gen_samples.py

''' Generate images and shapes using pretrained network pickle.
Code adapted from following paper
"Efficient Geometry-aware 3D Generative Adversarial Networks."
See LICENSES/LICENSE_EG3D for original license.
'''
import os
import re
from typing import List, Optional, Tuple, Union

import click
import dnnlib
import numpy as np
import PIL.Image
import torch
from tqdm import tqdm
import mrcfile

import legacy
from camera_utils import LookAtPoseSampler, FOV_to_intrinsics
from torch_utils import misc
import importlib

def parse_range(s: Union[str, List]) -> List[int]:
    '''Parse a comma separated list of numbers or ranges and return a list of ints.

    Example: '1,2,5-10' returns [1, 2, 5, 6, 7]
    '''
    if isinstance(s, list): return s
    ranges = []
    range_re = re.compile(r'^(\d+)-(\d+)$')
    for p in s.split(','):
        m = range_re.match(p)
        if m:
            ranges.extend(range(int(m.group(1)), int(m.group(2))+1))
        else:
            ranges.append(int(p))
    return ranges

def create_samples(N=256, voxel_origin=[0, 0, 0], cube_length=2.0):
    # NOTE: the voxel_origin is actually the (bottom, left, down) corner, not the middle
    voxel_origin = np.array(voxel_origin) - cube_length/2
    voxel_size = cube_length / (N - 1)

    overall_index = torch.arange(0, N ** 3, 1, out=torch.LongTensor())
    samples = torch.zeros(N ** 3, 3)

    # transform first 3 columns
    # to be the x, y, z index
    samples[:, 2] = overall_index % N
    samples[:, 1] = (overall_index.float() / N) % N
    samples[:, 0] = ((overall_index.float() / N) / N) % N

    # transform first 3 columns
    # to be the x, y, z coordinate
    samples[:, 0] = (samples[:, 0] * voxel_size) + voxel_origin[2]
    samples[:, 1] = (samples[:, 1] * voxel_size) + voxel_origin[1]
    samples[:, 2] = (samples[:, 2] * voxel_size) + voxel_origin[0]

    num_samples = N ** 3

    return samples.unsqueeze(0), voxel_origin, voxel_size

def parse_comma_separated_list(s):
    if isinstance(s, list):
        return s
    if s is None or s.lower() == 'none' or s == '':
        return []
    return s.split(',')

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

@click.command()
@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
@click.option('--seeds', type=parse_range, help='List of random seeds (e.g., \'0,1,4-6\')', required=True)
@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=0.7, show_default=True)
@click.option('--trunc-cutoff', 'truncation_cutoff', type=int, help='Truncation cutoff', default=14, show_default=True)
@click.option('--class', 'class_idx', type=int, help='Class label (unconditional if not specified)')
@click.option('--outdir', help='Where to save the output images', type=str, required=True, metavar='DIR', default=None, show_default=True)
@click.option('--shapes', help='Export shapes as .mrc files viewable in ChimeraX', type=bool, required=False, metavar='BOOL', default=False, show_default=True)
@click.option('--shape-res', help='', type=int, required=False, metavar='int', default=512, show_default=True)
@click.option('--fov-deg', help='Field of View of camera in degrees', type=int, required=False, metavar='float', default=18.837, show_default=True)
@click.option('--shape-format', help='Shape Format', type=click.Choice(['.mrc', '.ply']), default='.mrc')
@click.option('--pose_cond', type=int, help='camera conditioned pose angle', default=90, show_default=True)


def generate_images(
    network_pkl: str,
    seeds: List[int],
    truncation_psi: float,
    truncation_cutoff: int,
    outdir: str,
    shapes: bool,
    shape_res: int,
    fov_deg: float,
    shape_format: str,
    class_idx: Optional[int],
    pose_cond: int,
):
    """Generate images using pretrained network pickle.

