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# Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
import os
import numpy as np
import torch
import nvdiffrast.torch as dr
import cv2
from video3d.render.render import render_uv
from . import util
from . import texture
from . import mlptexture
from ..utils import misc
######################################################################################
# Wrapper to make materials behave like a python dict, but register textures as
# torch.nn.Module parameters.
######################################################################################
class Material(torch.nn.Module):
def __init__(self, mat_dict):
super(Material, self).__init__()
self.mat_keys = set()
for key in mat_dict.keys():
self.mat_keys.add(key)
self[key] = mat_dict[key]
def __contains__(self, key):
return hasattr(self, key)
def __getitem__(self, key):
return getattr(self, key)
def __setitem__(self, key, val):
self.mat_keys.add(key)
setattr(self, key, val)
def __delitem__(self, key):
self.mat_keys.remove(key)
delattr(self, key)
def keys(self):
return self.mat_keys
######################################################################################
# .mtl material format loading / storing
######################################################################################
@torch.no_grad()
def load_mtl(fn, clear_ks=True):
import re
mtl_path = os.path.dirname(fn)
# Read file
with open(fn, 'r') as f:
lines = f.readlines()
# Parse materials
materials = []
for line in lines:
split_line = re.split(' +|\t+|\n+', line.strip())
prefix = split_line[0].lower()
data = split_line[1:]
if 'newmtl' in prefix:
material = Material({'name' : data[0]})
materials += [material]
elif materials:
if 'bsdf' in prefix or 'map_kd' in prefix or 'map_ks' in prefix or 'bump' in prefix:
material[prefix] = data[0]
else:
material[prefix] = torch.tensor(tuple(float(d) for d in data), dtype=torch.float32, device='cuda')
# Convert everything to textures. Our code expects 'kd' and 'ks' to be texture maps. So replace constants with 1x1 maps
for mat in materials:
if not 'bsdf' in mat:
mat['bsdf'] = 'pbr'
if 'map_kd' in mat:
mat['kd'] = texture.load_texture2D(os.path.join(mtl_path, mat['map_kd']))
else:
mat['kd'] = texture.Texture2D(mat['kd'])
if 'map_ks' in mat:
mat['ks'] = texture.load_texture2D(os.path.join(mtl_path, mat['map_ks']), channels=3)
else:
mat['ks'] = texture.Texture2D(mat['ks'])
if 'bump' in mat:
mat['normal'] = texture.load_texture2D(os.path.join(mtl_path, mat['bump']), lambda_fn=lambda x: x * 2 - 1, channels=3)
# Convert Kd from sRGB to linear RGB
mat['kd'] = texture.srgb_to_rgb(mat['kd'])
if clear_ks:
# Override ORM occlusion (red) channel by zeros. We hijack this channel
for mip in mat['ks'].getMips():
mip[..., 0] = 0.0
return materials
@torch.no_grad()
def save_mtl(fn, material, mesh=None, feat=None, resolution=[256, 256], prior_shape=None):
folder = os.path.dirname(fn)
file = os.path.basename(fn)
prefix = '_'.join(file.split('_')[:-1]) + '_'
with open(fn, "w") as f:
f.write('newmtl defaultMat\n')
if material is not None:
f.write('bsdf %s\n' % material['bsdf'])
if 'kd_ks_normal' in material.keys():
assert mesh is not None
glctx = dr.RasterizeGLContext()
mask, kd, ks, normal = render_uv(glctx, mesh, resolution, material['kd_ks_normal'], feat=feat, prior_shape=prior_shape)
hole_mask = 1. - mask
hole_mask = hole_mask.int()[0]
def uv_padding(image):
uv_padding_size = 4
inpaint_image = (
cv2.inpaint(
