def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', default='overhand_knot', help='name of dataset')
parser.add_argument('--method', choices=['geodesic_dist', 'shape_context', 'geodesic_dist+shape_context'], default='geodesic_dist', help='matching algorithm')
parser.add_argument('input', type=str)
parser.add_argument('--show_shape_context', action='store_true')
parser.add_argument('--save_input', type=str, default='')
args = parser.parse_args()
dataset = recognition.DataSet.LoadFromTaskDemos(args.dataset)
# read input to match
input_xyz = cloud_io.read_from_rsrc(args.input)
if args.save_input:
cloud_io.write_cloud(args.save_input, input_xyz)
# create the matcher
matcher = None
if args.method == 'geodesic_dist':
matcher = recognition.GeodesicDistMatcher(dataset)
elif args.method == 'shape_context':
matcher = recognition.ShapeContextMatcher(dataset)
elif args.method == 'geodesic_dist+shape_context':
matcher = recognition.CombinedNNMatcher(dataset, [recognition.GeodesicDistMatcher, recognition.ShapeContextMatcher], [1, 0.1])
else:
raise NotImplementedError
# do the matching
best_match_name, best_match_cost = matcher.match(input_xyz)
print 'Best match name:', best_match_name, 'with cost:', best_match_cost
draw_comparison(left_cloud=input_xyz, right_cloud=dataset[best_match_name], show_shape_context=args.show_shape_context)
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('clouds', nargs='+', help='cloud resource srings')
args = parser.parse_args()
assert len(args.clouds) == 2
import cloud_io
print 'loading clouds'
clouds = [cloud_io.read_from_rsrc(c) for c in args.clouds]
print 'processing'
clouds_ds = [recognition.downsample(c)[0] for c in clouds]
# hists_ds = [recognition.calc_shape_context_hists(dc) for dc in clouds_ds]
f, partners, hists0, hists1, costs = recognition.match_and_calc_shape_context(clouds_ds[0], clouds_ds[1], return_tuple=True)
left_data = {
'xyz': clouds[0],
'xyz_ds': clouds_ds[0],
'hists_ds': hists0
}
right_data = {
'xyz': clouds[1],
'xyz_ds': clouds_ds[1],
'hists_ds': hists1
}
corr_data = {
'f': f,
'partners': partners,
'costs': costs
}
print 'drawing'
draw_comparison(left_data, right_data, corr_data)
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('clouds', nargs='+', help='cloud resource srings')
args = parser.parse_args()
assert len(args.clouds) == 2
import cloud_io
print 'loading clouds'
clouds = [cloud_io.read_from_rsrc(c) for c in args.clouds]
print 'processing'
clouds_ds = [recognition.downsample(c)[0] for c in clouds]
# hists_ds = [recognition.calc_shape_context_hists(dc) for dc in clouds_ds]
f, partners, hists0, hists1, costs = recognition.match_and_calc_shape_context(
clouds_ds[0], clouds_ds[1], return_tuple=True)
left_data = {'xyz': clouds[0], 'xyz_ds': clouds_ds[0], 'hists_ds': hists0}
right_data = {'xyz': clouds[1], 'xyz_ds': clouds_ds[1], 'hists_ds': hists1}
corr_data = {'f': f, 'partners': partners, 'costs': costs}
print 'drawing'
draw_comparison(left_data, right_data, corr_data)
