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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 numpy as np
import torch
import os
import pathlib
import imageio
import util
import nvdiffrast.torch as dr
#----------------------------------------------------------------------------
# Environment map and Phong BRDF learning.
#----------------------------------------------------------------------------
def fit_env_phong(max_iter = 1000,
log_interval = 10,
display_interval = None,
display_res = 1024,
res = 1024,
lr_base = 1e-2,
lr_ramp = 1.0,
out_dir = None,
log_fn = None,
mp4save_interval = None,
mp4save_fn = None):
log_file = None
writer = None
if out_dir:
os.makedirs(out_dir, exist_ok=True)
if log_fn:
log_file = open(out_dir + '/' + log_fn, 'wt')
if mp4save_interval != 0:
writer = imageio.get_writer(f'{out_dir}/{mp4save_fn}', mode='I', fps=30, codec='libx264', bitrate='16M')
else:
mp4save_interval = None
# Texture adapted from https://github.com/WaveEngine/Samples/tree/master/Materials/EnvironmentMap/Content/Assets/CubeMap.cubemap
datadir = f'{pathlib.Path(__file__).absolute().parents[1]}/data'
with np.load(f'{datadir}/envphong.npz') as f:
pos_idx, pos, normals, env = f.values()
env = env.astype(np.float32)/255.0
env = np.stack(env)[:, ::-1].copy()
print("Mesh has %d triangles and %d vertices." % (pos_idx.shape[0], pos.shape[0]))
# Move all the stuff to GPU.
pos_idx = torch.as_tensor(pos_idx, dtype=torch.int32, device='cuda')
pos = torch.as_tensor(pos, dtype=torch.float32, device='cuda')
normals = torch.as_tensor(normals, dtype=torch.float32, device='cuda')
env = torch.as_tensor(env, dtype=torch.float32, device='cuda')
# Target Phong parameters.
phong_rgb = np.asarray([1.0, 0.8, 0.6], np.float32)
phong_exp = 25.0
phong_rgb_t = torch.as_tensor(phong_rgb, dtype=torch.float32, device='cuda')
# Learned variables: environment maps, phong color, phong exponent.
env_var = torch.ones_like(env) * .5
env_var.requires_grad_()
phong_var_raw = torch.as_tensor(np.random.uniform(size=[4]), dtype=torch.float32, device='cuda')
phong_var_raw.requires_grad_()
phong_var_mul = torch.as_tensor([1.0, 1.0, 1.0, 10.0], dtype=torch.float32, device='cuda')
# Render.
ang = 0.0
imgloss_avg, phong_avg = [], []
glctx = dr.RasterizeGLContext()
zero_tensor = torch.as_tensor(0.0, dtype=torch.float32, device='cuda')
one_tensor = torch.as_tensor(1.0, dtype=torch.float32, device='cuda')
# Adam optimizer for environment map and phong with a learning rate ramp.
optimizer = torch.optim.Adam([env_var, phong_var_raw], lr=lr_base)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda x: lr_ramp**(float(x)/float(max_iter)))
for it in range(max_iter + 1):
phong_var = phong_var_raw * phong_var_mul
# Random rotation/translation matrix for optimization.
r_rot = util.random_rotation_translation(0.25)
# Smooth rotation for display.
ang = ang + 0.01
a_rot = np.matmul(util.rotate_x(-0.4), util.rotate_y(ang))
# 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, -3.5), 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)
a_mvc = a_mvp
r_mvp = torch.as_tensor(r_mvp, dtype=torch.float32, device='cuda')
a_mvp = torch.as_tensor(a_mvp, dtype=torch.float32, device='cuda')
# Solve camera positions.
a_campos = torch.as_tensor(np.linalg.inv(a_mv)[:3, 3], dtype=torch.float32, device='cuda')
r_campos = torch.as_tensor(np.linalg.inv(r_mv)[:3, 3], dtype=torch.float32, device='cuda')
# Random light direction.
lightdir = np.random.normal(size=[3])
lightdir /= np.linalg.norm(lightdir) + 1e-8
lightdir = torch.as_tensor(lightdir, dtype=torch.float32, device='cuda')
def render_refl(ldir, cpos, mvp):
# Transform and rasterize.
viewvec = pos[..., :3] - cpos[np.newaxis, np.newaxis, :] # View vectors at vertices.
reflvec = viewvec - 2.0 * normals[np.newaxis, ...] * torch.sum(normals[np.newaxis, ...] * viewvec, -1, keepdim=True) # Reflection vectors at vertices.
reflvec = reflvec / torch.sum(reflvec**2, -1, keepdim=True)**0.5 # Normalize.
pos_clip = torch.matmul(pos, mvp.t())[np.newaxis, ...]
rast_out, rast_out_db = dr.rasterize(glctx, pos_clip, pos_idx, [res, res])
refl, refld = dr.interpolate(reflvec, rast_out, pos_idx, rast_db=rast_out_db, diff_attrs='all') # Interpolated reflection vectors.
