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import spaces
import torch
print("cuda is available: ", torch.cuda.is_available())
import gradio as gr
import os
import shutil
import rembg
import numpy as np
import math
import open3d as o3d
from PIL import Image
import torchvision
import trimesh
from skimage.io import imsave
import imageio
import cv2
import matplotlib.pyplot as pl
pl.ion()
CaPE_TYPE = "6DoF"
device = 'cuda' #if torch.cuda.is_available() else 'cpu'
weight_dtype = torch.float16
torch.backends.cuda.matmul.allow_tf32 = True # for gpu >= Ampere and pytorch >= 1.12
# EscherNet
# create angles in archimedean spiral with N steps
def get_archimedean_spiral(sphere_radius, num_steps=250):
# x-z plane, around upper y
'''
https://en.wikipedia.org/wiki/Spiral, section "Spherical spiral". c = a / pi
'''
a = 40
r = sphere_radius
translations = []
angles = []
# i = a / 2
i = 0.01
while i < a:
theta = i / a * math.pi
x = r * math.sin(theta) * math.cos(-i)
z = r * math.sin(-theta + math.pi) * math.sin(-i)
y = r * - math.cos(theta)
# translations.append((x, y, z)) # origin
translations.append((x, z, -y))
angles.append([np.rad2deg(-i), np.rad2deg(theta)])
# i += a / (2 * num_steps)
i += a / (1 * num_steps)
return np.array(translations), np.stack(angles)
def look_at(origin, target, up):
forward = (target - origin)
forward = forward / np.linalg.norm(forward)
right = np.cross(up, forward)
right = right / np.linalg.norm(right)
new_up = np.cross(forward, right)
rotation_matrix = np.column_stack((right, new_up, -forward, target))
matrix = np.row_stack((rotation_matrix, [0, 0, 0, 1]))
return matrix
import einops
import sys
sys.path.insert(0, "./6DoF/") # TODO change it when deploying
# use the customized diffusers modules
from diffusers import DDIMScheduler
from dataset import get_pose
from CN_encoder import CN_encoder
from pipeline_zero1to3 import Zero1to3StableDiffusionPipeline
pretrained_model_name_or_path = "kxic/EscherNet_demo"
resolution = 256
h,w = resolution,resolution
guidance_scale = 3.0
radius = 2.2
bg_color = [1., 1., 1., 1.]
image_transforms = torchvision.transforms.Compose(
[
torchvision.transforms.Resize((resolution, resolution)), # 256, 256
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize([0.5], [0.5])
]
)
xyzs_spiral, angles_spiral = get_archimedean_spiral(1.5, 200)
# only half toop
xyzs_spiral = xyzs_spiral[:100]
angles_spiral = angles_spiral[:100]
# Init pipeline
scheduler = DDIMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler", revision=None)
image_encoder = CN_encoder.from_pretrained(pretrained_model_name_or_path, subfolder="image_encoder", revision=None)
pipeline = Zero1to3StableDiffusionPipeline.from_pretrained(
pretrained_model_name_or_path,
revision=None,
scheduler=scheduler,
image_encoder=None,
safety_checker=None,
feature_extractor=None,
torch_dtype=weight_dtype,
)
pipeline.image_encoder = image_encoder.to(weight_dtype)
pipeline.set_progress_bar_config(disable=False)
# pipeline.enable_xformers_memory_efficient_attention()
# enable vae slicing
pipeline.enable_vae_slicing()
# pipeline = pipeline.to(device)
@spaces.GPU(duration=120)
def run_eschernet(tmpdirname, eschernet_input_dict, sample_steps, sample_seed, nvs_num, nvs_mode):
# set the random seed
generator = torch.Generator(device=device).manual_seed(sample_seed)
T_out = nvs_num
T_in = len(eschernet_input_dict['imgs'])
####### output pose
# TODO choose T_out number of poses sequentially from the spiral
xyzs = xyzs_spiral[::(len(xyzs_spiral) // T_out)]
angles_out = angles_spiral[::(len(xyzs_spiral) // T_out)]
####### input's max radius for translation scaling
radii = eschernet_input_dict['radii']
max_t = np.max(radii)
min_t = np.min(radii)
####### input pose
pose_in = []
for T_in_index in range(T_in):
pose = get_pose(np.linalg.inv(eschernet_input_dict['poses'][T_in_index]))
pose[1:3, :] *= -1 # coordinate system conversion
pose[3, 3] *= 1. / max_t * radius # scale radius to [1.5, 2.2]
pose_in.append(torch.from_numpy(pose))
####### input image
img = eschernet_input_dict['imgs'] / 255.
