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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
#----------------------------------------------------------------------------
# Texture learning with/without mipmaps.
#----------------------------------------------------------------------------
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 = '.',
log_fn = None,
texsave_interval = None,
texsave_fn = None,
imgsave_interval = None,
imgsave_fn = None):
if out_dir:
os.makedirs(out_dir, exist_ok=True)
# 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]))
# Transformation matrix input to TF graph.
mtx_in = tf.placeholder(tf.float32, [4, 4])
# Learned texture.
tex_var = tf.get_variable('tex', initializer=tf.constant_initializer(0.2), shape=tex.shape)
# Setup TF graph for reference rendering in high resolution.
pos_clip = tf.matmul(pos, mtx_in, transpose_b=True)[tf.newaxis, ...]
rast_out, rast_out_db = dr.rasterize(pos_clip, pos_idx, [ref_res, ref_res])
texc, texd = dr.interpolate(uv[tf.newaxis, ...], rast_out, uv_idx, rast_db=rast_out_db, diff_attrs='all')
color = dr.texture(tex[np.newaxis], texc, texd, filter_mode='linear-mipmap-linear', max_mip_level=max_mip_level)
color = color * tf.clip_by_value(rast_out[..., -1:], 0, 1) # Mask out background.
# Reduce the reference to correct size.
while color.shape[1] > res:
color = util.bilinear_downsample(color)
# TF Graph for rendered candidate.
if enable_mip:
# With mipmaps.
rast_out_opt, rast_out_db_opt = dr.rasterize(pos_clip, pos_idx, [res, res])
texc_opt, texd_opt = dr.interpolate(uv[tf.newaxis, ...], rast_out_opt, uv_idx, rast_db=rast_out_db_opt, diff_attrs='all')
color_opt = dr.texture(tex_var[np.newaxis], texc_opt, texd_opt, filter_mode='linear-mipmap-linear', max_mip_level=max_mip_level)
else:
# No mipmaps: no image-space derivatives anywhere.
rast_out_opt, _ = dr.rasterize(pos_clip, pos_idx, [res, res], output_db=False)
texc_opt, _ = dr.interpolate(uv[tf.newaxis, ...], rast_out_opt, uv_idx)
color_opt = dr.texture(tex_var[np.newaxis], texc_opt, filter_mode='linear')
color_opt = color_opt * tf.clip_by_value(rast_out_opt[..., -1:], 0, 1) # Mask out background.
# Measure only relevant portions of texture when calculating texture PSNR.
loss = tf.reduce_mean((color - color_opt)**2)
texmask = np.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
texloss = (tf.reduce_sum(texmask * (tex - tex_var)**2)/np.sum(texmask))**0.5 # RMSE within masked area.
# Training driven by image-space loss.
lr_in = tf.placeholder(tf.float32, [])
train_op = tf.train.AdamOptimizer(lr_in, 0.9, 0.99).minimize(loss, var_list=[tex_var])
# Open log file.
log_file = open(out_dir + '/' + log_fn, 'wt') if log_fn else None
# Render.
ang = 0.0
util.init_uninitialized_vars()
texloss_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))
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)
# Run training and measure texture-space RMSE loss.
texloss_val, _ = util.run([texloss, train_op], {mtx_in: r_mvp, lr_in: lr})
texloss_avg.append(texloss_val)
# Print/save log.
if log_interval and (it % log_interval == 0):
texloss_val, texloss_avg = np.mean(np.asarray(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 result images/textures.
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:
result_image = util.run(color_opt, {mtx_in: a_mvp})[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)
if save_texture:
util.save_image(out_dir + '/' + (texsave_fn % it), util.run(tex_var)[::-1])
# Done.
if log_file:
log_file.close()
#----------------------------------------------------------------------------
# Main function.
#----------------------------------------------------------------------------
def main():
display_interval = 0
enable_mip = None
def usage():
print("Usage: python earth.py [-v] [-mip|-nomip]")
exit()
for a in sys.argv[1:]:
if a == '-v': display_interval = 10
elif a == '-mip': enable_mip = True
elif a == '-nomip': enable_mip = False
else: usage()
if enable_mip is None:
usage()
# Initialize TensorFlow.
util.init_tf()
# Run.
out_dir = 'out/earth_mip' if enable_mip else 'out/earth_nomip'
fit_earth(max_iter=20000, log_interval=10, display_interval=display_interval, enable_mip=enable_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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