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import gradio as gr
# import pickle
# from datasets import load_from_disk
from plaid.containers.sample import Sample
# import pyvista as pv
import numpy as np
import pyrender
import trimesh
import matplotlib as mpl
import matplotlib.cm as cm
import os
# switch to "osmesa" or "egl" before loading pyrender
os.environ["PYOPENGL_PLATFORM"] = "egl"
os.system("wget https://zenodo.org/records/10124594/files/Tensile2d.tar.gz")
os.system("tar -xvf Tensile2d.tar.gz")
# FOLDER = "plot"
# dataset = load_from_disk("Rotor37")
field_names_train = ["sig11", "sig22", "sig12", "U1", "U2", "evrcum"]
def sample_info(sample_id_str, fieldn):
plaid_sample = Sample.load_from_dir(f"Tensile2d/dataset/samples/sample_"+str(sample_id_str).zfill(9))
nodes = plaid_sample.get_nodes()
field = plaid_sample.get_field(fieldn)
if nodes.shape[1] == 2:
nodes__ = np.zeros((nodes.shape[0],nodes.shape[1]+1))
nodes__[:,:-1] = nodes
nodes = nodes__
triangles = plaid_sample.get_elements()['TRI_3']
# generate colormap
norm = mpl.colors.Normalize(vmin=np.min(field), vmax=np.max(field))
cmap = cm.coolwarm
m = cm.ScalarMappable(norm=norm, cmap=cmap)
vertex_colors = m.to_rgba(field)[:,:3]
# generate mesh
trimesh = Trimesh(vertices = nodes, faces = triangles)
trimesh.visual.vertex_colors = vertex_colors
mesh = pyrender.Mesh.from_trimesh(trimesh, smooth=False)
# compose scene
scene = pyrender.Scene(ambient_light=[.1, .1, .3], bg_color=[0, 0, 0])
camera = pyrender.PerspectiveCamera( yfov=np.pi / 3.0)
light = pyrender.DirectionalLight(color=[1,1,1], intensity=1000.)
scene.add(mesh, pose= np.eye(4))
scene.add(light, pose= np.eye(4))
c = 3**-0.5
scene.add(camera, pose=[[ 1, 0, 0, 0],
[ 0, c, -c, -2],
[ 0, c, c, 1.2],
[ 0, 0, 0, 1]])
# render scene
r = pyrender.OffscreenRenderer(1024, 1024)
color, _ = r.render(scene)
str__ = f"loading sample {sample_id_str}"
return str__, color
if __name__ == "__main__":
with gr.Blocks() as demo:
d1 = gr.Slider(0, 999, value=0, label="Training sample id", info="Choose between 0 and 999")
d2 = gr.Dropdown(field_names_train, value=field_names_train[0], label="Field name")
output1 = gr.Text(label="Training sample info")
output2 = gr.Image(label="Training sample visualization")
d1.input(sample_info, [d1, d2], [output1, output2])
d2.input(sample_info, [d1, d2], [output1, output2])
demo.launch()
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