img[img[:, :, :, -1] == 0.] = bg_color
# TODO batch image_transforms
input_image = [image_transforms(Image.fromarray(np.uint8(im[:, :, :3] * 255.)).convert("RGB")) for im in img]
####### nvs pose
pose_out = []
for T_out_index in range(T_out):
azimuth, polar = angles_out[T_out_index]
if CaPE_TYPE == "4DoF":
pose_out.append(torch.tensor([np.deg2rad(polar), np.deg2rad(azimuth), 0., 0.]))
elif CaPE_TYPE == "6DoF":
pose = look_at(origin=np.array([0, 0, 0]), target=xyzs[T_out_index], up=np.array([0, 0, 1]))
pose = np.linalg.inv(pose)
pose[2, :] *= -1
pose_out.append(torch.from_numpy(get_pose(pose)))
# [B, T, C, H, W]
input_image = torch.stack(input_image, dim=0).to(device).to(weight_dtype).unsqueeze(0)
# [B, T, 4]
pose_in = np.stack(pose_in)
pose_out = np.stack(pose_out)
if CaPE_TYPE == "6DoF":
pose_in_inv = np.linalg.inv(pose_in).transpose([0, 2, 1])
pose_out_inv = np.linalg.inv(pose_out).transpose([0, 2, 1])
pose_in_inv = torch.from_numpy(pose_in_inv).to(device).to(weight_dtype).unsqueeze(0)
pose_out_inv = torch.from_numpy(pose_out_inv).to(device).to(weight_dtype).unsqueeze(0)
pose_in = torch.from_numpy(pose_in).to(device).to(weight_dtype).unsqueeze(0)
pose_out = torch.from_numpy(pose_out).to(device).to(weight_dtype).unsqueeze(0)
input_image = einops.rearrange(input_image, "b t c h w -> (b t) c h w")
assert T_in == input_image.shape[0]
assert T_in == pose_in.shape[1]
assert T_out == pose_out.shape[1]
# run inference
pipeline.to(device)
pipeline.enable_xformers_memory_efficient_attention()
if CaPE_TYPE == "6DoF":
with torch.autocast("cuda"):
image = pipeline(input_imgs=input_image, prompt_imgs=input_image,
poses=[[pose_out, pose_out_inv], [pose_in, pose_in_inv]],
height=h, width=w, T_in=T_in, T_out=T_out,
guidance_scale=guidance_scale, num_inference_steps=50, generator=generator,
output_type="numpy").images
elif CaPE_TYPE == "4DoF":
with torch.autocast("cuda"):
image = pipeline(input_imgs=input_image, prompt_imgs=input_image, poses=[pose_out, pose_in],
height=h, width=w, T_in=T_in, T_out=T_out,
guidance_scale=guidance_scale, num_inference_steps=50, generator=generator,
output_type="numpy").images
# save output image
output_dir = os.path.join(tmpdirname, "eschernet")
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
os.makedirs(output_dir, exist_ok=True)
# save to N imgs
for i in range(T_out):
imsave(os.path.join(output_dir, f'{i}.png'), (image[i] * 255).astype(np.uint8))
# make a gif
frames = [Image.fromarray((image[i] * 255).astype(np.uint8)) for i in range(T_out)]
frame_one = frames[0]
frame_one.save(os.path.join(output_dir, "output.gif"), format="GIF", append_images=frames,
save_all=True, duration=50, loop=1)
# get a video
video_path = os.path.join(output_dir, "output.mp4")
imageio.mimwrite(video_path, np.stack(frames), fps=10, codec='h264')
return image, video_path
# TODO mesh it
@spaces.GPU(duration=120)
def make3d():
pass
############################ Dust3r as Pose Estimation ############################
from scipy.spatial.transform import Rotation
import copy
from dust3r.inference import inference
from dust3r.model import AsymmetricCroCo3DStereo
from dust3r.image_pairs import make_pairs
from dust3r.utils.image import load_images, rgb
from dust3r.utils.device import to_numpy
