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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import argparse
import os
import pathlib
import numpy as np
import torch
import util
import nvdiffrast.torch as dr
#----------------------------------------------------------------------------
# Helpers.
def transform_pos(mtx, pos):
t_mtx = torch.from_numpy(mtx).cuda() if isinstance(mtx, np.ndarray) else mtx
posw = torch.cat([pos, torch.ones([pos.shape[0], 1]).cuda()], axis=1)
return torch.matmul(posw, t_mtx.t())[None, ...]
def render(glctx, mtx, pos, pos_idx, uv, uv_idx, tex, resolution, enable_mip, max_mip_level):
pos_clip = transform_pos(mtx, pos)
rast_out, rast_out_db = dr.rasterize(glctx, pos_clip, pos_idx, resolution=[resolution, resolution])
if enable_mip:
texc, texd = dr.interpolate(uv[None, ...], rast_out, uv_idx, rast_db=rast_out_db, diff_attrs='all')
color = dr.texture(tex[None, ...], texc, texd, filter_mode='linear-mipmap-linear', max_mip_level=max_mip_level)
else:
texc, _ = dr.interpolate(uv[None, ...], rast_out, uv_idx)
color = dr.texture(tex[None, ...], texc, filter_mode='linear')
color = color * torch.clamp(rast_out[..., -1:], 0, 1) # Mask out background.
return color
#----------------------------------------------------------------------------
def fit_earth(max_iter = 20000,
log_interval = 10,
display_interval = None,
display_res = 1024,
enable_mip = True,
res = 512,
ref_res = 4096,
lr_base = 1e-2,
lr_ramp = 0.1,
out_dir = None,
log_fn = None,
texsave_interval = None,
texsave_fn = None,
imgsave_interval = None,
imgsave_fn = None):
log_file = None
if out_dir:
os.makedirs(out_dir, exist_ok=True)
if log_fn:
log_file = open(out_dir + '/' + log_fn, 'wt')
else:
imgsave_interval, texsave_interval = None, None
# Mesh and texture adapted from "3D Earth Photorealistic 2K" model at
# https://www.turbosquid.com/3d-models/3d-realistic-earth-photorealistic-2k-1279125
datadir = f'{pathlib.Path(__file__).absolute().parents[1]}/data'
with np.load(f'{datadir}/earth.npz') as f:
pos_idx, pos, uv_idx, uv, tex = f.values()
tex = tex.astype(np.float32)/255.0
max_mip_level = 9 # Texture is a 4x3 atlas of 512x512 maps.
print("Mesh has %d triangles and %d vertices." % (pos_idx.shape[0], pos.shape[0]))
# Some input geometry contains vertex positions in (N, 4) (with v[:,3]==1). Drop
# the last column in that case.
if pos.shape[1] == 4: pos = pos[:, 0:3]
# Create position/triangle index tensors
pos_idx = torch.from_numpy(pos_idx.astype(np.int32)).cuda()
vtx_pos = torch.from_numpy(pos.astype(np.float32)).cuda()
uv_idx = torch.from_numpy(uv_idx.astype(np.int32)).cuda()
vtx_uv = torch.from_numpy(uv.astype(np.float32)).cuda()
tex = torch.from_numpy(tex.astype(np.float32)).cuda()
tex_opt = torch.full(tex.shape, 0.2, device='cuda', requires_grad=True)
glctx = dr.RasterizeGLContext()
ang = 0.0
# Adam optimizer for texture with a learning rate ramp.
optimizer = torch.optim.Adam([tex_opt], lr=lr_base)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda x: lr_ramp**(float(x)/float(max_iter)))
# Render.
ang = 0.0
texloss_avg = []
for it in range(max_iter + 1):
# Random rotation/translation matrix for optimization.
r_rot = util.random_rotation_translation(0.25)
# Smooth rotation for display.
a_rot = np.matmul(util.rotate_x(-0.4), util.rotate_y(ang))
dist = np.random.uniform(0.0, 48.5)
