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gui.py
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922 lines (843 loc) · 41.8 KB
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import argparse
import math
from pathlib import Path
import dearpygui.dearpygui as dpg
import numpy as np
import torch
from torch import Tensor
import cv2
import my_ext
from my_ext import ops_3d, utils
from my_ext.utils.gui import Viewer3D
import datasets
from lietorch import SE3
from networks import options, make
from networks.sk_gs import SkeletonGaussianSplatting
from networks.renderer.gaussian_render_origin import render_gs_offical
D_NERF_SCENES = ['bouncingballs', 'hellwarrior', 'hook', 'jumpingjacks', 'lego', 'mutant', 'standup', 'trex']
ZJU_SCENES = ['366', '377', '381', '384', '387']
WIM_SCENES = ['atlas', 'baxter', 'cassie', 'iiwa', 'nao', 'pandas', 'spot']
# noinspection PyArgumentList
class SP_GS_GUI:
net: SkeletonGaussianSplatting
def __init__(self):
my_ext.my_logger.basic_config()
self.net, self.db, init_stage = self.build_dataset_and_net()
# self.image_index = 0
self.camera_index = 0
self.mean_point_scale = self.net.get_scaling.mean()
self.sp_colors = None
if hasattr(self.net, 'num_superpoints'):
self.sp_colors = utils.get_colors(self.net.num_superpoints).cuda().float()
self.net.gs_rasterizer = render_gs_offical
self.device = torch.device('cuda')
self.joint_color = torch.tensor([[1., 0, 0], [1., 0, 0]], device=self.device)
self.now_pose = torch.zeros(self.net.num_superpoints, 3, device=self.device)
self.now_joints = None
self.saved_videos = []
dpg.create_context()
dpg.create_viewport(
title='Superpoint Gaussian Splatting',
width=self.db.image_size[0],
height=self.db.image_size[1]
)
self.is_vary_time = False
self.is_vary_view = False
self.is_vary_pose = False
self.is_save_video = False
dpg.push_container_stack(dpg.add_window(tag='Primary Window'))
self.viewer = Viewer3D(self.rendering, size=self.db.image_size, no_resize=False, no_move=True)
with (dpg.window(tag='control', label='FPS:', collapsed=False, no_close=True, width=256, height=800)):
with dpg.collapsing_header(label='camears', default_open=False):
self.control_camera()
with dpg.collapsing_header(label='render', default_open=True):
self.control_render(init_stage)
with dpg.collapsing_header(label='show', default_open=True):
self.control_show()
with dpg.collapsing_header(label='joints', default_open=True):
self.control_joint()
dpg.pop_container_stack()
with dpg.handler_registry():
dpg.add_mouse_drag_handler(callback=self.viewer.callback_mouse_drag)
dpg.add_mouse_wheel_handler(callback=self.viewer.callback_mouse_wheel)
dpg.add_mouse_release_handler(callback=self.viewer.callback_mouse_release)
# dpg.add_mouse_wheel_handler(callback=self.callback_mouse_wheel)
dpg.add_mouse_move_handler(callback=self.callback_mouse_hover)
dpg.add_mouse_click_handler(callback=self.callback_mouse_click)
# dpg.add_key_press_handler(callback=self.callback_keypress)
self.change_image_index()
dpg.setup_dearpygui()
dpg.show_viewport()
# dpg.set_primary_window(self.viewer.win_tag, True)
dpg.set_primary_window("Primary Window", True)
# dpg.start_dearpygui()
def control_camera(self):
# dpg.add_text(tag='fps')
with dpg.group(horizontal=True):
dpg.add_text('fovy')
dpg.add_slider_float(
min_value=15.,
max_value=180.,
default_value=math.degrees(self.viewer.fovy),
callback=lambda *args: self.viewer.set_fovy(dpg.get_value('set_fovy')),
tag='set_fovy'
)
with dpg.group():
item_width = 50
with dpg.group(horizontal=True):
dpg.add_text('eye')
dpg.add_input_float(tag='eye_x', step=0, width=item_width)
dpg.add_input_float(tag='eye_y', step=0, width=item_width)
dpg.add_input_float(tag='eye_z', step=0, width=item_width)