from dust3r.viz import add_scene_cam, CAM_COLORS, OPENGL, pts3d_to_trimesh, cat_meshes
from dust3r.cloud_opt import global_aligner, GlobalAlignerMode
import functools
import math
@spaces.GPU
def _convert_scene_output_to_glb(outdir, imgs, pts3d, mask, focals, cams2world, cam_size=0.05,
cam_color=None, as_pointcloud=False,
transparent_cams=False, silent=False, same_focals=False):
assert len(pts3d) == len(mask) <= len(imgs) <= len(cams2world)
if not same_focals:
assert (len(cams2world) == len(focals))
pts3d = to_numpy(pts3d)
imgs = to_numpy(imgs)
focals = to_numpy(focals)
cams2world = to_numpy(cams2world)
scene = trimesh.Scene()
# add axes
scene.add_geometry(trimesh.creation.axis(axis_length=0.5, axis_radius=0.001))
# full pointcloud
if as_pointcloud:
pts = np.concatenate([p[m] for p, m in zip(pts3d, mask)])
col = np.concatenate([p[m] for p, m in zip(imgs, mask)])
pct = trimesh.PointCloud(pts.reshape(-1, 3), colors=col.reshape(-1, 3))
scene.add_geometry(pct)
else:
meshes = []
for i in range(len(imgs)):
meshes.append(pts3d_to_trimesh(imgs[i], pts3d[i], mask[i]))
mesh = trimesh.Trimesh(**cat_meshes(meshes))
scene.add_geometry(mesh)
# add each camera
for i, pose_c2w in enumerate(cams2world):
if isinstance(cam_color, list):
camera_edge_color = cam_color[i]
else:
camera_edge_color = cam_color or CAM_COLORS[i % len(CAM_COLORS)]
if same_focals:
focal = focals[0]
else:
focal = focals[i]
add_scene_cam(scene, pose_c2w, camera_edge_color,
None if transparent_cams else imgs[i], focal,
imsize=imgs[i].shape[1::-1], screen_width=cam_size)
rot = np.eye(4)
rot[:3, :3] = Rotation.from_euler('y', np.deg2rad(180)).as_matrix()
scene.apply_transform(np.linalg.inv(cams2world[0] @ OPENGL @ rot))
outfile = os.path.join(outdir, 'scene.glb')
if not silent:
print('(exporting 3D scene to', outfile, ')')
scene.export(file_obj=outfile)
return outfile
@spaces.GPU(duration=120)
def get_3D_model_from_scene(outdir, silent, scene, min_conf_thr=3, as_pointcloud=False, mask_sky=False,
clean_depth=False, transparent_cams=False, cam_size=0.05, same_focals=False):
"""
extract 3D_model (glb file) from a reconstructed scene
"""
if scene is None:
return None
# post processes
if clean_depth:
scene = scene.clean_pointcloud()
if mask_sky:
scene = scene.mask_sky()
# get optimized values from scene
rgbimg = to_numpy(scene.imgs)
focals = to_numpy(scene.get_focals().cpu())
# cams2world = to_numpy(scene.get_im_poses().cpu())
# TODO use the vis_poses
cams2world = scene.vis_poses
# 3D pointcloud from depthmap, poses and intrinsics
# pts3d = to_numpy(scene.get_pts3d())
# TODO use the vis_poses
pts3d = scene.vis_pts3d
scene.min_conf_thr = float(scene.conf_trf(torch.tensor(min_conf_thr)))
msk = to_numpy(scene.get_masks())
return _convert_scene_output_to_glb(outdir, rgbimg, pts3d, msk, focals, cams2world, as_pointcloud=as_pointcloud,
transparent_cams=transparent_cams, cam_size=cam_size, silent=silent,
same_focals=same_focals)
@spaces.GPU(duration=120)
def get_reconstructed_scene(outdir, model, device, silent, image_size, filelist, schedule, niter, min_conf_thr,
as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size,
scenegraph_type, winsize, refid, same_focals):
"""
from a list of images, run dust3r inference, global aligner.
