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
import gc

from absl import app
import torch
import gin
from internal import configs
from internal import datasets
from internal import models
from internal import train_utils
from internal import utils

from matplotlib import cm
import mediapy as media
import numpy as np

configs.define_common_flags()


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)))

    def idx_to_str(idx):
        return 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,
    }

    for k in ['color', 'normals', 'acc', 'distance_mean', 'distance_median']:
        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', 'normals'] 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.')
                img = utils.load_img(img_file)
                if k in ['color', 'normals']:
                    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):

    config = configs.load_config(save_config=False)

    # Setup device.
    if torch.cuda.is_available():
        device = torch.device('cuda')
        torch.set_default_tensor_type('torch.cuda.FloatTensor')
    else:
        device = torch.device('cpu')
        torch.set_default_tensor_type('torch.FloatTensor')

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

    # Set random number generator seeds.
    torch.manual_seed(20221019)
    np.random.seed(20221019)

    # Create model.
    setup = train_utils.setup_model(config, dataset=dataset)
    model, _, _, render_eval_fn, _ = setup
    state = dict(step=0, model=model.state_dict())

    # Load states from checkpoint.
    if utils.isdir(config.checkpoint_dir):
        files = sorted([f for f in os.listdir(config.checkpoint_dir)
                        if f.startswith('checkpoint')],
                       key=lambda x: int(x.split('_')[-1]))
        # if there are checkpoints in the dir, load the latest checkpoint
        if files:
            checkpoint_name = files[-1]
            state = torch.load(os.path.join(
                config.checkpoint_dir, checkpoint_name))
            model.load_state_dict(state['model'])
            model.eval()
            model.to(device)
    else:
        utils.makedirs(config.checkpoint_dir)

    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)

    def path_fn(x):
        return os.path.join(out_dir, x)

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

    def idx_to_str(idx):
        return 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 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')
        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()
        batch = dataset.generate_ray_batch(idx)
        train_frac = 1.

        with torch.no_grad():
            rendering = models.render_image(
                functools.partial(render_eval_fn, train_frac),
                batch.rays, config)

        print(f'Rendered in {(time.time() - eval_start_time):0.3f}s')

        save_fn(
            utils.save_img_u8, rendering['rgb'], path_fn(f'color_{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'))
        save_fn(
            utils.save_img_u8, rendering['roughness'], path_fn(
                f'rho_{idx_str}.png'),
            mask=rendering['acc'])

    num_files = len(glob.glob(path_fn('acc_*.tiff')))
    if 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)


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