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infer_panorama.py
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import os
os.environ['OPENCV_IO_ENABLE_OPENEXR'] = '1'
from pathlib import Path
import sys
if (_package_root := str(Path(__file__).absolute().parents[2])) not in sys.path:
sys.path.insert(0, _package_root)
from typing import *
import itertools
import json
import warnings
import click
@click.command(help='Inference script for panorama images')
@click.option('--input', '-i', 'input_path', type=click.Path(exists=True), required=True, help='Input image or folder path. "jpg" and "png" are supported.')
@click.option('--output', '-o', 'output_path', type=click.Path(), default='./output', help='Output folder path')
@click.option('--pretrained', 'pretrained_model_name_or_path', type=str, default='Ruicheng/moge-vitl', help='Pretrained model name or path. Defaults to "Ruicheng/moge-vitl"')
@click.option('--device', 'device_name', type=str, default='cuda', help='Device name (e.g. "cuda", "cuda:0", "cpu"). Defaults to "cuda"')
@click.option('--resize', 'resize_to', type=int, default=None, help='Resize the image(s) & output maps to a specific size. Defaults to None (no resizing).')
@click.option('--resolution_level', type=int, default=9, help='An integer [0-9] for the resolution level of inference. The higher, the better but slower. Defaults to 9. Note that it is irrelevant to the output resolution.')
@click.option('--threshold', type=float, default=0.03, help='Threshold for removing edges. Defaults to 0.03. Smaller value removes more edges. "inf" means no thresholding.')
@click.option('--batch_size', type=int, default=4, help='Batch size for inference. Defaults to 4.')
@click.option('--splitted', 'save_splitted', is_flag=True, help='Whether to save the splitted images. Defaults to False.')
@click.option('--maps', 'save_maps_', is_flag=True, help='Whether to save the output maps and fov(image, depth, mask, points, fov).')
@click.option('--glb', 'save_glb_', is_flag=True, help='Whether to save the output as a.glb file. The color will be saved as a texture.')
@click.option('--ply', 'save_ply_', is_flag=True, help='Whether to save the output as a.ply file. The color will be saved as vertex colors.')
@click.option('--show', 'show', is_flag=True, help='Whether show the output in a window. Note that this requires pyglet<2 installed as required by trimesh.')
def main(
input_path: str,
output_path: str,
pretrained_model_name_or_path: str,
device_name: str,
resize_to: int,
resolution_level: int,
threshold: float,
batch_size: int,
save_splitted: bool,
save_maps_: bool,
save_glb_: bool,
save_ply_: bool,
show: bool,
):
# Lazy import
import cv2
import numpy as np
from numpy import ndarray
import torch
from PIL import Image
from tqdm import tqdm, trange
import trimesh
import trimesh.visual
from scipy.sparse import csr_array, hstack, vstack
from scipy.ndimage import convolve
from scipy.sparse.linalg import lsmr
import utils3d
from moge.model.v1 import MoGeModel
from moge.utils.io import save_glb, save_ply
from moge.utils.vis import colorize_depth
from moge.utils.panorama import spherical_uv_to_directions, get_panorama_cameras, split_panorama_image, merge_panorama_depth
device = torch.device(device_name)
include_suffices = ['jpg', 'png', 'jpeg', 'JPG', 'PNG', 'JPEG']
if Path(input_path).is_dir():
image_paths = sorted(itertools.chain(*(Path(input_path).rglob(f'*.{suffix}') for suffix in include_suffices)))
else:
image_paths = [Path(input_path)]
if len(image_paths) == 0:
raise FileNotFoundError(f'No image files found in {input_path}')
# Write outputs
if not any([save_maps_, save_glb_, save_ply_]):
warnings.warn('No output format specified. Defaults to saving all. Please use "--maps", "--glb", or "--ply" to specify the output.')
