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webui.py
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import time
import numpy as np
import torch
import torchvision
import rembg
from gaussiansplatting.scene.colmap_loader import qvec2rotmat
from gaussiansplatting.scene.cameras import Simple_Camera
from threestudio.utils.dpt import DPT
from torchvision.ops import masks_to_boxes
from gaussiansplatting.utils.graphics_utils import fov2focal
import viser
import viser.transforms as tf
from dataclasses import dataclass, field
from viser.theme import TitlebarButton, TitlebarConfig, TitlebarImage
from PIL import Image
from tqdm import tqdm
import cv2
import numpy as np
import sys
import shutil
import torch
from torchvision.transforms.functional import to_pil_image, to_tensor
from kornia.geometry.quaternion import Quaternion
# import threestudio
# import os
# from threestudio.systems.base import BaseLift3DSystem
# from pathlib import Path
# import subprocess
# import rembg
# from threestudio.utils.clip_metrics import ClipSimilarity
# from threestudio.utils.lama import InpaintModel
# from threestudio.utils.ops import binary_cross_entropy
from threestudio.utils.typing import *
from threestudio.utils.transform import rotate_gaussians
from gaussiansplatting.gaussian_renderer import render, point_cloud_render
from gaussiansplatting.scene import GaussianModel
from gaussiansplatting.scene.vanilla_gaussian_model import (
GaussianModel as VanillaGaussianModel,
)
# from gaussiansplatting.utils.graphics_utils import fov2focal
from gaussiansplatting.arguments import (
PipelineParams,
OptimizationParams,
)
from omegaconf import OmegaConf
# from gaussiansplatting.utils.general_utils import inverse_sigmoid
# from gaussiansplatting.gaussian_renderer import camera2rasterizer
from argparse import ArgumentParser
from threestudio.utils.misc import (
get_device,
step_check,
dilate_mask,
erode_mask,
fill_closed_areas,
)
from threestudio.utils.sam import LangSAMTextSegmentor
from threestudio.utils.camera import camera_ray_sample_points, project, unproject
# from threestudio.utils.dpt import DPT
# from threestudio.utils.config import parse_structured
from gaussiansplatting.scene.camera_scene import CamScene
import math
from GUI.EditGuidance import EditGuidance
from GUI.DelGuidance import DelGuidance
# from GUI.AddGuidance import AddGuidance
import os
import random
import ui_utils
import datetime
import subprocess
from pathlib import Path
from threestudio.utils.transform import (
rotate_gaussians,
translate_gaussians,
scale_gaussians,
default_model_mtx,
)
class WebUI:
def __init__(self, cfg) -> None:
self.gs_source = cfg.gs_source
self.colmap_dir = cfg.colmap_dir
self.port = 8084
# training cfg
self.use_sam = False
self.guidance = None
self.stop_training = False
self.inpaint_end_flag = False
self.scale_depth = True
self.depth_end_flag = False
self.seg_scale = True
self.seg_scale_end = False
# from original system
self.points3d = []
self.gaussian = GaussianModel(
sh_degree=0,
anchor_weight_init_g0=1.0,
anchor_weight_init=0.1,
anchor_weight_multiplier=2,
)
# load
self.gaussian.load_ply(self.gs_source)
self.gaussian.max_radii2D = torch.zeros(
(self.gaussian.get_xyz.shape[0]), device="cuda"
)
# front end related
self.colmap_cameras = None
self.render_cameras = None
# diffusion model
self.ip2p = None
self.ctn_ip2p = None
self.ladi = None
self.ctn_inpaint = None
self.ctn_ip2p = None
self.training = False
if self.colmap_dir is not None:
# scene = CamScene(self.colmap_dir, h=512, w=512)
images_dir = os.path.join(self.colmap_dir, "images")
image_files = os.listdir(images_dir)
if image_files:
first_image_path = os.path.join(images_dir, image_files[0])
with Image.open(first_image_path) as img:
w, h = img.size
print(w)
print(h)
scene = CamScene(self.colmap_dir, h, w)
self.cameras_extent = scene.cameras_extent
self.colmap_cameras = scene.cameras
self.background_tensor = torch.tensor(
