Working Trimesh learning environment with new quality actions analysis and with old geometrical analysis. There are still some kinds of meshes which create problems with the new analysis

Author: ArzhelaR

environment/actions/triangular_actions.py

@@ -3,7 +3,6 @@
 import copy
 import numpy as np
 from mesh_model.mesh_analysis.global_mesh_analysis import NodeAnalysis
-from mesh_model.mesh_analysis.trimesh_analysis import TriMeshQualityAnalysis
 from mesh_model.mesh_struct.mesh_elements import Node, Dart
 from view.mesh_plotter.mesh_plots import plot_mesh
 
@@ -103,7 +102,7 @@ def split_edge(mesh_analysis, n1: Node, n2: Node) -> True:
 
     d2, d1, d11, d21, d211, n1, n2, n3, n4 = mesh_analysis.mesh.active_triangles(d)
 
-    # create a new node in the middle of [n1, n2]
+    # Create a new node in the middle of [n1, n2]
     N5 = mesh_analysis.mesh.add_node((n1.x() + n2.x()) / 2, (n1.y() + n2.y()) / 2)
 
     # modify existing triangles
@@ -254,20 +253,31 @@ def collapse_edge(mesh_analysis, n1: Node, n2: Node) -> True:
                 plot_mesh(mesh_analysis.mesh)
                 raise ValueError("Potential infinite loop in action collapse")
 
-
-        if mesh_analysis.is_star_vertex(n1, mid):
-            n1.set_xy(mid[0], mid[1])
-        elif mesh_analysis.is_star_vertex(n1, near_n1):
-            n1.set_xy(near_n1[0], near_n1[1])
-        elif mesh_analysis.is_star_vertex(n1, near_n2):
-            n1.set_xy(near_n2[0], near_n2[1])
-        elif mesh_analysis.is_star_vertex(n1, np.array([n1.x(), n1.y()])):
-            pass
-        elif mesh_analysis.is_star_vertex(n1, np.array([n2x, n2y])):
-            n1.set_xy(n2x, n2y)
+        found, new_x, new_y = mesh_analysis.find_star_vertex(n1)
+        if found:
+            n1.set_xy(new_x, new_y)
         else:
+            plot_mesh(mesh_before)
+            plot_mesh(mesh_analysis.mesh)
+            mesh_analysis.find_star_vertex(n1, plot=True)
             raise ValueError("No star vertex found")
 
+        # if mesh_analysis.is_star_vertex(n1, mid):
+        #     n1.set_xy(mid[0], mid[1])
+        # elif mesh_analysis.is_star_vertex(n1, near_n1):
+        #     n1.set_xy(near_n1[0], near_n1[1])
+        # elif mesh_analysis.is_star_vertex(n1, near_n2):
+        #     n1.set_xy(near_n2[0], near_n2[1])
+        # elif mesh_analysis.is_star_vertex(n1, np.array([n1.x(), n1.y()])):
+        #     pass
+        # elif mesh_analysis.is_star_vertex(n1, np.array([n2x, n2y])):
+        #     n1.set_xy(n2x, n2y)
+        # else:
+        #     plot_mesh(mesh_before)
+        #     plot_mesh(mesh_analysis.mesh)
+        #     mesh_analysis.is_star_vertex(n1, np.array([n1.x(), n1.y()]), True)
+        #     raise ValueError("No star vertex found")
+
         # Update dart quality
         n1_analysis = NodeAnalysis(n1)
         adj_darts = n1_analysis.adjacent_darts()
@@ -336,10 +346,10 @@ def check_mesh(mesh_analysis, m=None) -> bool:
             plot_mesh(m)
             plot_mesh(mesh_analysis.mesh)
             return False
-        #if dart_info[5] not in [-1,0,1,2]:
-            #plot_mesh(m)
-            #plot_mesh(mesh_analysis.mesh)
-            #return False
+        if dart_info[5] not in [-1,0,1,2]:
+            plot_mesh(m)
+            plot_mesh(mesh_analysis.mesh)
+            return False
     return True
 
 

environment/gymnasium_envs/trimesh_full_env/envs/trimesh.py

@@ -2,6 +2,7 @@
 import pygame
 import imageio
 import sys
+import os
 
 import numpy as np
 import gymnasium as gym
@@ -15,7 +16,7 @@
 
 from mesh_model.random_trimesh import random_mesh
 from mesh_model.mesh_struct.mesh_elements import Dart
-from mesh_model.mesh_analysis.trimesh_analysis import TriMeshQualityAnalysis
+from mesh_model.mesh_analysis.trimesh_analysis import TriMeshQualityAnalysis, TriMeshOldAnalysis
 from environment.gymnasium_envs.trimesh_full_env.envs.mesh_conv import get_x
 from environment.actions.triangular_actions import flip_edge, split_edge, collapse_edge, check_mesh
 from view.mesh_plotter.mesh_plots import plot_mesh
@@ -40,19 +41,28 @@ def __init__(
             mesh=None,
             mesh_size=9,
             max_episode_steps=20,
-            n_darts_selected=20,
+            n_darts_selected=7,
             deep=6,
             with_quality_obs=False,
             action_restriction=False,
-            render_mode=None
+            render_mode=None,
+            analysis_type = "quality"
     ) -> None:
 
         assert render_mode is None or render_mode in self.metadata["render_modes"]
         self.render_mode = render_mode
 
