forzar bifurcaciones

128.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:40d4f621c535f8f1776594be90796143d3d4ed5dc9b15b50518bbae7ce9d8183
+size 6714625

32.pth

@@ -1,3 +1,3 @@
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-oid sha256:4aee59c935e5203ff354f7a34a9e1ea99a4ee7f43a2bcd028d84335bba312f2f
-size 6713611
+oid sha256:6c7c0a29c7c3f405dcd7d49539f6eda289b7950698b1c1fd37caa2b8b17cf91f
+size 5012235

64.pth

@@ -1,3 +1,3 @@
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-oid sha256:37f658c9e5db8fc3340a133ef1be94c3b1127d2eb74a6d7f94ab812e989c2dff
-size 6713611
+oid sha256:d5de52f94fa1744fa8a6a197c5aec3ddb51be758d94f9b5ba104871a9c63fb95
+size 1758859

64bueno.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:37f658c9e5db8fc3340a133ef1be94c3b1127d2eb74a6d7f94ab812e989c2dff
+size 6713611

app.ipynb

@@ -34,6 +34,68 @@
     "from meshSubplot import plotTree"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def decode_testing(v, max, decoder, mult, min):\n",
+    "    def decode_node(v, max, decoder, mult, min, nbif):\n",
+    "\n",
+    "        cl = decoder.nodeClassifier(v)\n",
+    "        _, label = torch.max(cl, 1)\n",
+    "        label = label.data\n",
+    "        \n",
+    "        \n",
+    "        if label == 1 and createNode.count <= max:\n",
+    "            #print(\"label 1\")\n",
+    "            right, radius = decoder.internalDecoder(v)\n",
+    "                \n",
+    "            d = createNode(1, radius) \n",
+    "            \n",
+    "            d.right = decode_node(right, max, decoder, mult, min, nbif = nbif)\n",
+    "            return d\n",
+    "\n",
+    "        elif label == 2 and createNode.count <= max:\n",
+    "            #print(\"label 2\")\n",
+    "            left, right, radius = decoder.bifurcationDecoder(v)\n",
+    "                \n",
+    "            d = createNode(1, radius)\n",
+    "            \n",
+    "            d.right = decode_node(right, max, decoder, mult, min, nbif = nbif+1)\n",
+    "            d.left = decode_node(left, max, decoder, mult, min, nbif = nbif+1)\n",
+    "        \n",
+    "            return d\n",
+    "\n",
+    "        elif label == 0 : ##output del classifier\n",
+    "            #print(\"label 0\")\n",
+    "            if nbif >= 2:\n",
+    "                #print(\"mayor que min\")\n",
+    "                radio = decoder.featureDecoder(v)\n",
+    "                return createNode(1,radio)\n",
+    "        \n",
+    "            else:\n",
+    "                #print(\"menor que min\")\n",
+    "                left, right, radius = decoder.bifurcationDecoder(v)\n",
+    "                d = createNode(1, radius)\n",
+    "                d.right = decode_node(right, max, decoder, mult, min, nbif = nbif+1)\n",
+    "                d.left = decode_node(left, max, decoder, mult, min, nbif = nbif+1)\n",
+    "                return d\n",
+    "\n",
+    "        '''\n",
+    "        elif label == 0 : ##output del classifier\n",
+    "            print(\"0\", createNode.count)\n",
+    "            radio = decoder.featureDecoder(v)\n",
+    "            return createNode(1,radio)  \n",
+    "        '''\n",
+    "\n",
+    "    createNode.count = 0\n",
+    "    dec = decode_node (v, max, decoder, mult, min, nbif = 0)\n",
+    "\n",
+    "    return dec"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,

app.py

@@ -12,6 +12,8 @@ from torch.utils.data import Dataset, DataLoader
 import os
 from resamplear import *
 import open3d as o3d
+import pymeshlab as pm
+
 
 from vedo import *
 def count_fn(f):
@@ -30,7 +32,7 @@ def createNode(data, radius, left = None, right = None):
         return Node(data, radius, left, right)
 
 def decode_testing(v, max, decoder, mult, min):
-    def decode_node(v, max, decoder, mult, min):
+    def decode_node(v, max, decoder, mult, min, nbif):
 
         cl = decoder.nodeClassifier(v)
         _, label = torch.max(cl, 1)
@@ -43,7 +45,7 @@ def decode_testing(v, max, decoder, mult, min):
                 
             d = createNode(1, radius) 
             
-            d.right = decode_node(right, max, decoder, mult, min)
+            d.right = decode_node(right, max, decoder, mult, min, nbif = nbif)
             return d
 
         elif label == 2 and createNode.count <= max:
@@ -52,23 +54,24 @@ def decode_testing(v, max, decoder, mult, min):
                 
             d = createNode(1, radius)
             
-            d.right = decode_node(right, max, decoder, mult, min)
-            d.left = decode_node(left, max, decoder, mult, min)
+            d.right = decode_node(right, max, decoder, mult, min, nbif = nbif+1)
+            d.left = decode_node(left, max, decoder, mult, min, nbif = nbif+1)
         
             return d
 
         elif label == 0 : ##output del classifier
             #print("label 0")
-            if createNode.count>min:
+            if nbif >= 2:
                 #print("mayor que min")
                 radio = decoder.featureDecoder(v)
                 return createNode(1,radio)
         
             else:
                 #print("menor que min")
-                right, radius = decoder.internalDecoder(v)
-                d = createNode(1, radius) 
-                d.right = decode_node(right, max, decoder, mult, min)
+                left, right, radius = decoder.bifurcationDecoder(v)
+                d = createNode(1, radius)
+                d.right = decode_node(right, max, decoder, mult, min, nbif = nbif+1)
+                d.left = decode_node(left, max, decoder, mult, min, nbif = nbif+1)
                 return d
 