then run get_3D_model_from_scene
"""
# remove the directory if it already exists
if os.path.exists(outdir):
shutil.rmtree(outdir)
os.makedirs(outdir, exist_ok=True)
imgs, imgs_rgba = load_images(filelist, size=image_size, verbose=not silent, do_remove_background=True)
if len(imgs) == 1:
imgs = [imgs[0], copy.deepcopy(imgs[0])]
imgs[1]['idx'] = 1
if scenegraph_type == "swin":
scenegraph_type = scenegraph_type + "-" + str(winsize)
elif scenegraph_type == "oneref":
scenegraph_type = scenegraph_type + "-" + str(refid)
pairs = make_pairs(imgs, scene_graph=scenegraph_type, prefilter=None, symmetrize=True)
output = inference(pairs, model, device, batch_size=1, verbose=not silent)
mode = GlobalAlignerMode.PointCloudOptimizer if len(imgs) > 2 else GlobalAlignerMode.PairViewer
scene = global_aligner(output, device=device, mode=mode, verbose=not silent, same_focals=same_focals)
lr = 0.01
if mode == GlobalAlignerMode.PointCloudOptimizer:
loss = scene.compute_global_alignment(init='mst', niter=niter, schedule=schedule, lr=lr)
# outfile = get_3D_model_from_scene(outdir, silent, scene, min_conf_thr, as_pointcloud, mask_sky,
# clean_depth, transparent_cams, cam_size, same_focals=same_focals)
# also return rgb, depth and confidence imgs
# depth is normalized with the max value for all images
# we apply the jet colormap on the confidence maps
rgbimg = scene.imgs
# depths = to_numpy(scene.get_depthmaps())
# confs = to_numpy([c for c in scene.im_conf])
# cmap = pl.get_cmap('jet')
# depths_max = max([d.max() for d in depths])
# depths = [d / depths_max for d in depths]
# confs_max = max([d.max() for d in confs])
# confs = [cmap(d / confs_max) for d in confs]
imgs = []
rgbaimg = []
for i in range(len(rgbimg)): # when only 1 image, scene.imgs is two
imgs.append(rgbimg[i])
# imgs.append(rgb(depths[i]))
# imgs.append(rgb(confs[i]))
# imgs.append(imgs_rgba[i])
if len(imgs_rgba) == 1 and i == 1:
imgs.append(imgs_rgba[0])
rgbaimg.append(np.array(imgs_rgba[0]))
else:
imgs.append(imgs_rgba[i])
rgbaimg.append(np.array(imgs_rgba[i]))
rgbaimg = np.array(rgbaimg)
# for eschernet
# get optimized values from scene
rgbimg = to_numpy(scene.imgs)
focals = to_numpy(scene.get_focals().cpu())
cams2world = to_numpy(scene.get_im_poses().cpu())
# 3D pointcloud from depthmap, poses and intrinsics
pts3d = to_numpy(scene.get_pts3d())
scene.min_conf_thr = float(scene.conf_trf(torch.tensor(min_conf_thr)))