[0, 0, 0], dtype=torch.float32, device="cuda"
)
self.edit_frames = {}
self.origin_frames = {}
self.masks_2D = {}
self.text_segmentor = LangSAMTextSegmentor().to(get_device())
self.sam_predictor = self.text_segmentor.model.sam
self.sam_predictor.is_image_set = True
self.sam_features = {}
self.semantic_gauassian_masks = {}
self.semantic_gauassian_masks["ALL"] = torch.ones_like(self.gaussian._opacity)
self.parser = ArgumentParser(description="Training script parameters")
self.pipe = PipelineParams(self.parser)
# status
self.display_semantic_mask = False
self.display_point_prompt = False
self.viewer_need_update = False
self.system_need_update = False
self.inpaint_again = True
self.scale_depth = True
self.server = viser.ViserServer(port=self.port)
self.add_theme()
self.draw_flag = True
with self.server.add_gui_folder("Render Setting"):
self.resolution_slider = self.server.add_gui_slider(
"Resolution", min=384, max=4096, step=2, initial_value=2048
)
self.FoV_slider = self.server.add_gui_slider(
"FoV Scaler", min=0.2, max=2, step=0.1, initial_value=1
)
self.fps = self.server.add_gui_text(
"FPS", initial_value="-1", disabled=True
)
self.renderer_output = self.server.add_gui_dropdown(
"Renderer Output",
[
"comp_rgb",
],
)
self.save_button = self.server.add_gui_button("Save Gaussian")
self.frame_show = self.server.add_gui_checkbox(
"Show Frame", initial_value=False
)
with self.server.add_gui_folder("Semantic Tracing"):
self.sam_enabled = self.server.add_gui_checkbox(
"Enable SAM",
initial_value=False,
)
self.add_sam_points = self.server.add_gui_checkbox(
"Add SAM Points", initial_value=False
)
self.sam_group_name = self.server.add_gui_text(
"SAM Group Name", initial_value="table"
)
self.clear_sam_pins = self.server.add_gui_button(
"Clear SAM Pins",
)
self.text_seg_prompt = self.server.add_gui_text(
"Text Seg Prompt", initial_value="a bike"
)
self.semantic_groups = self.server.add_gui_dropdown(
"Semantic Group",
options=["ALL"],
)
self.seg_cam_num = self.server.add_gui_slider(
"Seg Camera Nums", min=6, max=200, step=1, initial_value=24
)
self.mask_thres = self.server.add_gui_slider(
"Seg Threshold", min=0.2, max=0.99999, step=0.00001, initial_value=0.7, visible=False
)
self.show_semantic_mask = self.server.add_gui_checkbox(
"Show Semantic Mask", initial_value=False
)
self.seg_scale_end_button = self.server.add_gui_button(
"End Seg Scale!",
visible=False,
)
self.submit_seg_prompt = self.server.add_gui_button("Tracing Begin!")
with self.server.add_gui_folder("Edit Setting"):
self.edit_type = self.server.add_gui_dropdown(
"Edit Type", ("Edit", "Delete", "Add")
)
self.guidance_type = self.server.add_gui_dropdown(
"Guidance Type", ("InstructPix2Pix", "ControlNet-Pix2Pix", "Ladi-Vton")
)
self.edit_frame_show = self.server.add_gui_checkbox(
"Show Edit Frame", initial_value=True, visible=False
)
self.edit_text = self.server.add_gui_text(
"Text",
initial_value="",
visible=True,
)
self.edit_image = self.server.add_gui_text(
"Image",
initial_value="",
visible=True,
) #value should be the path of image prompt
self.draw_bbox = self.server.add_gui_checkbox(
"Draw Bounding Box", initial_value=False, visible=False
)
self.left_up = self.server.add_gui_vector2(
"Left UP",
initial_value=(0, 0),
step=1,
visible=False,
)
self.right_down = self.server.add_gui_vector2(
"Right Down",
initial_value=(0, 0),
step=1,
visible=False,
)
self.inpaint_seed = self.server.add_gui_slider(
"Inpaint Seed", min=0, max=1000, step=1, initial_value=0, visible=False
)
self.refine_text = self.server.add_gui_text(
"Refine Text",
initial_value="",
visible=False,
)
self.inpaint_end = self.server.add_gui_button(
"End 2D Inpainting!",
visible=False,
)
self.depth_scaler = self.server.add_gui_slider(
"Depth Scale", min=0.0, max=5.0, step=0.01, initial_value=1.0, visible=False
)
self.depth_end = self.server.add_gui_button(
"End Depth Scale!",
visible=False,
)
self.edit_begin_button = self.server.add_gui_button("Edit Begin!")
