WIP-ADD: use Rhino rTree to find the index of the closest neighbor

Author: eleniv3d

…nents/DF_cloud_to_cloud_distance/code.py …mponents/DF_cloud_cloud_distance/code.pysrc/gh/components/DF_cloud_to_cloud_distance/code.py renamed to src/gh/components/DF_cloud_cloud_distance/code.py src/gh/components/DF_cloud_to_cloud_distance/code.py renamed to src/gh/components/DF_cloud_cloud_distance/code.py

@@ -17,7 +17,7 @@
 import diffCheck.df_util
 
 
-class CloudToCloudDistance(component):
+class CloudCloudDistance(component):
     def RunScript(self,
         i_cloud_source:  typing.List[rg.PointCloud],
         i_cloud_target: typing.List[rg.PointCloud],
@@ -26,25 +26,26 @@ def RunScript(self,
             The cloud-to-cloud component computes the distance between each point in the source point cloud and its nearest neighbour in thr target point cloud.
 
             :param i_cloud_source: a list of source point cloud
-            :param i_cloud_target: a list of target point cloud to align to
+            :param i_cloud_target: a list of target point cloud to calculate distances to
 
             :return o_distances : list of calculated distances for each point
             :return o_mse: the average squared difference between corresponding points of source and target
-            :return o_max_deviation: the max deviation between source and target (Hausdorff Distance)
+            :return o_max_deviation: the max deviation between source and target
             :return o_min_deviation: the min deviation between source and target
             :return o_std_deviation: the standard deviation between source and target
+            :returns o_resluts: the results of the comparison all in one object
         """
 
         if i_cloud_source is None or i_cloud_target is None:
             ghenv.Component.AddRuntimeMessage(RML.Warning, "Please provide both objects of type point clouds to compare")
             return None
-        
+
         # swap
         if i_swap is True:
             temp = i_cloud_source
             i_cloud_source = i_cloud_target
             i_cloud_target = temp
-            
+
         # conversion
         df_cloud_source_list = [df_cvt_bindings.cvt_rhcloud_2_dfcloud(i_cl_s) for i_cl_s in i_cloud_source]
         df_cloud_target_list = [df_cvt_bindings.cvt_rhcloud_2_dfcloud(i_cl_t) for i_cl_t in i_cloud_target]
@@ -56,7 +57,7 @@ def RunScript(self,
 
 
 if __name__ == "__main__":
-    com = CloudToCloudDistance()
+    com = CloudCloudDistance()
     o_distances, o_mse, o_max_deviation, o_min_deviation, o_std_deviation, o_results = com.RunScript(
         i_cloud_source,
         i_cloud_target,

…ents/DF_cloud_to_cloud_distance/icon.png …ponents/DF_cloud_cloud_distance/icon.pngsrc/gh/components/DF_cloud_to_cloud_distance/icon.png renamed to src/gh/components/DF_cloud_cloud_distance/icon.png src/gh/components/DF_cloud_to_cloud_distance/icon.png renamed to src/gh/components/DF_cloud_cloud_distance/icon.png

@@ -16,27 +16,30 @@
 from diffCheck.df_geometries import DFBeam
 
 
-class CloudToMeshDistance(component):
+class CloudMeshDistance(component):
     def RunScript(self,
         i_cloud_source: typing.List[rg.PointCloud],
         i_beam: typing.List[DFBeam],
         i_signed_flag: bool,
         i_swap: bool):
+
         """
             The cloud-to-cloud component computes the distance between each point in the source point cloud and its nearest neighbour in thr target point cloud.
 
-            :param i_cloud_source: source point cloud
-            :param i_mesh_target: target point cloud to align to
+            :param i_cloud_source: a list of point clouds
+            :param i_beam: a list of DF beams
+            :param i_signed_flag: calculate the sign of the distances
+            :param i_swap: swap source and target
 
             :return o_distances : list of calculated distances for each point
             :return o_mse: the average squared difference between corresponding points of source and target
-            :return o_max_deviation: the max deviation between source and target (Hausdorff Distance)
+            :return o_max_deviation: the max deviation between source and target
             :return o_min_deviation: the min deviation between source and target
+            :returns o_resluts: the results of the comparison all in one object
         """
         if i_cloud_source is None or i_beam is None:
             ghenv.Component.AddRuntimeMessage(RML.Warning, "Please provide an object of type point cloud and an object of type mesh to compare")
             return None
-        
 
         # conversion
         df_cloud_source_list = [df_cvt_bindings.cvt_rhcloud_2_dfcloud(i_cl_s) for i_cl_s in i_cloud_source]
@@ -49,7 +52,7 @@ def RunScript(self,
 
 
 if __name__ == "__main__":
-    com = CloudToMeshDistance()
+    com = CloudMeshDistance()
     o_distances, o_mse, o_max_deviation, o_min_deviation, o_std_deviation, o_results = com.RunScript(
         i_cloud_source,
         i_beam,