msk = to_numpy(scene.get_masks())
obj_mask = rgbaimg[..., 3] > 0
# TODO set global coordinate system at the center of the scene, z-axis is up
pts = np.concatenate([p[m] for p, m in zip(pts3d, msk)]).reshape(-1, 3)
pts_obj = np.concatenate([p[m&obj_m] for p, m, obj_m in zip(pts3d, msk, obj_mask)]).reshape(-1, 3)
centroid = np.mean(pts_obj, axis=0) # obj center
obj2world = np.eye(4)
obj2world[:3, 3] = -centroid # T_wc
# get z_up vector
# TODO fit a plane and get the normal vector
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(pts)
plane_model, inliers = pcd.segment_plane(distance_threshold=0.01, ransac_n=3, num_iterations=1000)
# get the normalised normal vector dim = 3
normal = plane_model[:3] / np.linalg.norm(plane_model[:3])
# the normal direction should be pointing up
if normal[1] < 0:
normal = -normal
# print("normal", normal)
# # TODO z-up 180
# z_up = np.array([[1,0,0,0],
# [0,-1,0,0],
# [0,0,-1,0],
# [0,0,0,1]])
# obj2world = z_up @ obj2world
# # avg the y
# z_up_avg = cams2world[:,:3,3].sum(0) / np.linalg.norm(cams2world[:,:3,3].sum(0), axis=-1) # average direction in cam coordinate
# # import pdb; pdb.set_trace()
# rot_axis = np.cross(np.array([0, 0, 1]), z_up_avg)
# rot_angle = np.arccos(np.dot(np.array([0, 0, 1]), z_up_avg) / (np.linalg.norm(z_up_avg) + 1e-6))
# rot = Rotation.from_rotvec(rot_angle * rot_axis)
# z_up = np.eye(4)
# z_up[:3, :3] = rot.as_matrix()
# get the rotation matrix from normal to z-axis
z_axis = np.array([0, 0, 1])
rot_axis = np.cross(normal, z_axis)
rot_angle = np.arccos(np.dot(normal, z_axis) / (np.linalg.norm(normal) + 1e-6))
rot = Rotation.from_rotvec(rot_angle * rot_axis)
z_up = np.eye(4)
z_up[:3, :3] = rot.as_matrix()
obj2world = z_up @ obj2world
# flip 180
flip_rot = np.array([[1, 0, 0, 0],
[0, -1, 0, 0],
[0, 0, -1, 0],
[0, 0, 0, 1]])
obj2world = flip_rot @ obj2world
# get new cams2obj
cams2obj = []
for i, cam2world in enumerate(cams2world):
cams2obj.append(obj2world @ cam2world)
# TODO transform pts3d to the new coordinate system
for i, pts in enumerate(pts3d):
pts3d[i] = (obj2world @ np.concatenate([pts, np.ones_like(pts)[..., :1]], axis=-1).transpose(2, 0, 1).reshape(4,
-1)) \
.reshape(4, pts.shape[0], pts.shape[1]).transpose(1, 2, 0)[..., :3]
cams2world = np.array(cams2obj)
# TODO rewrite hack
scene.vis_poses = cams2world.copy()
scene.vis_pts3d = pts3d.copy()
