def test_classify_image_mean_var_file():
"""Classify an image from files using mean and variance."""
local = os.path.dirname(os.path.abspath(__file__))
example.get_sample(local)
h, j, v, z = [os.path.join(local, f"{b}.fits") for b in "hjvz"]
Classifier.classify(h=h,
j=j,
v=v,
z=z,
out_dir=local,
out_type="mean_var")
outs = dh.get_expected_morpheus_output(out_type="mean_var")
for k in outs:
np.testing.assert_allclose(
outs[k],
fits.getdata(os.path.join(local, f"{k}.fits")),
atol=1e-5,
err_msg=f"{k} failed comparison",
)
os.remove(os.path.join(local, f"{k}.fits"))
for b in "hjvz":
os.remove(os.path.join(local, f"{b}.fits"))
def test_classify_mean_var_parallel_cpu():
"""Classify an image in parallel with two cpus."""
local = os.path.dirname(os.path.abspath(__file__))
os.mkdir(os.path.join(local, "output"))
out_dir = os.path.join(local, "output")
example.get_sample(local)
h, j, v, z = [os.path.join(local, f"{b}.fits") for b in "hjvz"]
outs = dh.get_expected_morpheus_output(out_type="mean_var")
classified = Classifier.classify(
h=h,
j=j,
v=v,
z=z,
out_dir=out_dir,
out_type="mean_var",
cpus=2,
parallel_check_interval=0.25, # check every 15 seconds
)
for k in outs:
np.testing.assert_allclose(outs[k],
classified[k],
atol=1e-5,
err_msg=f"{k} failed comparison")
shutil.rmtree(out_dir)
for b in [h, j, v, z]:
os.remove(b)
def test_classify_image_rank_vote_in_mem():
"""Classify an image in memory using rank vote."""
h, j, v, z = example.get_sample()
expected_outs = dh.get_expected_morpheus_output()
outs = Classifier.classify(h=h, j=j, v=v, z=z, out_dir=None)
for k in outs:
np.testing.assert_allclose(outs[k],
expected_outs[k],
err_msg=f"{k} failed comparison")
def test_classify_image_mean_var():
"""Classify an image from files using mean and variance."""
h, j, v, z = example.get_sample()
outs = Classifier.classify(h=h, j=j, v=v, z=z, out_type="mean_var")
expected_outs = dh.get_expected_morpheus_output(out_type="mean_var")
for k in outs:
np.testing.assert_allclose(outs[k],
expected_outs[k],
atol=1e-5,
err_msg=f"{k} failed comparison")
def main():
args = _parse_args(sys.argv[1:])
if args.action == "None":
Classifier.classify(
h=args.h,
j=args.j,
v=args.v,
z=args.z,
batch_size=args.batch_size,
out_dir=args.out_dir,
gpus=args.gpus,
cpus=args.cpus,
)
elif args.action == "catalog":
classified = Classifier.classify(
h=args.h,
j=args.j,
v=args.v,
z=args.z,
batch_size=args.batch_size,
out_dir=args.out_dir,
gpus=args.gpus,
cpus=args.cpus,
)
segmap = Classifier.segmap_from_classified(classified,
args.h,
out_dir=args.out_dir)
Classifier.catalog_from_classified(
classified,
args.h,
segmap,
out_file=os.path.join(args.out_dir, "colorized.png"),
)
elif args.action == "segmap":
classified = Classifier.classify(
h=args.h,
j=args.j,
v=args.v,
z=args.z,
batch_size=args.batch_size,
out_dir=args.out_dir,
gpus=args.gpus,
cpus=args.cpus,
)
Classifier.segmap_from_classified(classified,
args.h,
out_dir=args.out_dir)
elif args.action == "colorize":
classified = Classifier.classify(
h=args.h,
j=args.j,
v=args.v,
z=args.z,
batch_size=args.batch_size,
out_dir=args.out_dir,
gpus=args.gpus,
cpus=args.cpus,
)
Classifier.colorize_classified(classified, out_dir=args.out_dir)
elif args.action == "all":
classified = Classifier.classify(
h=args.h,
j=args.j,
v=args.v,
z=args.z,
batch_size=args.batch_size,
out_dir=args.out_dir,
gpus=args.gpus,
cpus=args.cpus,
)
segmap = Classifier.segmap_from_classified(classified,
args.h,
out_dir=args.out_dir)
Classifier.catalog_from_classified(
classified,
args.h,
segmap,
out_file=os.path.join(args.out_dir, "colorized.png"),
)
Classifier.colorize_classified(classified, out_dir=args.out_dir)
# Copyright 2018 Ryan Hausen
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
# ==============================================================================
"""Simple classification example reading from disk."""
from morpheus.classifier import Classifier
from morpheus.data import example
# this saves the sample numpy arrays as FITS files in 'out_dir'
example.get_sample(out_dir=".")
h, j, v, z = [f"{band}.fits" for band in "hjvz"]
morphs = Classifier.classify(h=h, j=j, v=v, z=z)
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
# ==============================================================================
"""Simple classification example on disk."""
from morpheus.classifier import Classifier
from morpheus.data import example
# this saves the sample numpy arrays as fits files in 'out_dir'
example.get_sample(out_dir=".")
h, j, v, z = [f"{band}.fits" for band in "hjvz"]
Classifier.classify(h=h, j=j, v=v, z=z, out_dir=".")