self.edit_end_button = self.server.add_gui_button(
"End Editing!", visible=False
)
with self.server.add_gui_folder("Advanced Options"):
self.edit_cam_num = self.server.add_gui_slider(
"Camera Num", min=12, max=200, step=1, initial_value=48
)
self.edit_train_steps = self.server.add_gui_slider(
"Total Step", min=0, max=5000, step=100, initial_value=1500
)
self.densify_until_step = self.server.add_gui_slider(
"Densify Until Step",
min=0,
max=5000,
step=50,
initial_value=1300,
)
self.densification_interval = self.server.add_gui_slider(
"Densify Interval",
min=25,
max=1000,
step=25,
initial_value=100,
)
self.max_densify_percent = self.server.add_gui_slider(
"Max Densify Percent",
min=0.0,
max=1.0,
step=0.001,
initial_value=0.01,
)
self.min_opacity = self.server.add_gui_slider(
"Min Opacity",
min=0.0,
max=0.1,
step=0.0001,
initial_value=0.005,
)
self.per_editing_step = self.server.add_gui_slider(
"Edit Interval", min=4, max=48, step=1, initial_value=10
)
self.edit_begin_step = self.server.add_gui_slider(
"Edit Begin Step", min=0, max=5000, step=100, initial_value=0
)
self.edit_until_step = self.server.add_gui_slider(
"Edit Until Step", min=0, max=5000, step=100, initial_value=1000
)
self.inpaint_scale = self.server.add_gui_slider(
"Inpaint Scale", min=0.1, max=10, step=0.1, initial_value=1, visible=False
)
self.mask_dilate = self.server.add_gui_slider(
"Mask Dilate", min=1, max=30, step=1, initial_value=15, visible=False
)
self.fix_holes = self.server.add_gui_checkbox(
"Fix Holes", initial_value=True, visible=False
)
with self.server.add_gui_folder("Learning Rate Scaler"):
self.gs_lr_scaler = self.server.add_gui_slider(
"XYZ LR Init", min=0.0, max=10.0, step=0.1, initial_value=3.0
)
self.gs_lr_end_scaler = self.server.add_gui_slider(
"XYZ LR End", min=0.0, max=10.0, step=0.1, initial_value=2.0
)
self.color_lr_scaler = self.server.add_gui_slider(
"Color LR", min=0.0, max=10.0, step=0.1, initial_value=3.0
)
self.opacity_lr_scaler = self.server.add_gui_slider(
"Opacity LR", min=0.0, max=10.0, step=0.1, initial_value=2.0
)
self.scaling_lr_scaler = self.server.add_gui_slider(
"Scale LR", min=0.0, max=10.0, step=0.1, initial_value=2.0
)
self.rotation_lr_scaler = self.server.add_gui_slider(
"Rotation LR", min=0.0, max=10.0, step=0.1, initial_value=2.0
)
with self.server.add_gui_folder("Loss Options"):
self.lambda_l1 = self.server.add_gui_slider(
"Lambda L1", min=0, max=100, step=1, initial_value=10
)
self.lambda_p = self.server.add_gui_slider(
"Lambda Perceptual", min=0, max=100, step=1, initial_value=10
)
self.anchor_weight_init_g0 = self.server.add_gui_slider(
"Anchor Init G0", min=0., max=10., step=0.05, initial_value=0.05
)
self.anchor_weight_init = self.server.add_gui_slider(
"Anchor Init", min=0., max=10., step=0.05, initial_value=0.1
)
self.anchor_weight_multiplier = self.server.add_gui_slider(
"Anchor Multiplier", min=1., max=10., step=0.1, initial_value=1.3
)
self.lambda_anchor_color = self.server.add_gui_slider(
"Lambda Anchor Color", min=0, max=500, step=1, initial_value=0
)
self.lambda_anchor_geo = self.server.add_gui_slider(
"Lambda Anchor Geo", min=0, max=500, step=1, initial_value=50
)
self.lambda_anchor_scale = self.server.add_gui_slider(
"Lambda Anchor Scale", min=0, max=500, step=1, initial_value=50
)
self.lambda_anchor_opacity = self.server.add_gui_slider(
"Lambda Anchor Opacity", min=0, max=500, step=1, initial_value=50
)
self.anchor_term = [self.anchor_weight_init_g0, self.anchor_weight_init,
self.anchor_weight_multiplier,
