Module: sarpyx.cli.algorithms.AdaptiveThresholding
File: sarpyx/cli/algorithms/AdaptiveThresholding.py
No module docstring available; module purpose is inferred from implementation.
Exported Symbols (__all__)
No explicit __all__ list. Public symbols inferred from implementation.
Public Functions (0)
No public top-level functions detected.
Public Classes (1)
AdaptiveThresholding class
Adaptive thresholding class for vessel detection using boxcar approach.
File location: sarpyx/cli/algorithms/AdaptiveThresholding.py:5
Class Signature
class AdaptiveThresholdingConstructor Parameters
Return Type
AdaptiveThresholding instances.
Exceptions
Construction/runtime exceptions are inferred from implementation and method-level documentation.
Side Effects
See method-level side effects below.
Example Usage
from sarpyx.cli.algorithms.AdaptiveThresholding import AdaptiveThresholding
obj = AdaptiveThresholding(...) # inferred from implementation
Edge Cases
No class-level edge-case section is explicitly documented; rely on method-level checks and raised exceptions.
Public Methods (2)
AdaptiveThresholding.detect_vessels method
Detect vessels using adaptive thresholding.
File location: sarpyx/cli/algorithms/AdaptiveThresholding.py:79
Signature
detect_vessels(self, image: np.ndarray) -> Tuple[np.ndarray, np.ndarray]Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
image | np.ndarray | yes | - | Input SAR image as 2D numpy array. |
Return Type
Tuple[np.ndarray, np.ndarray]
Tuple of (detection_map, threshold_map) where detection_map is binary and threshold_map contains the adaptive threshold values.
Exceptions
None explicitly documented; inferred from implementation.
Side Effects
- inferred from implementation
Example Usage
from sarpyx.cli.algorithms.AdaptiveThresholding import AdaptiveThresholding
# Constructor arguments are inferred from implementation.
obj = AdaptiveThresholding(...) # inferred from implementation
result = obj.detect_vessels(image=<image>)Edge Cases
No explicit edge-case section found; behavior is inferred from implementation.
AdaptiveThresholding.set_pfa method
Set new probability of false alarm.
File location: sarpyx/cli/algorithms/AdaptiveThresholding.py:122
Signature
set_pfa(self, pfa: float) -> NoneParameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
pfa | float | yes | - | New probability of false alarm (e.g., 1e-6). |
Return Type
None
inferred from implementation.
Exceptions
None explicitly documented; inferred from implementation.
Side Effects
- inferred from implementation
Example Usage
from sarpyx.cli.algorithms.AdaptiveThresholding import AdaptiveThresholding
# Constructor arguments are inferred from implementation.
obj = AdaptiveThresholding(...) # inferred from implementation
result = obj.set_pfa(pfa=<pfa>)Edge Cases
No explicit edge-case section found; behavior is inferred from implementation.