EMatcher Class
Manages a complete matching context in EasyMatch.
Remarks
A matching context consists of a learned pattern and of the parameters required to locate one or more instances of the pattern in a search field.
Namespace: Euresys::Open_eVision_2_11
Methods
Releases the pointer to the image object that has been passed to the EMatcher object.
Copies the learnt pattern in the supplied image. If no pattern has been learned, an exception with code EError_NoPatternLearnt will be thrown.
Draws a graphical representation of a given occurrence of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
Draws a graphical representation of all occurrences of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
Draws a graphical representation of all occurrences of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
Draws a graphical representation of a given occurrence of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
Constructs a matching context.
Toggle advanced learning.
Current angle step.
Contrast mode.
Correlation mode.
"Don't care" threshold.
Filtering mode.
Index of the last reduction.
Minimum score applied as a selection criterion in the early stages of the matching process.
Interpolation mode.
Flag indicating whether isotropic (as opposed to anisotropic) scaling is used.
Maximum angle, in the current angle unit.
Maximum number of positions at the first stage of the matching process.
Maximum number of positions.
Maximum scale factor for isotropic scaling.
Maximum horizontal scale factor for anisotropic scaling.
Maximum vertical scale factor for anisotropic scaling.
Minimum angle, in the current angle unit.
Minimum reduced area parameter.
Minimum scale factor for isotropic scaling.
Minimum horizontal scale factor for anisotropic scaling.
Minimum vertical scale factor for anisotropic scaling.
Minimum score.
Number of good matches found, as defined by EMatcher::MinScore and EMatcher::MaxPositions properties.
Number of reduction steps used in the matching process.
Learnt pattern height.
Returns TRUE after a learning operation has been successfully performed, indicating that the EMatcher object is ready for matching, and FALSE otherwise.
Pixel type of the learnt pattern.
Learnt pattern width.
Gets the physical pixel dimensions.
Returns an EMatchPosition object containing the position coordinates.
Returns a vector of EMatchPosition objects, each containing the position coordinates and other matching results.
Current value of scale step.
Current value of scale X step.
Current value of scale Y step.
Version number of the EMatcher object.
Learns a pattern to subsequently match in an image.
Loads the EMatcher. The given ESerializer must have been created for reading.
Matches the pattern against an image.
Saves the EMatcher. The given ESerializer must have been created for writing.
Toggle advanced learning.
Contrast mode.
Correlation mode.
"Don't care" threshold.
Sets the extension of the matching ROI, i.e. puts the horizontal and vertical distances, in pixels, that the found pattern occurrences may fall outside the matching ROI. Such occurrences partially outside the ROI have their score corrected by the ratio between the pattern area outside the ROI and the pattern area inside.
Filtering mode.
Index of the last reduction.
Minimum score applied as a selection criterion in the early stages of the matching process.
Interpolation mode.
Maximum angle, in the current angle unit.
Maximum number of positions at the first stage of the matching process.
Maximum number of positions.
Maximum scale factor for isotropic scaling.
Maximum horizontal scale factor for anisotropic scaling.
Maximum vertical scale factor for anisotropic scaling.
Minimum angle, in the current angle unit.
Minimum reduced area parameter.
Minimum scale factor for isotropic scaling.
Minimum horizontal scale factor for anisotropic scaling.
Minimum vertical scale factor for anisotropic scaling.
Minimum score.
Sets the physical pixel dimensions.
EMatcher Class
Manages a complete matching context in EasyMatch.
Remarks
A matching context consists of a learned pattern and of the parameters required to locate one or more instances of the pattern in a search field.
Namespace: Euresys.Open_eVision_2_11
Properties
Toggle advanced learning.
Current angle step.
Contrast mode.
Correlation mode.
"Don't care" threshold.
Filtering mode.
Index of the last reduction.
Minimum score applied as a selection criterion in the early stages of the matching process.
Interpolation mode.
Flag indicating whether isotropic (as opposed to anisotropic) scaling is used.
Maximum angle, in the current angle unit.
Maximum number of positions at the first stage of the matching process.
Maximum number of positions.
Maximum scale factor for isotropic scaling.
Maximum horizontal scale factor for anisotropic scaling.
Maximum vertical scale factor for anisotropic scaling.
Minimum angle, in the current angle unit.
Minimum reduced area parameter.
Minimum scale factor for isotropic scaling.
Minimum horizontal scale factor for anisotropic scaling.
Minimum vertical scale factor for anisotropic scaling.
Minimum score.
Number of good matches found, as defined by EMatcher::MinScore and EMatcher::MaxPositions properties.
Number of reduction steps used in the matching process.
Learnt pattern height.
Returns TRUE after a learning operation has been successfully performed, indicating that the EMatcher object is ready for matching, and FALSE otherwise.
Pixel type of the learnt pattern.
Learnt pattern width.
Returns a vector of EMatchPosition objects, each containing the position coordinates and other matching results.
Current value of scale step.
Current value of scale X step.
Current value of scale Y step.
Methods
Releases the pointer to the image object that has been passed to the EMatcher object.
Copies the learnt pattern in the supplied image. If no pattern has been learned, an exception with code NoPatternLearnt will be thrown.
Draws a graphical representation of a given occurrence of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
Draws a graphical representation of all occurrences of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
Draws a graphical representation of all occurrences of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
Draws a graphical representation of a given occurrence of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
Constructs a matching context.
Gets the physical pixel dimensions.
Returns an EMatchPosition object containing the position coordinates.
Learns a pattern to subsequently match in an image.
Loads the EMatcher. The given ESerializer must have been created for reading.
Matches the pattern against an image.
Saves the EMatcher. The given ESerializer must have been created for writing.
Sets the extension of the matching ROI, i.e. puts the horizontal and vertical distances, in pixels, that the found pattern occurrences may fall outside the matching ROI. Such occurrences partially outside the ROI have their score corrected by the ratio between the pattern area outside the ROI and the pattern area inside.
Sets the physical pixel dimensions.