-        self.mesh_size = mesh_size
-        self.mesh = mesh if mesh is not None else random_mesh(mesh_size)
-        self.m_analysis = TriMeshQualityAnalysis(self.mesh)
+        # If a mesh has been entered, it is used, otherwise a random mesh is generated.
+        if mesh is not None:
+            self.config = {"mesh": mesh}
+            self.mesh = copy.deepcopy(mesh)
+            self.mesh_size = 0
+        else:
+            self.config = {"mesh": None}
+            self.mesh_size = mesh_size
+            self.mesh = random_mesh(mesh_size)
+        self.analysis_type = analysis_type
+        self.m_analysis = TriMeshQualityAnalysis(self.mesh) if self.analysis_type == "quality" else TriMeshOldAnalysis(self.mesh)
         self._nodes_scores, self._mesh_score, self._ideal_score, self._nodes_adjacency = self.m_analysis.global_score()
         self._ideal_rewards = (self._mesh_score - self._ideal_score)*10
         self.next_mesh_score = 0
@@ -69,6 +79,12 @@ def __init__(
         self.darts_selected = []
         self.max_steps = max_episode_steps
 
+        self.actions_info = {
+            "n_flip": 0,
+            "n_split": 0,
+            "n_collapse": 0,
+        }
+
         self.observation_space = spaces.Box(
             low=-15,  # nodes min degree : 15
             high=15,  # nodes max degree : 15
@@ -100,15 +116,19 @@ def __init__(
 
             self.recording = False
             self.frames = []
+            self._render_frame()
 
     def reset(self, seed=None, options=None):
         # We need the following line to seed self.np_random
         super().reset(seed=seed)
         if options is not None:
             self.mesh = options['mesh']
+        elif self.config["mesh"] is not None:
+            self.mesh = copy.deepcopy(self.config["mesh"])
         else:
             self.mesh = random_mesh(self.mesh_size)
-        self.m_analysis = TriMeshQualityAnalysis(self.mesh)
+
+        self.m_analysis = TriMeshQualityAnalysis(self.mesh) if self.analysis_type=="quality" else TriMeshOldAnalysis(self.mesh)
         self._nodes_scores, self._mesh_score, self._ideal_score, self._nodes_adjacency = self.m_analysis.global_score()
         self._ideal_rewards = (self._mesh_score - self._ideal_score) * 10
         self.nb_invalid_actions = 0
@@ -117,9 +137,15 @@ def reset(self, seed=None, options=None):
         self.observation = self._get_obs()
         info = self._get_info(terminated=False,valid_act=(None,None,None), action=(None,None), mesh_reward=None)
 
+        self.actions_info = {
+            "n_flip": 0,
+            "n_split": 0,
+            "n_collapse": 0,
+        }
+
         if self.render_mode == "human":
-            self._render_frame()
             self.recording = True
+            self._render_frame()
         else:
             self.recording = False
 
@@ -173,10 +199,13 @@ def step(self, action: np.ndarray):
         # before_mesh = deepcopy(self.mesh)
         if action[0] == Actions.FLIP.value:
             valid_action, valid_topo, valid_geo = flip_edge(self.m_analysis, n1, n2)
+            self.actions_info["n_flip"] += 1
         elif action[0] == Actions.SPLIT.value:
             valid_action, valid_topo, valid_geo = split_edge(self.m_analysis, n1, n2)
+            self.actions_info["n_split"] += 1
         elif action[0] == Actions.COLLAPSE.value:
             valid_action, valid_topo, valid_geo = collapse_edge(self.m_analysis, n1, n2)
+            self.actions_info["n_collapse"] += 1
         else:
             raise ValueError("Action not defined")
 
@@ -214,7 +243,14 @@ def step(self, action: np.ndarray):
         #Saving episode rendering as gif
         if terminated or self.ep_len>= self.max_steps:
             if self.recording and self.frames:
-                imageio.mimsave(f"training/episode_recording/episode_{self.episode_count}.gif", self.frames, fps=1)
+                base_path = f"training/episode_recording/del/episode_star_{self.episode_count}"
+                filename = base_path + ".gif"
+                index = 1
+                while os.path.exists(filename):
+                    filename = f"{base_path}_{index}.gif"
+                    index += 1
+
+                imageio.mimsave(filename, self.frames, fps=1)
                 print("Image recorded")
                 self.episode_count +=1
 

mesh_files/tri-delaunay.msh

@@ -0,0 +1,125 @@
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