# Modelview and modelview + projection matrices.
proj = util.projection(x=0.4, n=1.0, f=200.0)
r_mv = np.matmul(util.translate(0, 0, -1.5 - dist), r_rot)
r_mvp = np.matmul(proj, r_mv).astype(np.float32)
a_mv = np.matmul(util.translate(0, 0, -3.5), a_rot)
a_mvp = np.matmul(proj, a_mv).astype(np.float32)
# Measure texture-space RMSE loss
with torch.no_grad():
texmask = torch.zeros_like(tex)
tr = tex.shape[1]//4
texmask[tr+13:2*tr-13, 25:-25, :] += 1.0
texmask[25:-25, tr+13:2*tr-13, :] += 1.0
# Measure only relevant portions of texture when calculating texture
# PSNR.
texloss = (torch.sum(texmask * (tex - tex_opt)**2)/torch.sum(texmask))**0.5 # RMSE within masked area.
texloss_avg.append(float(texloss))
# Render reference and optimized frames. Always enable mipmapping for reference.
color = render(glctx, r_mvp, vtx_pos, pos_idx, vtx_uv, uv_idx, tex, ref_res, True, max_mip_level)
color_opt = render(glctx, r_mvp, vtx_pos, pos_idx, vtx_uv, uv_idx, tex_opt, res, enable_mip, max_mip_level)
# Reduce the reference to correct size.
while color.shape[1] > res:
color = util.bilinear_downsample(color)
# Compute loss and perform a training step.
loss = torch.mean((color - color_opt)**2) # L2 pixel loss.
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
# Print/save log.
if log_interval and (it % log_interval == 0):
texloss_val = np.mean(np.asarray(texloss_avg))
texloss_avg = []
psnr = -10.0 * np.log10(texloss_val**2) # PSNR based on average RMSE.
s = "iter=%d,loss=%f,psnr=%f" % (it, texloss_val, psnr)
print(s)
if log_file:
log_file.write(s + '\n')
# Show/save image.
display_image = display_interval and (it % display_interval == 0)
save_image = imgsave_interval and (it % imgsave_interval == 0)
save_texture = texsave_interval and (it % texsave_interval) == 0
if display_image or save_image:
ang = ang + 0.1
with torch.no_grad():
result_image = render(glctx, a_mvp, vtx_pos, pos_idx, vtx_uv, uv_idx, tex_opt, res, enable_mip, max_mip_level)[0].cpu().numpy()
if display_image:
util.display_image(result_image, size=display_res, title='%d / %d' % (it, max_iter))
if save_image:
util.save_image(out_dir + '/' + (imgsave_fn % it), result_image)
if save_texture:
texture = tex_opt.cpu().numpy()[::-1]
util.save_image(out_dir + '/' + (texsave_fn % it), texture)
# Done.
if log_file:
log_file.close()
#----------------------------------------------------------------------------
def main():
parser = argparse.ArgumentParser(description='Earth texture fitting example')
parser.add_argument('--outdir', help='Specify output directory', default='')
parser.add_argument('--mip', action='store_true', default=False)
parser.add_argument('--display-interval', type=int, default=0)
parser.add_argument('--max-iter', type=int, default=10000)
args = parser.parse_args()
# Set up logging.
if args.outdir:
ms = 'mip' if args.mip else 'nomip'
out_dir = f'{args.outdir}/earth_{ms}'
print (f'Saving results under {out_dir}')
else:
out_dir = None
print ('No output directory specified, not saving log or images')
# Run.
fit_earth(max_iter=args.max_iter, log_interval=10, display_interval=args.display_interval, enable_mip=args.mip, out_dir=out_dir, log_fn='log.txt', texsave_interval=1000, texsave_fn='tex_%06d.png', imgsave_interval=1000, imgsave_fn='img_%06d.png')
# Done.
print("Done.")
#----------------------------------------------------------------------------
if __name__ == "__main__":
main()
#----------------------------------------------------------------------------
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