self.lambda_anchor_color, self.lambda_anchor_geo,
self.lambda_anchor_scale, self.lambda_anchor_opacity, ]
@self.inpaint_seed.on_update
def _(_):
self.inpaint_again = True
@self.depth_scaler.on_update
def _(_):
self.scale_depth = True
@self.mask_thres.on_update
def _(_):
self.seg_scale = True
@self.edit_type.on_update
def _(_):
if self.edit_type.value == "Edit":
self.edit_text.visible = True
self.edit_image.visible = True
self.refine_text.visible = False
for term in self.anchor_term:
term.visible = True
self.inpaint_scale.visible = False
self.mask_dilate.visible = False
self.fix_holes.visible = False
self.per_editing_step.visible = True
self.edit_begin_step.visible = True
self.edit_until_step.visible = True
self.draw_bbox.visible = False
self.left_up.visible = False
self.right_down.visible = False
self.inpaint_seed.visible = False
self.inpaint_end.visible = False
self.depth_scaler.visible = False
self.depth_end.visible = False
self.edit_frame_show.visible = True
self.guidance_type.visible = True
elif self.edit_type.value == "Delete":
self.edit_text.visible = True
self.refine_text.visible = False
for term in self.anchor_term:
term.visible = True
self.inpaint_scale.visible = True
self.mask_dilate.visible = True
self.fix_holes.visible = True
self.edit_cam_num.value = 24
self.densification_interval.value = 50
self.per_editing_step.visible = False
self.edit_begin_step.visible = False
self.edit_until_step.visible = False
self.draw_bbox.visible = False
self.left_up.visible = False
self.right_down.visible = False
self.inpaint_seed.visible = False
self.inpaint_end.visible = False
self.depth_scaler.visible = False
self.depth_end.visible = False
self.edit_frame_show.visible = True
self.guidance_type.visible = False
elif self.edit_type.value == "Add":
self.edit_text.visible = True
self.refine_text.visible = False
for term in self.anchor_term:
term.visible = False
self.inpaint_scale.visible = False
self.mask_dilate.visible = False
self.fix_holes.visible = False
self.per_editing_step.visible = True
self.edit_begin_step.visible = True
self.edit_until_step.visible = True
self.draw_bbox.visible = True
self.left_up.visible = True
self.right_down.visible = True
self.inpaint_seed.visible = False
self.inpaint_end.visible = False
self.depth_scaler.visible = False
self.depth_end.visible = False
self.edit_frame_show.visible = False
self.guidance_type.visible = False
@self.save_button.on_click
def _(_):
current_time = datetime.datetime.now()
formatted_time = current_time.strftime("%Y-%m-%d-%H:%M")
self.gaussian.save_ply(os.path.join("ui_result", "{}.ply".format(formatted_time)))
@self.inpaint_end.on_click
def _(_):
self.inpaint_end_flag = True
@self.seg_scale_end_button.on_click
def _(_):
self.seg_scale_end = True
@self.depth_end.on_click
def _(_):
self.depth_end_flag = True
@self.edit_end_button.on_click
def _(event: viser.GuiEvent):
self.stop_training = True
@self.edit_begin_button.on_click
def _(event: viser.GuiEvent):
self.edit_begin_button.visible = False
self.edit_end_button.visible = True
if self.training:
return
self.training = True
self.configure_optimizers()
self.gaussian.update_anchor_term(
anchor_weight_init_g0=self.anchor_weight_init_g0.value,
anchor_weight_init=self.anchor_weight_init.value,
anchor_weight_multiplier=self.anchor_weight_multiplier.value,
)
if self.edit_type.value == "Add":
# self.add(self.camera)
self.add(self.camera)
else:
self.edit_frame_show.visible = True
edit_cameras, train_frames, train_frustums = ui_utils.sample_train_camera(self.colmap_cameras,
