def eval_result_folder(result_dir):
gt_point_cloud_dir = os.path.join(result_dir, 'pcloud', 'gt')
result_point_cloud_dir = os.path.join(result_dir, 'pcloud',
'reconstruction')
re_pc_names = os.listdir(result_point_cloud_dir)
re_pc_names.sort()
# randomly evaluate a part of the results
if num_samples is not None:
re_pc_names = sample(re_pc_names, num_samples)
all_acc_percentage = []
all_acc_avg_dist = []
all_comp_percentage = []
all_comp_avg_dist = []
for re_pc_n in (re_pc_names):
gt_pc_filename = os.path.join(gt_point_cloud_dir, re_pc_n)
re_pc_filename = os.path.join(result_point_cloud_dir, re_pc_n)
gt_pc_pts = pc_util.read_ply_xyz(gt_pc_filename)
if not gt_isvalid(gt_pc_pts):
print('Invalid gt point cloud, skip.')
continue
re_pc_pts = pc_util.read_ply_xyz(re_pc_filename)
if re_pc_pts.shape[0] < 2048:
re_pc_pts = pc_util.sample_point_cloud(re_pc_pts, 2048)
acc_perct, acc_avg_dist = evaluation_utils.accuracy(re_pc_pts,
gt_pc_pts,
thre=thre)
comp_perct, comp_avg_dist = evaluation_utils.completeness(re_pc_pts,
gt_pc_pts,
thre=thre)
all_acc_percentage.append(acc_perct)
all_acc_avg_dist.append(acc_avg_dist)
all_comp_percentage.append(comp_perct)
all_comp_avg_dist.append(comp_avg_dist)
avg_acc_perct = np.mean(all_acc_percentage)
avg_acc_avg_dist = np.mean(all_acc_avg_dist)
avg_comp_perct = np.mean(all_comp_percentage)
avg_comp_avg_dist = np.mean(all_comp_avg_dist)
f1_score = evaluation_utils.compute_F1_score(avg_acc_perct, avg_comp_perct)
print('%s:' % (result_dir.split('/')[-1]))
print(
'\tacc_avg_distance, acc_percentage, completeness-avg_distance, completeness-percentage, F1: %s,%s,%s,%s,%s'
% (str(avg_acc_avg_dist), str(avg_acc_perct), str(avg_comp_avg_dist),
str(avg_comp_perct), str(f1_score)))
def get_gt_point_clouds(cls_id, model_name, sample_nb=2048):
SCAN_PC_DIR = '/workspace/pcl2pcl-gan/pc2pc/data/ShapeNet_v1_point_cloud'
pc_dir = os.path.join(SCAN_PC_DIR, cls_id, 'point_cloud_clean')
mn = model_name
gt_pc_filename = os.path.join(pc_dir, mn+'_clean.ply')
if not os.path.exists(gt_pc_filename):
print('GT points not found: %s'%(gt_pc_filename))
return np.zeros((sample_nb, 3))
gt_pc = pc_util.read_ply_xyz(gt_pc_filename)
if 'v1' in SCAN_PC_DIR:
# for v1 data, rotate it to align with v2 (-z face)
gt_pc = pc_util.rotate_point_cloud_by_axis_angle(gt_pc, [0,1,0], 90)
gt_pc = pc_util.sample_point_cloud(gt_pc, sample_nb)
return gt_pc
def get_gt_point_clouds(cls_name, model_names, sample_nb=2048):
cls_id = shapenet_pc_dataset.get_cls_id(cls_name)
pc_dir = os.path.join(SCAN_PC_DIR, cls_id, 'point_cloud_clean')
pc_arr = []
for mn in model_names:
mn = mn.split('_')[0]
gt_pc_filename = os.path.join(pc_dir, mn + '_clean.ply')
if not os.path.exists(gt_pc_filename):
print('GT points not found: %s' % (gt_pc_filename))
pc_arr.append(np.zeros((sample_nb, 3)))
continue
gt_pc = pc_util.read_ply_xyz(gt_pc_filename)
if 'v1' in SCAN_PC_DIR:
# for v1 data, rotate it to align with v2
gt_pc = pc_util.rotate_point_cloud_by_axis_angle(
gt_pc, [0, 1, 0], 90)
gt_pc = pc_util.sample_point_cloud(gt_pc, sample_nb)
pc_arr.append(gt_pc)
pc_arr = np.array(pc_arr)
return pc_arr
import pc_util
reconstructed_pc_dir = '/workspace/pointnet2/pc2pc/data/ShapeNet_v2_point_cloud/02691156/point_cloud_clean'
output_dir = os.path.join(os.path.dirname(reconstructed_pc_dir),
os.path.basename(reconstructed_pc_dir) + '_gt_df')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
resolution = 32
all_recon_ply_names = os.listdir(reconstructed_pc_dir)
for rpn in tqdm(all_recon_ply_names):
if '1d63eb2b1f78aa88acf77e718d93f3e1' not in rpn:
continue
ply_filename = os.path.join(reconstructed_pc_dir, rpn)
