine all camera parameters of a binocular stereo system. Instance represents: Initial values for the internal parameters of camera 1.

Find the best matches of a calibrated descriptor model in an image and return their 3D pose. Instance represents: Camera parameter (inner orientation) obtained from camera calibration.

Input image where the model should be found. The handle to the descriptor model. The detector's parameter names. Default: [] Values of the detector's parameters. Default: [] The descriptor's parameter names. Default: [] Values of the descriptor's parameters. Default: [] Minimum score of the instances of the models to be found. Default: 0.2 Maximal number of found instances. Default: 1 Score type to be evaluated in Score. Default: "num_points" Score of the found instances according to the ScoreType input. 3D pose of the object.

Find the best matches of a calibrated descriptor model in an image and return their 3D pose. Instance represents: Camera parameter (inner orientation) obtained from camera calibration.

Create a descriptor model for calibrated perspective matching. Instance represents: The parameters of the internal orientation of the camera.

Input image whose domain will be used to create the model. The reference pose of the object in the reference image. The type of the detector. Default: "lepetit" The detector's parameter names. Default: [] Values of the detector's parameters. Default: [] The descriptor's parameter names. Default: [] Values of the descriptor's parameters. Default: [] The seed for the random number generator. Default: 42 The handle to the descriptor model.

Prepare a deformable model for planar calibrated matching from XLD contours. Instance represents: The parameters of the internal orientation of the camera.

Input contours that will be used to create the model. The reference pose of the object. Maximum number of pyramid levels. Default: "auto" This parameter is not used. Default: [] This parameter is not used. Default: [] Step length of the angles (resolution). Default: "auto" Minimum scale of the pattern in row direction. Default: 1.0 This parameter is not used. Default: [] Scale step length (resolution) in row direction. Default: "auto" Minimum scale of the pattern in column direction. Default: 1.0 This parameter is not used. Default: [] Scale step length (resolution) in the column direction. Default: "auto" Kind of optimization used for generating the model. Default: "auto" Match metric. Default: "ignore_local_polarity" Minimum contrast of the objects in the search images. Default: 5 The generic parameter names. Default: [] Values of the generic parameter. Default: [] Handle of the model.

Prepare a deformable model for planar calibrated matching from XLD contours. Instance represents: The parameters of the internal orientation of the camera.

Create a deformable model for calibrated perspective matching. Instance represents: The parameters of the internal orientation of the camera.

Input image whose domain will be used to create the model. The reference pose of the object in the reference image. Maximum number of pyramid levels. Default: "auto" This parameter is not used. Default: [] This parameter is not used. Default: [] Step length of the angles (resolution). Default: "auto" Minimum scale of the pattern in row direction. Default: 1.0 This parameter is not used. Default: [] Scale step length (resolution) in row direction. Default: "auto" Minimum scale of the pattern in column direction. Default: 1.0 This parameter is not used. Default: [] Scale step length (resolution) in the column direction. Default: "auto" Kind of optimization used for generating the model. Default: "none" Match metric. Default: "use_polarity" Thresholds or hysteresis thresholds for the contrast of the object in the template image. Default: "auto" Minimum contrast of the objects in the search images. Default: "auto" The parameter names. Default: [] Values of the parameters. Default: [] Handle of the model.

Create a deformable model for calibrated perspective matching. Instance represents: The parameters of the internal orientation of the camera.

Project the edges of a 3D shape model into image coordinates. Instance represents: Internal camera parameters.

Handle of the 3D shape model. 3D pose of the 3D shape model in the world coordinate system. Remove hidden surfaces? Default: "true" Smallest face angle for which the edge is displayed Default: 0.523599 Contour representation of the model view.

Project the edges of a 3D shape model into image coordinates. Instance represents: Internal camera parameters.

Prepare a 3D object model for matching. Instance represents: Internal camera parameters.

Handle of the 3D object model. Reference orientation: Rotation around x-axis or x component of the Rodriguez vector (in radians or without unit). Default: 0 Reference orientation: Rotation around y-axis or y component of the Rodriguez vector (in radians or without unit). Default: 0 Reference orientation: Rotation around z-axis or z component of the Rodriguez vector (in radians or without unit). Default: 0 Meaning of the rotation values of the reference orientation. Default: "gba" Minimum longitude of the model views. Default: -0.35 Maximum longitude of the model views. Default: 0.35 Minimum latitude of the model views. Default: -0.35 Maximum latitude of the model views. Default: 0.35 Minimum camera roll angle of the model views. Default: -3.1416 Maximum camera roll angle of the model views. Default: 3.1416 Minimum camera-object-distance of the model views. Default: 0.3 Maximum camera-object-distance of the model views. Default: 0.4 Minimum contrast of the objects in the search images. Default: 10 Names of (optional) parameters for controlling the behavior of the operator. Default: [] Values of the optional generic parameters. Default: [] Handle of the 3D shape model.

Prepare a 3D object model for matching. Instance represents: Internal camera parameters.

Remove points from a 3D object model by projecting it to a virtual view and removing all points outside of a given region. Instance represents: Internal camera parameters.

Region in the image plane. Handle of the 3D object model. 3D pose of the world coordinate system in camera coordinates. Handle of the reduced 3D object model.

Remove points from a 3D object model by projecting it to a virtual view and removing all points outside of a given region. Instance represents: Internal camera parameters.

Region in the image plane. Handle of the 3D object model. 3D pose of the world coordinate system in camera coordinates. Handle of the reduced 3D object model.

Render 3D object models to get an image. Instance represents: Camera parameters of the scene.

Handles of the 3D object models. 3D poses of the objects. Names of the generic parameters. Default: [] Values of the generic parameters. Default: [] Rendered scene.

Render 3D object models to get an image. Instance represents: Camera parameters of the scene.

Handles of the 3D object models. 3D poses of the objects. Names of the generic parameters. Default: [] Values of the generic parameters. Default: [] Rendered scene.

Display 3D object models. Instance represents: Camera parameters of the scene.

Window handle. Handles of the 3D object models. 3D poses of the objects. Default: [] Names of the generic parameters. Default: [] Values of the generic parameters. Default: []

Display 3D object models. Instance represents: Camera parameters of the scene.

Window handle. Handles of the 3D object models. 3D poses of the objects. Default: [] Names of the generic parameters. Default: [] Values of the generic parameters. Default: []

Transform 3D points from a 3D object model to images. Instance represents: Camera parameters.

Image with the Y-Coordinates of the 3D points. Image with the Z-Coordinates of the 3D points. Handle of the 3D object model. Type of the conversion. Default: "cartesian" Pose of the 3D object model. Image with the X-Coordinates of the 3D points.

Project a 3D object model into image coordinates. Instance represents: Internal camera parameters.

Handle of the 3D object model. 3D pose of the world coordinate system in camera coordinates. Name of the generic parameter. Default: [] Value of the generic parameter. Default: [] Projected model contours.

Project a 3D object model into image coordinates. Instance represents: Internal camera parameters.

Add a camera to a 3D scene. Instance represents: Parameters of the new camera.

Handle of the 3D scene. Index of the new camera in the 3D scene.

Compute the calibrated scene flow between two stereo image pairs. Instance represents: Internal camera parameters of the rectified camera 1.

Input image 1 at time @f$t_{1}$t_1. Input image 2 at time @f$t_{1}$t_1. Input image 1 at time @f$t_{2}$t_2. Input image 2 at time @f$t_{2}$t_2. Disparity between input images 1 and 2 at time @f$t_{1}$t_1. Weight of the regularization term relative to the data term (derivatives of the optical flow). Default: 40.0 Weight of the regularization term relative to the data term (derivatives of the disparity change). Default: 40.0 Parameter name(s) for the algorithm. Default: "default_parameters" Parameter value(s) for the algorithm. Default: "accurate" Internal camera parameters of the rectified camera 2. Pose of the rectified camera 2 in relation to the rectified camera 1. Handle of the 3D object model.

Compute the calibrated scene flow between two stereo image pairs. Instance represents: Internal camera parameters of the rectified camera 1.

Compute an absolute pose out of point correspondences between world and image coordinates. Instance represents: The inner camera parameters from camera calibration.

X-Component of world coordinates. Y-Component of world coordinates. Z-Component of world coordinates. Row-Component of image coordinates. Column-Component of image coordinates. Kind of algorithm Default: "iterative" Type of pose quality to be returned in Quality. Default: "error" Pose quality. Pose.

Compute an absolute pose out of point correspondences between world and image coordinates. Instance represents: The inner camera parameters from camera calibration.

Calibrate the radial distortion. Modified instance represents: Internal camera parameters.

Contours that are available for the calibration. Width of the images from which the contours were extracted. Default: 640 Height of the images from which the contours were extracted. Default: 480 Threshold for the classification of outliers. Default: 0.05 Seed value for the random number generator. Default: 42 Determines the distortion model. Default: "division" Determines how the distortion center will be estimated. Default: "variable" Controls the deviation of the distortion center from the image center; larger values allow larger deviations from the image center; 0 switches the penalty term off. Default: 0.0 Contours that were used for the calibration

Compute a camera matrix from internal camera parameters. Instance represents: Internal camera parameters.

Width of the images that correspond to CameraMatrix. Height of the images that correspond to CameraMatrix. 3x3 projective camera matrix that corresponds to CameraParam.

Compute the internal camera parameters from a camera matrix. Modified instance represents: Internal camera parameters.

3x3 projective camera matrix that determines the internal camera parameters. Kappa. Width of the images that correspond to CameraMatrix. Height of the images that correspond to CameraMatrix.

Determine the 3D pose of a rectangle from its perspective 2D projection Instance represents: Internal camera parameters.

Contour(s) to be examined. Width of the rectangle in meters. Height of the rectangle in meters. Weighting mode for the optimization phase. Default: "nonweighted" Clipping factor for the elimination of outliers (typical: 1.0 for 'huber' and 3.0 for 'tukey'). Default: 2.0 Covariances of the pose values. Root-mean-square value of the final residual error. 3D pose of the rectangle.

Determine the 3D pose of a rectangle from its perspective 2D projection Instance represents: Internal camera parameters.

Determine the 3D pose of a circle from its perspective 2D projection. Instance represents: Internal camera parameters.

Contours to be examined. Radius of the circle in object space. Type of output parameters. Default: "pose" 3D pose of the second circle. 3D pose of the first circle.

Determine the 3D pose of a circle from its perspective 2D projection. Instance represents: Internal camera parameters.

Contours to be examined. Radius of the circle in object space. Type of output parameters. Default: "pose" 3D pose of the second circle. 3D pose of the first circle.

Generate a projection map that describes the mapping of images corresponding to a changing radial distortion. Instance represents: Old camera parameters.

New camera parameters. Type of the mapping. Default: "bilinear" Image containing the mapping data.

Generate a projection map that describes the mapping between the image plane and a the plane z=0 of a world coordinate system. Instance represents: Internal camera parameters.

3D pose of the world coordinate system in camera coordinates. Width of the images to be transformed. Height of the images to be transformed. Width of the resulting mapped images in pixels. Height of the resulting mapped images in pixels. Scale or unit. Default: "m" Type of the mapping. Default: "bilinear" Image containing the mapping data.

Generate a projection map that describes the mapping between the image plane and a the plane z=0 of a world coordinate system. Instance represents: Internal camera parameters.

Rectify an image by transforming it into the plane z=0 of a world coordinate system. Instance represents: Internal camera parameters.

Input image. 3D pose of the world coordinate system in camera coordinates. Width of the resulting image in pixels. Height of the resulting image in pixels. Scale or unit Default: "m" Type of interpolation. Default: "bilinear" Transformed image.

Rectify an image by transforming it into the plane z=0 of a world coordinate system. Instance represents: Internal camera parameters.

Transform image points into the plane z=0 of a world coordinate system. Instance represents: Internal camera parameters.

3D pose of the world coordinate system in camera coordinates. Row coordinates of the points to be transformed. Default: 100.0 Column coordinates of the points to be transformed. Default: 100.0 Scale or dimension Default: "m" X coordinates of the points in the world coordinate system. Y coordinates of the points in the world coordinate system.

Transform image points into the plane z=0 of a world coordinate system. Instance represents: Internal camera parameters.

Perform a hand-eye calibration. Instance represents: Internal camera parameters.

