def shape_net_test():
std_out_path = "/home/zgong8/PerceptualMetric/examples/data_out/decimation_low_{}.obj"
std_out_path_ori = "/home/zgong8/PerceptualMetric/examples/data_out/decimation_low_ori_{}.obj"
for i in range(10):
in_path = random_shape_net_object()
print(in_path)
out_path = std_out_path.format(i)
out_path_ori = std_out_path_ori.format(i)
p = Pipeline(1)
d = None
while d is None:
c = {}
for k, f in DecimationDeformer.get_applicable_configs().items():
q = np.random.uniform()
c[k] = f(q)
tmp_d = DecimationDeformer(c)
if tmp_d.get_score() == 1:
d = tmp_d
p.plug(d)
p.process_shape_file(in_path, out_path)
p = Pipeline(0)
p.process_shape_file(in_path, out_path_ori)
def test():
in_path = os.path.join(curr_dir, data_out_dir, "mc5b.obj")
out_path = os.path.join(curr_dir, data_out_dir, "mc5b_decimation.obj")
p = Pipeline(1)
d = DecimationDeformer({"ratio": 5.0})
p.plug(d)
p.process_shape_file(in_path, out_path)
def head_test():
in_path = os.path.join(curr_dir, data_in_dir, file_name)
out_path = os.path.join(curr_dir, data_out_dir, "marching_cubes_head_test.obj")
p = Pipeline(1)
d = PointCloudDeformer({})
p.plug(d)
p.process_shape_file(in_path, out_path)
def test():
in_path = os.path.join(curr_dir, data_out_dir, file_name)
out_path = os.path.join(curr_dir, data_out_dir, "mc5b_biharmonic.obj")
p = Pipeline(1)
c = {
PIVOTS: np.floor(random() * 4 + 1),
FIRST: random(),
SECOND: random(),
THIRD: random(),
FOURTH: random()
}
d = BiharmonicDeformer(c)
p.plug(d)
p.process_shape_file(in_path, out_path)
def shape_net_test():
std_out_path = "/home/zgong8/PerceptualMetric/examples/data_out/bi_high_{}.obj"
for i in range(10):
in_path = random_shape_net_object()
print(in_path)
out_path = std_out_path.format(i)
p = Pipeline(1)
c = {
PIVOTS: 1,
FIRST: random(),
SECOND: random(),
THIRD: random(),
FOURTH: random(),
DIS: 1.6
}
d = BiharmonicDeformer(c)
p.plug(d)
p.process_shape_file(in_path, out_path)
def shape_net_test():
# in_path = random_shape_net_object()
in_path = "/home/zgong8/ShapeNetCore.v2/03593526/2872c4b8e2f86201b7d0f3d89bbac230/models/model_normalized.obj"
for i in range(1):
print(in_path)
out_path = os.path.join(curr_dir, data_out_dir,
"rc_low_{}.obj".format(i))
out_path_ori = os.path.join(curr_dir, data_out_dir,
"rc_low_{}_ori.obj".format(i))
p = Pipeline(1)
d = None
while d is None:
c = {
't_x': 0.2905716521257016,
't_y': 0.4142014801845495,
't_z': 0.4329088231382898,
'theta': 0.22074621042125056,
'phi': 0.04610239008672457,
'pivots': 0.8523162841916193
}
for k, f in RandomCageDeformer.get_applicable_configs().items():
q = np.random.uniform()
c[k] = f(q)
tmp_d = RandomCageDeformer(c)
if tmp_d.get_score() == 1:
d = tmp_d
p.plug(d)
print(p)
p.process_shape_file(in_path, out_path)
p = Pipeline(0)
p.process_shape_file(in_path, out_path_ori)
import os
from pipeline.Pipeline import Pipeline
from module.deformers.VoxelizeDeformer import VoxelizeDeformer, NUM_CUBES
curr_dir = os.path.dirname(os.path.realpath(__file__))
data_in_dir = "data_in"
data_out_dir = "data_out"
file_name = "head.obj"
in_path = os.path.join(curr_dir, data_in_dir, file_name)
out_path = os.path.join(curr_dir, data_out_dir, "vox_head_test_32.obj")
p = Pipeline(1)
d = VoxelizeDeformer({NUM_CUBES: 128})
p.plug(d)
p.process_shape_file(in_path, out_path)
