# 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