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import gradio as gr |
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from plaid.containers.sample import Sample |
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import numpy as np |
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import pyrender |
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from trimesh import Trimesh |
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import matplotlib as mpl |
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import matplotlib.cm as cm |
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import os |
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os.environ["PYOPENGL_PLATFORM"] = "egl" |
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os.system("wget https://zenodo.org/records/10124594/files/Tensile2d.tar.gz") |
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os.system("tar -xvf Tensile2d.tar.gz") |
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field_names_train = ["sig11", "sig22", "sig12", "U1", "U2", "evrcum"] |
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def sample_info(sample_id_str, fieldn): |
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plaid_sample = Sample.load_from_dir(f"Tensile2d/dataset/samples/sample_"+str(sample_id_str).zfill(9)) |
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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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triangles = plaid_sample.get_elements()['TRI_3'] |
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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 = np.zeros((field.shape[0], 3)) |
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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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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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scene.add(mesh, pose= np.eye(4)) |
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scene.add(light, pose= np.eye(4)) |
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c = 3**-0.5 |
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scene.add(camera, pose=[[ 1, 0, 0, 0], |
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[ 0, c, -c, -2], |
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[ 0, c, c, 1.2], |
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[ 0, 0, 0, 1]]) |
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r = pyrender.OffscreenRenderer(1024, 1024) |
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color, _ = r.render(scene) |
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str__ = f"loading sample {sample_id_str}" |
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return str__, color |
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if __name__ == "__main__": |
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with gr.Blocks() as demo: |
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d1 = gr.Slider(0, 499, value=0, label="Training sample id", info="Choose between 0 and 499") |
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d2 = gr.Dropdown(field_names_train, value=field_names_train[0], label="Field name") |
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output1 = gr.Text(label="Training sample info") |
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output2 = gr.Image(label="Training sample visualization") |
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d1.input(sample_info, [d1, d2], [output1, output2]) |
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d2.input(sample_info, [d1, d2], [output1, output2]) |
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demo.launch() |
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