# TODO save cams2world and rgbimg to each file, file name "000.npy", "001.npy", ... and "000.png", "001.png", ...
for i, (img, img_rgba, pose) in enumerate(zip(rgbimg, rgbaimg, cams2world)):
np.save(os.path.join(outdir, f"{i:03d}.npy"), pose)
pl.imsave(os.path.join(outdir, f"{i:03d}.png"), img)
pl.imsave(os.path.join(outdir, f"{i:03d}_rgba.png"), img_rgba)
# np.save(os.path.join(outdir, f"{i:03d}_focal.npy"), to_numpy(focal))
# save the min/max radius of camera
radii = np.linalg.norm(np.linalg.inv(cams2world)[..., :3, 3])
np.save(os.path.join(outdir, "radii.npy"), radii)
eschernet_input = {"poses": cams2world,
"radii": radii,
"imgs": rgbaimg}
outfile = get_3D_model_from_scene(outdir, silent, scene, min_conf_thr, as_pointcloud, mask_sky,
clean_depth, transparent_cams, cam_size, same_focals=same_focals)
return scene, outfile, imgs, eschernet_input
def set_scenegraph_options(inputfiles, winsize, refid, scenegraph_type):
num_files = len(inputfiles) if inputfiles is not None else 1
max_winsize = max(1, math.ceil((num_files - 1) / 2))
if scenegraph_type == "swin":
winsize = gr.Slider(label="Scene Graph: Window Size", value=max_winsize,
minimum=1, maximum=max_winsize, step=1, visible=True)
refid = gr.Slider(label="Scene Graph: Id", value=0, minimum=0,
maximum=num_files - 1, step=1, visible=False)
elif scenegraph_type == "oneref":
winsize = gr.Slider(label="Scene Graph: Window Size", value=max_winsize,
minimum=1, maximum=max_winsize, step=1, visible=False)
refid = gr.Slider(label="Scene Graph: Id", value=0, minimum=0,
maximum=num_files - 1, step=1, visible=True)
else:
winsize = gr.Slider(label="Scene Graph: Window Size", value=max_winsize,
minimum=1, maximum=max_winsize, step=1, visible=False)
refid = gr.Slider(label="Scene Graph: Id", value=0, minimum=0,
maximum=num_files - 1, step=1, visible=False)
return winsize, refid
def get_examples(path):
objs = []
for obj_name in sorted(os.listdir(path)):
img_files = []
for img_file in sorted(os.listdir(os.path.join(path, obj_name))):
img_files.append(os.path.join(path, obj_name, img_file))
objs.append([img_files])
print("objs = ", objs)
return objs
def preview_input(inputfiles):
if inputfiles is None:
return None
imgs = []
for img_file in inputfiles:
img = pl.imread(img_file)
imgs.append(img)
return imgs
def main():
# dustr init
silent = False
image_size = 224
weights_path = 'checkpoints/DUSt3R_ViTLarge_BaseDecoder_224_linear.pth'
model = AsymmetricCroCo3DStereo.from_pretrained(weights_path).to(device)
# dust3r will write the 3D model inside tmpdirname
# with tempfile.TemporaryDirectory(suffix='dust3r_gradio_demo') as tmpdirname:
tmpdirname = os.path.join('logs/user_object')
# remove the directory if it already exists
if os.path.exists(tmpdirname):
shutil.rmtree(tmpdirname)
os.makedirs(tmpdirname, exist_ok=True)
if not silent:
print('Outputing stuff in', tmpdirname)
recon_fun = functools.partial(get_reconstructed_scene, tmpdirname, model, device, silent, image_size)
model_from_scene_fun = functools.partial(get_3D_model_from_scene, tmpdirname, silent)
generate_mvs = functools.partial(run_eschernet, tmpdirname)
_HEADER_ = '''
<h2><b>[CVPR'24 Oral] EscherNet: A Generative Model for Scalable View Synthesis</b></h2>
<b>EscherNet</b> is a multiview diffusion model for scalable generative any-to-any number/pose novel view synthesis.
Image views are treated as tokens and the camera pose is encoded by <b>CaPE (Camera Positional Encoding)</b>.
<a href='https://kxhit.github.io/EscherNet' target='_blank'>Project</a> <b>|</b>
<a href='https://github.com/kxhit/EscherNet' target='_blank'>GitHub</a> <b>|</b>
<a href='https://arxiv.org/abs/2402.03908' target='_blank'>ArXiv</a>
<h4><b>Tips:</b></h4>
- Our model can take <b>any number input images</b>. The more images you provide, the better the results.
- Our model can generate <b>any number and any pose</b> novel views. You can specify the number of views you want to generate. In this demo, we set novel views on an <b>archemedian spiral</b> for simplicity.
