Spaces:
Sleeping
Sleeping
File size: 14,422 Bytes
20a4a01 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 |
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# gradio demo
# --------------------------------------------------------
import argparse
import math
import builtins
import datetime
import gradio
import os
import torch
import numpy as np
import functools
import trimesh
import copy
from scipy.spatial.transform import Rotation
from dust3r.inference import inference
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 matplotlib.pyplot as pl
def get_args_parser():
parser = argparse.ArgumentParser()
parser_url = parser.add_mutually_exclusive_group()
parser_url.add_argument("--local_network", action='store_true', default=False,
help="make app accessible on local network: address will be set to 0.0.0.0")
parser_url.add_argument("--server_name", type=str, default=None, help="server url, default is 127.0.0.1")
parser.add_argument("--image_size", type=int, default=512, choices=[512, 224], help="image size")
parser.add_argument("--server_port", type=int, help=("will start gradio app on this port (if available). "
"If None, will search for an available port starting at 7860."),
default=None)
parser_weights = parser.add_mutually_exclusive_group(required=True)
parser_weights.add_argument("--weights", type=str, help="path to the model weights", default=None)
parser_weights.add_argument("--model_name", type=str, help="name of the model weights",
choices=["DUSt3R_ViTLarge_BaseDecoder_512_dpt",
"DUSt3R_ViTLarge_BaseDecoder_512_linear",
"DUSt3R_ViTLarge_BaseDecoder_224_linear"])
parser.add_argument("--device", type=str, default='cuda', help="pytorch device")
parser.add_argument("--tmp_dir", type=str, default=None, help="value for tempfile.tempdir")
parser.add_argument("--silent", action='store_true', default=False,
help="silence logs")
return parser
def set_print_with_timestamp(time_format="%Y-%m-%d %H:%M:%S"):
builtin_print = builtins.print
def print_with_timestamp(*args, **kwargs):
now = datetime.datetime.now()
formatted_date_time = now.strftime(time_format)
builtin_print(f'[{formatted_date_time}] ', end='') # print with time stamp
builtin_print(*args, **kwargs)
builtins.print = print_with_timestamp
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):
assert len(pts3d) == len(mask) <= len(imgs) <= len(cams2world) == len(focals)
pts3d = to_numpy(pts3d)
imgs = to_numpy(imgs)
focals = to_numpy(focals)
cams2world = to_numpy(cams2world)
scene = trimesh.Scene()
# 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)]
add_scene_cam(scene, pose_c2w, camera_edge_color,
None if transparent_cams else imgs[i], focals[i],
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
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):
"""
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 = scene.imgs
focals = scene.get_focals().cpu()
cams2world = 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())
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)
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):
"""
from a list of images, run dust3r inference, global aligner.
then run get_3D_model_from_scene
"""
imgs = load_images(filelist, size=image_size, verbose=not silent)
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)
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)
# 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 = []
for i in range(len(rgbimg)):
imgs.append(rgbimg[i])
imgs.append(rgb(depths[i]))
imgs.append(rgb(confs[i]))
return scene, outfile, imgs
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 = gradio.Slider(label="Scene Graph: Window Size", value=max_winsize,
minimum=1, maximum=max_winsize, step=1, visible=True)
refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0,
maximum=num_files - 1, step=1, visible=False)
elif scenegraph_type == "oneref":
winsize = gradio.Slider(label="Scene Graph: Window Size", value=max_winsize,
minimum=1, maximum=max_winsize, step=1, visible=False)
refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0,
maximum=num_files - 1, step=1, visible=True)
else:
winsize = gradio.Slider(label="Scene Graph: Window Size", value=max_winsize,
minimum=1, maximum=max_winsize, step=1, visible=False)
refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0,
maximum=num_files - 1, step=1, visible=False)
return winsize, refid
def main_demo(tmpdirname, model, device, image_size, server_name, server_port, silent=False):
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)
with gradio.Blocks(css=""".gradio-container {margin: 0 !important; min-width: 100%};""", title="DUSt3R Demo") as demo:
# scene state is save so that you can change conf_thr, cam_size... without rerunning the inference
scene = gradio.State(None)
gradio.HTML('<h2 style="text-align: center;">DUSt3R Demo</h2>')
with gradio.Column():
inputfiles = gradio.File(file_count="multiple")
with gradio.Row():
schedule = gradio.Dropdown(["linear", "cosine"],
value='linear', label="schedule", info="For global alignment!")
niter = gradio.Number(value=300, precision=0, minimum=0, maximum=5000,
label="num_iterations", info="For global alignment!")
scenegraph_type = gradio.Dropdown([("complete: all possible image pairs", "complete"),
("swin: sliding window", "swin"),
("oneref: match one image with all", "oneref")],
value='complete', label="Scenegraph",
info="Define how to make pairs",
interactive=True)
winsize = gradio.Slider(label="Scene Graph: Window Size", value=1,
minimum=1, maximum=1, step=1, visible=False)
refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0, maximum=0, step=1, visible=False)
run_btn = gradio.Button("Run")
with gradio.Row():
# adjust the confidence threshold
min_conf_thr = gradio.Slider(label="min_conf_thr", value=3.0, minimum=1.0, maximum=20, step=0.1)
# adjust the camera size in the output pointcloud
cam_size = gradio.Slider(label="cam_size", value=0.05, minimum=0.001, maximum=0.1, step=0.001)
with gradio.Row():
as_pointcloud = gradio.Checkbox(value=False, label="As pointcloud")
# two post process implemented
mask_sky = gradio.Checkbox(value=False, label="Mask sky")
clean_depth = gradio.Checkbox(value=True, label="Clean-up depthmaps")
transparent_cams = gradio.Checkbox(value=False, label="Transparent cameras")
outmodel = gradio.Model3D()
outgallery = gradio.Gallery(label='rgb,depth,confidence', columns=3, height="100%")
# events
scenegraph_type.change(set_scenegraph_options,
inputs=[inputfiles, winsize, refid, scenegraph_type],
outputs=[winsize, refid])
inputfiles.change(set_scenegraph_options,
inputs=[inputfiles, winsize, refid, scenegraph_type],
outputs=[winsize, refid])
run_btn.click(fn=recon_fun,
inputs=[inputfiles, schedule, niter, min_conf_thr, as_pointcloud,
mask_sky, clean_depth, transparent_cams, cam_size,
scenegraph_type, winsize, refid],
outputs=[scene, outmodel, outgallery])
min_conf_thr.release(fn=model_from_scene_fun,
inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
clean_depth, transparent_cams, cam_size],
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],
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],
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],
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],
outputs=outmodel)
transparent_cams.change(model_from_scene_fun,
inputs=[scene, min_conf_thr, as_pointcloud, mask_sky,
clean_depth, transparent_cams, cam_size],
outputs=outmodel)
demo.launch(share=False, server_name=server_name, server_port=server_port)
|