def main(argv):
filters = []
features = featuresfirststep.two_measure_features
classifier = featuresfirststep.highest_on_left
step_size = 1
# Process command line options. Anything remaining will be considered
# to be filters for features.
for arg in argv:
if arg == "--three-measures":
features = featuresfirststep.measure_features
elif arg == "--all-features":
features = featuresfirststep.all_features
elif arg == "--highest":
# Default
pass
elif arg == "--nearest":
classifier = featuresfirststep.nearest_on_left
elif arg == "--high-and-near":
classifier = featuresfirststep.highest_and_near_on_left
elif arg == "--double-step":
step_size = 2
else:
filters.append(arg)
scenes = load_scenes()
# Print the contents of the ARFF file to screen (use output
# redirection to save to file)
print get_arff_header(features(filters))
print "@DATA"
instances = get_instances(scenes, classifier, features(filters), step_size)
for instance in instances:
print instance
def main(argv):
scenes = load_scenes()
# Make sure that the number of steps is appropriate.
if steps < max(len(scene.maxima) for scene in scenes):
print ("Warning - number of steps smaller than the number of lens "
"positions in the largest scene.")
# Calculate various statistics.
lens_positions = [float(i) / (steps - 1) for i in range(steps)]
print_statistics(scenes, lens_positions, probability_left_peak)
print_statistics(scenes, lens_positions, probability_not_left_peak)
print_statistics(scenes, lens_positions, probability_right_peak)
print_statistics(scenes, lens_positions, probability_not_right_peak)
print_statistics(scenes, lens_positions, probability_highest_left)
print_statistics(scenes, lens_positions, probability_highest_right)
print_statistics(scenes, lens_positions, probability_most_left)
print_statistics(scenes, lens_positions, probability_most_right)
print_statistics(scenes, lens_positions, probability_nearest_left)
print_statistics(scenes, lens_positions, probability_nearest_right)
print_statistics(scenes, lens_positions, probability_nearhighest_left)
print_statistics(scenes, lens_positions, probability_nearhighest_right)
def main(argv):
params = featuresturn.ParameterSet()
# Process command line options.
for arg in argv:
if arg == "--closest-peak":
params.peakHandling = featuresturn.PeakHandling.CLOSEST
elif arg == "--backtrack-faster":
params.backtrackHandling = featuresturn.BacktrackHandling.FASTER
scenes = load_scenes()
json_file = create_json(scenes, params)
print json_file
def main(argv):
# Parse script arguments
try:
opts, _ = getopt.getopt(argv, "d:lv",
["double-step", "closest-peak", "backtrack-faster",
"use-weights", "show-random-sample",
"leave-out="])
except getopt.GetoptError:
print_script_usage()
sys.exit(2)
features = all_features
step_size = 1
show_random_sample = False
leave_out = ""
params = ParameterSet()
# Process command line options. Anything remaining will be considered
# to be filters for features.
for opt, arg in opts:
if opt in ("-d", "--double-step"):
step_size = 2
elif opt in ("-lv", "--leave-out"):
leave_out = arg
elif opt == "--closest-peak":
params.peakHandling = PeakHandling.CLOSEST
elif opt == "--backtrack-faster":
params.backtrackHandling = BacktrackHandling.FASTER
elif opt == "--use-weights":
params.outlierHandling = OutlierHandling.WEIGHTING
params.uniformSamplingRate = 0.10
elif opt == "--show-random-sample":
show_random_sample = True
else:
print_script_usage()
sys.exit(2)
random.seed(seed)
scenes = load_scenes(folder="focusraw/",
excluded_scenes=["cat.txt", "moon.txt",
"projector2.txt", "projector3.txt", leave_out])
if show_random_sample:
simulate_samples(scenes, features(), step_size, params)
else:
simulate_full(scenes, features(), step_size, params)
def main(argv):
scenes_folder = "focusraw/"
scenes = load_scenes(folder=scenes_folder)
ratio1 = [ ratio(scene.fvalues[0], scene.fvalues[8])
for scene in scenes ]
ratio2 = [ ratio(scene.fvalues[8], scene.fvalues[16])
for scene in scenes ]
first_maxima = [ scene.maxima[0] for scene in scenes ]
print "library(plotrix)"
rtools.print_array_assignment("ratio1", ratio1)
rtools.print_array_assignment("ratio2", ratio2)
rtools.print_array_assignment("first_maxima", first_maxima)
print "plot(ratio1, ratio2, xlim=c(-0.070,0.030), ylim=c(-0.070,0.010))"
print "# To avoid overlapping labels"
print "thigmophobe.labels(ratio1,ratio2,labels=first_maxima,cex=1.0)"
def main():
scenes_folder = "focusraw/"
scenes = load_scenes(folder=scenes_folder,
excluded_scenes=["cat.txt", "moon.txt"])
xs = []
ys = []
for scene in scenes:
for maxima in scene.maxima:
xs.append(float(maxima) / scene.step_count)
ys.append(scene.fvalues[maxima])
# For alpha blending
print "library(scales)"
print "plot(-1, -1, xlim=c(0,1), ylim=c(0,1))"
for x, y in zip(xs, ys):
print "segments(%f, 0, %f, %f, col=alpha(\"black\", 0.5))" % (x, x, y)
return
def main(argv):
# Parse script arguments
try:
opts, _ = getopt.getopt(argv, "", [ "lowlight", "low-light",
"lowlightgauss", "low-light-gauss",
"scene-to-print=" ])
except getopt.GetoptError:
print_script_usage()
sys.exit(2)
scene_to_print = None
scenes_folder = "focusraw/"
for opt, arg in opts:
if opt in ("--lowlight", "--low-light"):
scenes_folder = "lowlightraw/"
elif opt in ("--lowlightgauss", "--low-light-gauss"):
scenes_folder = "lowlightgaussraw/"
elif opt == "--scene-to-print":
scene_to_print = arg
else:
print_script_usage()
sys.exit(2)
random.seed(seed)
scenes = load_scenes(folder=scenes_folder,
excluded_scenes=["cat.txt", "moon.txt",
"projector2.txt", "projector3.txt"])
search_perfect(scenes)
print "\n"
search_standard(scenes, scene_to_print)
print "\n"
search_sweep(scenes, False)
print "\n"
search_sweep(scenes, True)
print "\n"
search_full(scenes)
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)
