def main(argv):
# Parse script arguments
try:
opts, _ = getopt.getopt(argv, "s:t:c:d",
["scene=", "tree=", "classifier=",
"double-step"])
except getopt.GetoptError:
print_script_usage()
sys.exit(2)
features = featuresfirststep.all_features_dict()
scene = None
tree = None
step_size = 1
for opt, arg in opts:
if opt in ("-s", "--scene"):
scene = Scene(arg)
load_maxima([scene])
elif opt in ("-t", "--tree"):
tree = evaluatetree.read_decision_tree(arg, features)
elif opt in ("-d", "--double-step"):
step_size = 2
if scene is None or tree is None:
print_script_usage()
sys.exit(2)
print_R_script(scene, tree, step_size)
def main(argv):
# Parse script arguments
try:
opts, _ = getopt.getopt(argv, "s:t:c:d",
["scene=", "tree=", "classifier=",
"double-step"])
except getopt.GetoptError:
print_script_usage()
sys.exit(2)
features = featuresfirststep.all_features_dict()
scene = None
tree = None
classifier = None
step_size = 1
for opt, arg in opts:
if opt in ("-s", "--scene"):
scene = load_scene(arg)
elif opt in ("-t", "--tree"):
tree = evaluatetree.read_decision_tree(arg, features)
elif opt in ("-c", "--classifier"):
if arg == "highest":
classifier = featuresfirststep.highest_on_left
elif arg == "nearest":
classifier = featuresfirststep.nearest_on_left
elif arg == "near_high":
classifier = featuresfirststep.highest_and_near_on_left
elif opt in ("-d", "--double-step"):
step_size = 2
if scene is None or tree is None:
print_script_usage()
sys.exit(2)
print_R_script(scene, tree, classifier, step_size)
def main(argv):
# Parse script arguments
try:
opts, _ = getopt.getopt(argv, "d:uo",
["left-right-tree=", "lowlight", "low-light",
"lowlightgauss", "low-light-gauss",
"action-tree=", "double-step", "backlash", "noise",
"specific-scene=", "perfect-file=",
"use-only="])
except getopt.GetoptError:
print_script_usage()
sys.exit(2)
params = BenchmarkParameters()
specific_scene = None
use_only_file = None
scenes_folder = "focusraw/"
for opt, arg in opts:
if opt in ("-d", "--double-step"):
params.step_size = 2
raise Exception("Simulator does not support double step size yet.")
elif opt in ("-uo", "--use-only"):
use_only_file = arg
elif opt in ("--lowlight", "--low-light"):
scenes_folder = "lowlightraw/"
elif opt in ("--lowlightgauss", "--low-light-gauss"):
scenes_folder = "lowlightgaussraw/"
elif opt == "--left-right-tree":
params.left_right_tree = evaluatetree.read_decision_tree(
arg, featuresfirststep.all_features_dict())
elif opt == "--action-tree":
params.action_tree = evaluatetree.read_decision_tree(
arg, featuresturn.all_features_dict())
elif opt == "--specific-scene":
specific_scene = arg
elif opt == "--perfect-file":
params.perfect_classification = load_classifications(arg)
elif opt == "--backlash":
params.backlash = True
elif opt == "--noise":
params.noise = True
else:
print_script_usage()
sys.exit(2)
random.seed(seed)
# Make sure simulator has everything it needs.
if params.missing_params():
print_script_usage()
sys.exit(2)
scenes = load_scenes(folder=scenes_folder,
excluded_scenes=["cat.txt", "moon.txt",
"projector2.txt", "projector3.txt"])
if use_only_file:
scenes = [scene for scene in scenes if scene.filename == use_only_file]
if specific_scene is None:
benchmark_scenes(params, scenes)
else:
benchmark_specific(params, scenes, specific_scene)