    Examples:

    \b
    # Generate an image using pre-trained model.
    python gen_samples.py --trunc=0.7 --seeds=0-2 --network model/hyplanehead-ckpt.pkl --outdir=output
    """

    print('Loading networks from "%s"...' % network_pkl)
    print(network_pkl, type(network_pkl))
    device = torch.device('cuda:0')
    with dnnlib.util.open_url(network_pkl) as f:
        G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore

    network_pkl_name = os.path.basename(network_pkl)
        
    os.makedirs(outdir, exist_ok=True)
    print(f'create outdir at f{outdir}')

    pose_cond_rad = pose_cond/180*np.pi

    intrinsics = FOV_to_intrinsics(fov_deg, device=device)


    # Generate images.
    for seed_idx, seed in enumerate(seeds):
        print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
        # cond camera settings
        cam_pivot = torch.tensor([0, 0, 0], device=device)
        cam_radius = G.rendering_kwargs.get('avg_camera_radius', 2.7)
        conditioning_cam2world_pose = LookAtPoseSampler.sample(pose_cond_rad, np.pi/2, cam_pivot, radius=cam_radius, device=device)
        conditioning_params = torch.cat([conditioning_cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9)], 1)
        
        # z and w
        z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device)
        
        imgs = []
        angle_p = -0.2
        for idx, angles in enumerate([(0, angle_p), (-45/180*np.pi, angle_p), (-90/180*np.pi, angle_p), (-135/180*np.pi, angle_p), (-np.pi, angle_p)]):
            angle_y = angles[0]
            angle_p = angles[1]
            # rand camera setting
            cam2world_pose = LookAtPoseSampler.sample(np.pi/2 + angle_y, np.pi/2 + angle_p, cam_pivot, radius=cam_radius, device=device)
            camera_params = torch.cat([cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9)], 1)

            ws = G.mapping(z, conditioning_params, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff)
            G_out = G.synthesis(ws, camera_params)

            img = G_out['image']
            img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
            imgs.append(img)


        img = torch.cat(imgs, dim=2)
        PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/seed{seed:04d}.png')


        if shapes:
            # extract a shape.mrc with marching cubes. You can view the .mrc file using ChimeraX from UCSF.
            max_batch=1000000

            samples, voxel_origin, voxel_size = create_samples(N=shape_res, voxel_origin=[0, 0, 0], cube_length=G.rendering_kwargs['box_warp'] * 1)#.reshape(1, -1, 3)
            samples = samples.to(z.device)
            sigmas = torch.zeros((samples.shape[0], samples.shape[1], 1), device=z.device)
            transformed_ray_directions_expanded = torch.zeros((samples.shape[0], max_batch, 3), device=z.device)
            transformed_ray_directions_expanded[..., -1] = -1

            head = 0
            with tqdm(total = samples.shape[1]) as pbar:
                with torch.no_grad():
                    while head < samples.shape[1]:
                        torch.manual_seed(0)
                        sigma = G.sample(samples[:, head:head+max_batch], transformed_ray_directions_expanded[:, :samples.shape[1]-head], z, conditioning_params, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, noise_mode='const')['sigma']
                        sigmas[:, head:head+max_batch] = sigma
                        head += max_batch
                        pbar.update(max_batch)

            sigmas = sigmas.reshape((shape_res, shape_res, shape_res)).cpu().numpy()
            sigmas = np.flip(sigmas, 0)

            # Trim the border of the extracted cube
            pad = int(30 * shape_res / 256)
            pad_value = -1000
            sigmas[:pad] = pad_value
            sigmas[-pad:] = pad_value
            sigmas[:, :pad] = pad_value
            sigmas[:, -pad:] = pad_value
            sigmas[:, :, :pad] = pad_value
            sigmas[:, :, -pad:] = pad_value

            if shape_format == '.ply':
                from shape_utils import convert_sdf_samples_to_ply
                convert_sdf_samples_to_ply(np.transpose(sigmas, (2, 1, 0)), [0, 0, 0], 1, os.path.join(outdir, f'seed{seed:04d}.ply'), level=10)
            elif shape_format == '.mrc': # output mrc
                with mrcfile.new_mmap(os.path.join(outdir, f'seed{seed:04d}.mrc'), overwrite=True, shape=sigmas.shape, mrc_mode=2) as mrc:
                    mrc.data[:] = sigmas


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

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

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