def shape_net_test():
for i in range(10):
in_path = random_shape_net_object()
print(in_path)
out_path = os.path.join(curr_dir, data_out_dir,
"arap_high_{}.obj".format(i))
out_path_ori = os.path.join(curr_dir, data_out_dir,
"arap_high_{}_ori.obj".format(i))
p = Pipeline(1)
d = None
while d is None:
c = {}
for k, f in ARAPDeformer.get_applicable_configs().items():
q = np.random.uniform()
c[k] = f(q)
tmp_d = ARAPDeformer(c)
if tmp_d.get_score() == 3:
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 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 string_to_pipeline(string):
each_deformation_finder = re.compile("<.*?}")
deformation_strings = each_deformation_finder.findall(string)
p = Pipeline(len(deformation_strings))
for deformation_string in deformation_strings:
p.plug(substring_to_deformation(deformation_string))
return p
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 string_to_pipelines(string):
each_deformation_finder = re.compile("<.*?}")
deformation_strings = each_deformation_finder.findall(string)
pipelines = []
for deformation_string in deformation_strings:
p = Pipeline(1)
p.plug(substring_to_deformation(deformation_string))
pipelines.append(p)
return pipelines
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, "mc5b_decimation.obj")
out_path = os.path.join(curr_dir, data_out_dir, "mc5b_arap.obj")
p = Pipeline(1)
c = {}
for k, f in ARAPDeformer.get_applicable_configs().items():
q = np.random.uniform()
c[k] = f(q)
d = ARAPDeformer(c)
p.plug(d)
p.process_shape_file(in_path, out_path)
def shape_net_test():
for i in range(10):
in_path = random_shape_net_object()
print(in_path)
out_path = os.path.join(curr_dir, data_out_dir, "pcd{}.obj".format(i))
p = Pipeline(1)
d = PointCloudDeformer({})
p.plug(d)
p.process_shape_file(in_path, out_path)
def get_random_pipeline():
q = np.random.uniform()
slots = int(np.floor(q * 10 + 1))
p = Pipeline(slots)
while not p.is_full():
p.plug(get_random_deformation())
# p_str = str(p)
# vd = p_str.find("VoxelizeDeformer")
# if vd != -1:
# dd = p_str.find("DecimationDeformer", vd)
# if dd != -1:
# p = None
return p
def shape_net_test2():
std_out_path = "/home/zgong8/PerceptualMetric/examples/data_out/mc{}.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)
d = MarchingCubesDeformer({})
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 test():
std_out_path = "/home/zgong8/PerceptualMetric/examples/data_out/decimation_sub_{}.obj"
std_out_path_ori = "/home/zgong8/PerceptualMetric/examples/data_out/decimation_sub_{}_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(2)
d = DecimationDeformer({"ratio": 3.0})
p.plug(d)
df = SubdivisionDeformer({STEPS: 1})
p.plug(df)
p.process_shape_file(in_path, out_path)
p = Pipeline(0)
p.process_shape_file(in_path, out_path_ori)
def shape_net_test():
std_out_path1 = "/home/zgong8/PerceptualMetric/examples/data_out/sd1_{}.obj"
std_out_path2 = "/home/zgong8/PerceptualMetric/examples/data_out/sd2_{}.obj"
std_out_path3 = "/home/zgong8/PerceptualMetric/examples/data_out/sd3_{}.obj"
for i in range(10):
in_path = random_shape_net_object()
print(in_path)
out_path1 = std_out_path1.format(i)
out_path2 = std_out_path2.format(i)
out_path3 = std_out_path3.format(i)
p = Pipeline(1)
df = SubdivisionDeformer({STEPS: 1})
p.plug(df)
p.process_shape_file(in_path, out_path1)
p = Pipeline(1)
df = SubdivisionDeformer({STEPS: 2})
p.plug(df)
p.process_shape_file(in_path, out_path2)
p = Pipeline(1)
df = SubdivisionDeformer({STEPS: 3})
p.plug(df)
p.process_shape_file(in_path, out_path3)
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)