fabiencasenave commited on
Commit
ce1f1b7
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verified ·
1 Parent(s): 00e3190

Update app.py

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Files changed (1) hide show
  1. app.py +59 -44
app.py CHANGED
@@ -40,49 +40,63 @@ def sample_info(sample_id_str, fieldn):
40
 
41
  nodes = plaid_sample.get_nodes()
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  field = plaid_sample.get_field(fieldn)
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- if nodes.shape[1] == 2:
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- nodes__ = np.zeros((nodes.shape[0],nodes.shape[1]+1))
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- nodes__[:,:-1] = nodes
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- nodes = nodes__
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-
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-
 
 
 
 
 
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  triangles = plaid_sample.get_elements()['TRI_3']
 
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- # generate colormap
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- if np.linalg.norm(field) > 0:
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- norm = mpl.colors.Normalize(vmin=np.min(field), vmax=np.max(field))
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- cmap = cm.coolwarm
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- m = cm.ScalarMappable(norm=norm, cmap=cmap)
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- vertex_colors = m.to_rgba(field)[:,:3]
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- else:
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- vertex_colors = 1+np.zeros((field.shape[0], 3))
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- vertex_colors[:,0] = 0.2298057
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- vertex_colors[:,1] = 0.01555616
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- vertex_colors[:,2] = 0.15023281
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-
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- # generate mesh
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- trimesh = Trimesh(vertices = nodes, faces = triangles)
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- trimesh.visual.vertex_colors = vertex_colors
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- mesh = pyrender.Mesh.from_trimesh(trimesh, smooth=False)
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-
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- # compose scene
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- scene = pyrender.Scene(ambient_light=[.1, .1, .3], bg_color=[0, 0, 0])
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- camera = pyrender.PerspectiveCamera( yfov=np.pi / 3.0)
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- light = pyrender.DirectionalLight(color=[1,1,1], intensity=1000.)
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-
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- scene.add(mesh, pose= np.eye(4))
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- scene.add(light, pose= np.eye(4))
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-
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- scene.add(camera, pose=[[ 1, 0, 0, 0],
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- [ 0, 1, 0, 0],
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- [ 0, 0, 1, 3],
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- [ 0, 0, 0, 1]])
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-
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- # render scene
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- r = pyrender.OffscreenRenderer(1024, 1024)
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- color, _ = r.render(scene)
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  str__ = f"Training sample {sample_id_str}\n"
@@ -106,20 +120,21 @@ def sample_info(sample_id_str, fieldn):
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  for fname in hf_dataset.description['out_fields_names']:
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  str__ += f"- {fname}\n"
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- return str__, color
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111
 
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  if __name__ == "__main__":
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- with gr.Blocks() as demo:
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  gr.Markdown(_HEADER_)
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  with gr.Row(variant="panel"):
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- with gr.Column():
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  d1 = gr.Slider(0, nb_samples-1, value=0, label="Training sample id", info="Choose between 0 and "+str(nb_samples-1))
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  output1 = gr.Text(label="Training sample info")
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- with gr.Column():
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  d2 = gr.Dropdown(field_names_train, value=field_names_train[0], label="Field name")
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- output2 = gr.Image(label="Training sample visualization")
 
123
 
124
  d1.input(sample_info, [d1, d2], [output1, output2])
125
  d2.input(sample_info, [d1, d2], [output1, output2])
 
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41
  nodes = plaid_sample.get_nodes()
42
  field = plaid_sample.get_field(fieldn)
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+ # if nodes.shape[1] == 2:
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+ # nodes__ = np.zeros((nodes.shape[0],nodes.shape[1]+1))
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+ # nodes__[:,:-1] = nodes
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+ # nodes = nodes__
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+
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+ norm = (field - field.min()) / (field.max() - field.min())
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+ colormap_func = mpl.pyplot.get_cmap('viridis')
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+ rgb_colors = colormap_func(norm)[:, :3]
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+
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+ nb_nodes = nodes.shape[0]
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+
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  triangles = plaid_sample.get_elements()['TRI_3']
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+ nb_tris = tris.shape[0]
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+ assert field.shape[0] == nb_nodes
 
 
 
 
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+ with open("visu.obj", 'w') as f:
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+ for i in range(nb_nodes):
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+ f.write(f"v {nodes[i,0]} {nodes[i,1]} {0.} {rgb_colors[i,0]} {rgb_colors[i,1]} {rgb_colors[i,2]}\n")
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+
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+ for i in range(nb_tris):
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+ f.write(f"f {quads[i,0] + 1} {quads[i,1] + 1} {quads[i,2] + 1} \n")
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+
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+ # # generate colormap
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+ # if np.linalg.norm(field) > 0:
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+ # norm = mpl.colors.Normalize(vmin=np.min(field), vmax=np.max(field))
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+ # cmap = cm.coolwarm
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+ # m = cm.ScalarMappable(norm=norm, cmap=cmap)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # vertex_colors = m.to_rgba(field)[:,:3]
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+ # else:
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+ # vertex_colors = 1+np.zeros((field.shape[0], 3))
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+ # vertex_colors[:,0] = 0.2298057
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+ # vertex_colors[:,1] = 0.01555616
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+ # vertex_colors[:,2] = 0.15023281
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+
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+ # # generate mesh
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+ # trimesh = Trimesh(vertices = nodes, faces = triangles)
81
+ # trimesh.visual.vertex_colors = vertex_colors
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+ # mesh = pyrender.Mesh.from_trimesh(trimesh, smooth=False)
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+
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+ # # compose scene
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+ # scene = pyrender.Scene(ambient_light=[.1, .1, .3], bg_color=[0, 0, 0])
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+ # camera = pyrender.PerspectiveCamera( yfov=np.pi / 3.0)
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+ # light = pyrender.DirectionalLight(color=[1,1,1], intensity=1000.)
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+
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+ # scene.add(mesh, pose= np.eye(4))
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+ # scene.add(light, pose= np.eye(4))
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+
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+ # scene.add(camera, pose=[[ 1, 0, 0, 0],
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+ # [ 0, 1, 0, 0],
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+ # [ 0, 0, 1, 3],
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+ # [ 0, 0, 0, 1]])
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+
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+ # # render scene
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+ # r = pyrender.OffscreenRenderer(1024, 1024)
99
+ # color, _ = r.render(scene)
100
 
101
 
102
  str__ = f"Training sample {sample_id_str}\n"
 
120
  for fname in hf_dataset.description['out_fields_names']:
121
  str__ += f"- {fname}\n"
122
 
123
+ return str__, "./visu.obj"
124
 
125
 
126
  if __name__ == "__main__":
127
 
128
+ with gr.Blocks(fill_width=True) as demo:
129
  gr.Markdown(_HEADER_)
130
  with gr.Row(variant="panel"):
131
+ with gr.Column(scale=1):
132
  d1 = gr.Slider(0, nb_samples-1, value=0, label="Training sample id", info="Choose between 0 and "+str(nb_samples-1))
133
  output1 = gr.Text(label="Training sample info")
134
+ with gr.Column(scale=2, min_width=300):
135
  d2 = gr.Dropdown(field_names_train, value=field_names_train[0], label="Field name")
136
+ # output2 = gr.Image(label="Training sample visualization")
137
+ output2 = gr.Model3D(label="Training sample visualization")
138
 
139
  d1.input(sample_info, [d1, d2], [output1, output2])
140
  d2.input(sample_info, [d1, d2], [output1, output2])