Linear list containing all the x coordinates of the calibration points (in the order of the images). Linear list containing all the y coordinates of the calibration points (in the order of the images). Linear list containing all the z coordinates of the calibration points (in the order of the images). Linear list containing all row coordinates of the calibration points (in the order of the images). Linear list containing all the column coordinates of the calibration points (in the order of the images). Number of the calibration points for each image. Known 3D pose of the robot for each image (moving camera: robot base in robot tool coordinates; stationary camera: robot tool in robot base coordinates). Method of hand-eye calibration. Default: "nonlinear" Type of quality assessment. Default: "error_pose" Computed 3D pose of the calibration points in robot base coordinates (moving camera) or in robot tool coordinates (stationary camera), respectively. Quality assessment of the result. Computed relative camera pose: 3D pose of the robot tool (moving camera) or robot base (stationary camera), respectively, in camera coordinates.

Perform a hand-eye calibration. Instance represents: Internal camera parameters.

Change the radial distortion of contours. Instance represents: Internal camera parameter for Contours.

Original contours. Internal camera parameter for ContoursRectified. Resulting contours with modified radial distortion.

Change the radial distortion of pixel coordinates. Instance represents: The inner camera parameters of the camera used to create the input pixel coordinates.

Original row component of pixel coordinates. Original column component of pixel coordinates. The inner camera parameters of a camera. Row component of pixel coordinates after changing the radial distortion. Column component of pixel coordinates after changing the radial distortion.

Change the radial distortion of an image. Instance represents: Internal camera parameter for Image.

Original image. Region of interest in ImageRectified. Internal camera parameter for Image. Resulting image with modified radial distortion.

Determine new camera parameters in accordance to the specified radial distortion. Instance represents: Internal camera parameters (original).

Mode Default: "adaptive" Desired radial distortions. Default: 0.0 Internal camera parameters (modified).

Determine new camera parameters in accordance to the specified radial distortion. Instance represents: Internal camera parameters (original).

Mode Default: "adaptive" Desired radial distortions. Default: 0.0 Internal camera parameters (modified).

Compute the line of sight corresponding to a point in the image. Instance represents: Internal camera parameters.

Row coordinate of the pixel. Column coordinate of the pixel. X coordinate of the first point on the line of sight in the camera coordinate system Y coordinate of the first point on the line of sight in the camera coordinate system Z coordinate of the first point on the line of sight in the camera coordinate system X coordinate of the second point on the line of sight in the camera coordinate system Y coordinate of the second point on the line of sight in the camera coordinate system Z coordinate of the second point on the line of sight in the camera coordinate system

Project 3D points into (sub-)pixel image coordinates. Instance represents: Internal camera parameters.

X coordinates of the 3D points to be projected in the camera coordinate system. Y coordinates of the 3D points to be projected in the camera coordinate system. Z coordinates of the 3D points to be projected in the camera coordinate system. Row coordinates of the projected points (in pixels). Column coordinates of the projected points (in pixels).

Convert internal camera parameters and a 3D pose into a 3x4 projection matrix. Instance represents: Internal camera parameters.

3D pose. 3x4 projection matrix.

Deserialize the serialized internal camera parameters. Modified instance represents: Internal camera parameters.

Handle of the serialized item.

Serialize the internal camera parameters. Instance represents: Internal camera parameters.

Handle of the serialized item.

Read internal camera parameters from a file. Modified instance represents: Internal camera parameters.

File name of internal camera parameters. Default: "campar.dat"

Write internal camera parameters into a file. Instance represents: Internal camera parameters.

File name of internal camera parameters. Default: "campar.dat"

Simulate an image with calibration plate. Instance represents: Internal camera parameters.

File name of the calibration plate description. Default: "calplate_320mm.cpd" External camera parameters (3D pose of the calibration plate in camera coordinates). Gray value of image background. Default: 128 Gray value of calibration plate. Default: 80 Gray value of calibration marks. Default: 224 Scaling factor to reduce oversampling. Default: 1.0 Simulated calibration image.

Project and visualize the 3D model of the calibration plate in the image. Instance represents: Internal camera parameters.

Window in which the calibration plate should be visualized. File name of the calibration plate description. Default: "calplate_320.cpd" External camera parameters (3D pose of the calibration plate in camera coordinates). Scaling factor for the visualization. Default: 1.0

Determine all camera parameters by a simultaneous minimization process. Instance represents: Initial values for the internal camera parameters.

Ordered tuple with all x coordinates of the calibration marks (in meters). Ordered tuple with all y coordinates of the calibration marks (in meters). Ordered tuple with all z coordinates of the calibration marks (in meters). Ordered tuple with all row coordinates of the extracted calibration marks (in pixels). Ordered tuple with all column coordinates of the extracted calibration marks (in pixels). Ordered tuple with all initial values for the external camera parameters. Camera parameters to be estimated. Default: "all" Ordered tuple with all external camera parameters. Average error distance in pixels. Internal camera parameters.

Determine all camera parameters by a simultaneous minimization process. Instance represents: Initial values for the internal camera parameters.

Extract rectangularly arranged 2D calibration marks from the image and calculate initial values for the external camera parameters. Instance represents: Initial values for the internal camera parameters.

Input image. Region of the calibration plate. File name of the calibration plate description. Default: "caltab_100.descr" Initial threshold value for contour detection. Default: 128 Loop value for successive reduction of StartThresh. Default: 10 Minimum threshold for contour detection. Default: 18 Filter parameter for contour detection, see edges_image. Default: 0.9 Minimum length of the contours of the marks. Default: 15.0 Maximum expected diameter of the marks. Default: 100.0 Tuple with column coordinates of the detected marks. Estimation for the external camera parameters. Tuple with row coordinates of the detected marks.

Define type, parameters, and relative pose of a camera in a camera setup model. Instance represents: Internal camera parameters.

Handle to the camera setup model. Index of the camera in the setup. Type of the camera. Default: [] Pose of the camera relative to the setup's coordinate system.

Define type, parameters, and relative pose of a camera in a camera setup model. Instance represents: Internal camera parameters.

Handle to the camera setup model. Index of the camera in the setup. Type of the camera. Default: [] Pose of the camera relative to the setup's coordinate system.

Set type and initial parameters of a camera in a calibration data model. Instance represents: Initial camera internal parameters.

Handle of a calibration data model. Camera index. Default: 0 Type of the camera. Default: []

Set type and initial parameters of a camera in a calibration data model. Instance represents: Initial camera internal parameters.

Handle of a calibration data model. Camera index. Default: 0 Type of the camera. Default: []

Represents an instance of a classifier.

Create a new classifier. Modified instance represents: Handle of the classifier.

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Train a classificator using a multi-channel image. Instance represents: Handle of the classifier.

Foreground pixels to be trained. Background pixels to be trained (rejection class). Multi-channel training image.

Classify pixels using hyper-cuboids. Instance represents: Handle of the classifier.

Multi channel input image. Classification result.

Deserialize a serialized classifier. Instance represents: Handle of the classifier.

Handle of the serialized item.

Serialize a classifier. Instance represents: Handle of the classifier.

Handle of the serialized item.

Save a classifier in a file. Instance represents: Handle of the classifier.

Name of the file which contains the written data.

Set system parameters for classification. Instance represents: Handle of the classifier.

Name of the wanted parameter. Default: "split_error" Value of the parameter. Default: 0.1

Set system parameters for classification. Instance represents: Handle of the classifier.

Name of the wanted parameter. Default: "split_error" Value of the parameter. Default: 0.1

Read a classifier from a file. Instance represents: Handle of the classifier.

Filename of the classifier.

Train the classifier with one data set. Instance represents: Handle of the classifier.

Number of the data set to train. Name of the protocol file. Default: "training_prot" Number of arrays of attributes to learn. Default: 500 Classification error for termination. Default: 0.05 Error during the assignment. Default: 100

Train the classifier. Instance represents: Handle of the classifier.

Array of attributes to learn. Default: [1.0,1.5,2.0] Class to which the array has to be assigned. Default: 1

Get information about the current parameter. Instance represents: Handle of the classifier.

Name of the system parameter. Default: "split_error" Value of the system parameter.

Destroy the classifier. Instance represents: Handle of the classifier.

Create a new classifier. Modified instance represents: Handle of the classifier.

Describe the classes of a box classifier. Instance represents: Handle of the classifier.

Highest dimension for output. Default: 3 Indices of the boxes. Lower bounds of the boxes (for each dimension). Higher bounds of the boxes (for each dimension). Number of training samples that were used to define this box (for each dimension). Number of training samples that were assigned incorrectly to the box. Indices of the classes.

Describe the classes of a box classifier. Instance represents: Handle of the classifier.

Classify a set of arrays. Instance represents: Handle of the classifier.

Key of the test data. Error during the assignment.

Classify a tuple of attributes with rejection class. Instance represents: Handle of the classifier.

Array of attributes which has to be classified. Default: 1.0 Number of the class, to which the array of attributes had been assigned or -1 for the rejection class.

Classify a tuple of attributes. Instance represents: Handle of the classifier.

Array of attributes which has to be classified. Default: 1.0 Number of the class to which the array of attributes had been assigned.

Represents an instance of a Gaussian mixture model.

Read a Gaussian Mixture Model from a file. Modified instance represents: GMM handle.

File name.

Create a Gaussian Mixture Model for classification Modified instance represents: GMM handle.

Number of dimensions of the feature space. Default: 3 Number of classes of the GMM. Default: 5 Number of centers per class. Default: 1 Type of the covariance matrices. Default: "spherical" Type of preprocessing used to transform the feature vectors. Default: "normalization" Preprocessing parameter: Number of transformed features (ignored for Preprocessing $=$ 'none' and Preprocessing $=$ 'normalization'). Default: 10 Seed value of the random number generator that is used to initialize the GMM with random values. Default: 42

Create a Gaussian Mixture Model for classification Modified instance represents: GMM handle.

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Classify an image with a Gaussian Mixture Model. Instance represents: GMM handle.

Input image. Threshold for the rejection of the classification. Default: 0.5 Segmented classes.

Add training samples from an image to the training data of a Gaussian Mixture Model. Instance represents: GMM handle.

Training image. Regions of the classes to be trained. Standard deviation of the Gaussian noise added to the training data. Default: 0.0

Get the training data of a Gaussian Mixture Model (GMM). Instance represents: Handle of a GMM that contains training data.

Handle of the training data of the classifier.

Add training data to a Gaussian Mixture Model (GMM). Instance represents: Handle of a GMM which receives the training data.

Handle of training data for a classifier.

Selects an optimal combination from a set of features to classify the provided data. Modified instance represents: A trained GMM classifier using only the selected features.

Handle of the training data. Method to perform the selection. Default: "greedy" Names of generic parameters to configure the classifier. Default: [] Values of generic parameters to configure the classifier. Default: [] The achieved score using two-fold cross-validation. The selected feature set, contains indices or names.

Selects an optimal combination from a set of features to classify the provided data. Modified instance represents: A trained GMM classifier using only the selected features.

Create a look-up table using a gaussian mixture model to classify byte images. Instance represents: GMM handle.

Names of the generic parameters that can be adjusted for the LUT classifier creation. Default: [] Values of the generic parameters that can be adjusted for the LUT classifier creation. Default: [] Handle of the LUT classifier.

Clear a Gaussian Mixture Model.

GMM handle.

Clear a Gaussian Mixture Model. Instance represents: GMM handle.

Clear the training data of a Gaussian Mixture Model.

GMM handle.

Clear the training data of a Gaussian Mixture Model. Instance represents: GMM handle.

Deserialize a serialized Gaussian Mixture Model. Modified instance represents: GMM handle.

Handle of the serialized item.

Serialize a Gaussian Mixture Model (GMM). Instance represents: GMM handle.

Handle of the serialized item.

Read a Gaussian Mixture Model from a file. Modified instance represents: GMM handle.

File name.

Write a Gaussian Mixture Model to a file. Instance represents: GMM handle.

File name.

Read the training data of a Gaussian Mixture Model from a file. Instance represents: GMM handle.

File name.

Write the training data of a Gaussian Mixture Model to a file. Instance represents: GMM handle.

File name.

Calculate the class of a feature vector by a Gaussian Mixture Model. Instance represents: GMM handle.

Feature vector. Number of best classes to determine. Default: 1 A-posteriori probability of the classes. Probability density of the feature vector. Normalized k-sigma-probability for the feature vector. Result of classifying the feature vector with the GMM.

Evaluate a feature vector by a Gaussian Mixture Model. Instance represents: GMM handle.

