def parse_command_line():
parser = argparse.ArgumentParser(
description="""Train, validate, and test a classifier that will detect clouds in
remote sensing data.""")
parser.add_argument("-p", "--prepare-data", help="Prepare training and validation data.",
action="store_true")
parser.add_argument("-t", "--train", help="""Train classifier. Use --graph to generate quality
graphs""", action="store_true")
parser.add_argument("-g", "--graph", help="Generate training graphs.", action="store_true")
parser.add_argument("--predict", help="""Make a prediction about a single image.
Provide path to image.""", nargs=1, type=str)
parser.add_argument("--note", help="Adds extra note onto generated quality graph.", type=str)
args = vars(parser.parse_args())
if os.environ.get("CAFFE_HOME") == None:
print "You must set CAFFE_HOME to point to where Caffe is installed. Example:"
print "export CAFFE_HOME=/usr/local/caffe"
exit(1)
# Ensure the random number generator always starts from the same place for consistent tests.
random.seed(0)
if args["prepare_data"] == True:
prepare_data()
if args["train"] == True:
train(args["graph"], note=args["note"])
if args["predict"] != None:
image_path = args["predict"][0]
predict.predict(image_path)
def predict(self):
"""@todo: Docstring for predict.
:returns: @todo
"""
input_file = tkFileDialog.Open().show()
if not input_file:
tkMessageBox.showwarning('Bad input', 'Incorrect input file name')
return
model = self.get_model_path()
if not model:
return
predict(input_file, model['path'])
convert(
os.path.join(os.path.dirname(input_file), 'predicted_'+ os.path.basename(input_file)))