recon_pc = pc_util.read_ply_xyz(ply_filename)
recon_pc = pc_util.rotate_point_cloud_by_axis_angle(
recon_pc, [0, 1, 0], -90)
recon_df, recon_df_arr = pc2df_utils.convert_pc2df(recon_pc,
resolution=resolution)
#output_filename = os.path.join(output_dir, rpn[:-4]+'.txt')
output_filename = os.path.join(output_dir, rpn[:-10] + '__0__.txt')
with open(output_filename, 'w') as file:
file.write('%d %d %d ' % (resolution, resolution, resolution))
for ele in recon_df_arr:
file.write('%f ' % (ele))
import pc_util
point_cloud_dir = '../data/ShapeNet_v1_point_cloud/03636649/point_cloud_clean_full'
output_dir = '../data/ShapeNet_v1_point_cloud/03636649/point_cloud_clean_full_ds'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
down_sample_rate = 0.125
ply_filename_list = [
os.path.join(point_cloud_dir, f) for f in os.listdir(point_cloud_dir)
]
ply_filename_list.sort()
for pf in tqdm(ply_filename_list):
points = pc_util.read_ply_xyz(pf)
choice = np.random.choice(points.shape[0],
int(points.shape[0] * down_sample_rate))
sampled_points = points[choice]
if sampled_points.shape[0] < 1000:
print('Skip, probably empty scan. %s' % (pf))
continue
# ensure that the bbox is centerred at the original
pts_min = np.amin(sampled_points, axis=0, keepdims=True)
pts_max = np.amax(sampled_points, axis=0, keepdims=True)
bbox_center = (pts_min + pts_max) / 2.0
sampled_points = sampled_points - bbox_center
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(BASE_DIR)
sys.path.append(os.path.join(ROOT_DIR, '../utils'))
import pc_util
dataset_dir = '/workspace/pointnet2/pc2pc/data/3D-EPN_dataset/test-images_dim32_sdf_pc'
output_dir = '/workspace/pointnet2/pc2pc/data/3D-EPN_dataset/test-images_dim32_sdf_pc_processed'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
cls_ids = os.listdir(dataset_dir)
for cls_id in cls_ids:
cls_dir = os.path.join(dataset_dir, cls_id)
output_cls_dir = os.path.join(output_dir, cls_id, 'point_cloud')
if not os.path.exists(output_cls_dir):
os.makedirs(output_cls_dir)
point_cloud_names = os.listdir(cls_dir)
for pc_n in tqdm(point_cloud_names):
pc_filename = os.path.join(cls_dir, pc_n)
out_pc_filename = os.path.join(output_cls_dir, pc_n)
points = pc_util.read_ply_xyz(pc_filename)
rotated_points = pc_util.rotate_point_cloud_by_axis_angle(
points, [0, 1, 0], 90)
pc_util.write_ply(rotated_points, out_pc_filename)
os.makedirs(out_dir)
if not os.path.exists(out_gt_dir):
os.mkdir(out_gt_dir)
if not os.path.exists(out_recon_dir):
os.mkdir(out_recon_dir)
if not os.path.exists(out_recon_mesh_dir):
os.mkdir(out_recon_mesh_dir)
df = read_df_from_txt(df_txt_fn)
df_mesh = get_isosurface(df, iso_val)
cls_id, mdl_id = get_clsId_modelId(df_txt_fn)
scanned_pc_filename = os.path.join(SHAPENET_POINTCLOUD_DIR, cls_id, 'point_cloud_clean', mdl_id+'_clean.ply')
if not os.path.exists(scanned_pc_filename):
print('No scanning available: %s'%(scanned_pc_filename))
continue
scan_pc = pc_util.read_ply_xyz(scanned_pc_filename)
if 'v1' in SHAPENET_POINTCLOUD_DIR:
# for v1 data, rotate it to align with v2
scan_pc = pc_util.rotate_point_cloud_by_axis_angle(scan_pc, [0,1,0], 90)
scan_pc =pc_util.sample_point_cloud(scan_pc, points_sample_nb)
pc_util.write_ply(scan_pc, os.path.join(out_gt_dir, mdl_id+'.ply'))
df_mesh = align_dfmesh_scanpc(df_mesh, dim, scan_pc)
df_mesh.export(os.path.join(out_recon_mesh_dir, mdl_id+'.ply'))
#recon_samples, _ = trimesh.sample.sample_surface_even(df_mesh, points_sample_nb)
recon_samples, _ = trimesh.sample.sample_surface(df_mesh, points_sample_nb)
pc_util.write_ply(np.array(recon_samples), os.path.join(out_recon_dir, mdl_id+'.ply'))