(image.detach().cpu().numpy() * 255).astype(np.uint8),
(hole_mask.detach().cpu().numpy() * 255).astype(np.uint8),
uv_padding_size,
cv2.INPAINT_TELEA,
)
/ 255.0
)
return torch.from_numpy(inpaint_image).to(image)
kd = uv_padding(kd[0])[None]
batch_size = kd.shape[0]
f.write(f'map_Kd {prefix}texture_kd.png\n')
misc.save_images(folder, kd.permute(0,3,1,2).detach().cpu().numpy(), fnames=[prefix + "texture_kd"] * batch_size)
f.write(f'map_Ks {prefix}texture_ks.png\n')
misc.save_images(folder, ks.permute(0,3,1,2).detach().cpu().numpy(), fnames=[prefix + "texture_ks"] * batch_size)
# disable normal
# f.write(f'bump {prefix}texture_n.png\n')
# misc.save_images(folder, normal.permute(0,3,1,2).detach().cpu().numpy(), fnames=[prefix + "texture_n"] * batch_size)
if 'kd' in material.keys():
f.write('map_Kd texture_kd.png\n')
texture.save_texture2D(os.path.join(folder, 'texture_Kd.png'), texture.rgb_to_srgb(material['kd']))
if 'ks' in material.keys():
f.write('map_Ks texture_ks.png\n')
texture.save_texture2D(os.path.join(folder, 'texture_Ks.png'), material['ks'])
if 'normal' in material.keys():
f.write('bump texture_n.png\n')
texture.save_texture2D(os.path.join(folder, 'texture_n.png'), material['normal'], lambda_fn=lambda x:(util.safe_normalize(x)+1)*0.5)
else:
f.write('Kd 1 1 1\n')
f.write('Ks 0 0 0\n')
f.write('Ka 0 0 0\n')
f.write('Tf 1 1 1\n')
f.write('Ni 1\n')
f.write('Ns 0\n')
######################################################################################
# Merge multiple materials into a single uber-material
######################################################################################
def _upscale_replicate(x, full_res):
x = x.permute(0, 3, 1, 2)
x = torch.nn.functional.pad(x, (0, full_res[1] - x.shape[3], 0, full_res[0] - x.shape[2]), 'replicate')
return x.permute(0, 2, 3, 1).contiguous()
def merge_materials(materials, texcoords, tfaces, mfaces):
assert len(materials) > 0
for mat in materials:
assert mat['bsdf'] == materials[0]['bsdf'], "All materials must have the same BSDF (uber shader)"
assert ('normal' in mat) is ('normal' in materials[0]), "All materials must have either normal map enabled or disabled"
uber_material = Material({
'name' : 'uber_material',
'bsdf' : materials[0]['bsdf'],
})
textures = ['kd', 'ks', 'normal']
# Find maximum texture resolution across all materials and textures
max_res = None
for mat in materials:
for tex in textures:
tex_res = np.array(mat[tex].getRes()) if tex in mat else np.array([1, 1])
max_res = np.maximum(max_res, tex_res) if max_res is not None else tex_res
# Compute size of compund texture and round up to nearest PoT
full_res = 2**np.ceil(np.log2(max_res * np.array([1, len(materials)]))).astype(np.int)
# Normalize texture resolution across all materials & combine into a single large texture
for tex in textures:
if tex in materials[0]:
tex_data = torch.cat(tuple(util.scale_img_nhwc(mat[tex].data, tuple(max_res)) for mat in materials), dim=2) # Lay out all textures horizontally, NHWC so dim2 is x
tex_data = _upscale_replicate(tex_data, full_res)
uber_material[tex] = texture.Texture2D(tex_data)
# Compute scaling values for used / unused texture area
s_coeff = [full_res[0] / max_res[0], full_res[1] / max_res[1]]
# Recompute texture coordinates to cooincide with new composite texture
new_tverts = {}
new_tverts_data = []
for fi in range(len(tfaces)):
matIdx = mfaces[fi]
for vi in range(3):
ti = tfaces[fi][vi]
if not (ti in new_tverts):
new_tverts[ti] = {}