Feature vector. Probability density of the feature vector. Normalized k-sigma-probability for the feature vector. A-posteriori probability of the classes.

Train a Gaussian Mixture Model. Instance represents: GMM handle.

Maximum number of iterations of the expectation maximization algorithm Default: 100 Threshold for relative change of the error for the expectation maximization algorithm to terminate. Default: 0.001 Mode to determine the a-priori probabilities of the classes Default: "training" Regularization value for preventing covariance matrix singularity. Default: 0.0001 Number of executed iterations per class Number of found centers per class

Compute the information content of the preprocessed feature vectors of a GMM. Instance represents: GMM handle.

Type of preprocessing used to transform the feature vectors. Default: "principal_components" Cumulative information content of the transformed feature vectors. Relative information content of the transformed feature vectors.

Return the number of training samples stored in the training data of a Gaussian Mixture Model (GMM). Instance represents: GMM handle.

Number of stored training samples.

Return a training sample from the training data of a Gaussian Mixture Models (GMM). Instance represents: GMM handle.

Index of the stored training sample. Class of the training sample. Feature vector of the training sample.

Add a training sample to the training data of a Gaussian Mixture Model. Instance represents: GMM handle.

Feature vector of the training sample to be stored. Class of the training sample to be stored. Standard deviation of the Gaussian noise added to the training data. Default: 0.0

Return the parameters of a Gaussian Mixture Model. Instance represents: GMM handle.

Number of classes of the GMM. Minimum number of centers per GMM class. Maximum number of centers per GMM class. Type of the covariance matrices. Number of dimensions of the feature space.

Create a Gaussian Mixture Model for classification Modified instance represents: GMM handle.

Represents an instance of a k-NearestNeighbor classifier.

Read the k-NN classifier from a file. Modified instance represents: Handle of the k-NN classifier.

File name of the classifier.

Create a k-nearest neighbors (k-NN) classifier. Modified instance represents: Handle of the k-NN classifier.

Number of dimensions of the feature. Default: 10

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Classify an image with a k-Nearest-Neighbor classifier. Instance represents: Handle of the k-NN classifier.

Input image. Distance of the pixel's nearest neighbor. Threshold for the rejection of the classification. Default: 0.5 Segmented classes.

Add training samples from an image to the training data of a k-Nearest-Neighbor classifier. Instance represents: Handle of the k-NN classifier.

Training image. Regions of the classes to be trained.

Get the training data of a k-nearest neighbors (k-NN) classifier. Instance represents: Handle of the k-NN classifier that contains training data.

Handle of the training data of the classifier.

Add training data to a k-nearest neighbors (k-NN) classifier. Instance represents: Handle of a k-NN which receives the training data.

Training data for a classifier.

Selects an optimal subset from a set of features to solve a certain classification problem. Modified instance represents: A trained k-NN classifier using only the selected features.

Handle of the training data. Method to perform the selection. Default: "greedy" Names of generic parameters to configure the selection process and the classifier. Default: [] Values of generic parameters to configure the selection process and the classifier. Default: [] The achieved score using two-fold cross-validation. The selected feature set, contains indices or names.

Selects an optimal subset from a set of features to solve a certain classification problem. Modified instance represents: A trained k-NN classifier using only the selected features.

Clear a k-NN classifier. Instance represents: Handle of the k-NN classifier.

Return the number of training samples stored in the training data of a k-nearest neighbors (k-NN) classifier. Instance represents: Handle of the k-NN classifier.

Number of stored training samples.

Return a training sample from the training data of a k-nearest neighbors (k-NN) classifier. Instance represents: Handle of the k-NN classifier.

Index of the training sample. Class of the training sample. Feature vector of the training sample.

Deserialize a serialized k-NN classifier. Modified instance represents: Handle of the k-NN classifier.

Handle of the serialized item.

Serialize a k-NN classifier. Instance represents: Handle of the k-NN classifier.

Handle of the serialized item.

Read the k-NN classifier from a file. Modified instance represents: Handle of the k-NN classifier.

File name of the classifier.

Save the k-NN classifier in a file. Instance represents: Handle of the k-NN classifier.

Name of the file in which the classifier will be written.

Get parameters of a k-NN classification. Instance represents: Handle of the k-NN classifier.

Names of the parameters that can be read from the k-NN classifier. Default: ["method","k"] Values of the selected parameters.

Set parameters for k-NN classification. Instance represents: Handle of the k-NN classifier.

Names of the generic parameters that can be adjusted for the k-NN classifier. Default: ["method","k","max_num_classes"] Values of the generic parameters that can be adjusted for the k-NN classifier. Default: ["classes_distance",5,1]

Search for the next neighbors for a given feature vector. Instance represents: Handle of the k-NN classifier.

Features that should be classified. A rating for the results. This value contains either a distance, a frequency or a weighted frequency. The classification result, either class IDs or sample indices.

Creates the search trees for a k-NN classifier. Instance represents: Handle of the k-NN classifier.

Names of the generic parameters that can be adjusted for the k-NN classifier creation. Default: [] Values of the generic parameters that can be adjusted for the k-NN classifier creation. Default: []

Add a sample to a k-nearest neighbors (k-NN) classifier. Instance represents: Handle of the k-NN classifier.

List of features to add. Class IDs of the features.

Add a sample to a k-nearest neighbors (k-NN) classifier. Instance represents: Handle of the k-NN classifier.

List of features to add. Class IDs of the features.

Create a k-nearest neighbors (k-NN) classifier. Modified instance represents: Handle of the k-NN classifier.

Number of dimensions of the feature. Default: 10

Create a look-up table using a k-nearest neighbors classifier (k-NN) to classify byte images. Instance represents: Handle of the k-NN classifier.

Represents an instance of a classification lookup table

Create a look-up table using a k-nearest neighbors classifier (k-NN) to classify byte images. Modified instance represents: Handle of the LUT classifier.

Handle of the k-NN classifier. Names of the generic parameters that can be adjusted for the LUT classifier creation. Default: [] Values of the generic parameters that can be adjusted for the LUT classifier creation. Default: []

Create a look-up table using a gaussian mixture model to classify byte images. Modified instance represents: Handle of the LUT classifier.

GMM handle. Names of the generic parameters that can be adjusted for the LUT classifier creation. Default: [] Values of the generic parameters that can be adjusted for the LUT classifier creation. Default: []

Create a look-up table using a Support-Vector-Machine to classify byte images. Modified instance represents: Handle of the LUT classifier.

SVM handle. Names of the generic parameters that can be adjusted for the LUT classifier creation. Default: [] Values of the generic parameters that can be adjusted for the LUT classifier creation. Default: []

Create a look-up table using a multi-layer perceptron to classify byte images. Modified instance represents: Handle of the LUT classifier.

MLP handle. Names of the generic parameters that can be adjusted for the LUT classifier creation. Default: [] Values of the generic parameters that can be adjusted for the LUT classifier creation. Default: []

Classify a byte image using a look-up table. Instance represents: Handle of the LUT classifier.

Input image. Segmented classes.

Clear a look-up table classifier.

Handle of the LUT classifier.

Clear a look-up table classifier. Instance represents: Handle of the LUT classifier.

Create a look-up table using a k-nearest neighbors classifier (k-NN) to classify byte images. Modified instance represents: Handle of the LUT classifier.

Create a look-up table using a gaussian mixture model to classify byte images. Modified instance represents: Handle of the LUT classifier.

Create a look-up table using a Support-Vector-Machine to classify byte images. Modified instance represents: Handle of the LUT classifier.

Create a look-up table using a multi-layer perceptron to classify byte images. Modified instance represents: Handle of the LUT classifier.

Represents an instance of a multilayer perceptron.

Read a multilayer perceptron from a file. Modified instance represents: MLP handle.

File name.

Create a multilayer perceptron for classification or regression. Modified instance represents: MLP handle.

Number of input variables (features) of the MLP. Default: 20 Number of hidden units of the MLP. Default: 10 Number of output variables (classes) of the MLP. Default: 5 Type of the activation function in the output layer of the MLP. Default: "softmax" Type of preprocessing used to transform the feature vectors. Default: "normalization" Preprocessing parameter: Number of transformed features (ignored for Preprocessing $=$ 'none' and Preprocessing $=$ 'normalization'). Default: 10 Seed value of the random number generator that is used to initialize the MLP with random values. Default: 42

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Classify an image with a multilayer perceptron. Instance represents: MLP handle.

Input image. Threshold for the rejection of the classification. Default: 0.5 Segmented classes.

Add training samples from an image to the training data of a multilayer perceptron. Instance represents: MLP handle.

Training image. Regions of the classes to be trained.

Get the training data of a multilayer perceptron (MLP). Instance represents: Handle of a MLP that contains training data.

Handle of the training data of the classifier.

Add training data to a multilayer perceptron (MLP). Instance represents: MLP handle which receives the training data.

Training data for a classifier.

Selects an optimal combination of features to classify the provided data. Modified instance represents: A trained MLP classifier using only the selected features.

Create a look-up table using a multi-layer perceptron to classify byte images. Instance represents: MLP handle.

Clear a multilayer perceptron.

MLP handle.

Clear a multilayer perceptron. Instance represents: MLP handle.

Clear the training data of a multilayer perceptron.

MLP handle.

Clear the training data of a multilayer perceptron. Instance represents: MLP handle.

Deserialize a serialized multilayer perceptron. Modified instance represents: MLP handle.

Handle of the serialized item.

Serialize a multilayer perceptron (MLP). Instance represents: MLP handle.

Handle of the serialized item.

Read a multilayer perceptron from a file. Modified instance represents: MLP handle.

File name.

Write a multilayer perceptron to a file. Instance represents: MLP handle.

File name.

Read the training data of a multilayer perceptron from a file. Instance represents: MLP handle.

File name.

Write the training data of a multilayer perceptron to a file. Instance represents: MLP handle.

File name.

Calculate the class of a feature vector by a multilayer perceptron. Instance represents: MLP handle.

Feature vector. Number of best classes to determine. Default: 1 Confidence(s) of the class(es) of the feature vector. Result of classifying the feature vector with the MLP.

Calculate the class of a feature vector by a multilayer perceptron. Instance represents: MLP handle.

Feature vector. Number of best classes to determine. Default: 1 Confidence(s) of the class(es) of the feature vector. Result of classifying the feature vector with the MLP.

Calculate the evaluation of a feature vector by a multilayer perceptron. Instance represents: MLP handle.

Feature vector. Result of evaluating the feature vector with the MLP.

Train a multilayer perceptron. Instance represents: MLP handle.

Maximum number of iterations of the optimization algorithm. Default: 200 Threshold for the difference of the weights of the MLP between two iterations of the optimization algorithm. Default: 1.0 Threshold for the difference of the mean error of the MLP on the training data between two iterations of the optimization algorithm. Default: 0.01 Mean error of the MLP on the training data as a function of the number of iterations of the optimization algorithm. Mean error of the MLP on the training data.

Compute the information content of the preprocessed feature vectors of a multilayer perceptron. Instance represents: MLP handle.

Return the number of training samples stored in the training data of a multilayer perceptron. Instance represents: MLP handle.

Number of stored training samples.

Return a training sample from the training data of a multilayer perceptron. Instance represents: MLP handle.

Number of stored training sample. Target vector of the training sample. Feature vector of the training sample.

Get the parameters of a rejection class. Instance represents: MLP handle.

Names of the generic parameters to return. Default: "sampling_strategy" Values of the generic parameters.

Get the parameters of a rejection class. Instance represents: MLP handle.

Names of the generic parameters to return. Default: "sampling_strategy" Values of the generic parameters.

Set the parameters of a rejection class. Instance represents: MLP handle.

Names of the generic parameters. Default: "sampling_strategy" Values of the generic parameters. Default: "hyperbox_around_all_classes"

Set the parameters of a rejection class. Instance represents: MLP handle.

Names of the generic parameters. Default: "sampling_strategy" Values of the generic parameters. Default: "hyperbox_around_all_classes"

Add a training sample to the training data of a multilayer perceptron. Instance represents: MLP handle.

Feature vector of the training sample to be stored. Class or target vector of the training sample to be stored.

Add a training sample to the training data of a multilayer perceptron. Instance represents: MLP handle.

Feature vector of the training sample to be stored. Class or target vector of the training sample to be stored.

Return the regularization parameters of a multilayer perceptron. Instance represents: MLP handle.

