projection_video.py

# Copyright (c) 2021, 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.

"""Project given image to the latent space of pretrained network pickle."""
import os

import click
import imageio
import numpy as np
import PIL.Image
import torch
from tqdm import tqdm

import dnnlib
import legacy
import functools

#----------------------------------------------------------------------------

@click.command()
@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
@click.option('--target', 'target_fname', help='Target image file to project to', required=True, metavar='FILE')
@click.option('--w-steps',                help='W steps file name', required=True, metavar='FILE')
@click.option('--outdir',                 help='Where to save the output images', required=True, metavar='DIR')
def run_projection(
    network_pkl: str,
    target_fname: str,
    w_steps: str,
    outdir: str,
):
    # Load networks.
    print('Loading networks from "%s"...' % network_pkl)
    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
    with dnnlib.util.open_url(network_pkl) as fp:
        G = legacy.load_network_pkl(fp)['G_ema'].requires_grad_(False).to(device) # type: ignore
    
    if not torch.cuda.is_available():
        # https://github.com/NVlabs/stylegan2-ada-pytorch/pull/121/files
        G.synthesis.forward = functools.partial(G.synthesis.forward, force_fp32=True)

    # Load target image.
    target_pil = PIL.Image.open(target_fname).convert('RGB')
    w, h = target_pil.size
    s = min(w, h)
    target_pil = target_pil.crop(((w - s) // 2, (h - s) // 2, (w + s) // 2, (h + s) // 2))
    target_pil = target_pil.resize((G.img_resolution, G.img_resolution), PIL.Image.LANCZOS)
    target_uint8 = np.array(target_pil, dtype=np.uint8)

    projected_w_steps = np.load(w_steps)['w']
    # Render debug output: optional video and projected image and W vector.
    os.makedirs(outdir, exist_ok=True)
    video = imageio.get_writer(f'{outdir}/proj.mp4', mode='I', fps=10, codec='libx264', bitrate='16M')
    print (f'Saving optimization progress video "{outdir}/proj.mp4"')
    for projected_w in tqdm(projected_w_steps):
        projected_w = torch.from_numpy(projected_w).to(device)
        synth_image = G.synthesis(projected_w.unsqueeze(0), noise_mode='const')
        synth_image = (synth_image + 1) * (255/2)
        synth_image = synth_image.permute(0, 2, 3, 1).clamp(0, 255).to(torch.uint8)[0].cpu().numpy()
        video.append_data(np.concatenate([target_uint8, synth_image], axis=1))
    video.close()

#----------------------------------------------------------------------------

if __name__ == "__main__":
    run_projection() # pylint: disable=no-value-for-parameter

#----------------------------------------------------------------------------