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)