Additional Inherited Members | |
 Public Member Functions inherited from ILMatch | |
| void | GetPatCenter (double *patX, double *patY, double *patAngle) |
| LPVPatCenterMode | GetPatCenterMode () |
| ILPoints * | GetPatFeature () |
| void | GetPatImage (ILImage *img) |
| void | GetPatMask (ILImage *mask) |
| void | GetPatPruneMask (ILImage *mask) |
| BOOL | IsLearnt () |
| LPVErrorCode | Learn (ILImage *img, ILRegion *region) |
| LPVErrorCode | LearnWithShape (ILImage *img, ILRegion *region, ILRegion *shapeRegion) |
| LPVErrorCode | LearnWithShapeImage (ILImage *img, ILRegion *region, ILImage *shapeImg) |
| LPVErrorCode | Match (ILImage *img, ILRegion *region, ILMatchResults **results) |
| LPVErrorCode | Prune (ILImage *img, ILRegion *region) |
| void | SetPatCenter (LPVPatCenterMode centerMode, double patX, double patY, double patAngle) |
| Public Member Functions inherited from ILObject | |
| ILObject * | Copy () |
| LPVErrorCode | Load (LString filename) |
| void | Reset () |
| LPVErrorCode | Save (LString filename) |
| BOOL | Valid () |
| Properties inherited from ILMatch | |
| int | AcceptScore [get, set] |
| The minimum acceptable score, in range from 1 to 100. Higher score indicates more strict evaluation standard. The searching wouldn't go through all candidates. It firstly sort the candidates by its approximate similarity to template, then optimize their position/angle/scale to sub-pixel level one-by-one. It stops if there are enough results met the specified acceptable score. | |
| int | AccuracyLevel [get, set] |
| Accuracy level for the matching, higher level usually means more accurate and stable result but slower. It should be either 0(Low), 1(Middle, default) or 2(High). | |
| int | AngleBias [get, set] |
| The angle bias in range from -180 to 180. By default, it's 0. | |
| int | AngleTolerance [get, set] |
| The angle tolerance in range from 0 to 180. By default, it's 5. The matching angle range is based on the bias and tolerance as \( AngleBias \pm AngleTolerance \) . | |
| double | DetailLevel [get, set] |
| Level of detail of the pattern template, in range of 0 ~ 1. By default, it's set to 0.5. Higher level indicates more details contained in the trained template, while it may introduce more noise and slow down the detection. You should set the proper detail level firstly, then train the template via Learn(), since the template features should be re-extracted again in the training process. If at the same time DetailLevel and ShortThreshold / WeakThreshold is specified, we'll filter according to ShortThreshold / WeakThreshold firstly, then adjust to the required detail level. . More... | |
| BOOL | ExcludeBoundary [get, set] |
| Whether to exclude the matching result on the boundary of the input image or region. | |
| int | GrayValueWeight [get, set] |
| The combination weight of the evaluation score based on the similarity of the matching result and the template image, using normalized cross correlation (NCC). By default, it's 0, means the gray score does not take any effect. The final score of the result is calculated as: \( Score = Score_{shape} \times (1 - w) + Score_{gray} \times w \) . | |
| BOOL | IgnoreMissing [get, set] |
| Whether the missing parts contribute negatively to the matching result. By default is true, the missing features are ignored when scoring and missing percentage check is skipped. | |
| BOOL | IgnorePolarity [get, set] |
| Whether to ignore polarity in matching. By default, it's off. | |
| BOOL | IsotropicScale [get, set] |
| Whether to keep aspect ratio when scaling, default to true. | |
| int | MaxCount [get, set] |
| The expected maximum count of the matching results. | |
| int | MissingTolerance [get, set] |
| The maximum acceptable missing rate of the matching features in percentage. The value range is 0 ~ 100. By default, it's 50, means 50% missing is acceptable. Note missing part check is only enabled when IgnoreMissing is false. | |
| int | Overlap [get, set] |
| The maximum acceptable overlap between the nearby matching results in percentage. The value range is 1 ~ 80. By default, it's 50, means 50% overlap. The recommended setting is around 30 ~ 70. | |
| int | ScaleBias [get, set] |
| The scale bias in range from 50 to 150%. By default, it's 100. | |
| int | ScaleTolerance [get, set] |
| The scale tolerance in range from 0 to 50%. By default, it's 0. The matching scale range is based on the bias and tolerance as \( ScaleBias \pm ScaleTolerance \) . | |
| int | ShortThreshold [get, set] |
| The short threshold is used to filter trained feature points extracted from short edge segments. It's a 0 ~ 100 percentage value related to the longer side of the training image or region, aka. the actual threshold is calculated as \( Max(Width, Height) \times ShortThreshold / 100 \) By default, it's set to 0 to skip the filtering. You should set the proper short threshold firstly, then train the template via Learn(), since the template features should be re-extracted again in the training process. More... | |
| BOOL | StrictScore [get, set] |
| Whether to enable strict scoring in matching. By default, it's off. If it's on, scoring is done on the original image with most detail information. If it's off, scoring is done on the scaled and smoothed image which is much faster but with less detail. | |
| BOOL | UseCache [get, set] |
| Whether to make use of cache in matching, it will slightly speed up the matching while consume more memory. By default, it's on. | |
| int | WeakThreshold [get, set] |
| The weak threshold is used to filter trained feature points of low contrast, in range of 0 ~ 100. By default, it's set to 0 to skip the filtering. You should set the proper weak threshold firstly, then train the template via Learn(), since the template features should be re-extracted again in the training process. More... | |
This class implements the interface ILMatch, and is used to create an instance of pattern matching tool.
