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import torch
import trimesh
from lib.common.BNI_utils import (
depth_inverse_transform,
double_side_bilateral_normal_integration,
verts_inverse_transform,
)
class BNI:
def __init__(self, dir_path, name, BNI_dict, cfg, device):
self.scale = 256.0
self.cfg = cfg
self.name = name
self.normal_front = BNI_dict["normal_F"]
self.normal_back = BNI_dict["normal_B"]
self.mask = BNI_dict["mask"]
self.depth_front = BNI_dict["depth_F"]
self.depth_back = BNI_dict["depth_B"]
self.depth_mask = BNI_dict["depth_mask"]
# hparam:
# k --> smaller, keep continuity
# lambda --> larger, more depth-awareness
self.k = self.cfg['k']
self.lambda1 = self.cfg['lambda1']
self.boundary_consist = self.cfg['boundary_consist']
self.cut_intersection = self.cfg['cut_intersection']
self.F_B_surface = None
self.F_B_trimesh = None
self.F_depth = None
self.B_depth = None
self.device = device
self.export_dir = dir_path
# code: https://github.com/hoshino042/bilateral_normal_integration
# paper: Bilateral Normal Integration
def extract_surface(self, verbose=True):
bni_result = double_side_bilateral_normal_integration(
normal_front=self.normal_front,
normal_back=self.normal_back,
normal_mask=self.mask,
depth_front=self.depth_front * self.scale,
depth_back=self.depth_back * self.scale,
depth_mask=self.depth_mask,
k=self.k,
lambda_normal_back=1.0,
lambda_depth_front=self.lambda1,
lambda_depth_back=self.lambda1,
lambda_boundary_consistency=self.boundary_consist,
cut_intersection=self.cut_intersection,
)
F_verts = verts_inverse_transform(bni_result["F_verts"], self.scale)
B_verts = verts_inverse_transform(bni_result["B_verts"], self.scale)
self.F_depth = depth_inverse_transform(bni_result["F_depth"], self.scale)
self.B_depth = depth_inverse_transform(bni_result["B_depth"], self.scale)
F_B_verts = torch.cat((F_verts, B_verts), dim=0)
F_B_faces = torch.cat(
(bni_result["F_faces"], bni_result["B_faces"] + bni_result["F_faces"].max() + 1), dim=0
)
self.F_B_trimesh = trimesh.Trimesh(
F_B_verts.float(), F_B_faces.long(), process=False, maintain_order=True
)
# self.F_trimesh = trimesh.Trimesh(
# F_verts.float(), bni_result["F_faces"].long(), process=False, maintain_order=True
# )
# self.B_trimesh = trimesh.Trimesh(
# B_verts.float(), bni_result["B_faces"].long(), process=False, maintain_order=True
# )
if __name__ == "__main__":
import os.path as osp
import numpy as np
from tqdm import tqdm
root = "/home/yxiu/Code/ECON/results/examples/BNI"
npy_file = f"{root}/304e9c4798a8c3967de7c74c24ef2e38.npy"
bni_dict = np.load(npy_file, allow_pickle=True).item()
default_cfg = {'k': 2, 'lambda1': 1e-4, 'boundary_consist': 1e-6}
# for k in [1, 2, 4, 10, 100]:
# default_cfg['k'] = k
# for k in [1e-8, 1e-4, 1e-2, 1e-1, 1]:
# default_cfg['lambda1'] = k
# for k in [1e-4, 1e-2, 0]:
# default_cfg['boundary_consist'] = k
bni_object = BNI(
osp.dirname(npy_file), osp.basename(npy_file), bni_dict, default_cfg,
torch.device('cuda:0')
)
bni_object.extract_surface()
bni_object.F_trimesh.export(osp.join(osp.dirname(npy_file), "F.obj"))
bni_object.B_trimesh.export(osp.join(osp.dirname(npy_file), "B.obj"))
bni_object.F_B_trimesh.export(osp.join(osp.dirname(npy_file), "BNI.obj"))
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