Merge pull request #11 from BrainLesion/hendrik_dev

Hendrik dev

examples/example_cfos_3d.py

@@ -1,6 +1,6 @@
 from auxiliary.nifti.io import read_nifti
 
-from panoptica import CCABackend, SemanticSegmentationEvaluator
+from panoptica import SemanticPair, Panoptic_Evaluator, ConnectedComponentsInstanceApproximator, CCABackend, NaiveOneToOneMatching
 
 pred_masks = read_nifti(
     input_nifti_path="/home/florian/flow/cfos_analysis/data/ablation/2021-11-25_23-50-56_2021-10-25_19-38-31_tr_dice_bce_11/patchvolume_695_2.nii.gz"
@@ -9,11 +9,14 @@
     input_nifti_path="/home/florian/flow/cfos_analysis/data/reference/patchvolume_695_2/patchvolume_695_2_binary.nii.gz",
 )
 
-eva = SemanticSegmentationEvaluator(cca_backend=CCABackend.cc3d)
-res = eva.evaluate(
-    reference_mask=ref_masks,
-    prediction_mask=pred_masks,
+sample = SemanticPair(pred_masks, ref_masks)
+
+evaluator = Panoptic_Evaluator(
+    expected_input=SemanticPair,
+    instance_approximator=ConnectedComponentsInstanceApproximator(cca_backend=CCABackend.cc3d),
+    instance_matcher=NaiveOneToOneMatching(),
     iou_threshold=0.5,
 )
 
-print(res)
+result, debug_data = evaluator.evaluate(sample)
+print(result)

examples/example_spine_instance.py

@@ -1,21 +1,19 @@
 from auxiliary.nifti.io import read_nifti
+import numpy as np
 
-from panoptica import InstanceSegmentationEvaluator
-
-ref_masks = read_nifti(
-    "examples/spine_seg/instance_example/sub-0007_mod-T2w_seg-vert_msk.nii.gz"
-)
-pred_masks = read_nifti(
-    "examples/spine_seg/instance_example/sub-0007_mod-T2w_seg-vert_msk_new.nii.gz"
-)
+from panoptica import MatchedInstancePair, Panoptic_Evaluator, NaiveOneToOneMatching
 
+ref_masks = read_nifti("repo/examples/spine_seg/instance_example/sub-0007_mod-T2w_seg-vert_msk.nii.gz")
+pred_masks = read_nifti("repo/examples/spine_seg/instance_example/sub-0007_mod-T2w_seg-vert_msk_new.nii.gz")
 
-eva = InstanceSegmentationEvaluator()
+sample = MatchedInstancePair(prediction_arr=pred_masks, reference_arr=ref_masks)
 
-res = eva.evaluate(
-    reference_mask=ref_masks,
-    prediction_mask=pred_masks,
+evaluator = Panoptic_Evaluator(
+    expected_input=MatchedInstancePair,
+    instance_approximator=None,
+    instance_matcher=NaiveOneToOneMatching(),
     iou_threshold=0.5,
 )
 
-print(res)
+result, debug_data = evaluator.evaluate(sample)
+print(result)

examples/example_spine_semantic.py

@@ -1,20 +1,19 @@
 from auxiliary.nifti.io import read_nifti
 
-from panoptica import CCABackend, SemanticSegmentationEvaluator
+from panoptica import SemanticPair, Panoptic_Evaluator, ConnectedComponentsInstanceApproximator, CCABackend, NaiveOneToOneMatching
+
+ref_masks = read_nifti("examples/spine_seg/semantic_example/sub-0007_mod-T2w_seg-spine_msk.nii.gz")
+pred_masks = read_nifti("examples/spine_seg/semantic_example/sub-0007_mod-T2w_seg-spine_msk_new.nii.gz")
 
-ref_masks = read_nifti(
-    "examples/spine_seg/semantic_example/sub-0007_mod-T2w_seg-spine_msk.nii.gz"
-)
-pred_masks = read_nifti(
-    "examples/spine_seg/semantic_example/sub-0007_mod-T2w_seg-spine_msk_new.nii.gz"
-)
 
+sample = SemanticPair(pred_masks, ref_masks)
 
-eva = SemanticSegmentationEvaluator(cca_backend=CCABackend.cc3d)
-res = eva.evaluate(
-    reference_mask=ref_masks,
-    prediction_mask=pred_masks,
+evaluator = Panoptic_Evaluator(
+    expected_input=SemanticPair,
+    instance_approximator=ConnectedComponentsInstanceApproximator(cca_backend=CCABackend.cc3d),
+    instance_matcher=NaiveOneToOneMatching(),
     iou_threshold=0.5,
 )
 
