File size: 4,484 Bytes
5ea1b6f 5eb7f8a 5ea1b6f 5eb7f8a ede25fc 2e0c5aa 5ea1b6f 2e121c3 2e0c5aa ede25fc 5ea1b6f ede25fc 2e0c5aa 5eb7f8a ede25fc fa53b56 ede25fc fa53b56 ede25fc 878eb5c fa53b56 878eb5c ede25fc 525ce44 3e4a0db 525ce44 5ea1b6f 5eb7f8a 2e0c5aa d9403e1 3aabd0b d9403e1 27ec183 e7895e8 65555e4 2e0c5aa 65555e4 2e0c5aa 65555e4 2e0c5aa f369ed3 2495238 3aabd0b 65555e4 2e0c5aa 65555e4 2e0c5aa 1036858 5ea1b6f 764e17a c257e9e 764e17a 878eb5c 525ce44 878eb5c 525ce44 878eb5c 5ea1b6f 2e0c5aa 5ea1b6f 878eb5c 5ea1b6f ede25fc 5ea1b6f ede25fc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
import gradio as gr
import pickle
from datasets import load_dataset
from plaid.containers.sample import Sample
import numpy as np
import pyrender
from trimesh 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")
hf_dataset = load_dataset("PLAID-datasets/AirfRANS_remeshed", split="all_samples")
nb_samples = 1000
field_names_train = ["Ux", "Uy", "p", "nut", "implicit_distance"]
_HEADER_ = '''
<h2><b>Visualization demo of <a href='https://huggingface.co/datasets/PLAID-datasets/AirfRANS_remeshed' target='_blank'><b>AirfRANS_remeshed dataset</b></b></h2>
'''
def round_num(num)->str:
return '%s' % float('%.3g' % num)
def sample_info(sample_id_str, fieldn):
sample_ = hf_dataset[int(sample_id_str)]["sample"]
plaid_sample = Sample.model_validate(pickle.loads(sample_))
# 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
if np.linalg.norm(field) > 0:
norm = mpl.colors.Normalize(vmin=np.min(field), vmax=np.max(field))
cmap = cm.seismic#cm.coolwarm
m = cm.ScalarMappable(norm=norm, cmap=cmap)
vertex_colors = m.to_rgba(field)[:,:3]
else:
vertex_colors = 1+np.zeros((field.shape[0], 3))
vertex_colors[:,0] = 0.2298057
vertex_colors[:,1] = 0.01555616
vertex_colors[:,2] = 0.15023281
# 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))
scene.add(camera, pose=[[ 1, 0, 0, 1],
[ 0, 1, 0, 0],
[ 0, 0, 1, 6],
[ 0, 0, 0, 1]])
# render scene
r = pyrender.OffscreenRenderer(1024, 1024)
color, _ = r.render(scene)
str__ = f"Training sample {sample_id_str}\n"
str__ += str(plaid_sample)+"\n"
if len(hf_dataset.description['in_scalars_names'])>0:
str__ += "\ninput scalars:\n"
for sname in hf_dataset.description['in_scalars_names']:
str__ += f"- {sname}: {round_num(plaid_sample.get_scalar(sname))}\n"
if len(hf_dataset.description['out_scalars_names'])>0:
str__ += "\noutput scalars:\n"
for sname in hf_dataset.description['out_scalars_names']:
str__ += f"- {sname}: {round_num(plaid_sample.get_scalar(sname))}\n"
str__ += f"\n\nMesh number of nodes: {nodes.shape[0]}\n"
if len(hf_dataset.description['in_fields_names'])>0:
str__ += "\ninput fields:\n"
for fname in hf_dataset.description['in_fields_names']:
str__ += f"- {fname}\n"
if len(hf_dataset.description['out_fields_names'])>0:
str__ += "\noutput fields:\n"
for fname in hf_dataset.description['out_fields_names']:
str__ += f"- {fname}\n"
return str__, color
if __name__ == "__main__":
with gr.Blocks() as demo:
gr.Markdown(_HEADER_)
with gr.Row(variant="panel"):
with gr.Column():
d1 = gr.Slider(0, nb_samples-1, value=0, label="Training sample id", info="Choose between 0 and "+str(nb_samples-1))
output1 = gr.Text(label="Training sample info")
with gr.Column():
d2 = gr.Dropdown(field_names_train, value=field_names_train[0], label="Field name")
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()
|