self.edit_cam_num.value,
self.server)
if self.edit_type.value == "Edit":
self.edit(edit_cameras, train_frames, train_frustums)
elif self.edit_type.value == "Delete":
self.delete(edit_cameras, train_frames, train_frustums)
ui_utils.remove_all(train_frames)
ui_utils.remove_all(train_frustums)
self.edit_frame_show.visible = False
self.guidance = None
self.training = False
self.gaussian.anchor_postfix()
self.edit_begin_button.visible = True
self.edit_end_button.visible = False
@self.submit_seg_prompt.on_click
def _(_):
if not self.sam_enabled.value:
text_prompt = self.text_seg_prompt.value
print("[Segmentation Prompt]", text_prompt)
_, semantic_gaussian_mask = self.update_mask(text_prompt)
else:
text_prompt = self.sam_group_name.value
# buggy here, if self.sam_enabled == True, will raise strange errors. (Maybe caused by multi-threading access to the same SAM model)
self.sam_enabled.value = False
self.add_sam_points.value = False
# breakpoint()
_, semantic_gaussian_mask = self.update_sam_mask_with_point_prompt(
save_mask=True
)
self.semantic_gauassian_masks[text_prompt] = semantic_gaussian_mask
if text_prompt not in self.semantic_groups.options:
self.semantic_groups.options += (text_prompt,)
self.semantic_groups.value = text_prompt
@self.semantic_groups.on_update
def _(_):
semantic_mask = self.semantic_gauassian_masks[self.semantic_groups.value]
self.gaussian.set_mask(semantic_mask)
self.gaussian.apply_grad_mask(semantic_mask)
@self.edit_frame_show.on_update
def _(_):
if self.guidance is not None:
for _ in self.guidance.train_frames:
_.visible = self.edit_frame_show.value
for _ in self.guidance.train_frustums:
_.visible = self.edit_frame_show.value
self.guidance.visible = self.edit_frame_show.value
with torch.no_grad():
self.frames = []
random.seed(0)
frame_index = random.sample(
range(0, len(self.colmap_cameras)),
min(len(self.colmap_cameras), 20),
)
for i in frame_index:
self.make_one_camera_pose_frame(i)
@self.frame_show.on_update
def _(_):
for frame in self.frames:
frame.visible = self.frame_show.value
self.server.world_axes.visible = self.frame_show.value
@self.server.on_scene_click
def _(pointer):
self.click_cb(pointer)
@self.clear_sam_pins.on_click
def _(_):
self.clear_points3d()
def make_one_camera_pose_frame(self, idx):
cam = self.colmap_cameras[idx]
T_world_camera = tf.SE3.from_rotation_and_translation(
tf.SO3(cam.qvec), cam.T
).inverse()
wxyz = T_world_camera.rotation().wxyz
position = T_world_camera.translation()
# breakpoint()
frame = self.server.add_frame(
f"/colmap/frame_{idx}",
wxyz=wxyz,
position=position,
axes_length=0.2,
axes_radius=0.01,
visible=False,
)
self.frames.append(frame)
@frame.on_click
def _(event: viser.GuiEvent):
client = event.client
assert client is not None
T_world_current = tf.SE3.from_rotation_and_translation(
tf.SO3(client.camera.wxyz), client.camera.position
)
T_world_target = tf.SE3.from_rotation_and_translation(
tf.SO3(frame.wxyz), frame.position
) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5]))
T_current_target = T_world_current.inverse() @ T_world_target
for j in range(5):
T_world_set = T_world_current @ tf.SE3.exp(
T_current_target.log() * j / 4.0
)
with client.atomic():
client.camera.wxyz = T_world_set.rotation().wxyz
client.camera.position = T_world_set.translation()
time.sleep(1.0 / 15.0)
client.camera.look_at = frame.position
if not hasattr(self, "begin_call"):
def begin_trans(client):
assert client is not None
T_world_current = tf.SE3.from_rotation_and_translation(