…DF_cloud_to_cloud_distance/metadata.json …ts/DF_cloud_cloud_distance/metadata.jsonsrc/gh/components/DF_cloud_to_cloud_distance/metadata.json renamed to src/gh/components/DF_cloud_cloud_distance/metadata.json src/gh/components/DF_cloud_to_cloud_distance/metadata.json renamed to src/gh/components/DF_cloud_cloud_distance/metadata.json

@@ -20,7 +20,8 @@ def RunScript(self,
                   i_results,
                   i_viz_settings):
         """
-        Adds color to the mesh or point cloud and generates a corresponding legend and histogram
+        Adds color to the mesh or point cloud
+        and generates a corresponding legend and histogram
         """
 
         values, min_value, max_value = df_vizualization.filter_values_based_on_valuetype(i_results, i_viz_settings)

…onents/DF_cloud_to_mesh_distance/code.py …omponents/DF_cloud_mesh_distance/code.pysrc/gh/components/DF_cloud_to_mesh_distance/code.py renamed to src/gh/components/DF_cloud_mesh_distance/code.py src/gh/components/DF_cloud_to_mesh_distance/code.py renamed to src/gh/components/DF_cloud_mesh_distance/code.py

@@ -10,12 +10,8 @@
 import Grasshopper as gh
 from Grasshopper.Kernel import GH_RuntimeMessageLevel as RML
 
-import diffCheck
 from diffCheck import df_vizualization
 
-import diffCheck.df_util
-
-
 class VisualizationSettings(component):
     def RunScript(self,
         i_value_type,
@@ -39,13 +35,13 @@ def RunScript(self,
         if i_histogram_scale_factor is None: i_histogram_scale_factor = 0.01
 
         # pack settings
-        o_viz_settings = df_vizualization.DFVizSettings(i_value_type, 
-                                                        i_upper_threshold, 
-                                                        i_lower_threshold, 
-                                                        i_palette, 
-                                                        i_legend_height, 
-                                                        i_legend_width, 
-                                                        i_legend_plane, 
+        o_viz_settings = df_vizualization.DFVizSettings(i_value_type,
+                                                        i_upper_threshold,
+                                                        i_lower_threshold,
+                                                        i_palette,
+                                                        i_legend_height,
+                                                        i_legend_width,
+                                                        i_legend_plane,
                                                         i_histogram_scale_factor)
 
         return o_viz_settings

…nents/DF_cloud_to_mesh_distance/icon.png …mponents/DF_cloud_mesh_distance/icon.pngsrc/gh/components/DF_cloud_to_mesh_distance/icon.png renamed to src/gh/components/DF_cloud_mesh_distance/icon.png src/gh/components/DF_cloud_to_mesh_distance/icon.png renamed to src/gh/components/DF_cloud_mesh_distance/icon.png

@@ -15,25 +15,15 @@ def cloud_2_cloud_comparison(source_list, target_list):
     """
     results = DFVizResults()
     for source, target in zip(source_list, target_list):
-        distances = cloud_2_cloud_distance(source, target)
+        distances = np.asarray(source.compute_distance(target))
         results.add(source, target, distances)
 
     return results
 
 
-def cloud_2_cloud_distance(source, target):
-    """
-        Compute the Euclidean distance for every point of a source pcd to its
-        closest point on a target pointcloud
-    """
-
-    return np.asarray(source.compute_distance(target))
-
-
 def cloud_2_rhino_mesh_comparison(cloud_source_list, rhino_mesh_target_list, signed_flag, swap):
     """
-        Compute the Euclidean distance for every point of a source pcd to its
-        closest point on a target pointcloud
+        Computes distances between a pcd and a mesh
     """
     results = DFVizResults()
 
@@ -66,30 +56,48 @@ def cloud_2_rhino_mesh_comparison(cloud_source_list, rhino_mesh_target_list, sig
 
 #     return distances
 
+
 def rhino_mesh_2_cloud_distance(source, target, signed=False):
     """
         Calculate the distance between every vertex of a Rhino Mesh to its closest point on a PCD
     """
-    #make a Df point cloud containing all the vertices of the source rhino mesh
+    # make a Df point cloud containing all the vertices of the source rhino mesh
     df_pcd_from_mesh_vertices = diffcheck_bindings.dfb_geometry.DFPointCloud()
     df_pcd_from_mesh_vertices.points = [[pt.X, pt.Y, pt.Z] for pt in source.Vertices]
-    #calculate the distances
+    # calculate the distances
     distances = np.asarray(df_pcd_from_mesh_vertices.compute_distance(target))
 
     if signed:
+        # build an RTree containing all the points of the target
+        tree = rg.RTree()
+        for i, ver in enumerate(target.points): 
+            tree.Insert(rg.Point3d(ver[0], ver[1], ver[2]), i)
+
         for idx, p in enumerate(source.Vertices):
 