Name of the regularization parameter to return. Default: "weight_prior" Value of the regularization parameter.

Set the regularization parameters of a multilayer perceptron. Instance represents: MLP handle.

Name of the regularization parameter to set. Default: "weight_prior" Value of the regularization parameter. Default: 1.0

Set the regularization parameters of a multilayer perceptron. Instance represents: MLP handle.

Name of the regularization parameter to set. Default: "weight_prior" Value of the regularization parameter. Default: 1.0

Return the parameters of a multilayer perceptron. Instance represents: MLP handle.

Number of hidden units of the MLP. Number of output variables (classes) of the MLP. Type of the activation function in the output layer of the MLP. Type of preprocessing used to transform the feature vectors. Preprocessing parameter: Number of transformed features. Number of input variables (features) of the MLP.

Create a multilayer perceptron for classification or regression. Modified instance represents: MLP handle.

Represents an instance of a support vector machine.

Read a support vector machine from a file. Modified instance represents: SVM handle.

File name.

Create a support vector machine for pattern classification. Modified instance represents: SVM handle.

Number of input variables (features) of the SVM. Default: 10 The kernel type. Default: "rbf" Additional parameter for the kernel function. In case of RBF kernel the value for gamma@f$ Default: 0.02 Regularisation constant of the SVM. Default: 0.05 Number of classes. Default: 5 The mode of the SVM. Default: "one-versus-one" Type of preprocessing used to transform the feature vectors. Default: "normalization" Preprocessing parameter: Number of transformed features (ignored for Preprocessing $=$ 'none' and Preprocessing $=$ 'normalization'). Default: 10

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Classify an image with a support vector machine. Instance represents: SVM handle.

Input image. Segmented classes.

Add training samples from an image to the training data of a support vector machine. Instance represents: SVM handle.

Training image. Regions of the classes to be trained.

Get the training data of a support vector machine (SVM). Instance represents: Handle of a SVM that contains training data.

Handle of the training data of the classifier.

Add training data to a support vector machine (SVM). Instance represents: Handle of a SVM which receives the training data.

Training data for a classifier.

Selects an optimal combination of features to classify the provided data. Modified instance represents: A trained SVM classifier using only the selected features.

Create a look-up table using a Support-Vector-Machine to classify byte images. Instance represents: SVM handle.

Clear a support vector machine.

SVM handle.

Clear a support vector machine. Instance represents: SVM handle.

Clear the training data of a support vector machine.

SVM handle.

Clear the training data of a support vector machine. Instance represents: SVM handle.

Deserialize a serialized support vector machine (SVM). Modified instance represents: SVM handle.

Handle of the serialized item.

Serialize a support vector machine (SVM). Instance represents: SVM handle.

Handle of the serialized item.

Read a support vector machine from a file. Modified instance represents: SVM handle.

File name.

Write a support vector machine to a file. Instance represents: SVM handle.

File name.

Read the training data of a support vector machine from a file. Instance represents: SVM handle.

File name.

Write the training data of a support vector machine to a file. Instance represents: SVM handle.

File name.

Evaluate a feature vector by a support vector machine. Instance represents: SVM handle.

Feature vector. Result of evaluating the feature vector with the SVM.

Classify a feature vector by a support vector machine. Instance represents: SVM handle.

Feature vector. Number of best classes to determine. Default: 1 Result of classifying the feature vector with the SVM.

Approximate a trained support vector machine by a reduced support vector machine for faster classification. Instance represents: Original SVM handle.

Type of postprocessing to reduce number of SV. Default: "bottom_up" Minimum number of remaining SVs. Default: 2 Maximum allowed error of reduction. Default: 0.001 SVMHandle of reduced SVM.

Train a support vector machine. Instance represents: SVM handle.

Stop parameter for training. Default: 0.001 Mode of training. For normal operation: 'default'. If SVs already included in the SVM should be used for training: 'add_sv_to_train_set'. For alpha seeding: the respective SVM handle. Default: "default"

Train a support vector machine. Instance represents: SVM handle.

Compute the information content of the preprocessed feature vectors of a support vector machine Instance represents: SVM handle.

Return the number of support vectors of a support vector machine. Instance represents: SVM handle.

Number of SV of each sub-SVM. Total number of support vectors.

Return the index of a support vector from a trained support vector machine. Instance represents: SVM handle.

Index of the stored support vector. Index of the support vector in the training set.

Return the number of training samples stored in the training data of a support vector machine. Instance represents: SVM handle.

Number of stored training samples.

Return a training sample from the training data of a support vector machine. Instance represents: SVM handle.

Number of the stored training sample. Target vector of the training sample. Feature vector of the training sample.

Add a training sample to the training data of a support vector machine. Instance represents: SVM handle.

Feature vector of the training sample to be stored. Class of the training sample to be stored.

Add a training sample to the training data of a support vector machine. Instance represents: SVM handle.

Feature vector of the training sample to be stored. Class of the training sample to be stored.

Return the parameters of a support vector machine. Instance represents: SVM handle.

The kernel type. Additional parameter for the kernel. Regularization constant of the SVM. Number of classes of the test data. The mode of the SVM. Type of preprocessing used to transform the feature vectors. Preprocessing parameter: Number of transformed features (ignored for Preprocessing $=$ 'none' and Preprocessing $=$ 'normalization'). Number of input variables (features) of the SVM.

Create a support vector machine for pattern classification. Modified instance represents: SVM handle.

Represents an instance of a training data management class.

Read the training data for classifiers from a file. Modified instance represents: Handle of the training data.

File name of the training data.

Create a handle for training data for classifiers. Modified instance represents: Handle of the training data.

Number of dimensions of the feature vector. Default: 10

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Deserialize serialized training data for classifiers. Modified instance represents: Handle of the training data.

Handle of the serialized item.

Serialize training data for classifiers. Instance represents: Handle of the training data.

Handle of the serialized item.

Read the training data for classifiers from a file. Modified instance represents: Handle of the training data.

File name of the training data.

Save the training data for classifiers in a file. Instance represents: Handle of the training data.

Name of the file in which the training data will be written.

Select certain features from training data to create training data containing less features. Instance represents: Handle of the training data.

Indices or names to select the subfeatures or columns. Handle of the reduced training data.

Define subfeatures in training data. Instance represents: Handle of the training data that should be partitioned into subfeatures.

Length of the subfeatures. Names of the subfeatures.

Get the training data of a Gaussian Mixture Model (GMM). Modified instance represents: Handle of the training data of the classifier.

Handle of a GMM that contains training data.

Add training data to a Gaussian Mixture Model (GMM). Instance represents: Handle of training data for a classifier.

Handle of a GMM which receives the training data.

Get the training data of a multilayer perceptron (MLP). Modified instance represents: Handle of the training data of the classifier.

Handle of a MLP that contains training data.

Add training data to a multilayer perceptron (MLP). Instance represents: Training data for a classifier.

MLP handle which receives the training data.

Get the training data of a k-nearest neighbors (k-NN) classifier. Modified instance represents: Handle of the training data of the classifier.

Handle of the k-NN classifier that contains training data.

Add training data to a k-nearest neighbors (k-NN) classifier. Instance represents: Training data for a classifier.

Handle of a k-NN which receives the training data.

Get the training data of a support vector machine (SVM). Modified instance represents: Handle of the training data of the classifier.

Handle of a SVM that contains training data.

Add training data to a support vector machine (SVM). Instance represents: Training data for a classifier.

Handle of a SVM which receives the training data.

Return the number of training samples stored in the training data. Instance represents: Handle of training data.

Number of stored training samples.

Return a training sample from training data. Instance represents: Handle of training data for a classifier.

Number of stored training sample. Class of the training sample. Feature vector of the training sample.

Clears training data for classifiers. Instance represents: Handle of training data for a classifier.

Add a training sample to training data. Instance represents: Handle of the training data.

The order of the feature vector. Default: "row" Feature vector of the training sample. Class of the training sample.

Create a handle for training data for classifiers. Modified instance represents: Handle of the training data.

Number of dimensions of the feature vector. Default: 10

Selects an optimal combination of features to classify the provided data. Instance represents: Handle of the training data.

Method to perform the selection. Default: "greedy" Names of generic parameters to configure the selection process and the classifier. Default: [] Values of generic parameters to configure the selection process and the classifier. Default: [] The selected feature set, contains indices referring. The achieved score using two-fold cross-validation. A trained MLP classifier using only the selected features.

Selects an optimal combination of features to classify the provided data. Instance represents: Handle of the training data.

Method to perform the selection. Default: "greedy" Names of generic parameters to configure the selection process and the classifier. Default: [] Values of generic parameters to configure the selection process and the classifier. Default: [] The selected feature set, contains indices. The achieved score using two-fold cross-validation. A trained SVM classifier using only the selected features.

Selects an optimal combination of features to classify the provided data. Instance represents: Handle of the training data.

Method to perform the selection. Default: "greedy" Names of generic parameters to configure the selection process and the classifier. Default: [] Values of generic parameters to configure the selection process and the classifier. Default: [] The selected feature set, contains indices. The achieved score using two-fold cross-validation. A trained SVM classifier using only the selected features.

Selects an optimal combination from a set of features to classify the provided data. Instance represents: Handle of the training data.

Method to perform the selection. Default: "greedy" Names of generic parameters to configure the classifier. Default: [] Values of generic parameters to configure the classifier. Default: [] The selected feature set, contains indices or names. The achieved score using two-fold cross-validation. A trained GMM classifier using only the selected features.

Selects an optimal combination from a set of features to classify the provided data. Instance represents: Handle of the training data.

Selects an optimal subset from a set of features to solve a certain classification problem. Instance represents: Handle of the training data.

Method to perform the selection. Default: "greedy" Names of generic parameters to configure the selection process and the classifier. Default: [] Values of generic parameters to configure the selection process and the classifier. Default: [] The selected feature set, contains indices or names. The achieved score using two-fold cross-validation. A trained k-NN classifier using only the selected features.

Selects an optimal subset from a set of features to solve a certain classification problem. Instance represents: Handle of the training data.

Method to perform the selection. Default: "greedy" Names of generic parameters to configure the selection process and the classifier. Default: [] Values of generic parameters to configure the selection process and the classifier. Default: [] The selected feature set, contains indices or names. The achieved score using two-fold cross-validation. A trained k-NN classifier using only the selected features.

Represents an instance of a color space transformation lookup table

Creates the look-up-table for transformation of an image from the RGB color space to an arbitrary color space. Modified instance represents: Handle of the look-up-table for color space transformation.

Color space of the output image. Default: "hsv" Direction of color space transformation. Default: "from_rgb" Number of bits of the input image. Default: 8

Release the look-up-table needed for color space transformation. Instance represents: Handle of the look-up-table handle for the color space transformation.

Color space transformation using pre-generated look-up-table. Instance represents: Handle of the look-up-table for the color space transformation.

Input image (channel 1). Input image (channel 2). Input image (channel 3). Color-transformed output image (channel 2). Color-transformed output image (channel 3). Color-transformed output image (channel 1).

Color space of the output image. Default: "hsv" Direction of color space transformation. Default: "from_rgb" Number of bits of the input image. Default: 8

Represents an instance of a model for the component-based matching.

Read a component model from a file. Modified instance represents: Handle of the component model.

File name.

Prepare a component model for matching based on explicitly specified components and relations. Modified instance represents: Handle of the component model.

Input image from which the shape models of the model components should be created. Input regions from which the shape models of the model components should be created. Variation of the model components in row direction. Variation of the model components in column direction. Angle variation of the model components. Smallest rotation of the component model. Default: -0.39 Extent of the rotation of the component model. Default: 0.79 Lower hysteresis threshold for the contrast of the components in the model image. Default: "auto" Upper hysteresis threshold for the contrast of the components in the model image. Default: "auto" Minimum size of the contour regions in the model. Default: "auto" Minimum contrast of the components in the search images. Default: "auto" Minimum score of the instances of the components to be found. Default: 0.5 Maximum number of pyramid levels for the components. Default: "auto" Step length of the angles (resolution) for the components. Default: "auto" Kind of optimization for the components. Default: "auto" Match metric used for the components. Default: "use_polarity" Complete pregeneration of the shape models for the components if equal to 'true'. Default: "false" Ranking of the model components expressing the suitability to act as the root component.

Prepare a component model for matching based on explicitly specified components and relations. Modified instance represents: Handle of the component model.

