Merge pull request #33 from BrainLesion/logging_first

Logging first

examples/example_spine_instance.py

@@ -11,8 +11,7 @@
 
 pred_masks = read_nifti(directory + "/spine_seg/matched_instance/pred.nii.gz")
 
-sample = MatchedInstancePair(
-    prediction_arr=pred_masks, reference_arr=ref_masks)
+sample = MatchedInstancePair(prediction_arr=pred_masks, reference_arr=ref_masks)
 
 
 evaluator = Panoptic_Evaluator(

examples/example_spine_semantic.py

@@ -12,12 +12,8 @@
 
 directory = turbopath(__file__).parent
 
-ref_masks = read_nifti(
-    directory + "/spine_seg/semantic/ref.nii.gz"
-)
-pred_masks = read_nifti(
-    directory + "/spine_seg/semantic/pred.nii.gz"
-)
+ref_masks = read_nifti(directory + "/spine_seg/semantic/ref.nii.gz")
+pred_masks = read_nifti(directory + "/spine_seg/semantic/pred.nii.gz")
 
 sample = SemanticPair(pred_masks, ref_masks)
 

panoptica/evaluator.py

@@ -22,6 +22,8 @@ def __init__(
         expected_input: Type[SemanticPair] | Type[UnmatchedInstancePair] | Type[MatchedInstancePair] = MatchedInstancePair,
         instance_approximator: InstanceApproximator | None = None,
         instance_matcher: InstanceMatchingAlgorithm | None = None,
+        log_times: bool = False,
+        verbose: bool = False,
         iou_threshold: float = 0.5,
     ) -> None:
         """Creates a Panoptic_Evaluator, that saves some parameters to be used for all subsequent evaluations
@@ -36,22 +38,30 @@ def __init__(
         self.__instance_approximator = instance_approximator
         self.__instance_matcher = instance_matcher
         self.__iou_threshold = iou_threshold
+        self.__log_times = log_times
+        self.__verbose = verbose
 
     @measure_time
-    def evaluate(self, processing_pair: _ProcessingPair) -> tuple[PanopticaResult, dict[str, _ProcessingPair]]:
+    def evaluate(
+        self, processing_pair: SemanticPair | UnmatchedInstancePair | MatchedInstancePair | PanopticaResult
+    ) -> tuple[PanopticaResult, dict[str, _ProcessingPair]]:
         assert type(processing_pair) == self.__expected_input, f"input not of expected type {self.__expected_input}"
         return panoptic_evaluate(
             processing_pair=processing_pair,
             instance_approximator=self.__instance_approximator,
             instance_matcher=self.__instance_matcher,
             iou_threshold=self.__iou_threshold,
+            log_times=self.__log_times,
+            verbose=self.__verbose,
         )
 
 
 def panoptic_evaluate(
     processing_pair: SemanticPair | UnmatchedInstancePair | MatchedInstancePair | PanopticaResult,
     instance_approximator: InstanceApproximator | None = None,
     instance_matcher: InstanceMatchingAlgorithm | None = None,
+    log_times: bool = False,
+    verbose: bool = False,
     iou_threshold: float = 0.5,
     **kwargs,
 ) -> tuple[PanopticaResult, dict[str, _ProcessingPair]]:
@@ -82,13 +92,16 @@ def panoptic_evaluate(
     >>> panoptic_evaluate(SemanticPair(...), instance_approximator=InstanceApproximator(), iou_threshold=0.6)
     (PanopticaResult(...), {'UnmatchedInstanceMap': _ProcessingPair(...), 'MatchedInstanceMap': _ProcessingPair(...)})
     """
+    print("Panoptic: Start Evaluation")
     debug_data: dict[str, _ProcessingPair] = {}
     # First Phase: Instance Approximation
     if isinstance(processing_pair, PanopticaResult):
+        print("-- Input was Panoptic Result, will just return")
         return processing_pair, debug_data
 
     if isinstance(processing_pair, SemanticPair):
         assert instance_approximator is not None, "Got SemanticPair but not InstanceApproximator"
+        print("-- Got SemanticPair, will approximate instances")
         processing_pair = instance_approximator.approximate_instances(processing_pair)
         debug_data["UnmatchedInstanceMap"] = processing_pair.copy()
 
@@ -97,6 +110,7 @@ def panoptic_evaluate(
         processing_pair = _handle_zero_instances_cases(processing_pair)
 
     if isinstance(processing_pair, UnmatchedInstancePair):
+        print("-- Got UnmatchedInstancePair, will match instances")
         assert instance_matcher is not None, "Got UnmatchedInstancePair but not InstanceMatchingAlgorithm"
         processing_pair = instance_matcher.match_instances(processing_pair)
 
@@ -107,6 +121,7 @@ def panoptic_evaluate(
         processing_pair = _handle_zero_instances_cases(processing_pair)
 
     if isinstance(processing_pair, MatchedInstancePair):
+        print("-- Got MatchedInstancePair, will evaluate instances")
         processing_pair = evaluate_matched_instance(processing_pair, iou_threshold=iou_threshold)
 
     if isinstance(processing_pair, PanopticaResult):
@@ -131,7 +146,7 @@ def _handle_zero_instances_cases(
     n_reference_instance = processing_pair.n_reference_instance
     n_prediction_instance = processing_pair.n_prediction_instance
     # Handle cases where either the reference or the prediction is empty
-    if n_prediction_instance == 0 or n_reference_instance == 0:
+    if n_prediction_instance == 0 and n_reference_instance == 0:
         # Both references and predictions are empty, perfect match
         return PanopticaResult(
             num_ref_instances=0,

panoptica/instance_approximator.py

@@ -2,6 +2,7 @@
 from panoptica.utils.datatypes import SemanticPair, UnmatchedInstancePair, MatchedInstancePair
 from panoptica._functionals import _connected_components, CCABackend
 from panoptica.utils.numpy_utils import _get_smallest_fitting_uint
+from panoptica.timing import measure_time
 import numpy as np
 
