"--n_jobs", str(args.n_jobs),
"--factor_background",str(factor_background),
"--o_size",str(o_size),
"--d_size",str(d_size),
"--narrow_band",
"--n_estimators", str(n_estimators),
"--n_tests", str(n_tests),
"--max_depth", str(max_depth) ] )
if not_centered:
cmd.append( "--not_centered" )
if heart_only:
cmd.append( "--heart_only" )
if shape_model or args.shape_model_only:
cmd_shape_model = ( [ "python", "shape_model.py",
"--seg" ] + seg_files +
[ "--output_folder", output_folder + "/" + patient_id,
"--n_jobs", str(args.n_jobs) ] )
if args.shape_model_only:
cmd = cmd_shape_model
else:
cmd = cmd_shape_model + ["&&"] + cmd
sbatch( cmd,
mem=args.mem,
c=args.n_jobs,
partition=args.partition,
verbose=args.verbose,
dryrun=args.dryrun )
sys.path.insert(1, "../commonlib")
from SimpleSlurm import sbatch
n_jobs = 1
mem = 50
partition = "long"
detection_folder = "/vol/vipdata/data/fetal_data/OUTPUT/whole_body_shape_padding50/"
all_files = sorted(glob(detection_folder + "/*/prediction_2/img.nii.gz"))
for f in all_files:
dirname = f[: -len("img.nii.gz")]
cmd = [
"python",
"segment_random_walker.py",
"--img",
f,
"--seg",
dirname + "/final_seg.nii.gz",
"--output",
dirname + "/rw10.nii.gz",
"--narrow_band",
str(10),
"--thorax",
]
sbatch(cmd, mem=mem, c=n_jobs, partition=partition, verbose=False, dryrun=False)
sys.path.append("lib")
from SimpleSlurm import sbatch
n_jobs = 5
ground_truth_folder = "/vol/bitbucket/kpk09/detector/data_resampled"
output_folder = "/vol/medic02/users/kpk09/OUTPUT/stage1_prediction"
heart_only = True
detector = "/vol/medic02/users/kpk09/OUTPUT/stage1"
all_files = sorted(glob(ground_truth_folder+'/*_img.nii.gz'))
for f in all_files:
patient_id = os.path.basename(f)[:-len("_img.nii.gz")]
if os.path.exists( output_folder + "/" + patient_id + "/votes_heart.nii.gz"):
continue
cmd = [ "python", "predict.py",
"--input", f ,
"--output", output_folder + "/" + patient_id,
"--detector", detector + "/" + patient_id,
"--chunk_size", str(int(1e5)),
"--n_jobs", str(n_jobs),
"--narrow_band"
]
if heart_only:
cmd.append( "--heart_only" )
sbatch( cmd, mem=30, c=n_jobs, verbose=False, dryrun=True )