Prepare a component model for matching based on trained components. Modified instance represents: Handle of the component model.

Handle of the training result. Smallest rotation of the component model. Default: -0.39 Extent of the rotation of the component model. Default: 0.79 Minimum contrast of the components in the search images. Default: "auto" Minimum score of the instances of the components to be found. Default: 0.5 Maximum number of pyramid levels for the components. Default: "auto" Step length of the angles (resolution) for the components. Default: "auto" Kind of optimization for the components. Default: "auto" Match metric used for the components. Default: "use_polarity" Complete pregeneration of the shape models for the components if equal to 'true'. Default: "false" Ranking of the model components expressing the suitability to act as the root component.

Prepare a component model for matching based on trained components. Modified instance represents: Handle of the component model.

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Return the components of a found instance of a component model. Instance represents: Handle of the component model.

Start index of each found instance of the component model in the tuples describing the component matches. End index of each found instance of the component model to the tuples describing the component matches. Row coordinate of the found component matches. Column coordinate of the found component matches. Rotation angle of the found component matches. Score of the found component matches. Index of the found components. Index of the found instance of the component model to be returned. Mark the orientation of the components. Default: "false" Row coordinate of all components of the selected model instance. Column coordinate of all components of the selected model instance. Rotation angle of all components of the selected model instance. Score of all components of the selected model instance. Found components of the selected component model instance.

Return the components of a found instance of a component model. Instance represents: Handle of the component model.

Find the best matches of a component model in an image. Instance represents: Handle of the component model.

Input image in which the component model should be found. Index of the root component. Smallest rotation of the root component Default: -0.39 Extent of the rotation of the root component. Default: 0.79 Minimum score of the instances of the component model to be found. Default: 0.5 Number of instances of the component model to be found (or 0 for all matches). Default: 1 Maximum overlap of the instances of the component models to be found. Default: 0.5 Behavior if the root component is missing. Default: "stop_search" Behavior if a component is missing. Default: "prune_branch" Pose prediction of components that are not found. Default: "none" Minimum score of the instances of the components to be found. Default: 0.5 Subpixel accuracy of the component poses if not equal to 'none'. Default: "least_squares" Number of pyramid levels for the components used in the matching (and lowest pyramid level to use if $|NumLevelsComp| = 2n$). Default: 0 "Greediness" of the search heuristic for the components (0: safe but slow; 1: fast but matches may be missed). Default: 0.9 End index of each found instance of the component model in the tuples describing the component matches. Score of the found instances of the component model. Row coordinate of the found component matches. Column coordinate of the found component matches. Rotation angle of the found component matches. Score of the found component matches. Index of the found components. Start index of each found instance of the component model in the tuples describing the component matches.

Find the best matches of a component model in an image. Instance represents: Handle of the component model.

Free the memory of a component model. Instance represents: Handle of the component model.

Return the search tree of a component model. Instance represents: Handle of the component model.

Relations of components that are connected in the search tree. Index of the root component. Image for which the tree is to be returned. Default: "model_image" Component index of the start node of an arc in the search tree. Component index of the end node of an arc in the search tree. Row coordinate of the center of the rectangle representing the relation. Column index of the center of the rectangle representing the relation. Orientation of the rectangle representing the relation (radians). First radius (half length) of the rectangle representing the relation. Second radius (half width) of the rectangle representing the relation. Smallest relative orientation angle. Extent of the relative orientation angle. Search tree.

Return the search tree of a component model. Instance represents: Handle of the component model.

Return the parameters of a component model. Instance represents: Handle of the component model.

Ranking of the model components expressing their suitability to act as root component. Handles of the shape models of the individual model components. Minimum score of the instances of the components to be found.

Return the parameters of a component model. Instance represents: Handle of the component model.

Deserialize a serialized component model. Modified instance represents: Handle of the component model.

Handle of the serialized item.

Serialize a component model. Instance represents: Handle of the component model.

Handle of the serialized item.

Read a component model from a file. Modified instance represents: Handle of the component model.

File name.

Write a component model to a file. Instance represents: Handle of the component model.

File name.

Prepare a component model for matching based on explicitly specified components and relations. Modified instance represents: Handle of the component model.

Prepare a component model for matching based on trained components. Modified instance represents: Handle of the component model.

Represents an instance of a training result for the component-based matching.

Train components and relations for the component-based matching. Modified instance represents: Handle of the training result.

Input image from which the shape models of the initial components should be created. Contour regions or enclosing regions of the initial components. Training images that are used for training the model components. Contour regions of rigid model components. Lower hysteresis threshold for the contrast of the initial components in the image. Default: "auto" Upper hysteresis threshold for the contrast of the initial components in the image. Default: "auto" Minimum size of connected contour regions. Default: "auto" Minimum score of the instances of the initial components to be found. Default: 0.5 Search tolerance in row direction. Default: -1 Search tolerance in column direction. Default: -1 Angle search tolerance. Default: -1 Decision whether the training emphasis should lie on a fast computation or on a high robustness. Default: "speed" Criterion for solving ambiguous matches of the initial components in the training images. Default: "rigidity" Maximum contour overlap of the found initial components in a training image. Default: 0.2 Threshold for clustering the initial components. Default: 0.5

Train components and relations for the component-based matching. Modified instance represents: Handle of the training result.

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Prepare a component model for matching based on trained components. Instance represents: Handle of the training result.

Smallest rotation of the component model. Default: -0.39 Extent of the rotation of the component model. Default: 0.79 Minimum contrast of the components in the search images. Default: "auto" Minimum score of the instances of the components to be found. Default: 0.5 Maximum number of pyramid levels for the components. Default: "auto" Step length of the angles (resolution) for the components. Default: "auto" Kind of optimization for the components. Default: "auto" Match metric used for the components. Default: "use_polarity" Complete pregeneration of the shape models for the components if equal to 'true'. Default: "false" Ranking of the model components expressing the suitability to act as the root component. Handle of the component model.

Prepare a component model for matching based on trained components. Instance represents: Handle of the training result.

Free the memory of a component training result. Instance represents: Handle of the training result.

Return the relations between the model components that are contained in a training result. Instance represents: Handle of the training result.

Index of reference component. Image for which the component relations are to be returned. Default: "model_image" Row coordinate of the center of the rectangle representing the relation. Column index of the center of the rectangle representing the relation. Orientation of the rectangle representing the relation (radians). First radius (half length) of the rectangle representing the relation. Second radius (half width) of the rectangle representing the relation. Smallest relative orientation angle. Extent of the relative orientation angles. Region representation of the relations.

Return the relations between the model components that are contained in a training result. Instance represents: Handle of the training result.

Return the initial or model components in a certain image. Instance represents: Handle of the training result.

Type of returned components or index of an initial component. Default: "model_components" Image for which the components are to be returned. Default: "model_image" Mark the orientation of the components. Default: "false" Row coordinate of the found instances of all initial components or model components. Column coordinate of the found instances of all initial components or model components. Rotation angle of the found instances of all components. Score of the found instances of all components. Contour regions of the initial components or of the model components.

Return the initial or model components in a certain image. Instance represents: Handle of the training result.

Modify the relations within a training result. Instance represents: Handle of the training result.

Model component(s) relative to which the movement(s) should be modified. Default: "all" Model component(s) of which the relative movement(s) should be modified. Default: "all" Change of the position relation in pixels. Change of the orientation relation in radians.

Modify the relations within a training result. Instance represents: Handle of the training result.

Deserialize a component training result. Modified instance represents: Handle of the training result.

Handle of the serialized item.

Serialize a component training result. Instance represents: Handle of the training result.

Handle of the serialized item.

Read a component training result from a file. Modified instance represents: Handle of the training result.

File name.

Write a component training result to a file. Instance represents: Handle of the training result.

File name.

Adopt new parameters that are used to create the model components into the training result. Instance represents: Handle of the training result.

Training images that were used for training the model components. Criterion for solving the ambiguities. Default: "rigidity" Maximum contour overlap of the found initial components. Default: 0.2 Threshold for clustering the initial components. Default: 0.5 Contour regions of rigid model components.

Inspect the rigid model components obtained from the training. Instance represents: Handle of the training result.

Criterion for solving the ambiguities. Default: "rigidity" Maximum contour overlap of the found initial components. Default: 0.2 Threshold for clustering the initial components. Default: 0.5 Contour regions of rigid model components.

Train components and relations for the component-based matching. Modified instance represents: Handle of the training result.

Input image from which the shape models of the initial components should be created. Contour regions or enclosing regions of the initial components. Training images that are used for training the model components. Lower hysteresis threshold for the contrast of the initial components in the image. Default: "auto" Upper hysteresis threshold for the contrast of the initial components in the image. Default: "auto" Minimum size of connected contour regions. Default: "auto" Minimum score of the instances of the initial components to be found. Default: 0.5 Search tolerance in row direction. Default: -1 Search tolerance in column direction. Default: -1 Angle search tolerance. Default: -1 Decision whether the training emphasis should lie on a fast computation or on a high robustness. Default: "speed" Criterion for solving ambiguous matches of the initial components in the training images. Default: "rigidity" Maximum contour overlap of the found initial components in a training image. Default: 0.2 Threshold for clustering the initial components. Default: 0.5 Contour regions of rigid model components.

Train components and relations for the component-based matching. Modified instance represents: Handle of the training result.

Input image from which the shape models of the initial components should be created. Contour regions or enclosing regions of the initial components. Training images that are used for training the model components. Lower hysteresis threshold for the contrast of the initial components in the image. Default: "auto" Upper hysteresis threshold for the contrast of the initial components in the image. Default: "auto" Minimum size of connected contour regions. Default: "auto" Minimum score of the instances of the initial components to be found. Default: 0.5 Search tolerance in row direction. Default: -1 Search tolerance in column direction. Default: -1 Angle search tolerance. Default: -1 Decision whether the training emphasis should lie on a fast computation or on a high robustness. Default: "speed" Criterion for solving ambiguous matches of the initial components in the training images. Default: "rigidity" Maximum contour overlap of the found initial components in a training image. Default: 0.2 Threshold for clustering the initial components. Default: 0.5 Contour regions of rigid model components.

Class representing a compute device handle.

Open a compute device. Modified instance represents: Compute device handle.

Compute device Identifier.

Query compute device parameters. Instance represents: Compute device handle.

Name of the parameter to query. Default: "buffer_cache_capacity" Value of the parameter.

Set parameters of an compute device. Instance represents: Compute device handle.

Name of the parameter to set. Default: "buffer_cache_capacity" New parameter value.

Set parameters of an compute device. Instance represents: Compute device handle.

Name of the parameter to set. Default: "buffer_cache_capacity" New parameter value.

Close all compute devices.

Close a compute_device. Instance represents: Compute device handle.

Deactivate all compute devices.

Deactivate a compute device. Instance represents: Compute device handle.

Activate a compute device. Instance represents: Compute device handle.

Initialize a compute device. Instance represents: Compute device handle.

List of operators to prepare. Default: "all"

Open a compute device. Modified instance represents: Compute device handle.

Compute device Identifier.

Get information on a compute device.

Compute device handle. Name of Information to query. Default: "name" Returned information.

Get the list of available compute devices.

List of available compute devices.

Represents an instance of a condition synchronization object.

Create a condition variable synchronization object. Modified instance represents: Condition synchronization object.

Mutex attribute. Default: [] Mutex attribute value. Default: []

Create a condition variable synchronization object. Modified instance represents: Condition synchronization object.

Mutex attribute. Default: [] Mutex attribute value. Default: []

Destroy a condition synchronization object. Instance represents: Condition synchronization object.

Signal a condition synchronization object. Instance represents: Condition synchronization object.

Bounded wait on the signal of a condition synchronization object. Instance represents: Condition synchronization object.

Mutex synchronization object. Timeout in micro seconds.

wait on the signal of a condition synchronization object. Instance represents: Condition synchronization object.

Mutex synchronization object.

Create a condition variable synchronization object. Modified instance represents: Condition synchronization object.

Mutex attribute. Default: [] Mutex attribute value. Default: []

Create a condition variable synchronization object. Modified instance represents: Condition synchronization object.

Mutex attribute. Default: [] Mutex attribute value. Default: []

Represents an instance of a 2D data code reader.

Read a 2D data code model from a file and create a new model. Modified instance represents: Handle of the created 2D data code model.