 
@@ -47,6 +48,7 @@ def _approximate_instances(self, semantic_pair: SemanticPair, **kwargs) -> Unmat
         """
         pass
 
+    @measure_time
     def approximate_instances(self, semantic_pair: SemanticPair, **kwargs) -> UnmatchedInstancePair | MatchedInstancePair:
         """
         Perform instance approximation on the given SemanticPair.

panoptica/instance_evaluator.py

@@ -2,9 +2,11 @@
 from panoptica.utils.datatypes import MatchedInstancePair
 from panoptica.result import PanopticaResult
 from panoptica.metrics import _compute_iou, _compute_dice_coefficient, _average_symmetric_surface_distance
+from panoptica.timing import measure_time
 import numpy as np
 
 
+@measure_time
 def evaluate_matched_instance(matched_instance_pair: MatchedInstancePair, iou_threshold: float, **kwargs) -> PanopticaResult:
     """
     Map instance labels based on the provided labelmap and create a MatchedInstancePair.

panoptica/instance_matcher.py

@@ -8,6 +8,7 @@
     MatchedInstancePair,
     UnmatchedInstancePair,
 )
+from panoptica.timing import measure_time
 from scipy.optimize import linear_sum_assignment
 
 
@@ -50,6 +51,7 @@ def _match_instances(self, unmatched_instance_pair: UnmatchedInstancePair, **kwa
         """
         pass
 
+    @measure_time
     def match_instances(self, unmatched_instance_pair: UnmatchedInstancePair, **kwargs) -> MatchedInstancePair:
         """
         Perform instance matching on the given UnmatchedInstancePair.
@@ -114,6 +116,7 @@ def _match_instances(self, unmatched_instance_pair: UnmatchedInstancePair, **kwa
         """
         ref_labels = unmatched_instance_pair.ref_labels
         pred_labels = unmatched_instance_pair.pred_labels
+        # TODO bounding boxes first, then only calc iou over bboxes collisions
         iou_matrix = _calc_iou_matrix(
             unmatched_instance_pair.prediction_arr.flatten(),
             unmatched_instance_pair.reference_arr.flatten(),

panoptica/result.py

@@ -134,7 +134,7 @@ def rq(self) -> float:
             float: Recognition Quality (RQ).
         """
         if self.tp == 0:
-            return 0.0
+            return 0.0 if self.num_pred_instances + self.num_ref_instances > 0 else np.nan
         return self.tp / (self.tp + 0.5 * self.fp + 0.5 * self.fn)
 
     @property
@@ -146,7 +146,7 @@ def sq(self) -> float:
             float: Segmentation Quality (SQ).
         """
         if self.tp == 0:
-            return 0.0
+            return 0.0 if self.num_pred_instances + self.num_ref_instances > 0 else np.nan
         return np.sum(self._iou_list) / self.tp
 
     @property
@@ -178,7 +178,7 @@ def sq_dsc(self) -> float:
             float: Average Dice coefficient.
         """
         if self.tp == 0:
-            return 0.0
+            return 0.0 if self.num_pred_instances + self.num_ref_instances > 0 else np.nan
         return np.sum(self._dice_list) / self.tp
 
     @property
@@ -210,7 +210,7 @@ def instance_assd(self) -> float:
             float: average symmetric surface distance.
         """
         if self.tp == 0:
-            return 0.0
+            return np.nan if self.num_pred_instances + self.num_ref_instances == 0 else np.inf
         return np.sum(self._assd_list) / self.tp
 
     @property

panoptica/timing.py

@@ -7,7 +7,7 @@ def wrapper(*args, **kwargs):
         result = func(*args, **kwargs)
         end_time = time.time()
         elapsed_time = end_time - start_time
-        print(f"{func.__name__} took {elapsed_time} seconds to execute.")
+        print(f"-- {func.__name__} took {elapsed_time} seconds to execute.")
         return result
 
     return wrapper

panoptica/utils/datatypes.py

@@ -18,8 +18,8 @@ class _ProcessingPair(ABC):
     prediction_arr: np.ndarray
     reference_arr: np.ndarray
     # unique labels without zero
-    ref_labels: tuple[int]
-    pred_labels: tuple[int]
+    ref_labels: tuple[int, ...]
+    pred_labels: tuple[int, ...]
     n_dim: int
 
     def __init__(self, prediction_arr: np.ndarray, reference_arr: np.ndarray, dtype: type | None) -> None:
@@ -35,8 +35,8 @@ def __init__(self, prediction_arr: np.ndarray, reference_arr: np.ndarray, dtype:
         self.reference_arr = reference_arr
         self.dtype = dtype
         self.n_dim = reference_arr.ndim
-        self.ref_labels: tuple[int] = tuple(_unique_without_zeros(reference_arr))  # type:ignore
-        self.pred_labels: tuple[int] = tuple(_unique_without_zeros(prediction_arr))  # type:ignore
+        self.ref_labels: tuple[int, ...] = tuple(_unique_without_zeros(reference_arr))  # type:ignore
+        self.pred_labels: tuple[int, ...] = tuple(_unique_without_zeros(prediction_arr))  # type:ignore
 
     # Make all variables read-only!
     def __setattr__(self, attr, value):