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os.environ["CUDA_VISIBLE_DEVICES"] = "3"
import time
import torch
import argparse
import numpy as np
from PIL import Image
from torchvision import transforms
from collections import OrderedDict
import sys
sys.path.append(".")
sys.path.append("..")
import imageio
from tqdm import tqdm
from configs.paths_config import model_paths
from models.triencoder import TriEncoder
from models.eg3d.shape_utils import convert_sdf_samples_to_ply
device = "cuda:0"
def gen_rand_pose(device):
return (torch.rand(1, device=device) - 0.5) * torch.pi/6 + torch.pi/2
def layout_grid(img: np.ndarray, grid_w=None, grid_h=1, float_to_uint8=True, chw_to_hwc=True, to_numpy=True):
batch_size, channels, img_h, img_w = img.shape
if grid_w is None:
grid_w = batch_size // grid_h
assert batch_size == grid_w * grid_h
if float_to_uint8:
img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8)
img = img.reshape(grid_h, grid_w, channels, img_h, img_w)
img = img.permute(2, 0, 3, 1, 4)
img = img.reshape(channels, grid_h * img_h, grid_w * img_w)
if chw_to_hwc:
img = img.permute(1, 2, 0)
if to_numpy:
img = img.cpu().numpy()
return img
def generate_video(net, gen_triplanes, savepath, batch_size=1):
voxel_resolution = 512
video_kwargs = {}
video_out = imageio.get_writer(savepath, mode='I', fps=60, codec='libx264', **video_kwargs)
grid_h, grid_w = 1, 1
num_keyframes, w_frames = 12, 24
image_mode = "image"
for frame_idx in tqdm(range(num_keyframes * w_frames)):
imgs = []
for yi in range(grid_h):
for xi in range(grid_w):
pitch_range = 0.25
yaw_range = 0.35
camera_p = net.eural_to_camera(batch_size,
3.14/2 -0.05 + pitch_range * np.cos(2 * 3.14 * frame_idx / (num_keyframes * w_frames)),
3.14/2 + yaw_range * np.sin(2 * 3.14 * frame_idx / (num_keyframes * w_frames)),
)
render_res = net.triplane_renderer(gen_triplanes, camera_p)
img = render_res[image_mode][0]
if image_mode == 'image_depth':
img = -img
img = (img - img.min()) / (img.max() - img.min()) * 2 - 1
imgs.append(img)
video_out.append_data(layout_grid(torch.stack(imgs), grid_h=1, grid_w=1))
video_out.close()
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 extract_mesh(net, gen_triplanes, savepath):
shape_res = 512
max_batch=1000000
my_plane = gen_triplanes.reshape(1,3,32,256,256)
samples, voxel_origin, voxel_size = create_samples(N=shape_res, voxel_origin=[0, 0, 0], cube_length=net.triplane_generator.rendering_kwargs['box_warp'] * 1)#.reshape(1, -1, 3)
samples = samples.to(device)
sigmas = torch.zeros((samples.shape[0], samples.shape[1], 1), device=device)
transformed_ray_directions_expanded = torch.zeros((samples.shape[0], max_batch, 3), device=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 = net.triplane_renderer.sample_density(my_plane, samples[:, head:head+max_batch], transformed_ray_directions_expanded[:, :samples.shape[1]-head])
sigmas[:, head:head+max_batch] = sigma['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
convert_sdf_samples_to_ply(np.transpose(sigmas, (2, 1, 0)), [0, 0, 0], 1, savepath, level=10)
def multi_view_output(net, gen_triplanes, savepath, batch_size=1):
# observe in multiple views
image_mode = "image" # or "depth"
offset = np.linspace(-np.pi/6, np.pi/6, 8)
imgs_list = []
for p in offset:
camera_p = net.eural_to_camera(batch_size, np.pi/2, np.pi/2 + p) # pitch yawl
# print(camera_p)
render_res = net.triplane_renderer(gen_triplanes, camera_p)
img = render_res[image_mode][0]
imgs_list.append(img.clamp(-1,1).squeeze(0).cpu().detach().permute((1,2,0)).numpy())
image_list = (np.concatenate(imgs_list, axis=1) + 1) / 2 * 255
Image.fromarray(image_list.astype(np.uint8)).save(savepath)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, default=model_paths["encoder_render"], help='model path')
parser.add_argument('--image', type=str, default='./imgs/3.png', help='image path')
parser.add_argument('--output', type=str, default='./output', help='output path')
parser.add_argument('--save_mesh', type=bool, default=True, help='If true, save mesh')
parser.add_argument('--save_video', type=bool, default=True, help='If true, save video')
parser.add_argument('--mode', type=str, default='Normal', help='mode of triplane generation', choices=['LT', 'Normal'])
args = parser.parse_args()
model_path = args.model
image_path = args.image
output_path = args.output
mode = args.mode
if not os.path.exists(output_path):
os.mkdir(output_path)
# load model
model_dict = torch.load(model_path)
net = TriEncoder(mode=mode)
net.encoder.load_state_dict(model_dict['encoder_state_dict'])
net.triplane_renderer.load_state_dict(model_dict['renderer_state_dict'])
# load image
img = Image.open(image_path).resize((512, 512))
img_tensor = transforms.ToTensor()(img).unsqueeze(0).to(device)
if img_tensor.shape[1] == 4: # remove alpha channel
img_tensor = img_tensor[:, :3, :, :]
img_tensor = img_tensor * 2 - 1
# inference
net.encoder.to(device)
net.encoder.eval()
basename = os.path.basename(image_path).split('.')[0]
with torch.no_grad():
gen_triplanes = net.encoder(img_tensor)
multi_view_output(net, gen_triplanes, os.path.join(output_path, 'multi_view_' + basename + '.png'))
if args.save_video:
generate_video(net, gen_triplanes, os.path.join(output_path, 'video_' + basename + '.mp4'))
if args.save_mesh:
extract_mesh(net, gen_triplanes, os.path.join(output_path, 'mesh_' + basename + '.ply'))
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