Fix docs and interface for iou, dice

src/evops/metrics/DefaultBenchmark.py

@@ -50,4 +50,8 @@ def __fScore(
     precision = __precision(pred_labels, gt_labels, tp_condition)
     recall = __recall(pred_labels, gt_labels, tp_condition)
 
+    # Prevent division by zero
+    if precision + recall == 0:
+        return 0.0
+
     return 2 * precision * recall / (precision + recall)

src/evops/metrics/MeanBenchmark.py

@@ -23,7 +23,7 @@ def __mean(
     pred_labels: NDArray[Any, np.int32],
     gt_labels: NDArray[Any, np.int32],
     metric: Callable[
-        [NDArray[(Any, 3), np.float64], NDArray[Any, np.int32], NDArray[Any, np.int32]],
+        [NDArray[Any, np.int32], NDArray[Any, np.int32]],
         np.float64,
     ],
 ) -> np.float64:

src/evops/metrics/metrics.py

@@ -29,33 +29,31 @@
 
 
 def iou(
-    pred_labels: NDArray[Any, np.int32],
-    gt_labels: NDArray[Any, np.int32],
+    pred_indices: NDArray[Any, np.int32],
+    gt_indices: NDArray[Any, np.int32],
 ) -> np.float64:
     """
-    :param pc_points: source point cloud
     :param pred_indices: indices of points that belong to one plane obtained as a result of segmentation
     :param gt_indices: indices of points belonging to the reference plane
     :return: iou metric value for plane
     """
-    __iou_dice_mean_bechmark_asserts(pred_labels, gt_labels)
+    __iou_dice_mean_bechmark_asserts(pred_indices, gt_indices)
 
-    return __iou(pred_labels, gt_labels)
+    return __iou(pred_indices, gt_indices)
 
 
 def dice(
-    pred_labels: NDArray[Any, np.int32],
-    gt_labels: NDArray[Any, np.int32],
+    pred_indices: NDArray[Any, np.int32],
+    gt_indices: NDArray[Any, np.int32],
 ) -> np.float64:
     """
-    :param pc_points: source point cloud
-    :param pred_labels: labels of points that belong to one planes obtained as a result of segmentation
-    :param gt_labels: labels of points belonging to the reference planes
+    :param pred_indices: labels of points that belong to one plane obtained as a result of segmentation
+    :param gt_indices: labels of points belonging to the reference plane
     :return: iou metric value for plane
     """
-    __iou_dice_mean_bechmark_asserts(pred_labels, gt_labels)
+    __iou_dice_mean_bechmark_asserts(pred_indices, gt_indices)
 
-    return __dice(pred_labels, gt_labels)
+    return __dice(pred_indices, gt_indices)
 
 
 def precision(
@@ -64,7 +62,6 @@ def precision(
     tp_condition: str,
 ) -> np.float64:
     """
-    :param pc_points: source point cloud
     :param pred_labels: labels of points that belong to one planes obtained as a result of segmentation
     :param gt_labels: labels of points belonging to the reference planes
     :param tp_condition: helper function to calculate statistics: {'iou'}
@@ -81,7 +78,6 @@ def recall(
     tp_condition: str,
 ) -> np.float64:
     """
-    :param pc_points: source point cloud
     :param pred_labels: indices of points that belong to one plane obtained as a result of segmentation
     :param gt_labels: indices of points belonging to the reference plane
     :param tp_condition: helper function to calculate statistics: {'iou'}
@@ -98,7 +94,6 @@ def fScore(
     tp_condition: str,
 ) -> np.float64:
     """
-    :param pc_points: source point cloud
     :param pred_labels: indices of points that belong to one plane obtained as a result of segmentation
     :param gt_labels: indices of points belonging to the reference plane
     :param tp_condition: helper function to calculate statistics: {'iou'}
@@ -113,12 +108,11 @@ def mean(
     pred_labels: NDArray[Any, np.int32],
     gt_labels: NDArray[Any, np.int32],
     metric: Callable[
-        [NDArray[(Any, 3), np.float64], NDArray[Any, np.int32], NDArray[Any, np.int32]],
+        [NDArray[Any, np.int32], NDArray[Any, np.int32]],
         np.float64,
     ],
 ) -> np.float64:
     """
-    :param pc_points: source point cloud
     :param pred_labels: labels of points obtained as a result of segmentation
     :param gt_labels: reference labels of point cloud
     :param metric: metric function for which you want to get the mean value
@@ -135,7 +129,6 @@ def multi_value(
     overlap_threshold: np.float64 = 0.8,
 ) -> (np.float64, np.float64, np.float64, np.float64, np.float64, np.float64):
     """
-    :param pc_points: source point cloud
     :param pred_labels: labels of points obtained as a result of segmentation
     :param gt_labels: reference labels of point cloud
     :param overlap_threshold: minimum value at which the planes are considered intersected