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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 numpy as np
import tensorflow as tf
import os
import sys
import pathlib
import util
sys.path.insert(0, os.path.join(sys.path[0], '../..')) # for nvdiffrast
import nvdiffrast.tensorflow 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 = '.',
log_fn = None,
imgsave_interval = None,
imgsave_fn = None):
if out_dir:
os.makedirs(out_dir, exist_ok=True)
# 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
print("Mesh has %d triangles and %d vertices." % (pos_idx.shape[0], pos.shape[0]))
# Target Phong parameters.
phong_rgb = np.asarray([1.0, 0.8, 0.6], np.float32)
phong_exp = 25.0
# Inputs to TF graph.
mtx_in = tf.placeholder(tf.float32, [4, 4])
invmtx_in = tf.placeholder(tf.float32, [4, 4]) # Inverse.
campos_in = tf.placeholder(tf.float32, [3]) # Camera position in world space.
lightdir_in = tf.placeholder(tf.float32, [3]) # Light direction.
# Learned variables: environment maps, phong color, phong exponent.
env_var = tf.get_variable('env_var', initializer=tf.constant_initializer(0.5), shape=env.shape)
phong_var_raw = tf.get_variable('phong_var', initializer=tf.random_uniform_initializer(0.0, 1.0), shape=[4]) # R, G, B, exp.
phong_var = phong_var_raw * [1.0, 1.0, 1.0, 10.0] # Faster learning rate for the exponent.
# Transform and rasterize.
viewvec = pos[..., :3] - campos_in[np.newaxis, np.newaxis, :] # View vectors at vertices.
reflvec = viewvec - 2.0 * normals[tf.newaxis, ...] * tf.reduce_sum(normals[tf.newaxis, ...] * viewvec, axis=-1, keepdims=True) # Reflection vectors at vertices.
reflvec = reflvec / tf.reduce_sum(reflvec**2, axis=-1, keepdims=True)**0.5 # Normalize.
pos_clip = tf.matmul(pos, mtx_in, transpose_b=True)[tf.newaxis, ...]
rast_out, rast_out_db = dr.rasterize(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 / tf.reduce_sum(refl**2, axis=-1, keepdims=True)**0.5 # Normalize.
ldotr = tf.reduce_sum(-lightdir_in * refl, axis=-1, keepdims=True) # L dot R.
# Reference color. No need for AA because we are not learning geometry.
env = np.stack(env)[:, ::-1]
color = dr.texture(env[np.newaxis, ...], refl, refld, filter_mode='linear-mipmap-linear', boundary_mode='cube')
color = tf.reduce_sum(tf.stack(color), axis=0)
color = color + phong_rgb * tf.maximum(0.0, ldotr) ** phong_exp # Phong.
color = tf.maximum(color, 1.0 - tf.clip_by_value(rast_out[..., -1:], 0, 1)) # White background.
# Candidate rendering same up to this point, but uses learned texture and Phong parameters instead.
color_opt = dr.texture(env_var[tf.newaxis, ...], refl, uv_da=refld, filter_mode='linear-mipmap-linear', boundary_mode='cube')
color_opt = tf.reduce_sum(tf.stack(color_opt), axis=0)
color_opt = color_opt + phong_var[:3] * tf.maximum(0.0, ldotr) ** phong_var[3] # Phong.
color_opt = tf.maximum(color_opt, 1.0 - tf.clip_by_value(rast_out[..., -1:], 0, 1)) # White background.
# Training.
loss = tf.reduce_mean((color - color_opt)**2) # L2 pixel loss.
lr_in = tf.placeholder(tf.float32, [])
train_op = tf.train.AdamOptimizer(lr_in, 0.9, 0.99).minimize(loss, var_list=[env_var, phong_var_raw])
# Open log file.
log_file = open(out_dir + '/' + log_fn, 'wt') if log_fn else None
# Render.
ang = 0.0
util.init_uninitialized_vars()
imgloss_avg, phong_avg = [], []
for it in range(max_iter + 1):
lr = lr_base * lr_ramp**(float(it)/float(max_iter))
# 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)
# Solve camera positions.
a_campos = np.linalg.inv(a_mv)[:3, 3]
r_campos = np.linalg.inv(r_mv)[:3, 3]
# Random light direction.
lightdir = np.random.normal(size=[3])
lightdir /= np.linalg.norm(lightdir) + 1e-8
# Run training and measure image-space RMSE loss.
imgloss_val, phong_val, _ = util.run([loss, phong_var, train_op], {mtx_in: r_mvp, invmtx_in: np.linalg.inv(r_mvp), campos_in: r_campos, lightdir_in: lightdir, lr_in: lr})
imgloss_avg.append(imgloss_val**0.5)
phong_avg.append(phong_val)
# 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_image = imgsave_interval and (it % imgsave_interval == 0)
if display_image or save_image:
result_image = util.run(color_opt, {mtx_in: a_mvp, invmtx_in: np.linalg.inv(a_mvp), campos_in: a_campos, lightdir_in: lightdir})[0]
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)
# Done.
if log_file:
log_file.close()
#----------------------------------------------------------------------------
# Main function.
#----------------------------------------------------------------------------
def main():
display_interval = 0
for a in sys.argv[1:]:
if a == '-v':
display_interval = 10
else:
print("Usage: python envphong.py [-v]")
exit()
# Initialize TensorFlow.
util.init_tf()
# Run.
fit_env_phong(max_iter=1500, log_interval=10, display_interval=display_interval, out_dir='out/env_phong', log_fn='log.txt', imgsave_interval=100, imgsave_fn='img_%06d.png')
# Done.
print("Done.")
#----------------------------------------------------------------------------
if __name__ == "__main__":
main()
#----------------------------------------------------------------------------
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