tf.SO3(client.camera.wxyz), client.camera.position
)
T_world_target = tf.SE3.from_rotation_and_translation(
tf.SO3(frame.wxyz), frame.position
) @ tf.SE3.from_translation(np.array([0.0, 0.0, -0.5]))
T_current_target = T_world_current.inverse() @ T_world_target
for j in range(5):
T_world_set = T_world_current @ tf.SE3.exp(
T_current_target.log() * j / 4.0
)
with client.atomic():
client.camera.wxyz = T_world_set.rotation().wxyz
client.camera.position = T_world_set.translation()
client.camera.look_at = frame.position
self.begin_call = begin_trans
def configure_optimizers(self):
opt = OptimizationParams(
parser = ArgumentParser(description="Training script parameters"),
max_steps= self.edit_train_steps.value,
lr_scaler = self.gs_lr_scaler.value,
lr_final_scaler = self.gs_lr_end_scaler.value,
color_lr_scaler = self.color_lr_scaler.value,
opacity_lr_scaler = self.opacity_lr_scaler.value,
scaling_lr_scaler = self.scaling_lr_scaler.value,
rotation_lr_scaler = self.rotation_lr_scaler.value,
)
opt = OmegaConf.create(vars(opt))
# opt.update(self.training_args)
self.gaussian.spatial_lr_scale = self.cameras_extent
self.gaussian.training_setup(opt)
def render(
self,
cam,
local=False,
sam=False,
train=False,
) -> Dict[str, Any]:
self.gaussian.localize = local
render_pkg = render(cam, self.gaussian, self.pipe, self.background_tensor)
image, viewspace_point_tensor, _, radii = (
render_pkg["render"],
render_pkg["viewspace_points"],
render_pkg["visibility_filter"],
render_pkg["radii"],
)
if train:
self.viewspace_point_tensor = viewspace_point_tensor
self.radii = radii
self.visibility_filter = self.radii > 0.0
semantic_map = render(
cam,
self.gaussian,
self.pipe,
self.background_tensor,
override_color=self.gaussian.mask[..., None].float().repeat(1, 3),
)["render"]
semantic_map = torch.norm(semantic_map, dim=0)
semantic_map = semantic_map > 0.0 # 1, H, W
semantic_map_viz = image.detach().clone() # C, H, W
semantic_map_viz = semantic_map_viz.permute(1, 2, 0) # 3 512 512 to 512 512 3
semantic_map_viz[semantic_map] = 0.50 * semantic_map_viz[
semantic_map
] + 0.50 * torch.tensor([1.0, 0.0, 0.0], device="cuda")
semantic_map_viz = semantic_map_viz.permute(2, 0, 1) # 512 512 3 to 3 512 512
render_pkg["sam_masks"] = []
render_pkg["point2ds"] = []
if sam:
if hasattr(self, "points3d") and len(self.points3d) > 0:
sam_output = self.sam_predict(image, cam)
if sam_output is not None:
render_pkg["sam_masks"].append(sam_output[0])
render_pkg["point2ds"].append(sam_output[1])
self.gaussian.localize = False # reverse
render_pkg["semantic"] = semantic_map_viz[None]
render_pkg["masks"] = semantic_map[None] # 1, 1, H, W
image = image.permute(1, 2, 0)[None] # C H W to 1 H W C
render_pkg["comp_rgb"] = image # 1 H W C
depth = render_pkg["depth_3dgs"]
depth = depth.permute(1, 2, 0)[None]
render_pkg["depth"] = depth
render_pkg["opacity"] = depth / (depth.max() + 1e-5)
return {
**render_pkg,
}
@torch.no_grad()
def update_mask(self, text_prompt) -> None:
masks = []
weights = torch.zeros_like(self.gaussian._opacity)
weights_cnt = torch.zeros_like(self.gaussian._opacity, dtype=torch.int32)
total_view_num = len(self.colmap_cameras)
random.seed(0) # make sure same views
view_index = random.sample(
range(0, total_view_num),
min(total_view_num, self.seg_cam_num.value),
)
for idx in tqdm(view_index):
cur_cam = self.colmap_cameras[idx]
this_frame = render(
cur_cam, self.gaussian, self.pipe, self.background_tensor
)["render"]
# breakpoint()
# this_frame [c h w]
this_frame = this_frame.moveaxis(0, -1)[None, ...]
