Layered track pointer part of puzzle scene perceiver grouping. More...
Public Member Functions | |
| def | __init__ (self, iState=None, trackCfg=None) |
| Constructor for layered puzzle scene tracker. More... | |
| def | adapt (self) |
| Adapt the layer detection models. More... | |
| def | correct (self) |
| Apply correction process to the individual detectors. More... | |
| def | display_cv (self, I, ratio=None, window_name="trackpoints") |
| def | emptyDebug (self) |
| def | emptyState (self) |
| Get and empty state to recover its basic structure. More... | |
| def | getDebug (self) |
| def | getState (self) |
| Get the complete detector state, which involves the states of the individual layer detectors. More... | |
| def | info (self) |
| def | measure (self, I) |
| Apply detection to the source image pass. More... | |
| def | predict (self) |
| Generate prediction of expected measurement. More... | |
| def | process (self, I) |
| Apply entire predict to adapt process to source image(s). More... | |
Public Attributes | |
| glove | |
| pieces | |
Detailed Description
Layered track pointer part of puzzle scene perceiver grouping.
Layered trackpointers based on layered detector output. One for glove and one for pieces. The arm is not tracked since there should be no need to do so as its status is known from forward kinematics on joint measurements.
Constructor & Destructor Documentation
◆ __init__()
| def __init__ | ( | self, | |
iState = None, |
|||
trackCfg = None |
|||
| ) |
Constructor for layered puzzle scene tracker.
- Parameters
-
[in] iState Initial state of tracks. [in] trackCfg Trackpointer(s) configuration.
Member Function Documentation
◆ adapt()
| def adapt | ( | self | ) |
Adapt the layer detection models.
There is no adaptation.
◆ correct()
| def correct | ( | self | ) |
Apply correction process to the individual detectors.
Apply naive correction on a per detector basis. As a layered system, there might be interdependencies that would impact the correction step. Ignoring that for now since it does not immediately come to mind what needs to be done.
◆ display_cv()
| def display_cv | ( | self, | |
| I, | |||
ratio = None, |
|||
window_name = "trackpoints" |
|||
| ) |
◆ emptyDebug()
| def emptyDebug | ( | self | ) |
◆ emptyState()
| def emptyState | ( | self | ) |
Get and empty state to recover its basic structure.
- Parameters
-
[out] estate The empty state.
◆ getDebug()
| def getDebug | ( | self | ) |
◆ getState()
| def getState | ( | self | ) |
Get the complete detector state, which involves the states of the individual layer detectors.
- Parameters
-
[out] state The detector state for each layer, by layer.
◆ info()
| def info | ( | self | ) |
◆ measure()
| def measure | ( | self, | |
| I | |||
| ) |
Apply detection to the source image pass.
- Parameters
-
[in] I Layered detection image instance (structure/dataclass).
◆ predict()
| def predict | ( | self | ) |
Generate prediction of expected measurement.
The detectors are mostly going to be static models, which means that prediction does nothing. Just in case though, the prediction methods are called for them.
◆ process()
| def process | ( | self, | |
| I | |||
| ) |
Apply entire predict to adapt process to source image(s).
- Parameters
-
[in] I Layered detection image instance (structure/dataclass).
Member Data Documentation
◆ glove
| glove |
◆ pieces
| pieces |
The documentation for this class was generated from the following file:
- /home/pvela/python/surveillance/Surveillance/layers/PuzzleScene.py