-print(res)
+result, debug_data = evaluator.evaluate(sample)
+print(result)

panoptica/__init__.py

@@ -1,7 +1,5 @@
-from panoptica.instance_evaluation.instance_evaluator import (
-    InstanceSegmentationEvaluator,
-)
-from panoptica.semantic_evaluation.connected_component_backends import CCABackend
-from panoptica.semantic_evaluation.semantic_evaluator import (
-    SemanticSegmentationEvaluator,
-)
+from panoptica.instance_approximator import ConnectedComponentsInstanceApproximator, CCABackend
+from panoptica.instance_matcher import NaiveOneToOneMatching
+from panoptica.evaluator import Panoptic_Evaluator
+from panoptica.result import PanopticaResult
+from panoptica.utils.datatypes import SemanticPair, UnmatchedInstancePair, MatchedInstancePair

panoptica/_functionals.py

@@ -0,0 +1,91 @@
+import numpy as np
+from multiprocessing import Pool
+from panoptica.utils.metrics import _compute_instance_iou
+from panoptica.utils.constants import CCABackend
+
+
+def _calc_iou_matrix(prediction_arr: np.ndarray, reference_arr: np.ndarray, ref_labels: list[int], pred_labels: list[int]):
+    """
+    Calculate the Intersection over Union (IoU) matrix between reference and prediction arrays.
+
+    Args:
+        prediction_arr (np.ndarray): Numpy array containing the prediction labels.
+        reference_arr (np.ndarray): Numpy array containing the reference labels.
+        ref_labels (list[int]): List of unique reference labels.
+        pred_labels (list[int]): List of unique prediction labels.
+
+    Returns:
+        np.ndarray: IoU matrix where each element represents the IoU between a reference and prediction instance.
+
+    Example:
+    >>> _calc_iou_matrix(np.array([1, 2, 3]), np.array([4, 5, 6]), [1, 2, 3], [4, 5, 6])
+    array([[0. , 0. , 0. ],
+           [0. , 0. , 0. ],
+           [0. , 0. , 0. ]])
+    """
+    num_ref_instances = len(ref_labels)
+    num_pred_instances = len(pred_labels)
+
+    # Create a pool of worker processes to parallelize the computation
+    with Pool() as pool:
+        # Generate all possible pairs of instance indices for IoU computation
+        instance_pairs = [(reference_arr, prediction_arr, ref_idx, pred_idx) for ref_idx in ref_labels for pred_idx in pred_labels]
+
+        # Calculate IoU for all instance pairs in parallel using starmap
+        iou_values = pool.starmap(_compute_instance_iou, instance_pairs)
+
+    # Reshape the resulting IoU values into a matrix
+    iou_matrix = np.array(iou_values).reshape((num_ref_instances, num_pred_instances))
+    return iou_matrix
+
+
+def _map_labels(arr: np.ndarray, label_map: dict[np.integer, np.integer]) -> np.ndarray:
+    """
+    Maps labels in the given array according to the label_map dictionary.
+
+    Args:
+        label_map (dict): A dictionary that maps the original label values (str or int) to the new label values (int).
+
+    Returns:
+        np.ndarray: Returns a copy of the remapped array
+    """
+    data = arr.copy()
+    for v in np.unique(data):
+        if v in label_map:  # int needed to match non-integer data-types
+            data[arr == v] = label_map[v]
+    return data
+
+
+def _connected_components(
+    array: np.ndarray,
+    cca_backend: CCABackend,
+) -> tuple[np.ndarray, int]:
+    """
+    Label connected components in a binary array using a specified connected components algorithm.
+
+    Args:
+        array (np.ndarray): Binary array containing connected components.
+        cca_backend (CCABackend): Enum indicating the connected components algorithm backend (CCABackend.cc3d or CCABackend.scipy).
+
+    Returns:
+        tuple[np.ndarray, int]: A tuple containing the labeled array and the number of connected components.
+
+    Raises:
+        NotImplementedError: If the specified connected components algorithm backend is not implemented.
+
+    Example:
+    >>> _connected_components(np.array([[1, 0, 1], [0, 1, 1], [1, 0, 0]]), CCABackend.scipy)
+    (array([[1, 0, 2], [0, 3, 3], [4, 0, 0]]), 4)
+    """
+    if cca_backend == CCABackend.cc3d:
+        import cc3d
+
+        cc_arr, n_instances = cc3d.connected_components(array, return_N=True)
+    elif cca_backend == CCABackend.scipy:
+        from scipy.ndimage import label
+
+        cc_arr, n_instances = label(array)
+    else:
+        raise NotImplementedError(cca_backend)
+
+    return cc_arr, n_instances