save_maps_ = save_glb_ = save_ply_ = True
model = MoGeModel.from_pretrained(pretrained_model_name_or_path).to(device).eval()
for image_path in (pbar := tqdm(image_paths, desc='Total images', disable=len(image_paths) <= 1)):
image = cv2.cvtColor(cv2.imread(str(image_path)), cv2.COLOR_BGR2RGB)
height, width = image.shape[:2]
if resize_to is not None:
height, width = min(resize_to, int(resize_to * height / width)), min(resize_to, int(resize_to * width / height))
image = cv2.resize(image, (width, height), cv2.INTER_AREA)
splitted_extrinsics, splitted_intriniscs = get_panorama_cameras()
splitted_resolution = 512
splitted_images = split_panorama_image(image, splitted_extrinsics, splitted_intriniscs, splitted_resolution)
# Infer each view
print('Inferring...') if pbar.disable else pbar.set_postfix_str(f'Inferring')
splitted_distance_maps, splitted_masks = [], []
for i in trange(0, len(splitted_images), batch_size, desc='Inferring splitted views', disable=len(splitted_images) <= batch_size, leave=False):
image_tensor = torch.tensor(np.stack(splitted_images[i:i + batch_size]) / 255, dtype=torch.float32, device=device).permute(0, 3, 1, 2)
fov_x, fov_y = np.rad2deg(utils3d.numpy.intrinsics_to_fov(np.array(splitted_intriniscs[i:i + batch_size])))
fov_x = torch.tensor(fov_x, dtype=torch.float32, device=device)
output = model.infer(image_tensor, fov_x=fov_x, apply_mask=False)
distance_map, mask = output['points'].norm(dim=-1).cpu().numpy(), output['mask'].cpu().numpy()
splitted_distance_maps.extend(list(distance_map))
splitted_masks.extend(list(mask))
# Save splitted
if save_splitted:
splitted_save_path = Path(output_path, image_path.stem, 'splitted')
splitted_save_path.mkdir(exist_ok=True, parents=True)
for i in range(len(splitted_images)):
cv2.imwrite(str(splitted_save_path / f'{i:02d}.jpg'), cv2.cvtColor(splitted_images[i], cv2.COLOR_RGB2BGR))
cv2.imwrite(str(splitted_save_path / f'{i:02d}_distance_vis.png'), cv2.cvtColor(colorize_depth(splitted_distance_maps[i], splitted_masks[i]), cv2.COLOR_RGB2BGR))
# Merge
print('Merging...') if pbar.disable else pbar.set_postfix_str(f'Merging')
merging_width, merging_height = min(1920, width), min(960, height)
panorama_depth, panorama_mask = merge_panorama_depth(merging_width, merging_height, splitted_distance_maps, splitted_masks, splitted_extrinsics, splitted_intriniscs)
panorama_depth = panorama_depth.astype(np.float32)
panorama_depth = cv2.resize(panorama_depth, (width, height), cv2.INTER_LINEAR)
panorama_mask = cv2.resize(panorama_mask.astype(np.uint8), (width, height), cv2.INTER_NEAREST) > 0
points = panorama_depth[:, :, None] * spherical_uv_to_directions(utils3d.numpy.image_uv(width=width, height=height))
# Write outputs
print('Writing outputs...') if pbar.disable else pbar.set_postfix_str(f'Inferring')
save_path = Path(output_path, image_path.relative_to(input_path).parent, image_path.stem)
save_path.mkdir(exist_ok=True, parents=True)
if save_maps_:
cv2.imwrite(str(save_path / 'image.jpg'), cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
cv2.imwrite(str(save_path / 'depth_vis.png'), cv2.cvtColor(colorize_depth(panorama_depth, mask=panorama_mask), cv2.COLOR_RGB2BGR))
cv2.imwrite(str(save_path / 'depth.exr'), panorama_depth, [cv2.IMWRITE_EXR_TYPE, cv2.IMWRITE_EXR_TYPE_FLOAT])
cv2.imwrite(str(save_path / 'points.exr'), points, [cv2.IMWRITE_EXR_TYPE, cv2.IMWRITE_EXR_TYPE_FLOAT])
cv2.imwrite(str(save_path /'mask.png'), (panorama_mask * 255).astype(np.uint8))
# Export mesh & visulization
if save_glb_ or save_ply_ or show:
normals, normals_mask = utils3d.numpy.points_to_normals(points, panorama_mask)
faces, vertices, vertex_colors, vertex_uvs = utils3d.numpy.image_mesh(
points,
image.astype(np.float32) / 255,
utils3d.numpy.image_uv(width=width, height=height),
mask=panorama_mask & ~(utils3d.numpy.depth_edge(panorama_depth, rtol=threshold) & utils3d.numpy.normals_edge(normals, tol=5, mask=normals_mask)),
tri=True
)
if save_glb_:
save_glb(save_path / 'mesh.glb', vertices, faces, vertex_uvs, image)
if save_ply_:
save_ply(save_path / 'mesh.ply', vertices, faces, vertex_colors)
if show:
trimesh.Trimesh(
vertices=vertices,
vertex_colors=vertex_colors,
faces=faces,
process=False
).show()
if __name__ == '__main__':
main()