#parser.add_argument('--product',
#dest='product',
#default=False,
#action="store_true",
#help='Use product as additional feature')
parser.add_argument('--save', help='Save training results in a file')
parser.add_argument('--load', help='Load training results from a file')
options = parser.parse_args()
return options
if __name__ == "__main__":
history = minc.format_history(sys.argv)
options = parse_options()
# load prior and input image
if (options.prior is not None
or options.load is not None) and options.image is not None:
if options.debug: print("Loading images...")
# convert to float as we go
#images= [ minc.Image(i).data.astype(np.float32) for i in options.image ]
image = minc.Image(options.image).data.astype(np.float32)
if options.debug: print("Done")
clf = None
default=False,
help='Use image coordinates as additional features' )
parser.add_argument('--random', type=int,
dest="random",
help='Provide random state if needed' )
parser.add_argument('--save',help='Save training results in a file')
parser.add_argument('--load',help='Load training results from a file')
options = parser.parse_args()
return options
if __name__ == "__main__":
history=minc.format_history(sys.argv)
options = parse_options()
#print(repr(options))
# load prior and input image
if (options.prior is not None or options.load is not None) and options.image is not None:
if options.debug: print("Loading images...")
images= [ minc.Image(i).data for i in options.image ]
if options.coord:
# add features dependant on coordinates
c=np.mgrid[0:images[0].shape[0] , 0:images[0].shape[1] , 0:images[0].shape[2]]