def run_worker_process():
try:
lm.init('user::default', {})
lm.info()
lm.log.setSeverity(1000)
lm.log.log(lm.log.LogLevel.Err, lm.log.LogLevel.Info, '', 0, 'pid={}'.format(os.getpid()))
lm.dist.worker.init('dist::worker::default', {
'name': uuid.uuid4().hex,
'address': 'localhost',
'port': 5000,
'numThreads': 1
})
lm.dist.worker.run()
lm.dist.shutdown()
lm.shutdown()
except Exception:
tr = traceback.print_exc()
lm.log.log(lm.log.LogLevel.Err, lm.log.LogLevel.Info, '', 0, str(tr))
# + {"nbsphinx": "hidden"}
if not lm.Release:
lm.debug.attach_to_debugger()
# + {"raw_mimetype": "text/restructuredtext", "active": ""}
# Lightmetrica offers an extension for the Jupyter notebook to support logging or interactive progress reporting inside the notebook. The extension can be loaded by a line magic command as below.
# -
# %load_ext lightmetrica_jupyter
# + {"raw_mimetype": "text/restructuredtext", "active": ""}
# After importing the module, you can initialize the framwork by calling :cpp:func:`lm::init` function. You can pass various arguments to configure the framework to the function, but here we keep it empty so that everything is configured to be default.
# -
lm.init()
# + {"raw_mimetype": "text/restructuredtext", "active": ""}
# Logging and progress reporting in Jupyter notebook can be enabled by :cpp:func:`lm::log::init` and :cpp:func:`lm::progress::init` functions with corresponding types.
# -
lm.log.init('jupyter')
lm.progress.init('jupyter')
# + {"raw_mimetype": "text/restructuredtext", "active": ""}
# Once the framework has been initialized properly, you can get an splash message using :cpp:func:`lm::info()` function.
# -
lm.info()
# + {"raw_mimetype": "text/restructuredtext", "active": ""}
import os
import imageio
import pandas as pd
import numpy as np
# %matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import lmfunctest as ft
import lmscene
import lightmetrica as lm
os.getpid()
# %load_ext lightmetrica_jupyter
lm.init('user::default', {})
lm.parallel.init('parallel::openmp', {'numThreads': -1})
lm.log.init('logger::jupyter', {})
lm.info()
scenes = lmscene.scenes_small()
rmse_series = pd.Series(index=scenes)
for scene in scenes:
print("Testing [scene='{}']".format(scene))
lm.reset()
lm.asset('film_output', 'film::bitmap', {'w': 1920, 'h': 1080})
# Load scene and render
import os
import imageio
import pandas as pd
import numpy as np
import timeit
# %matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import lmfunctest as ft
import lmscene
import lightmetrica as lm
# %load_ext lightmetrica_jupyter
lm.init('user::default', {
'numThreads': -1,
'logger': 'logger::jupyter'
})
lm.asset('camera_main', 'camera::pinhole', {
'position': [5.101118, 1.083746, -2.756308],
'center': [4.167568, 1.078925, -2.397892],
'up': [0,1,0],
'vfov': 43.001194
})
lm.asset('obj_base_mat', 'material::diffuse', {
'Kd': [.8,.2,.2]
})
lm.asset('model_obj', 'model::wavefrontobj', {
'path': os.path.join(ft.env.scene_path, 'fireplace_room/fireplace_room.obj'),
'base_material': lm.asset('obj_base_mat')
})