Name of the 2D data code model file. Default: "data_code_model.dcm"

Create a model of a 2D data code class. Modified instance represents: Handle for using and accessing the 2D data code model.

Type of the 2D data code. Default: "Data Matrix ECC 200" Names of the generic parameters that can be adjusted for the 2D data code model. Default: [] Values of the generic parameters that can be adjusted for the 2D data code model. Default: []

Create a model of a 2D data code class. Modified instance represents: Handle for using and accessing the 2D data code model.

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Access iconic objects that were created during the search for 2D data code symbols. Instance represents: Handle of the 2D data code model.

Handle of the 2D data code candidate. Either an integer (usually the ResultHandle of find_data_code_2d) or a string representing a group of candidates. Default: "all_candidates" Name of the iconic object to return. Default: "candidate_xld" Objects that are created as intermediate results during the detection or evaluation of 2D data codes.

Access iconic objects that were created during the search for 2D data code symbols. Instance represents: Handle of the 2D data code model.

Get the alphanumerical results that were accumulated during the search for 2D data code symbols. Instance represents: Handle of the 2D data code model.

Handle of the 2D data code candidate. Either an integer (usually the ResultHandle of find_data_code_2d) or a string representing a group of candidates. Default: "all_candidates" Names of the results of the 2D data code to return. Default: "status" List with the results.

Get the alphanumerical results that were accumulated during the search for 2D data code symbols. Instance represents: Handle of the 2D data code model.

Handle of the 2D data code candidate. Either an integer (usually the ResultHandle of find_data_code_2d) or a string representing a group of candidates. Default: "all_candidates" Names of the results of the 2D data code to return. Default: "status" List with the results.

Detect and read 2D data code symbols in an image or train the 2D data code model. Instance represents: Handle of the 2D data code model.

Input image. If the image has a reduced domain, the data code search is reduced to that domain. This usually reduces the runtime of the operator. However, if the datacode is not fully inside the domain, the datacode might not be found correctly. In rare cases, data codes may be found outside the domain. If these results are undesirable, they have to be subsequently eliminated. Names of (optional) parameters for controlling the behavior of the operator. Default: [] Values of the optional generic parameters. Default: [] Handles of all successfully decoded 2D data code symbols. Decoded data strings of all detected 2D data code symbols in the image. XLD contours that surround the successfully decoded data code symbols. The order of the contour points reflects the orientation of the detected symbols. The contours begin in the top left corner (see 'orientation' at get_data_code_2d_results) and continue clockwise. Alignment{left} Figure[1][1][60]{get_data_code_2d_results-xld_qrcode} Order of points of SymbolXLDs Figure Alignment @f$

Detect and read 2D data code symbols in an image or train the 2D data code model. Instance represents: Handle of the 2D data code model.

Set selected parameters of the 2D data code model. Instance represents: Handle of the 2D data code model.

Names of the generic parameters that shall be adjusted for the 2D data code. Default: "polarity" Values of the generic parameters that are adjusted for the 2D data code. Default: "light_on_dark"

Set selected parameters of the 2D data code model. Instance represents: Handle of the 2D data code model.

Names of the generic parameters that shall be adjusted for the 2D data code. Default: "polarity" Values of the generic parameters that are adjusted for the 2D data code. Default: "light_on_dark"

Get one or several parameters that describe the 2D data code model. Instance represents: Handle of the 2D data code model.

Names of the generic parameters that are to be queried for the 2D data code model. Default: "polarity" Values of the generic parameters.

Get one or several parameters that describe the 2D data code model. Instance represents: Handle of the 2D data code model.

Names of the generic parameters that are to be queried for the 2D data code model. Default: "polarity" Values of the generic parameters.

Get for a given 2D data code model the names of the generic parameters or objects that can be used in the other 2D data code operators. Instance represents: Handle of the 2D data code model.

Name of the parameter group. Default: "get_result_params" List containing the names of the supported generic parameters.

Deserialize a serialized 2D data code model. Modified instance represents: Handle of the 2D data code model.

Handle of the serialized item.

Serialize a 2D data code model. Instance represents: Handle of the 2D data code model.

Handle of the serialized item.

Read a 2D data code model from a file and create a new model. Modified instance represents: Handle of the created 2D data code model.

Name of the 2D data code model file. Default: "data_code_model.dcm"

Writes a 2D data code model into a file. Instance represents: Handle of the 2D data code model.

Name of the 2D data code model file. Default: "data_code_model.dcm"

Delete a 2D data code model and free the allocated memory. Instance represents: Handle of the 2D data code model.

Create a model of a 2D data code class. Modified instance represents: Handle for using and accessing the 2D data code model.

Represents an instance of a deformable model for matching.

Read a deformable model from a file. Modified instance represents: Handle of the model.

File name.

Prepare a deformable model for planar calibrated matching from XLD contours. Modified instance represents: Handle of the model.

Input contours that will be used to create the model. The parameters of the internal orientation of the camera. The reference pose of the object. Maximum number of pyramid levels. Default: "auto" This parameter is not used. Default: [] This parameter is not used. Default: [] Step length of the angles (resolution). Default: "auto" Minimum scale of the pattern in row direction. Default: 1.0 This parameter is not used. Default: [] Scale step length (resolution) in row direction. Default: "auto" Minimum scale of the pattern in column direction. Default: 1.0 This parameter is not used. Default: [] Scale step length (resolution) in the column direction. Default: "auto" Kind of optimization used for generating the model. Default: "auto" Match metric. Default: "ignore_local_polarity" Minimum contrast of the objects in the search images. Default: 5 The generic parameter names. Default: [] Values of the generic parameter. Default: []

Prepare a deformable model for planar calibrated matching from XLD contours. Modified instance represents: Handle of the model.

Prepare a deformable model for planar uncalibrated matching from XLD contours. Modified instance represents: Handle of the model.

Input contours that will be used to create the model. Maximum number of pyramid levels. Default: "auto" This parameter is not used. Default: [] This parameter is not used. Default: [] Step length of the angles (resolution). Default: "auto" Minimum scale of the pattern in row direction. Default: 1.0 This parameter is not used. Default: [] Scale step length (resolution) in row direction. Default: "auto" Minimum scale of the pattern in column direction. Default: 1.0 This parameter is not used. Default: [] Scale step length (resolution) in the column direction. Default: "auto" Kind of optimization used for generating the model. Default: "auto" Match metric. Default: "ignore_local_polarity" Minimum contrast of the objects in the search images. Default: 5 The generic parameter names. Default: [] Values of the generic parameters. Default: []

Prepare a deformable model for planar uncalibrated matching from XLD contours. Modified instance represents: Handle of the model.

Create a deformable model for calibrated perspective matching. Modified instance represents: Handle of the model.

Input image whose domain will be used to create the model. The parameters of the internal orientation of the camera. The reference pose of the object in the reference image. Maximum number of pyramid levels. Default: "auto" This parameter is not used. Default: [] This parameter is not used. Default: [] Step length of the angles (resolution). Default: "auto" Minimum scale of the pattern in row direction. Default: 1.0 This parameter is not used. Default: [] Scale step length (resolution) in row direction. Default: "auto" Minimum scale of the pattern in column direction. Default: 1.0 This parameter is not used. Default: [] Scale step length (resolution) in the column direction. Default: "auto" Kind of optimization used for generating the model. Default: "none" Match metric. Default: "use_polarity" Thresholds or hysteresis thresholds for the contrast of the object in the template image. Default: "auto" Minimum contrast of the objects in the search images. Default: "auto" The parameter names. Default: [] Values of the parameters. Default: []

Create a deformable model for calibrated perspective matching. Modified instance represents: Handle of the model.

Creates a deformable model for uncalibrated, perspective matching. Modified instance represents: Handle of the model.

Input image whose domain will be used to create the model. Maximum number of pyramid levels. Default: "auto" This parameter is not used. Default: [] This parameter is not used. Default: [] Step length of the angles (resolution). Default: "auto" Minimum scale of the pattern in row direction. Default: 1.0 This parameter is not used. Default: [] Scale step length (resolution) in row direction. Default: "auto" Minimum scale of the pattern in column direction. Default: 1.0 This parameter is not used. Default: [] Scale step length (resolution) in column direction. Default: "auto" Kind of optimization used for generating the model. Default: "none" Match metric. Default: "use_polarity" Thresholds or hysteresis thresholds for the contrast of the object in the template image. Default: "auto" Minimum contrast of the objects in the search images. Default: "auto" The generic parameter names. Default: [] Values of the generic parameter. Default: []

Creates a deformable model for uncalibrated, perspective matching. Modified instance represents: Handle of the model.

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Return the origin (reference point) of a deformable model. Instance represents: Handle of the model.

Row coordinate of the origin of the deformable model. Column coordinate of the origin of the deformable model.

Set the origin (reference point) of a deformable model. Instance represents: Handle of the model.

Row coordinate of the origin of the deformable model. Column coordinate of the origin of the deformable model.

Set selected parameters of the deformable model. Instance represents: Handle of the model.

Parameter names. Parameter values.

Return the parameters of a deformable model. Instance represents: Handle of the model.

Names of the generic parameters that are to be queried for the deformable model. Default: "angle_start" Values of the generic parameters.

Return the parameters of a deformable model. Instance represents: Handle of the model.

Names of the generic parameters that are to be queried for the deformable model. Default: "angle_start" Values of the generic parameters.

Return the contour representation of a deformable model. Instance represents: Handle of the model.

Pyramid level for which the contour representation should be returned. Default: 1 Contour representation of the deformable model.

Deserialize a deformable model. Modified instance represents: Handle of the model.

Handle of the serialized item.

Serialize a deformable model. Instance represents: Handle of a model to be saved.

Handle of the serialized item.

Read a deformable model from a file. Modified instance represents: Handle of the model.

File name.

Write a deformable model to a file. Instance represents: Handle of a model to be saved.

The path and filename of the model to be saved.

Free the memory of a deformable model.

Handle of the model.

Free the memory of a deformable model. Instance represents: Handle of the model.

Find the best matches of a local deformable model in an image. Instance represents: Handle of the model.

Input image in which the model should be found. Vector field of the rectification transformation. Contours of the found instances of the model. Smallest rotation of the model. Default: -0.39 Extent of the rotation angles. Default: 0.79 Minimum scale of the model in row direction. Default: 1.0 Maximum scale of the model in row direction. Default: 1.0 Minimum scale of the model in column direction. Default: 1.0 Maximum scale of the model in column direction. Default: 1.0 Minumum score of the instances of the model to be found. Default: 0.5 Number of instances of the model to be found (or 0 for all matches). Default: 1 Maximum overlap of the instances of the model to be found. Default: 1.0 Number of pyramid levels used in the matching. Default: 0 "Greediness" of the search heuristic (0: safe but slow; 1: fast but matches may be missed). Default: 0.9 Switch for requested iconic result. Default: [] The general parameter names. Default: [] Values of the general parameters. Default: [] Scores of the found instances of the model. Row coordinates of the found instances of the model. Column coordinates of the found instances of the model. Rectified image of the found model.

Find the best matches of a calibrated deformable model in an image and return their 3D pose. Instance represents: Handle of the model.

Input image in which the model should be found. Smallest rotation of the model. Default: -0.39 Extent of the rotation angles. Default: 0.78 Minimum scale of the model in row direction. Default: 1.0 Maximum scale of the model in row direction. Default: 1.0 Minimum scale of the model in column direction. Default: 1.0 Maximum scale of the model in column direction. Default: 1.0 Minimum score of the instances of the model to be found. Default: 0.5 Number of instances of the model to be found (or 0 for all matches). Default: 1 Maximum overlap of the instances of the model to be found. Default: 1.0 Number of pyramid levels used in the matching (and lowest pyramid level to use if $|NumLevels| = 2$). Default: 0 "Greediness" of the search heuristic (0: safe but slow; 1: fast but matches may be missed). Default: 0.9 The general parameter names. Default: [] Values of the general parameters. Default: [] 6 standard deviations or 36 covariances of the pose parameters. Score of the found instances of the model. Pose of the object.

Find the best matches of a calibrated deformable model in an image and return their 3D pose. Instance represents: Handle of the model.

Find the best matches of a planar projective invariant deformable model in an image. Instance represents: Handle of the model.