- The pose estimation is done using <a href='https://github.com/naver/dust3r' target='_blank'>DUSt3R</a>. You can also provide your own poses or get pose via any SLAM system.
- The current checkpoint supports 6DoF camera pose and is trained on 30k 3D <a href='https://objaverse.allenai.org/' target='_blank'>Objaverse</a> objects for demo. Scaling is on the roadmap!
'''
_CITE_ = r"""
📝 <b>Citation</b>:
```bibtex
@article{kong2024eschernet,
title={EscherNet: A Generative Model for Scalable View Synthesis},
author={Kong, Xin and Liu, Shikun and Lyu, Xiaoyang and Taher, Marwan and Qi, Xiaojuan and Davison, Andrew J},
journal={arXiv preprint arXiv:2402.03908},
year={2024}
}
```
"""
with gr.Blocks() as demo:
gr.Markdown(_HEADER_)
mv_images = gr.State()
scene = gr.State(None)
eschernet_input = gr.State(None)
with gr.Row(variant="panel"):
# left column
with gr.Column():
with gr.Row():
input_image = gr.File(file_count="multiple")
# with gr.Row():
# # set the size of the window
# preview_image = gr.Gallery(label='Input Views', rows=1,
with gr.Row():
run_dust3r = gr.Button("Get Pose!", elem_id="dust3r")
with gr.Row():
processed_image = gr.Gallery(label='Input Views', columns=2, height="100%")
with gr.Row(variant="panel"):
# input examples under "examples" folder
gr.Examples(
examples=get_examples('examples'),
# examples=[
# [['examples/controller/frame000077.jpg', 'examples/controller/frame000032.jpg', 'examples/controller/frame000172.jpg']],
# [['examples/hairdryer/frame000081.jpg', 'examples/hairdryer/frame000162.jpg', 'examples/hairdryer/frame000003.jpg']],
# ],
inputs=[input_image],
label="Examples (click one set of images to start!)",
examples_per_page=20
)
# right column
with gr.Column():
with gr.Row():
outmodel = gr.Model3D()
with gr.Row():
gr.Markdown('''
<h4><b>Check if the pose and segmentation looks correct. If not, remove the incorrect images and try again.</b></h4>
''')
with gr.Row():
with gr.Group():
do_remove_background = gr.Checkbox(
label="Remove Background", value=True
)
sample_seed = gr.Number(value=42, label="Seed Value", precision=0)
sample_steps = gr.Slider(
label="Sample Steps",
minimum=30,
maximum=75,
value=50,
step=5,
visible=False
)
nvs_num = gr.Slider(
label="Number of Novel Views",
minimum=5,
maximum=100,
value=30,
step=1
)
nvs_mode = gr.Dropdown(["archimedes circle"], # "fixed 4 views", "fixed 8 views"
value="archimedes circle", label="Novel Views Pose Chosen", visible=True)
with gr.Row():
gr.Markdown('''
<h4><b>Choose your desired novel view poses number and generate! The more output images the longer it takes.</b></h4>
''')
with gr.Row():
submit = gr.Button("Submit", elem_id="eschernet", variant="primary")
with gr.Row():
# mv_show_images = gr.Image(
# label="Generated Multi-views",
# type="pil",
# width=379,
# interactive=False
# )
with gr.Column():
output_video = gr.Video(
label="video", format="mp4",
width=379,
autoplay=True,
interactive=False
)