-            rhp = rg.Point3d(p[0], p[1], p[2])
-            closest_meshPoint = source.ClosestMeshPoint(rhp, 1000)
-            closest_point = closest_meshPoint.Point
-            distance = rhp.DistanceTo(closest_point)
+            # find the index on the target that the vertex is closest to
+            search_point = p
+            sphere = rg.Sphere(search_point, distances[idx]*1.0001)
+            found_indices = []
+
+            # what if we find 2 indices
+            if len(found_indices) > 1:
+                pass
+
+            def search_callback(sender, e):
+                found_indices.append(e.Id)
+
+            tree.Search(sphere, search_callback)
+
+            df_closest_point = target.points[found_indices[0]]
+            closest_point = rg.Point3d(df_closest_point[0], df_closest_point[1], df_closest_point[2])
             # Calculate the direction from target to source
-            direction = rhp - closest_point
+            direction = p - closest_point
             # Calculate the signed distance
-            normal = source.NormalAt(closest_meshPoint)
+            normal = source.Normals[idx]
             dot_product = direction * normal
             if dot_product < 0:
-                distances[idx] = - distance
+                distances[idx] = - distances[idx]
 
     return np.asarray(distances)
 

…/DF_cloud_to_mesh_distance/metadata.json …nts/DF_cloud_mesh_distance/metadata.jsonsrc/gh/components/DF_cloud_to_mesh_distance/metadata.json renamed to src/gh/components/DF_cloud_mesh_distance/metadata.json src/gh/components/DF_cloud_to_mesh_distance/metadata.json renamed to src/gh/components/DF_cloud_mesh_distance/metadata.json

@@ -7,6 +7,7 @@
 from System.Drawing import Color
 from diffCheck import df_vizualization
 
+
 class DFVizSettings:
     """
     This class compiles the settings for the vizualization into one object
@@ -66,6 +67,7 @@ def __init__(self, name):
                 Color.FromArgb(248, 230, 33)
             ]
 
+
 def interpolate_color(color1, color2, t):
     """
     Interpolate between two colors.
@@ -74,6 +76,7 @@ def interpolate_color(color1, color2, t):
     r = int(color1.R + (color2.R - color1.R) * t)
     g = int(color1.G + (color2.G - color1.G) * t)
     b = int(color1.B + (color2.B - color1.B) * t)
+
     return Color.FromArgb(r, g, b)
 
 
@@ -96,7 +99,7 @@ def value_to_color(value, min_value, max_value, palette):
         t = (value - min_value) / (max_value - min_value)
 
     # Determine the segment in the colormap
-    n = len(colormap)- 1
+    n = len(colormap) - 1
     idx = int(t * n)
     if idx >= n:
         idx = n - 1
@@ -132,7 +135,7 @@ def color_mesh(mesh, values, min_value, max_value, palette):
     """
 
     mesh.VertexColors.Clear()
-    
+
     for i, vertex in enumerate(mesh.Vertices):
         # check if values is a list
         if isinstance(values, list):
@@ -163,9 +166,9 @@ def create_legend(min_value, max_value, palette, steps=10, plane=rg.Plane.WorldX
             mesh = rg.Mesh()
             for pt in rect_pts:
                 mesh.Vertices.Add(pt)
-            # color mesh
+
             mesh.Faces.AddFace(0, 1, 2, 3)
-            
+            # color mesh
             mesh.VertexColors.Add(previous_color.R, previous_color.G, previous_color.B)
             mesh.VertexColors.Add(previous_color.R, previous_color.G, previous_color.B)
             mesh.VertexColors.Add(color.R, color.G, color.B)
@@ -200,7 +203,9 @@ def create_legend(min_value, max_value, palette, steps=10, plane=rg.Plane.WorldX
     return legend_geometry
 
 
-def create_histogram(values, min_value, max_value, steps=100, plane=rg.Plane.WorldXY, total_height=10, scaling_factor = 0.01, spacing=0):
+def create_histogram(values, min_value, max_value, steps=100,
+                     plane=rg.Plane.WorldXY, total_height=10,
+                     scaling_factor=0.01, spacing=0):
     """
     Create a histogram in Rhino with a polyline representing value frequencies.
     """
@@ -261,7 +266,7 @@ def filter_values_based_on_valuetype(results, settings):
         values = results.distances_mse
         min_value = min(values)
         max_value = max(values)
-        
+
     elif settings.valueType == "MAX":
 
         values = results.distances_max_deviation