if not (matIdx in new_tverts[ti]): # create new vertex
new_tverts_data.append([(matIdx + texcoords[ti][0]) / s_coeff[1], texcoords[ti][1] / s_coeff[0]]) # Offset texture coodrinate (x direction) by material id & scale to local space. Note, texcoords are (u,v) but texture is stored (w,h) so the indexes swap here
new_tverts[ti][matIdx] = len(new_tverts_data) - 1
tfaces[fi][vi] = new_tverts[ti][matIdx] # reindex vertex
return uber_material, new_tverts_data, tfaces
######################################################################################
# Utility functions for material
######################################################################################
def initial_guess_material(cfgs, mlp=False, init_mat=None, tet_bbox=None):
kd_min = torch.tensor(cfgs.get('kd_min', [0., 0., 0., 0.]), dtype=torch.float32)
kd_max = torch.tensor(cfgs.get('kd_max', [1., 1., 1., 1.]), dtype=torch.float32)
ks_min = torch.tensor(cfgs.get('ks_min', [0., 0., 0.]), dtype=torch.float32)
ks_max = torch.tensor(cfgs.get('ks_max', [0., 0., 0.]), dtype=torch.float32)
nrm_min = torch.tensor(cfgs.get('nrm_min', [-1., -1., 0.]), dtype=torch.float32)
nrm_max = torch.tensor(cfgs.get('nrm_max', [1., 1., 1.]), dtype=torch.float32)
if mlp:
num_layers = cfgs.get("num_layers_tex", 5)
nf = cfgs.get("hidden_size", 128)
enable_encoder = cfgs.get("enable_encoder", False)
feat_dim = cfgs.get("latent_dim", 64) if enable_encoder else 0
mlp_min = torch.cat((kd_min[0:3], ks_min, nrm_min), dim=0)
mlp_max = torch.cat((kd_max[0:3], ks_max, nrm_max), dim=0)
min_max = torch.stack((mlp_min, mlp_max), dim=0)
out_chn = 9
mlp_map_opt = mlptexture.MLPTexture3D(tet_bbox, channels=out_chn, internal_dims=nf, hidden=num_layers-1, feat_dim=feat_dim, min_max=min_max)
mat = Material({'kd_ks_normal' : mlp_map_opt})
else:
# Setup Kd (albedo) and Ks (x, roughness, metalness) textures
if cfgs.random_textures or init_mat is None:
num_channels = 4 if cfgs.layers > 1 else 3
kd_init = torch.rand(size=cfgs.texture_res + [num_channels]) * (kd_max - kd_min)[None, None, 0:num_channels] + kd_min[None, None, 0:num_channels]
kd_map_opt = texture.create_trainable(kd_init , cfgs.texture_res, not cfgs.custom_mip, [kd_min, kd_max])
ksR = np.random.uniform(size=cfgs.texture_res + [1], low=0.0, high=0.01)
ksG = np.random.uniform(size=cfgs.texture_res + [1], low=ks_min[1].cpu(), high=ks_max[1].cpu())
ksB = np.random.uniform(size=cfgs.texture_res + [1], low=ks_min[2].cpu(), high=ks_max[2].cpu())
ks_map_opt = texture.create_trainable(np.concatenate((ksR, ksG, ksB), axis=2), cfgs.texture_res, not cfgs.custom_mip, [ks_min, ks_max])
else:
kd_map_opt = texture.create_trainable(init_mat['kd'], cfgs.texture_res, not cfgs.custom_mip, [kd_min, kd_max])
ks_map_opt = texture.create_trainable(init_mat['ks'], cfgs.texture_res, not cfgs.custom_mip, [ks_min, ks_max])
# Setup normal map
if cfgs.random_textures or init_mat is None or 'normal' not in init_mat:
normal_map_opt = texture.create_trainable(np.array([0, 0, 1]), cfgs.texture_res, not cfgs.custom_mip, [nrm_min, nrm_max])
else:
normal_map_opt = texture.create_trainable(init_mat['normal'], cfgs.texture_res, not cfgs.custom_mip, [nrm_min, nrm_max])
mat = Material({
'kd' : kd_map_opt,
'ks' : ks_map_opt,
'normal' : normal_map_opt
})
if init_mat is not None:
mat['bsdf'] = init_mat['bsdf']
elif "bsdf" in cfgs:
mat['bsdf'] = cfgs["bsdf"]
else:
mat['bsdf'] = 'pbr'
if not cfgs.get("perturb_normal", False):
mat['no_perturbed_nrm'] = True
return mat |