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def | __init__ (self, detCfg=None, detInst=None, processors=None) |
| Constructor for layered puzzle scene detector. More...
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def | adapt (self) |
| Adapt the layer detection models. More...
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def | correct (self) |
| Apply correction process to the individual detectors. More...
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def | detect (self, I) |
| Apply predict, measure, correct process to source image. More...
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def | emptyDebug (self) |
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def | emptyState (self) |
| Get and empty state to recover its basic structure. More...
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def | getDebug (self) |
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def | getState (self) |
| Get the complete detector state, which involves the states of the individual layer detectors. More...
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def | info (self) |
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def | load (inFile) |
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def | loadFrom (fPtr) |
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def | measure (self, I) |
| Apply detection to the source image pass. More...
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def | predict (self) |
| Generate prediction of expected measurement. More...
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def | process (self, I) |
| Apply entire predict to adapt process to source image. More...
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def | saveTo (self, fPtr) |
| Save the instantiated Detector to given HDF5 file. More...
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◆ __init__()
def __init__ |
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self, |
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detCfg = None , |
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detInst = None , |
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processors = None |
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Constructor for layered puzzle scene detector.
- Parameters
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[in] | detCfg | Detector configuration. |
[in] | detInst | Detection instances for the different layers. |
[in] | processors | Image processors for the different layers. |
Reimplemented in Calibrator.
◆ adapt()
Adapt the layer detection models.
This part is tricky as there may be dependencies across the layers in terms of what should be updated and what should not be. Applying simple filtering to establish what pixels should adapt and which ones shouldn't.
Reimplemented in Calibrator.
◆ buildFromCfg()
def buildFromCfg |
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theConfig | ) |
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Instantiate from stored configuration file (YAML).
◆ calibrate2config()
def calibrate2config |
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theStream, |
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outFile, |
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initModel = None |
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static |
◆ correct()
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.
Reimplemented in Calibrator.
◆ detect()
Apply predict, measure, correct process to source image.
Running detect alone elects not to adapt or update the underlying models. The static model is presumed to be sufficient and applied to the RGBD stream.
- Parameters
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[in] | I | Source RGB-D image (structure/dataclass). |
Reimplemented in Calibrator.
◆ emptyDebug()
◆ emptyState()
Get and empty state to recover its basic structure.
- Parameters
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[out] | estate | The empty state. |
Reimplemented in Calibrator.
◆ getDebug()
◆ getState()
Get the complete detector state, which involves the states of the individual layer detectors.
- Parameters
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[out] | state | The detector state for each layer, by layer. |
Reimplemented in Calibrator.
◆ info()
◆ load()
◆ loadFrom()
◆ measure()
Apply detection to the source image pass.
- Parameters
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[in] | I | An RGB-D image (structure/dataclass). |
Reimplemented in Calibrator.
◆ predict()
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.
Reimplemented in Calibrator.
◆ process()
Apply entire predict to adapt process to source image.
- Parameters
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[in] | I | Source RGB-D image (structure/dataclass). |
Reimplemented in Calibrator.
◆ saveTo()
def saveTo |
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self, |
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fPtr |
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Save the instantiated Detector to given HDF5 file.
The save to function writes the necessary information to re-instantiate a Detectors class object to the passed HDF5 file pointer/instance.
- Parameters
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[in] | fPtr | An HDF5 file point. |
Reimplemented in Calibrator.
◆ config
◆ depth
◆ glove
◆ imGlove
◆ mask
The documentation for this class was generated from the following file:
- /home/pvela/python/surveillance/Surveillance/layers/Glove.py