# Phong light.
refl = refl / (torch.sum(refl**2, -1, keepdim=True) + 1e-8)**0.5 # Normalize.
ldotr = torch.sum(-ldir * refl, -1, keepdim=True) # L dot R.
# Return
return refl, refld, ldotr, (rast_out[..., -1:] == 0)
# Render the reflections.
refl, refld, ldotr, mask = render_refl(lightdir, r_campos, r_mvp)
# Reference color. No need for AA because we are not learning geometry.
color = dr.texture(env[np.newaxis, ...], refl, uv_da=refld, filter_mode='linear-mipmap-linear', boundary_mode='cube')
color = color + phong_rgb_t * torch.max(zero_tensor, ldotr) ** phong_exp # Phong.
color = torch.where(mask, one_tensor, color) # White background.
# Candidate rendering same up to this point, but uses learned texture and Phong parameters instead.
color_opt = dr.texture(env_var[np.newaxis, ...], refl, uv_da=refld, filter_mode='linear-mipmap-linear', boundary_mode='cube')
color_opt = color_opt + phong_var[:3] * torch.max(zero_tensor, ldotr) ** phong_var[3] # Phong.
color_opt = torch.where(mask, one_tensor, color_opt) # White background.
# Compute loss and train.
loss = torch.mean((color - color_opt)**2) # L2 pixel loss.
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
# Collect losses.
imgloss_avg.append(loss.detach().cpu().numpy())
phong_avg.append(phong_var.detach().cpu().numpy())
# Print/save log.
if log_interval and (it % log_interval == 0):
imgloss_val, imgloss_avg = np.mean(np.asarray(imgloss_avg, np.float32)), []
phong_val, phong_avg = np.mean(np.asarray(phong_avg, np.float32), axis=0), []
phong_rgb_rmse = np.mean((phong_val[:3] - phong_rgb)**2)**0.5
phong_exp_rel_err = np.abs(phong_val[3] - phong_exp)/phong_exp
s = "iter=%d,phong_rgb_rmse=%f,phong_exp_rel_err=%f,img_rmse=%f" % (it, phong_rgb_rmse, phong_exp_rel_err, imgloss_val)
print(s)
if log_file:
log_file.write(s + '\n')
# Show/save result image.
display_image = display_interval and (it % display_interval == 0)
save_mp4 = mp4save_interval and (it % mp4save_interval == 0)
if display_image or save_mp4:
lightdir = np.asarray([.8, -1., .5, 0.0])
lightdir = np.matmul(a_mvc, lightdir)[:3]
lightdir /= np.linalg.norm(lightdir)
lightdir = torch.as_tensor(lightdir, dtype=torch.float32, device='cuda')
refl, refld, ldotr, mask = render_refl(lightdir, a_campos, a_mvp)
color_opt = dr.texture(env_var[np.newaxis, ...], refl, uv_da=refld, filter_mode='linear-mipmap-linear', boundary_mode='cube')
color_opt = color_opt + phong_var[:3] * torch.max(zero_tensor, ldotr) ** phong_var[3]
color_opt = torch.where(mask, one_tensor, color_opt)
result_image = color_opt.detach()[0].cpu().numpy()
if display_image:
util.display_image(result_image, size=display_res, title='%d / %d' % (it, max_iter))
if save_mp4:
writer.append_data(np.clip(np.rint(result_image*255.0), 0, 255).astype(np.uint8))
# Done.
if writer is not None:
writer.close()
if log_file:
log_file.close()
#----------------------------------------------------------------------------
# Main function.
#----------------------------------------------------------------------------
def main():
parser = argparse.ArgumentParser(description='Environment map fitting example')
parser.add_argument('--outdir', help='Specify output directory', default='')
parser.add_argument('--display-interval', type=int, default=0)
parser.add_argument('--mp4save-interval', type=int, default=10)
parser.add_argument('--max-iter', type=int, default=5000)
args = parser.parse_args()
# Set up logging.
if args.outdir:
out_dir = f'{args.outdir}/env_phong'
print (f'Saving results under {out_dir}')
else:
out_dir = None
print ('No output directory specified, not saving log or images')
# Run.
fit_env_phong(
max_iter=args.max_iter,
log_interval=100,
display_interval=args.display_interval,
out_dir=out_dir,
mp4save_interval=args.mp4save_interval,
mp4save_fn='progress.mp4'
)
# Done.
print("Done.")
#----------------------------------------------------------------------------
if __name__ == "__main__":
main()
#----------------------------------------------------------------------------
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