Input image in which the model should be found. Smallest rotation of the model. Default: -0.39 Extent of the rotation angles. Default: 0.78 Minimum scale of the model in row direction. Default: 1.0 Maximum scale of the model in row direction. Default: 1.0 Minimum scale of the model in column direction. Default: 1.0 Maximum scale of the model in column direction. Default: 1.0 Minimum score of the instances of the model to be found. Default: 0.5 Number of instances of the model to be found (or 0 for all matches). Default: 1 Maximum overlap of the instances of the model to be found. Default: 1.0 Number of pyramid levels used in the matching (and lowest pyramid level to use if $|NumLevels| = 2$). Default: 0 "Greediness" of the search heuristic (0: safe but slow; 1: fast but matches may be missed). Default: 0.9 The general parameter names. Default: [] Values of the general parameters. Default: [] Score of the found instances of the model. Homographies between model and found instances.

Find the best matches of a planar projective invariant deformable model in an image. Instance represents: Handle of the model.

Input image in which the model should be found. Smallest rotation of the model. Default: -0.39 Extent of the rotation angles. Default: 0.78 Minimum scale of the model in row direction. Default: 1.0 Maximum scale of the model in row direction. Default: 1.0 Minimum scale of the model in column direction. Default: 1.0 Maximum scale of the model in column direction. Default: 1.0 Minimum score of the instances of the model to be found. Default: 0.5 Number of instances of the model to be found (or 0 for all matches). Default: 1 Maximum overlap of the instances of the model to be found. Default: 1.0 Number of pyramid levels used in the matching (and lowest pyramid level to use if $|NumLevels| = 2$). Default: 0 "Greediness" of the search heuristic (0: safe but slow; 1: fast but matches may be missed). Default: 0.9 The general parameter names. Default: [] Values of the general parameters. Default: [] Score of the found instances of the model. Homographies between model and found instances.

Set the metric of a local deformable model that was created from XLD contours. Instance represents: Handle of the model.

Input image used for the determination of the polarity. Vector field of the local deformation. Match metric. Default: "use_polarity"

Set the metric of a planar calibrated deformable model that was created from XLD contours. Instance represents: Handle of the model.

Input image used for the determination of the polarity. Pose of the model in the image. Match metric. Default: "use_polarity"

Set the metric of a planar uncalibrated deformable model that was created from XLD contours. Instance represents: Handle of the model.

Input image used for the determination of the polarity. Transformation matrix. Match metric. Default: "use_polarity"

Prepare a deformable model for local deformable matching from XLD contours. Modified instance represents: Handle of the model.

Input contours that will be used to create the model. Maximum number of pyramid levels. Default: "auto" This parameter is not used. Default: [] This parameter is not used. Default: [] Step length of the angles (resolution). Default: "auto" Minimum scale of the pattern in row direction. Default: 1.0 This parameter is not used. Default: [] Scale step length (resolution) in row direction. Default: "auto" Minimum scale of the pattern in column direction. Default: 1.0 This parameter is not used. Default: [] Scale step length (resolution) in column direction. Default: "auto" Kind of optimization used for generating the model. Default: "auto" Match metric. Default: "ignore_local_polarity" Minimum contrast of the objects in the search images. Default: 5 The generic parameter names. Default: [] Values of the generic parameters. Default: []

Prepare a deformable model for local deformable matching from XLD contours. Modified instance represents: Handle of the model.

Input contours that will be used to create the model. Maximum number of pyramid levels. Default: "auto" This parameter is not used. Default: [] This parameter is not used. Default: [] Step length of the angles (resolution). Default: "auto" Minimum scale of the pattern in row direction. Default: 1.0 This parameter is not used. Default: [] Scale step length (resolution) in row direction. Default: "auto" Minimum scale of the pattern in column direction. Default: 1.0 This parameter is not used. Default: [] Scale step length (resolution) in column direction. Default: "auto" Kind of optimization used for generating the model. Default: "auto" Match metric. Default: "ignore_local_polarity" Minimum contrast of the objects in the search images. Default: 5 The generic parameter names. Default: [] Values of the generic parameters. Default: []

Prepare a deformable model for planar calibrated matching from XLD contours. Modified instance represents: Handle of the model.

Prepare a deformable model for planar uncalibrated matching from XLD contours. Modified instance represents: Handle of the model.

Creates a deformable model for local, deformable matching. Modified instance represents: Handle of the model.

Create a deformable model for calibrated perspective matching. Modified instance represents: Handle of the model.

Creates a deformable model for uncalibrated, perspective matching. Modified instance represents: Handle of the model.

Represents an instance of a deformable surface matching result.

Get details of a result from deformable surface based matching. Instance represents: Handle of the deformable surface matching result.

Name of the result property. Default: "sampled_scene" Index of the result property. Default: 0 Value of the result property.

Get details of a result from deformable surface based matching. Instance represents: Handle of the deformable surface matching result.

Name of the result property. Default: "sampled_scene" Index of the result property. Default: 0 Value of the result property.

Free the memory of a deformable surface matching result.

Handle of the deformable surface matching result.

Free the memory of a deformable surface matching result. Instance represents: Handle of the deformable surface matching result.

Refine the position and deformation of a deformable surface model in a 3D scene.

Handle of the deformable surface model. Handle of the 3D object model containing the scene. Relative sampling distance of the scene. Default: 0.05 Initial deformation of the 3D object model Names of the generic parameters. Default: [] Values of the generic parameters. Default: [] Handle of the matching result. Score of the refined model.

Refine the position and deformation of a deformable surface model in a 3D scene. Modified instance represents: Handle of the matching result.

Find the best match of a deformable surface model in a 3D scene.

Handle of the deformable surface model. Handle of the 3D object model containing the scene. Scene sampling distance relative to the diameter of the surface model. Default: 0.05 Minimum score of the returned match. Default: 0 Names of the generic parameters. Default: [] Values of the generic parameters. Default: [] Handle of the matching result. Score of the found instances of the surface model.

Find the best match of a deformable surface model in a 3D scene. Modified instance represents: Handle of the matching result.

Represents an instance of a deformable surface model.

Read a deformable surface model from a file. Modified instance represents: Handle of the read deformable surface model.

Name of the file to read.

Create the data structure needed to perform deformable surface-based matching. Modified instance represents: Handle of the deformable surface model.

Handle of the 3D object model. Sampling distance relative to the object's diameter Default: 0.05 Names of the generic parameters. Default: [] Values of the generic parameters. Default: []

Create the data structure needed to perform deformable surface-based matching. Modified instance represents: Handle of the deformable surface model.

Handle of the 3D object model. Sampling distance relative to the object's diameter Default: 0.05 Names of the generic parameters. Default: [] Values of the generic parameters. Default: []

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Free the memory of a deformable surface model.

Handle of the deformable surface model.

Free the memory of a deformable surface model. Instance represents: Handle of the deformable surface model.

Deserialize a deformable surface model. Modified instance represents: Handle of the deformable surface model.

Handle of the serialized item.

Serialize a deformable surface_model. Instance represents: Handle of the deformable surface model.

Handle of the serialized item.

Read a deformable surface model from a file. Modified instance represents: Handle of the read deformable surface model.

Name of the file to read.

Write a deformable surface model to a file. Instance represents: Handle of the deformable surface model to write.

File name to write to.

Refine the position and deformation of a deformable surface model in a 3D scene. Instance represents: Handle of the deformable surface model.

Handle of the 3D object model containing the scene. Relative sampling distance of the scene. Default: 0.05 Initial deformation of the 3D object model Names of the generic parameters. Default: [] Values of the generic parameters. Default: [] Handle of the matching result. Score of the refined model.

Refine the position and deformation of a deformable surface model in a 3D scene. Instance represents: Handle of the deformable surface model.

Find the best match of a deformable surface model in a 3D scene. Instance represents: Handle of the deformable surface model.

Handle of the 3D object model containing the scene. Scene sampling distance relative to the diameter of the surface model. Default: 0.05 Minimum score of the returned match. Default: 0 Names of the generic parameters. Default: [] Values of the generic parameters. Default: [] Handle of the matching result. Score of the found instances of the surface model.

Find the best match of a deformable surface model in a 3D scene. Instance represents: Handle of the deformable surface model.

Return the parameters and properties of a deformable surface model. Instance represents: Handle of the deformable surface model.

Name of the parameter. Default: "sampled_model" Value of the parameter.

Return the parameters and properties of a deformable surface model. Instance represents: Handle of the deformable surface model.

Name of the parameter. Default: "sampled_model" Value of the parameter.

Add a reference point to a deformable surface model. Instance represents: Handle of the deformable surface model.

x-coordinates of a reference point. x-coordinates of a reference point. x-coordinates of a reference point. Index of the new reference point.

Add a reference point to a deformable surface model. Instance represents: Handle of the deformable surface model.

x-coordinates of a reference point. x-coordinates of a reference point. x-coordinates of a reference point. Index of the new reference point.

Add a sample deformation to a deformable surface model Instance represents: Handle of the deformable surface model.

Handle of the deformed 3D object model.

Add a sample deformation to a deformable surface model Instance represents: Handle of the deformable surface model.

Handle of the deformed 3D object model.

Create the data structure needed to perform deformable surface-based matching. Modified instance represents: Handle of the deformable surface model.

Handle of the 3D object model. Sampling distance relative to the object's diameter Default: 0.05 Names of the generic parameters. Default: [] Values of the generic parameters. Default: []

Create the data structure needed to perform deformable surface-based matching. Modified instance represents: Handle of the deformable surface model.

Handle of the 3D object model. Sampling distance relative to the object's diameter Default: 0.05 Names of the generic parameters. Default: [] Values of the generic parameters. Default: []

Represents an instance of a descriptor model.

Read a descriptor model from a file. Modified instance represents: Handle of the model.

File name.

Create a descriptor model for calibrated perspective matching. Modified instance represents: The handle to the descriptor model.

Input image whose domain will be used to create the model. The parameters of the internal orientation of the camera. The reference pose of the object in the reference image. The type of the detector. Default: "lepetit" The detector's parameter names. Default: [] Values of the detector's parameters. Default: [] The descriptor's parameter names. Default: [] Values of the descriptor's parameters. Default: [] The seed for the random number generator. Default: 42

Prepare a descriptor model for interest point matching. Modified instance represents: The handle to the descriptor model.

Input image whose domain will be used to create the model. The type of the detector. Default: "lepetit" The detector's parameter names. Default: [] Values of the detector's parameters. Default: [] The descriptor's parameter names. Default: [] Values of the descriptor's parameters. Default: [] The seed for the random number generator. Default: 42

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Free the memory of a descriptor model.

Handle of the descriptor model.

Free the memory of a descriptor model. Instance represents: Handle of the descriptor model.

Deserialize a descriptor model. Modified instance represents: Handle of the model.

Handle of the serialized item.

Serialize a descriptor model. Instance represents: Handle of a model to be saved.

Handle of the serialized item.

Read a descriptor model from a file. Modified instance represents: Handle of the model.

File name.

Write a descriptor model to a file. Instance represents: Handle of a model to be saved.

The path and filename of the model to be saved.

Find the best matches of a calibrated descriptor model in an image and return their 3D pose. Instance represents: The handle to the descriptor model.

Input image where the model should be found. The detector's parameter names. Default: [] Values of the detector's parameters. Default: [] The descriptor's parameter names. Default: [] Values of the descriptor's parameters. Default: [] Minimum score of the instances of the models to be found. Default: 0.2 Maximal number of found instances. Default: 1 Camera parameter (inner orientation) obtained from camera calibration. Score type to be evaluated in Score. Default: "num_points" Score of the found instances according to the ScoreType input. 3D pose of the object.

Find the best matches of a calibrated descriptor model in an image and return their 3D pose. Instance represents: The handle to the descriptor model.

Find the best matches of a descriptor model in an image. Instance represents: The handle to the descriptor model.

Input image where the model should be found. The detector's parameter names. Default: [] Values of the detector's parameters. Default: [] The descriptor's parameter names. Default: [] Values of the descriptor's parameters. Default: [] Minimum score of the instances of the models to be found. Default: 0.2 Maximal number of found instances. Default: 1 Score type to be evaluated in Score. Default: "num_points" Score of the found instances according to the ScoreType input. Homography between model and found instance.

Find the best matches of a descriptor model in an image. Instance represents: The handle to the descriptor model.

Input image where the model should be found. The detector's parameter names. Default: [] Values of the detector's parameters. Default: [] The descriptor's parameter names. Default: [] Values of the descriptor's parameters. Default: [] Minimum score of the instances of the models to be found. Default: 0.2 Maximal number of found instances. Default: 1 Score type to be evaluated in Score. Default: "num_points" Score of the found instances according to the ScoreType input. Homography between model and found instance.

