/
extract_shapes.py
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/
extract_shapes.py
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from cv2 import normalize
import plyfile
import argparse
import torch
import numpy as np
import skimage.measure
import scipy
import mrcfile
import os
from tqdm import tqdm
import os
import re
from typing import List, Optional, Tuple, Union
import math
import click
import dnnlib
import PIL.Image
import copy
import legacy
import torch.nn as nn
import torchvision
import cv2
from torch_utils import misc
from torchvision.utils import save_image
#----------------------------------------------------------------------------
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 parse_vec2(s: Union[str, Tuple[float, float]]) -> Tuple[float, float]:
'''Parse a floating point 2-vector of syntax 'a,b'.
Example:
'0,1' returns (0,1)
'''
if isinstance(s, tuple): return s
parts = s.split(',')
if len(parts) == 2:
return (float(parts[0]), float(parts[1]))
raise ValueError(f'cannot parse 2-vector {s}')
#----------------------------------------------------------------------------
def make_transform(translate: Tuple[float,float], angle: float):
m = np.eye(3)
s = np.sin(angle/360.0*np.pi*2)
c = np.cos(angle/360.0*np.pi*2)
m[0][0] = c
m[0][1] = s
m[0][2] = translate[0]
m[1][0] = -s
m[1][1] = c
m[1][2] = translate[1]
return m
#----------------------------------------------------------------------------
def create_samples(N=512, 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 sample_generator_ide3d(generator, aux_img_net, z, c, max_batch=100000, voxel_resolution=256, voxel_origin=[0,0,0], cube_length=2.0, psi=0.5, **kwargs):
n_regions = 3
head = 0
samples, voxel_origin, voxel_size = create_samples(voxel_resolution, voxel_origin, cube_length)
samples = 0.9 * (samples).to(z.device)
sigmas = torch.zeros((samples.shape[0], samples.shape[1], 1), device=z.device)
with torch.no_grad():
# mapping latent codes into W space
ws = generator.mapping(z, c, truncation_psi=psi)
# TODO: compute nerf input
voxel_block_ws = []
block_ws = []
with torch.autograd.profiler.record_function('split_ws'):
misc.assert_shape(ws, [None, generator.synthesis.num_ws, generator.synthesis.w_dim])
ws = ws.to(torch.float32)
w_idx = 0
for res in generator.synthesis.voxel_block_resolutions:
block = getattr(generator.synthesis, f'vb{res}')
voxel_block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb))
w_idx += block.num_conv
for res in generator.synthesis.block_resolutions:
block = getattr(generator.synthesis, f'b{res}')
block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb))
w_idx += block.num_conv
x_v = img_v = seg_v = None
for res, cur_ws in zip(generator.synthesis.voxel_block_resolutions, voxel_block_ws):
block = getattr(generator.synthesis, f'vb{res}')
x_v, img_v, seg_v = block(x_v, img_v, cur_ws, condition_img=seg_v)
render_kwargs = {}
render_kwargs["img_size"] = generator.synthesis.render_size
render_kwargs["nerf_noise"] = 0
render_kwargs["ray_start"] = 2.25
render_kwargs["ray_end"] = 3.3
render_kwargs["fov"] = 18
render_kwargs.update(kwargs)
P = c[:, :16].reshape(-1, 4, 4)
P = P.detach()
render_kwargs["camera"] = P
# Sequentially evaluate siren with max_batch_size to avoid OOM
while head < samples.shape[1]:
tail = head + max_batch
coarse_output = generator.synthesis.renderer.sample_voxel(img_v, seg_v, samples[:, head:tail]).reshape(samples.size(0), -1, 52)
sigmas[:, head:head+max_batch] = coarse_output[:, :, -1:]
head += max_batch
sigmas = sigmas.reshape((voxel_resolution, voxel_resolution, voxel_resolution)).cpu().numpy()
return sigmas
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--network', type=str)
parser.add_argument('--seeds', type=parse_range)
parser.add_argument('--trunc', type=float, default=1.)
parser.add_argument('--noise-mode', default='const')
parser.add_argument('--cube_size', type=float, default=0.3)
parser.add_argument('--voxel_resolution', type=int, default=256)
parser.add_argument('--outdir', type=str, default='shapes')
opt = parser.parse_args()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
with dnnlib.util.open_url(opt.network) as f:
G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore
aux_img_net = nn.Sequential(
nn.Conv2d(32, 3, 1),
nn.Tanh()
).to(device)
render_params = {
"h_stddev": 0.,
"v_stddev": 0.,
"num_steps": 96
}
label = torch.zeros([1, G.c_dim], device=device)
if G.c_dim != 0:
label = torch.tensor([1,0,0,0, 0,1,0,0, 0,0,1,2.7, 0,0,0,1, 4.2647, 0, 0.5, 0, 4.2647, 0.5, 0, 0, 1])[None].float().to(device)
os.makedirs(opt.outdir, exist_ok=True)
for seed in tqdm(opt.seeds):
torch.manual_seed(seed)
z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device)
voxel_grid = sample_generator_ide3d(G, aux_img_net, z, label, cube_length=opt.cube_size, voxel_resolution=opt.voxel_resolution, psi=opt.trunc, **render_params)
with mrcfile.new_mmap(os.path.join(opt.outdir, f'{seed}.mrc'), overwrite=True, shape=voxel_grid.shape, mrc_mode=2) as mrc:
mrc.data[:] = voxel_grid
np.save(os.path.join(opt.outdir, f'{seed}.npy'), voxel_grid)