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():
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 random_shape_sample():
for i in range(5, 10):
in_path = random_shape_net_object()
out_path_ori = r"/home/zgong8/data_out/base-" + str(i) + "-ori.obj"
out_path1 = r"/home/zgong8/data_out/base-" + str(i) + "-1.obj"
file_path1 = r"/home/zgong8/data_out/base-" + str(i) + "-1.txt"
out_path2 = r"/home/zgong8/data_out/base-" + str(i) + "-2.obj"
file_path2 = r"/home/zgong8/data_out/base-" + str(i) + "-2.txt"
empty_pipeline = Pipeline(0)
empty_pipeline.process_shape_file(in_path, out_path_ori)
deformation_level = i % 3 + 1
status = False
while not status:
ppl, status = run_leveled_pipeline(3, in_path, out_path1)
with open(file_path1, 'w') as f:
f.write(in_path)
f.write("\n")
f.write(str(ppl))
f.write("\n")
f.write("status: ")
f.write(str(status))
f.write("\n")
f.write("score: ")
f.write(str(ppl.estimate_deform_score()))
if status:
f.write("\n")
f.write("manifold: ")
f.write(str(is_manifold(file=out_path1)))
f.write("\n")
f.write("cut_at: ")
f.write(str(ppl.cut_by_point_cloud))
f.close()
status = False
while not status:
ppl, status = run_leveled_pipeline(1, in_path, out_path2)
with open(file_path2, 'w') as f:
f.write(in_path)
f.write("\n")
f.write(str(ppl))
f.write("\n")
f.write("status: ")
f.write(str(status))
f.write("\n")
f.write("score: ")
f.write(str(ppl.estimate_deform_score()))
if status:
f.write("\n")
f.write("manifold: ")
f.write(str(is_manifold(file=out_path2)))
f.write("\n")
f.write("cut_at: ")
f.write(str(ppl.cut_by_point_cloud))
f.close()
print("{} done!".format(str(i)))
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 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 shape_net_test():
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_ori = std_out_path.format(i, "")
out_path_a = std_out_path.format(i, "a")
out_path_b = std_out_path.format(i, "b")
subprocess.run(["cp", in_path, out_path_ori])
subprocess.run([
"/home/zgong8/libigl/tutorial/build/bin/705_MarchingCubes",
in_path, out_path_a, out_path_b
])
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_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 random_shape_net_test():
file = random_shape_net_object()
print(file)
print(is_manifold(file=file))