render.py

# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Render script."""

import concurrent.futures
import functools
import glob
import os
import time
from internal import utils
import ipdb
from absl import app
import gin

from internal import configs

import jax
from jax import random
from rich import print
from matplotlib import cm
import mediapy as media
import numpy as np

configs.define_common_flags()
jax.config.parse_flags_with_absl()

# Step III: Add a new key here.
EXTRA_VIS_KEYS = [
  'L', 'R', 'R_norm',
  'Lx5', 'rgb_Lx5',
  'Lx8', 'rgb_Lx8',
  'Lg22', 'rgb_Lg22', 'L_avg0.6', 'rgb_L_avg0.6',
  'L_enhanced', 'rgb_enhanced',
  'L_avg_g22_x8', 'rgb_L_avg_g22_x8',
  'gamma',
  'alpha',
  # 'rgb_norm'
]


def create_videos(config, base_dir, out_dir, out_name, num_frames):
  """Creates videos out of the images saved to disk."""
  names = [n for n in config.checkpoint_dir.split('/') if n]
  # Last two parts of checkpoint path are experiment name and scene name.
  exp_name, scene_name = names[-2:]
  video_prefix = f'{scene_name}_{exp_name}_{out_name}'

  zpad = max(3, len(str(num_frames - 1)))
  idx_to_str = lambda idx: str(idx).zfill(zpad)

  utils.makedirs(base_dir)

  # Load one example frame to get image shape and depth range.
  depth_file = os.path.join(out_dir, f'distance_mean_{idx_to_str(0)}.tiff')
  depth_frame = utils.load_img(depth_file)
  shape = depth_frame.shape
  p = config.render_dist_percentile
  distance_limits = np.percentile(depth_frame.flatten(), [p, 100 - p])
  lo, hi = [config.render_dist_curve_fn(x) for x in distance_limits]
  print(f'Video shape is {shape[:2]}')

  video_kwargs = {
    'shape': shape[:2],
    'codec': 'h264',
    'fps': config.render_video_fps,
    'crf': config.render_video_crf,
  }

  # make videos based on img file prefix
  color_tags = [
                 'color', 'normals',
               ] + EXTRA_VIS_KEYS
  all_tags = color_tags + ['acc', 'distance_mean', 'distance_median']

  for k in all_tags:
    video_file = os.path.join(base_dir, f'{video_prefix}_{k}.mp4')
    input_format = 'gray' if k == 'acc' else 'rgb'
    file_ext = 'png' if k in color_tags else 'tiff'
    idx = 0
    file0 = os.path.join(out_dir, f'{k}_{idx_to_str(0)}.{file_ext}')
    if not utils.file_exists(file0):
      print(f'Images missing for tag {k}')
      continue
    print(f'Making video {video_file}...')
    with media.VideoWriter(
        video_file, **video_kwargs, input_format=input_format) as writer:
      for idx in range(num_frames):
        img_file = os.path.join(out_dir, f'{k}_{idx_to_str(idx)}.{file_ext}')
        if not utils.file_exists(img_file):
          ValueError(f'Image file {img_file} does not exist.')
        try:
          img = utils.load_img(img_file)
        except Exception as e:
          print(f'Get error when loading image {img_file}: {e}')
          continue

        if k in color_tags:
          img = img / 255.
        elif k.startswith('distance'):
          img = config.render_dist_curve_fn(img)
          img = np.clip((img - np.minimum(lo, hi)) / np.abs(hi - lo), 0, 1)
          img = cm.get_cmap('turbo')(img)[..., :3]

        frame = (np.clip(np.nan_to_num(img), 0., 1.) * 255.).astype(np.uint8)
        writer.add_image(frame)
        idx += 1


def main(unused_argv):
  from flax.training import checkpoints
  from internal import datasets
  from internal import models
  from internal import train_utils
  import jax.numpy as jnp

  config = configs.load_config(save_config=False)

  dataset = datasets.load_dataset('test', config.data_dir, config)

  key = random.PRNGKey(20200823)
  _, state, render_eval_pfn, _, _ = train_utils.setup_model(config, key)

  if config.rawnerf_mode:
    postprocess_fn = dataset.metadata['postprocess_fn']
  else:
    postprocess_fn = lambda z: z

  state = checkpoints.restore_checkpoint(config.checkpoint_dir, state, step=config.render_ckpt_step)
  try:
    tmp_params = checkpoints.restore_checkpoint(config.checkpoint_dir, None, step=config.render_ckpt_step)
    gb = tmp_params['params']['params']['NerfMLP_0']['global_gamma_base']
    # checkpoints.restore_checkpoint(config.checkpoint_dir, None)['params']['params']['NerfMLP_0']['global_gamma_base']
    print(f'[ I ] global_gamma_base at step {int(state.step)} is: {gb}, gamma={jax.nn.sigmoid(gb) * 2 + 1}')
  except:
    print('[ E ] global_gamma_base not found in state')

  step = int(state.step)
  print(f'Rendering checkpoint at step {step}.')