         '''
@@ -79,7 +82,7 @@ def decode_testing(v, max, decoder, mult, min):
         '''
 
     createNode.count = 0
-    dec = decode_node (v, max, decoder, mult, min)
+    dec = decode_node (v, max, decoder, mult, min, nbif = 0)
 
     return dec
 
@@ -201,7 +204,7 @@ def predict():
 
         
         z = torch.randn(1, latent_size)
-        generated_images = decode_testing(z, 100, Grassdecoder, mult, 1)
+        generated_images = decode_testing(z, 50, Grassdecoder, mult, 1)
         count = []
         numerar_nodos(generated_images, count)
         #tr(generated_images)
@@ -236,8 +239,8 @@ def predict():
         graph_resampled = G
         prepararAristas(graph_resampled)
         graphOfCenters = GrafoCentros(graph_resampled)
-        
-        if abs(max_radius/min_radius)<10:
+
+        if abs(max_radius/min_radius)<10 :
             try:
                 graphOfCenters.tile()
                 mesh = tm.Trimesh( graphOfCenters.getVertices(), graphOfCenters.getCaras() )
@@ -245,13 +248,19 @@ def predict():
                 m2 = mesh_o3d 
                 o3d.io.write_triangle_mesh("sinsub.obj", m2)
                 mesh_o3d = mesh_o3d.subdivide_loop(3)
-            
+                o3d.io.write_triangle_mesh("output.obj", mesh_o3d)
+                ms = pm.MeshSet()
+                ms.load_new_mesh('output.obj')
+                ms.compute_selection_by_self_intersections_per_face()
+                if ms.current_mesh().selected_face_number() > 0:
+                    raise Exception("autointerseccion")
                 v_matrix = np.asarray(mesh_o3d.vertices )
                 f_matrix = np.asarray(mesh_o3d.triangles )
                 v_matrix_2 = np.asarray(m2.vertices )
                 f_matrix_2 = np.asarray(m2.triangles )
                 success = True
-            
+                
+
             except Exception as e:
                 print("No se pudo generar")
                 print(str(e))
@@ -278,11 +287,6 @@ def predict():
             x=x, y=y, z=z, 
             i=f_matrix[:,0], j=f_matrix[:,1], k=f_matrix[:,2], 
             color='red'))
-
-    m = go.Mesh3d(
-            x=v_matrix[:,0], y=v_matrix[:,1], z=v_matrix[:,2], 
-            i=f_matrix[:,0], j=f_matrix[:,1], k=f_matrix[:,2], 
-            color='red')
     
     fig.update_layout(
         scene = dict(
@@ -300,7 +304,7 @@ def predict():
             color='red'))
     
 
-    o3d.io.write_triangle_mesh("output.obj", mesh_o3d)
+    
     #o3d.io.write_triangle_mesh("generadas/"+filename.split("_")[0]+".obj", mesh_o3d)
     return fig
 
@@ -313,7 +317,7 @@ Grassdecoder = Grassdecoder
 
 Grassdecoder.eval()
 
-checkpoint = torch.load("64.pth", map_location=torch.device('cpu'))
+checkpoint = torch.load("64bueno.pth", map_location=torch.device('cpu'))
 Grassdecoder.load_state_dict(checkpoint['decoder_state_dict'])
 
 gr.Interface( predict, 

lmodel-700.pth

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+oid sha256:57ad98302bf4787795e6ecbf0cef881bd19a2d49e08bc9baa75287820d6c251e
+size 6911275

model-700-moreparams-best.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:23b23467a6a67e8a1c482599ccc96eb86b00261dc88abc8b92b4a73987f0fa7d
+size 66460149

sinsub.obj

@@ -1,32 +1,186 @@
 # Created by Open3D 
 # object name: sinsub
-# number of vertices: 12
-# number of triangles: 16
-v 0.449378 0.00737204 0.0931694
-v 0.453679 -0.00822981 0.0918463
-v 0.443061 -0.0101272 0.0797087
-v 0.43876 0.00547462 0.0810317
-v 0.540627 0.0390201 0.0148946
-v 0.554566 0.0244183 0.00205357
-v 0.538313 0.0243431 -0.0155028
-v 0.524375 0.0389449 -0.00266178
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-f 4 8 7
-f 1 5 8
-f 6 10 9
-f 7 11 10
-f 8 12 11
-f 5 9 12
+# number of vertices: 64
+# number of triangles: 118
+v 0.268613 0.760725 0.82848
+v 0.266527 0.799368 0.833408
+v 0.272015 0.794773 0.871757
+v 0.2741 0.75613 0.866829
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+v 0.36726 0.381984 0.596562
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+v 0.394687 0.375637 0.586115
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src/mesh_gen/mesh.py

@@ -12,8 +12,8 @@ class Cuadrado:
         self.posicion = posicion
         self.normal = normal
         self.upVector = upVector
-        self.sideVector = self.normal.cross( self.upVector )/4
-        self.upVector = upVector/4
+        self.sideVector = self.normal.cross( self.upVector )/5
+        self.upVector = upVector/5
 
 
         self.vertices = [