research KNN from 2D points_skeleton NOT 3D

lidro/create_virtual_point/stats/knn_distance.py

@@ -4,29 +4,34 @@
 from sklearn.neighbors import NearestNeighbors
 
 
-def find_k_nearest_neighbors(point_2d: np.array, points_3d: np.array, k: int) -> np.array:
+def find_k_nearest_neighbors(points_skeleton: np.array, points_ground_3d: np.array, k: int) -> np.array:
     """Finds the K nearest neighbors of a given point from a list of 3D points
 
     Args:
-        point_2d (np.array): An array of 3D point [x, y, 0]
-        points_3D (np.array): An array of 3D points [x, y, z]
-        k (int): The number of nearest neighbors to find
+        points_skeleton (np.array): a single point representing the skeleton Hydro
+                                    an array of 2D point [x, y]
+        points_ground_3d (np.array): ground's points from LIDAR.
+                                    an array of 3D points [x, y, z]
+        k (int): the number of nearest neighbors to find
 
     Returns:
-        numpy.ndarray: An array of the K nearest 3D points [x, y, z]
+        numpy.ndarray: an array of the K nearest 3D points [x, y, z]
     """
-    # Convert point_2d to nump.arrat
-    point_2d_array = np.array(point_2d).reshape(1, -1)
+    # Extract 2D coordinates (x, y) from the 3D points
+    points_ground_2d = points_ground_3d[:, :2]
+
+    # Convert point_2d to nump.array
+    points_skeleton_array = np.array(points_skeleton).reshape(1, -1)
 
     # Initialize and fit the NearestNeighbors model
-    nbrs = NearestNeighbors(n_neighbors=k, algorithm="auto").fit(points_3d)
+    nbrs = NearestNeighbors(n_neighbors=k, algorithm="auto").fit(points_ground_2d)
 
     # Find the distances and indices of the nearest neighbors
-    indices = nbrs.kneighbors(point_2d_array, return_distance=False)
+    indices = nbrs.kneighbors(points_skeleton_array, return_distance=False)
 
     # Retrieve the points corresponding to the indices
     # Use indices  to retrieve the closest 3D points
-    k_nearest_points = points_3d[indices[0]]
+    k_nearest_points = points_ground_3d[indices[0]]
     # = points_3d[indices.flatten()]
 
     return k_nearest_points

lidro/main_create_virtual_point.py

@@ -98,7 +98,7 @@ def extract_points_around_skeleton_points_one_tile(filename):
         points_skeleton_clip = clip_points_with_box(points_list_skeleton, bbox)
         # Create list with Skeleton's points with Z for step 4
         points_skeleton_with_z_clip = [
-            ([geom.x, geom.y, 0]) for geom in points_skeleton_clip.geometry if isinstance(geom, Point)
+            ([geom.x, geom.y]) for geom in points_skeleton_clip.geometry if isinstance(geom, Point)
         ]
         # Step 4 : Extract a Z elevation value along the hydrographic skeleton
         logging.info(f"\nExtract a Z elevation value along the hydrographic skeleton for tile : {tilename}")

test/stats/test_knn_distance.py

@@ -12,7 +12,7 @@ def test_find_k_nearest_neighbors_default():
             [830867.61, 6290202.62, 2.89],
         ]
     )
-    point = np.array([[830574.89, 6290648.53, 0]])
+    point = np.array([[830574.89, 6290648.53]])
     k = 3
 
     result = find_k_nearest_neighbors(point, points_array, k)

test/vectors/test_las_around_point.py

@@ -22,7 +22,7 @@ def setup_module(module):
 
 def test_las_around_point_default():
     # Parameters
-    points_along_skeleton = [[830864.5181373736, 6290217.943739296, 0], [830866.5957826116, 6290208.162525126, 0]]
+    points_along_skeleton = [[830864.5181373736, 6290217.943739296], [830866.5957826116, 6290208.162525126]]
 
     points_clip = np.array(
         [