# Create a phong renderer by composing a rasterizer and a shader. The textured phong shader will
# interpolate the texture uv coordinates for each vertex, sample from a texture image and
# apply the Phong lighting model
renderer = MeshRenderer(rasterizer=MeshRasterizer(
cameras=cameras, raster_settings=raster_settings),
shader=SoftPhongShader(device=device,
cameras=cameras,
lights=lights))
img = renderer(mesh)
plt.figure(figsize=(10, 10))
plt.imshow(img[0].cpu().numpy())
plt.show()
if __name__ == '__main__':
file = Path(
"/home/lukas/rtm/rtm_files_3d/2020-08-24_11-20-27_111_RESULT.erfh5")
from Pipeline.data_loader_mesh import DataLoaderMesh
sensor_verts_path = Path(
"/home/lukas/rtm/sensor_verts_3d_272_subsampled.dump")
dl = DataLoaderMesh(sensor_verts_path=sensor_verts_path)
data = dl.get_sensor_flowfront_mesh(file)
sample = data[150][1]
mc = MeshCreator(file)
verts, faces, _ = mc.get_mesh_components()
show_vedo_mesh_old(verts, faces, sample)
# save_p3d_mesh(verts, faces, sample)
pass
return x
if __name__ == '__main__':
from Pipeline.data_loader_mesh import DataLoaderMesh
from pathlib import Path
dl = DataLoaderMesh(
sensor_verts_path=Path("/home/lukas/rtm/sensor_verts.dump"))
file = Path(
"/home/lukas/rtm/rtm_files/2019-07-24_16-32-40_308_RESULT.erfh5")
bs = 4
mesh = dl.get_batched_mesh_torch(bs, file)
# model = SensorMeshToFlowFrontModel(mesh)
# model = SensorMeshToDryspotModel(mesh, bs).cuda()
model = SensorMeshToDryspotResnet(mesh, bs).cuda()
instances = dl.get_sensor_flowfront_mesh(file)
data, labels = [], []
batch = instances[0:bs]
for d, l in batch:
data.append(d)
labels.append(l)
data = torch.Tensor(data).cuda()
lables = torch.Tensor(labels)
for i in tqdm(range(500)):
output = model(data)