mask = self.text_segmentor(this_frame, text_prompt)[0].to(get_device())
if self.use_sam:
print("Using SAM")
self.sam_features[idx] = self.sam_predictor.features
masks.append(mask)
self.gaussian.apply_weights(cur_cam, weights, weights_cnt, mask)
weights /= weights_cnt + 1e-7
self.seg_scale_end_button.visible = True
self.mask_thres.visible = True
self.show_semantic_mask.value = True
while True:
if self.seg_scale:
selected_mask = weights > self.mask_thres.value
selected_mask = selected_mask[:, 0]
self.gaussian.set_mask(selected_mask)
self.gaussian.apply_grad_mask(selected_mask)
self.seg_scale = False
if self.seg_scale_end:
self.seg_scale_end = False
break
time.sleep(0.01)
self.seg_scale_end_button.visible = False
self.mask_thres.visible = False
return masks, selected_mask
@property
def camera(self):
if len(list(self.server.get_clients().values())) == 0:
return None
if self.render_cameras is None and self.colmap_dir is not None:
self.aspect = list(self.server.get_clients().values())[0].camera.aspect
self.render_cameras = CamScene(
self.colmap_dir, h=-1, w=-1, aspect=self.aspect
).cameras
self.begin_call(list(self.server.get_clients().values())[0])
viser_cam = list(self.server.get_clients().values())[0].camera
# viser_cam.up_direction = tf.SO3(viser_cam.wxyz) @ np.array([0.0, -1.0, 0.0])
# viser_cam.look_at = viser_cam.position
R = tf.SO3(viser_cam.wxyz).as_matrix()
T = -R.T @ viser_cam.position
# T = viser_cam.position
if self.render_cameras is None:
fovy = viser_cam.fov * self.FoV_slider.value
else:
fovy = self.render_cameras[0].FoVy * self.FoV_slider.value
fovx = 2 * math.atan(math.tan(fovy / 2) * self.aspect)
# fovy = self.render_cameras[0].FoVy
# fovx = self.render_cameras[0].FoVx
# math.tan(self.render_cameras[0].FoVx / 2) / math.tan(self.render_cameras[0].FoVy / 2)
# math.tan(fovx/2) / math.tan(fovy/2)
# aspect = viser_cam.aspect
width = int(self.resolution_slider.value)
height = int(width / self.aspect)
return Simple_Camera(0, R, T, fovx, fovy, height, width, "", 0)
def click_cb(self, pointer):
if self.sam_enabled.value and self.add_sam_points.value:
assert hasattr(pointer, "click_pos"), "please install our forked viser"
click_pos = pointer.click_pos # tuple (float, float) W, H from 0 to 1
click_pos = torch.tensor(click_pos)
self.add_points3d(self.camera, click_pos)
self.viwer_need_update = True
elif self.draw_bbox.value:
assert hasattr(pointer, "click_pos"), "please install our forked viser"
click_pos = pointer.click_pos
click_pos = torch.tensor(click_pos)
cur_cam = self.camera
if self.draw_flag:
self.left_up.value = [
int(cur_cam.image_width * click_pos[0]),
int(cur_cam.image_height * click_pos[1]),
]
self.draw_flag = False
else:
new_value = [
int(cur_cam.image_width * click_pos[0]),
int(cur_cam.image_height * click_pos[1]),
]
if (self.left_up.value[0] < new_value[0]) and (
self.left_up.value[1] < new_value[1]
):
self.right_down.value = new_value
self.draw_flag = True
else:
self.left_up.value = new_value
def set_system(self, system):
self.system = system
def clear_points3d(self):
self.points3d = []
def add_points3d(self, camera, points2d, update_mask=False):
depth = render(camera, self.gaussian, self.pipe, self.background_tensor)[
"depth_3dgs"
]
unprojected_points3d = unproject(camera, points2d, depth)