  out_name = 'path_renders' if config.render_path else 'test_preds'
  out_name = f'{out_name}_step_{step}'
  base_dir = config.render_dir
  if base_dir is None:
    base_dir = os.path.join(config.checkpoint_dir, 'render')
  out_dir = os.path.join(base_dir, out_name)
  if not utils.isdir(out_dir):
    utils.makedirs(out_dir)

  path_fn = lambda x: os.path.join(out_dir, x)

  # Ensure sufficient zero-padding of image indices in output filenames.
  zpad = max(3, len(str(dataset.size - 1)))
  idx_to_str = lambda idx: str(idx).zfill(zpad)

  if config.render_save_async:
    async_executor = concurrent.futures.ThreadPoolExecutor(max_workers=4)
    async_futures = []

    def save_fn(fn, *args, **kwargs):
      async_futures.append(async_executor.submit(fn, *args, **kwargs))
  else:
    def save_fn(fn, *args, **kwargs):
      fn(*args, **kwargs)

  for idx in range(dataset.size):
    if idx % config.render_num_jobs != config.render_job_id:
      continue

    if config.render_img_num is not None and idx >= config.render_img_num:
      print(f'Only render {config.render_img_num} images, finished.')
      return
    # If current image and next image both already exist, skip ahead.
    idx_str = idx_to_str(idx)
    curr_file = path_fn(f'color_{idx_str}.png')
    next_idx_str = idx_to_str(idx + config.render_num_jobs)
    next_file = path_fn(f'color_{next_idx_str}.png')
    # ipdb.set_trace()

    # ucomment here!
    if utils.file_exists(curr_file) and utils.file_exists(next_file):
      print(f'Image {idx}/{dataset.size} already exists, skipping')
      continue

    print(f'Evaluating image {idx + 1}/{dataset.size}')
    eval_start_time = time.time()
    rays = dataset.generate_ray_batch(idx).rays
    train_frac = 1.
    rendering = models.render_image(
      functools.partial(render_eval_pfn, state.params, train_frac),
      rays, None, config)
    print(f'Rendered in {(time.time() - eval_start_time):0.3f}s')

    if jax.host_id() != 0:  # Only record via host 0.
      continue

    rendering['rgb'] = postprocess_fn(rendering['rgb'])

    save_fn(utils.save_img_u8, rendering['rgb'], path_fn(f'color_{idx_str}.png'))
    # ipdb.set_trace()
    # cv2.imwrite('/home/grads/hywang26/tmp/rawnerf_D5/render/path_renders_step_50002/color_000.tiff', np.array(rendering['rgb'][..., ::-1] * (2 ** 16)).astype('uint16'))

    if 'L' in rendering:
      # save R_norm
      save_fn(utils.save_img_u8, rendering['R'] / rendering['R'].max(), path_fn(f'R_norm_{idx_str}.png'))
      save_fn(utils.save_img_u8, rendering['rgb'] / rendering['rgb'].max(), path_fn(f'rgb_norm_{idx_str}.png'))

      for render_name in rendering.keys():
        if render_name in EXTRA_VIS_KEYS:
          # ipdb.set_trace()
          render_tensor = rendering[render_name]
          if render_tensor.shape[-1] == 1:
            print(f'Reshape single channel image {render_name} to 3-channel.')
            render_tensor = jnp.broadcast_to(render_tensor, [*render_tensor.shape[:-1], 3])
          save_fn(utils.save_img_u8, render_tensor, path_fn(f'{render_name}_{idx_str}.png'))

    if 'normals' in rendering:
      save_fn(
        utils.save_img_u8, rendering['normals'] / 2. + 0.5,
        path_fn(f'normals_{idx_str}.png'))
    save_fn(
      utils.save_img_f32, rendering['distance_mean'],
      path_fn(f'distance_mean_{idx_str}.tiff'))
    save_fn(
      utils.save_img_f32, rendering['distance_median'],
      path_fn(f'distance_median_{idx_str}.tiff'))
    save_fn(
      utils.save_img_f32, rendering['acc'], path_fn(f'acc_{idx_str}.tiff'))

  if config.render_save_async:
    # Wait until all worker threads finish.
    async_executor.shutdown(wait=True)

    # This will ensure that exceptions in child threads are raised to the
    # main thread.
    for future in async_futures:
      future.result()

  time.sleep(1)
  num_files = len(glob.glob(path_fn('acc_*.tiff')))
  time.sleep(10)
  if jax.host_id() == 0 and num_files == dataset.size:
    print(f'All files found, creating videos (job {config.render_job_id}).')
    create_videos(config, base_dir, out_dir, out_name, dataset.size)

  # A hack that forces Jax to keep all TPUs alive until every TPU is finished.
  x = jax.numpy.ones([jax.local_device_count()])
  x = jax.device_get(jax.pmap(lambda x: jax.lax.psum(x, 'i'), 'i')(x))
  print(x)


if __name__ == '__main__':
  with gin.config_scope('eval'):  # Use the same scope as eval.py
    app.run(main)