# with gr.Row():
# with gr.Tab("OBJ"):
# output_model_obj = gr.Model3D(
# label="Output Model (OBJ Format)",
# #width=768,
# interactive=False,
# )
# gr.Markdown("Note: Downloaded .obj model will be flipped. Export .glb instead or manually flip it before usage.")
# with gr.Tab("GLB"):
# output_model_glb = gr.Model3D(
# label="Output Model (GLB Format)",
# #width=768,
# interactive=False,
# )
# gr.Markdown("Note: The model shown here has a darker appearance. Download to get correct results.")
with gr.Row():
gr.Markdown('''The novel views are generated on an archimedean spiral. You can download the video''')
gr.Markdown(_CITE_)
# set dust3r parameter invisible to be clean
with gr.Column():
with gr.Row():
schedule = gr.Dropdown(["linear", "cosine"],
value='linear', label="schedule", info="For global alignment!", visible=False)
niter = gr.Number(value=300, precision=0, minimum=0, maximum=5000,
label="num_iterations", info="For global alignment!", visible=False)
scenegraph_type = gr.Dropdown(["complete", "swin", "oneref"],
value='complete', label="Scenegraph",
info="Define how to make pairs",
interactive=True, visible=False)
same_focals = gr.Checkbox(value=True, label="Focal", info="Use the same focal for all cameras", visible=False)
winsize = gr.Slider(label="Scene Graph: Window Size", value=1,
minimum=1, maximum=1, step=1, visible=False)
refid = gr.Slider(label="Scene Graph: Id", value=0, minimum=0, maximum=0, step=1, visible=False)
with gr.Row():
# adjust the confidence threshold
min_conf_thr = gr.Slider(label="min_conf_thr", value=3.0, minimum=1.0, maximum=20, step=0.1, visible=False)
# adjust the camera size in the output pointcloud
cam_size = gr.Slider(label="cam_size", value=0.05, minimum=0.01, maximum=0.5, step=0.001, visible=False)
with gr.Row():
as_pointcloud = gr.Checkbox(value=False, label="As pointcloud", visible=False)
# two post process implemented
mask_sky = gr.Checkbox(value=False, label="Mask sky", visible=False)
clean_depth = gr.Checkbox(value=True, label="Clean-up depthmaps", visible=False)
transparent_cams = gr.Checkbox(value=False, label="Transparent cameras", visible=False)
# events
# scenegraph_type.change(set_scenegraph_options,
# inputs=[input_image, winsize, refid, scenegraph_type],
# outputs=[winsize, refid])
input_image.change(set_scenegraph_options,
inputs=[input_image, winsize, refid, scenegraph_type],
outputs=[winsize, refid])
# min_conf_thr.release(fn=model_from_scene_fun,
# inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
# clean_depth, transparent_cams, cam_size, same_focals],
# outputs=outmodel)
# cam_size.change(fn=model_from_scene_fun,
# inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
# clean_depth, transparent_cams, cam_size, same_focals],
# outputs=outmodel)
# as_pointcloud.change(fn=model_from_scene_fun,
# inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
# clean_depth, transparent_cams, cam_size, same_focals],
# outputs=outmodel)
# mask_sky.change(fn=model_from_scene_fun,
# inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
# clean_depth, transparent_cams, cam_size, same_focals],
# outputs=outmodel)
# clean_depth.change(fn=model_from_scene_fun,
# inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
# clean_depth, transparent_cams, cam_size, same_focals],
# outputs=outmodel)
# transparent_cams.change(model_from_scene_fun,
# inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
# clean_depth, transparent_cams, cam_size, same_focals],
# outputs=outmodel)
run_dust3r.click(fn=recon_fun,
inputs=[input_image, schedule, niter, min_conf_thr, as_pointcloud,
mask_sky, clean_depth, transparent_cams, cam_size,
scenegraph_type, winsize, refid, same_focals],
outputs=[scene, outmodel, processed_image, eschernet_input])
# events
# preview images on input change
input_image.change(fn=preview_input,
inputs=[input_image],
outputs=[processed_image])
submit.click(fn=generate_mvs,
inputs=[eschernet_input, sample_steps, sample_seed,
nvs_num, nvs_mode],
outputs=[mv_images, output_video],
)#.success(
# # fn=make3d,
# # inputs=[mv_images],
# # outputs=[output_video, output_model_obj, output_model_glb]
# # )
demo.queue(max_size=10)
demo.launch(share=True, server_name="0.0.0.0", server_port=None)
if __name__ == '__main__':
main() |