self.points3d += unprojected_points3d.unbind(0)
if update_mask:
self.update_sam_mask_with_point_prompt(self.points3d)
@torch.no_grad()
def update_sam_mask_with_point_prompt(
self, points3d=None, save_mask=False, save_name="point_prompt_mask"
):
points3d = points3d if points3d is not None else self.points3d
masks = []
weights = torch.zeros_like(self.gaussian._opacity)
weights_cnt = torch.zeros_like(self.gaussian._opacity, dtype=torch.int32)
total_view_num = len(self.colmap_cameras)
random.seed(0) # make sure same views
view_index = random.sample(
range(0, total_view_num),
min(total_view_num, self.seg_cam_num.value),
)
for idx in tqdm(view_index):
cur_cam = self.colmap_cameras[idx]
assert len(points3d) > 0
points2ds = project(cur_cam, points3d)
img = render(cur_cam, self.gaussian, self.pipe, self.background_tensor)[
"render"
]
self.sam_predictor.set_image(
np.asarray(to_pil_image(img.cpu())),
)
self.sam_features[idx] = self.sam_predictor.features
# print(points2ds)
mask, _, _ = self.sam_predictor.predict(
point_coords=points2ds.cpu().numpy(),
point_labels=np.array([1] * points2ds.shape[0], dtype=np.int64),
box=None,
multimask_output=False,
)
mask = torch.from_numpy(mask).to(torch.bool).to(get_device())
self.gaussian.apply_weights(
cur_cam, weights, weights_cnt, mask.to(torch.float32)
)
masks.append(mask)
weights /= weights_cnt + 1e-7
self.seg_scale_end_button.visible = True
self.mask_thres.visible = True
self.show_semantic_mask.value = True
while True:
if self.seg_scale:
selected_mask = weights > self.mask_thres.value
selected_mask = selected_mask[:, 0]
self.gaussian.set_mask(selected_mask)
self.gaussian.apply_grad_mask(selected_mask)
self.seg_scale = False
if self.seg_scale_end:
self.seg_scale_end = False
break
time.sleep(0.01)
self.seg_scale_end_button.visible = False
self.mask_thres.visible = False
if save_mask:
for id, mask in enumerate(masks):
mask = mask.cpu().numpy()[0, 0]
img = Image.fromarray(mask)
os.makedirs("tmp",exist_ok=True)
img.save(f"./tmp/{save_name}-{id}.jpg")
return masks, selected_mask
@torch.no_grad()
def sam_predict(self, image, cam):
img = np.asarray(to_pil_image(image.cpu()))
self.sam_predictor.set_image(img)
if len(self.points3d) == 0:
return
_points2ds = project(cam, self.points3d)
_mask, _, _ = self.sam_predictor.predict(
point_coords=_points2ds.cpu().numpy(),
point_labels=np.array([1] * _points2ds.shape[0], dtype=np.int64),
box=None,
multimask_output=False,
)
_mask = torch.from_numpy(_mask).to(torch.bool).to(get_device())
return _mask.squeeze(), _points2ds
@torch.no_grad()
def prepare_output_image(self, output):
out_key = self.renderer_output.value
out_img = output[out_key][0] # H W C
if out_key == "comp_rgb":
if self.show_semantic_mask.value:
out_img = output["semantic"][0].moveaxis(0, -1)
elif out_key == "masks":
out_img = output["masks"][0].to(torch.float32)[..., None].repeat(1, 1, 3)
if out_img.dtype == torch.float32:
out_img = out_img.clamp(0, 1)
out_img = (out_img * 255).to(torch.uint8).cpu().to(torch.uint8)
out_img = out_img.moveaxis(-1, 0) # C H W
if self.sam_enabled.value:
if "sam_masks" in output and len(output["sam_masks"]) > 0:
try:
out_img = torchvision.utils.draw_segmentation_masks(
out_img, output["sam_masks"][0]