Query the interest points of the descriptor model or the last processed search image. Instance represents: The handle to the descriptor model.

Set of interest points. Default: "model" Subset of interest points. Default: "all" Row coordinates of interest points. Column coordinates of interest points.

Query the interest points of the descriptor model or the last processed search image. Instance represents: The handle to the descriptor model.

Set of interest points. Default: "model" Subset of interest points. Default: "all" Row coordinates of interest points. Column coordinates of interest points.

Return the parameters of a descriptor model. Instance represents: The object handle to the descriptor model.

The detectors parameter names. Values of the detectors parameters. The descriptors parameter names. Values of the descriptors parameters. The type of the detector.

Create a descriptor model for calibrated perspective matching. Modified instance represents: The handle to the descriptor model.

Prepare a descriptor model for interest point matching. Modified instance represents: The handle to the descriptor model.

Query alphanumerical results that were accumulated during descriptor-based matching. Instance represents: Handle of a descriptor model.

Handle of the object for which the results are queried. Default: "all" Name of the results to be queried. Default: "num_points" Returned results.

Query alphanumerical results that were accumulated during descriptor-based matching. Instance represents: Handle of a descriptor model.

Handle of the object for which the results are queried. Default: "all" Name of the results to be queried. Default: "num_points" Returned results.

Return the origin of a descriptor model. Instance represents: Handle of a descriptor model.

Position of origin in row direction. Position of origin in column direction.

Return the origin of a descriptor model. Instance represents: Handle of a descriptor model.

Position of origin in row direction. Position of origin in column direction.

Sets the origin of a descriptor model. Instance represents: Handle of a descriptor model.

Translation of origin in row direction. Default: 0 Translation of origin in column direction. Default: 0

Sets the origin of a descriptor model. Instance represents: Handle of a descriptor model.

Translation of origin in row direction. Default: 0 Translation of origin in column direction. Default: 0

Represents an instance of a Dictionary.

Create a new empty dictionary. Modified instance represents: Handle of the newly created dictionary.

Read a dictionary from a file. Modified instance represents: Dictionary handle.

File name. Name of the generic parameter. Default: [] Value of the generic parameter. Default: []

Read a dictionary from a file. Modified instance represents: Dictionary handle.

File name. Name of the generic parameter. Default: [] Value of the generic parameter. Default: []

Copy a dictionary. Instance represents: Dictionary handle.

Name of the generic parameter. Default: [] Value of the generic parameter. Default: [] Copied dictionary handle.

Copy a dictionary. Instance represents: Dictionary handle.

Name of the generic parameter. Default: [] Value of the generic parameter. Default: [] Copied dictionary handle.

Create a new empty dictionary. Modified instance represents: Handle of the newly created dictionary.

Retrieve an object associated with the key from the dictionary. Instance represents: Dictionary handle.

Key string. Object value retrieved from the dictionary.

Retrieve an object associated with the key from the dictionary. Instance represents: Dictionary handle.

Key string. Object value retrieved from the dictionary.

Query dictionary parameters or information about a dictionary. Instance represents: Dictionary handle.

Names of the dictionary parameters or info queries. Default: "keys" Dictionary keys the parameter/query should be applied to (empty for GenParamName = 'keys'). Values of the dictionary parameters or info queries.

Query dictionary parameters or information about a dictionary. Instance represents: Dictionary handle.

Retrieve a tuple associated with the key from the dictionary. Instance represents: Dictionary handle.

Key string. Tuple value retrieved from the dictionary.

Retrieve a tuple associated with the key from the dictionary. Instance represents: Dictionary handle.

Key string. Tuple value retrieved from the dictionary.

Read a dictionary from a file. Modified instance represents: Dictionary handle.

File name. Name of the generic parameter. Default: [] Value of the generic parameter. Default: []

Read a dictionary from a file. Modified instance represents: Dictionary handle.

File name. Name of the generic parameter. Default: [] Value of the generic parameter. Default: []

Remove keys from a dictionary. Instance represents: Dictionary handle.

Key to remove.

Remove keys from a dictionary. Instance represents: Dictionary handle.

Key to remove.

Add a key/object pair to the dictionary. Instance represents: Dictionary handle.

Object to be associated with the key. Key string.

Add a key/object pair to the dictionary. Instance represents: Dictionary handle.

Object to be associated with the key. Key string.

Add a key/tuple pair to the dictionary. Instance represents: Dictionary handle.

Key string. Tuple value to be associated with the key.

Add a key/tuple pair to the dictionary. Instance represents: Dictionary handle.

Key string. Tuple value to be associated with the key.

Write a dictionary to a file. Instance represents: Dictionary handle.

File name. Name of the generic parameter. Default: [] Value of the generic parameter. Default: []

Write a dictionary to a file. Instance represents: Dictionary handle.

File name. Name of the generic parameter. Default: [] Value of the generic parameter. Default: []

Represents an instance of a Deep Neural Network.

Read a deep-learning-based classifier from a file. Modified instance represents: Handle of the deep learning classifier.

File name. Default: "pretrained_dl_classifier_compact.hdl"

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Infer the class affiliations for a set of images using a deep-learning-based classifier. Instance represents: Handle of the deep-learning-based classifier.

Tuple of input images. Handle of the deep learning classification results.

Clear a deep-learning-based classifier.

Handle of the deep-learning-based classifier.

Clear a deep-learning-based classifier. Instance represents: Handle of the deep-learning-based classifier.

Deserialize a deep-learning-based classifier. Modified instance represents: Handle of the deep-learning-based classifier.

Handle of the serialized item.

Return the parameters of a deep-learning-based classifier. Instance represents: Handle of the deep-learning-based classifier.

Name of the generic parameter. Default: "gpu" Value of the generic parameter.

Return the parameters of a deep-learning-based classifier. Instance represents: Handle of the deep-learning-based classifier.

Name of the generic parameter. Default: "gpu" Value of the generic parameter.

Read a deep-learning-based classifier from a file. Modified instance represents: Handle of the deep learning classifier.

File name. Default: "pretrained_dl_classifier_compact.hdl"

Serialize a deep-learning-based classifier. Instance represents: Handle of the deep-learning-based classifier.

Handle of the serialized item.

Set the parameters of a deep-learning-based classifier. Instance represents: Handle of the deep-learning-based classifier.

Name of the generic parameter. Default: "classes" Value of the generic parameter. Default: ["class_1","class_2","class_3"]

Set the parameters of a deep-learning-based classifier. Instance represents: Handle of the deep-learning-based classifier.

Name of the generic parameter. Default: "classes" Value of the generic parameter. Default: ["class_1","class_2","class_3"]

Write a deep-learning-based classifier in a file. Instance represents: Handle of the deep-learning-based classifier.

File name.

Represents an instance of a Deep Neural Network inference step result.

Infer the class affiliations for a set of images using a deep-learning-based classifier. Modified instance represents: Handle of the deep learning classification results.

Tuple of input images. Handle of the deep-learning-based classifier.

Clear a handle containing the results of the deep-learning-based classification.

Handle of the deep learning classification results.

Clear a handle containing the results of the deep-learning-based classification. Instance represents: Handle of the deep learning classification results.

Retrieve classification results inferred by a deep-learning-based classifier. Instance represents: Handle of the deep learning classification results.

Index of the image in the batch. Default: "all" Name of the generic parameter. Default: "predicted_classes" Value of the generic parameter, either the confidence values, the class names or class indices.

Retrieve classification results inferred by a deep-learning-based classifier. Instance represents: Handle of the deep learning classification results.

Represents an instance of a Deep Neural Network training step result.

Perform a training step of a deep-learning-based classifier on a batch of images. Modified instance represents: Handle of the training results from the deep-learning-based classifier.

Images comprising the batch. Handle of the deep-learning-based classifier. Corresponding labels for each of the images. Default: []

Clear the handle of a deep-learning-based classifier training result.

Handle of the training results from the deep-learning-based classifier.

Clear the handle of a deep-learning-based classifier training result. Instance represents: Handle of the training results from the deep-learning-based classifier.

Return the results for the single training step of a deep-learning-based classifier. Instance represents: Handle of the training results from the deep-learning-based classifier.

Name of the generic parameter. Default: "loss" Value of the generic parameter.

Return the results for the single training step of a deep-learning-based classifier. Instance represents: Handle of the training results from the deep-learning-based classifier.

Name of the generic parameter. Default: "loss" Value of the generic parameter.

Perform a training step of a deep-learning-based classifier on a batch of images. Modified instance represents: Handle of the training results from the deep-learning-based classifier.

Images comprising the batch. Handle of the deep-learning-based classifier. Corresponding labels for each of the images. Default: []

Represents an instance of a Deep Neural Network.

Create a deep learning network for object detection. Modified instance represents: Deep learning model for object detection.

Deep learning classifier, used as backbone network. Default: "pretrained_dl_classifier_compact.hdl" Number of classes. Default: 3 Parameters for the object detection model. Default: []

Read a deep learning model from a file. Modified instance represents: Handle of the deep learning model.

Filename Default: "pretrained_dl_segmentation_compact.hdl"

Serialize object to binary stream in HALCON format

Deserialize object from binary stream in HALCON format

Apply a deep-learning-based network on a set of images for inference. Instance represents: Handle of the deep learning model.

Input data. Requested outputs. Default: [] Handle containing result data.

Clear a deep learning model.

Handle of the deep learning model.

Clear a deep learning model. Instance represents: Handle of the deep learning model.

Create a deep learning network for object detection. Modified instance represents: Deep learning model for object detection.

Deep learning classifier, used as backbone network. Default: "pretrained_dl_classifier_compact.hdl" Number of classes. Default: 3 Parameters for the object detection model. Default: []

Deserialize a deep learning model. Modified instance represents: Handle of the deep learning model.

Handle of the serialized item.

Return the parameters of a deep learning model. Instance represents: Handle of the deep learning model.

Name of the generic parameter. Default: "batch_size" Value of the generic parameter.

Read a deep learning model from a file. Modified instance represents: Handle of the deep learning model.

Filename Default: "pretrained_dl_segmentation_compact.hdl"

Serialize a deep learning model. Instance represents: Handle of the deep learning model.

Handle of the serialized item.

Set the parameters of a deep learning model. Instance represents: Handle of the deep learning model.

Name of the generic parameter. Default: "learning_rate" Value of the generic parameter. Default: 0.001

Set the parameters of a deep learning model. Instance represents: Handle of the deep learning model.

Name of the generic parameter. Default: "learning_rate" Value of the generic parameter. Default: 0.001

Train a deep learning model. Instance represents: Deep learning model handle.

Tuple of Dictionaries with input images and corresponding information. Dictionary with the train result data.

Write a deep learning model in a file. Instance represents: Handle of the deep learning model.

Filename

Represents an instance of a drawing object.

Create a circle which can be modified interactively. Modified instance represents: Handle of the drawing object.

Row coordinate of the center. Default: 100 Column coordinate of the center. Default: 100 Radius of the circle. Default: 80

Create a rectangle of any orientation which can be modified interactively. Modified instance represents: Handle of the drawing object.

Row coordinate of the center. Default: 150 Column coordinate of the center. Default: 150 Orientation of the first half axis in radians. Default: 0 First half axis. Default: 100 Second half axis. Default: 100

Create a rectangle parallel to the coordinate axis which can be modified interactively. Modified instance represents: Handle of the drawing object.

Row coordinate of the upper left corner. Default: 100 Column coordinate of the upper left corner. Default: 100 Row coordinate of the lower right corner. Default: 200 Column coordinate of the lower right corner. Default: 200

Adds a callback for the resize event, that is, this callback is executed whenever the user changes any of the dimensions of the draw object.

Callback function with the signature defined by HDrawingObjectCallback

Adds a callback for the attach event, that is, this callback is executed when a drawing object is attached to the window.

Callback function with the signature defined by HDrawingObjectCallback

Adds a callback for the detach event, that is, this callback is executed when a drawing object is detached from the window.

Callback function with the signature defined by HDrawingObjectCallback

Adds a callback for the resize event, that is, this callback is executed whenever the drawing object's position changes.

Callback function with the signature defined by HDrawingObjectCallback

Adds a callback for the resize event, that is, this callback is executed whenever the drawing object is selected.