with dpg.group(horizontal=True):
dpg.add_text('at ')
dpg.add_input_float(tag='at_x', step=0, width=item_width)
dpg.add_input_float(tag='at_y', step=0, width=item_width)
dpg.add_input_float(tag='at_z', step=0, width=item_width)
def change_eye(*args):
print('change camera position', args)
self.viewer.eye = self.viewer.eye.new_tensor([dpg.get_value(item) for item in
['eye_x', 'eye_y', 'eye_z']])
self.viewer.at = self.viewer.at.new_tensor([dpg.get_value(item) for item in
['at_x', 'at_y', 'at_z']])
self.viewer.need_update = True
dpg.add_button(label='change', callback=change_eye)
def change_image_index(self, *args, **kwargs):
if self.db is None:
return
image_index = self.image_index = dpg.get_value('img_id') % len(self.db)
dpg.set_value('img_id', image_index)
camera_id = self.db.camera_ids[image_index] if getattr(self.db, 'num_cameras', -1) > 0 else image_index
Tw2v = self.db.Tw2v[camera_id].cpu()
Tw2v = ops_3d.convert_coord_system_matrix(Tw2v, self.db.coord_dst, ops_3d.get_coord_system())
self.viewer.set_pose(Tw2v=Tw2v)
if hasattr(self.db, 'times') and self.db.times is not None:
dpg.set_value('time', self.db.times[image_index].item())
self.viewer.set_fovy(math.degrees((self.db.FoV[camera_id] if self.db.FoV.ndim == 2 else self.db.FoV)[1].item()))
self.viewer.resize(self.db.image_size[0], self.db.image_size[1])
print('change_image_index:', image_index, camera_id)
def control_render(self, init_stage=None):
with dpg.group(horizontal=True):
dpg.add_checkbox(label='offical rasterizer',
default_value=getattr(self.net, 'use_official_gaussians_render', True),
tag='official_rasterizer', callback=self.viewer.set_need_update)
with dpg.group(horizontal=True):
dpg.add_text('white background')
dpg.add_checkbox(tag='bg_white', callback=self.viewer.set_need_update)
with dpg.group(horizontal=True, show=hasattr(self.db, 'times') and self.db.times is not None):
dpg.add_slider_float(label='t', tag='time', max_value=1.0, callback=self.viewer.set_need_update)
def vary_time():
self.is_vary_time = not self.is_vary_time
if dpg.get_value('save_video'):
self.is_save_video = True
self.saved_videos = []
dpg.configure_item('save_video', label='(0)')
dpg.add_button(label='A', callback=vary_time)
# set camera by image_index
with dpg.group(horizontal=True):
dpg.add_input_int(label='img_id',
tag='img_id',
min_value=0,
min_clamped=True,
max_value=len(self.db) - 1,
max_clamped=True,
step=1,
callback=self.change_image_index
)
with dpg.group(horizontal=True):
dpg.add_text('cmp')
dpg.add_radio_button(items=['no', 'GT', "blend", "error"], tag='cmp_GT',
callback=self.viewer.set_need_update, horizontal=True, default_value='no')
# interpolate two camera
with dpg.group(horizontal=True):
dpg.add_text('view_int')
dpg.add_checkbox(tag='iterp_view', callback=self.vary_view)
dpg.add_input_int(
tag='img_id_1',
max_value=len(self.db.images) - 1,
max_clamped=True,
min_clamped=True,
step=0,
width=50,
callback=self.vary_view
)
dpg.add_input_int(
tag='img_id_2',
max_value=len(self.db.images) - 1,
max_clamped=True,
min_clamped=True,
step=0,
width=50,
callback=self.vary_view
)
def set_random_two_image_id():
img_id_1, img_id_2 = np.random.choice(len(self.db.images), 2).tolist()
dpg.set_value('img_id_1', img_id_1)
dpg.set_value('img_id_2', img_id_2)
self.vary_view()
dpg.add_button(label='R', callback=set_random_two_image_id)
with dpg.group(horizontal=True):
dpg.add_slider_float(tag='view_t', min_value=0, max_value=1, width=150, callback=self.vary_view)
dpg.add_input_int(tag='view_speed', default_value=120, width=50, step=0)
def switch_vary_view():
self.is_vary_view = not self.is_vary_view
print('switch vary view:', self.is_vary_view)
dpg.add_button(label='A', tag='A', callback=switch_vary_view)
def set_rotate_index_limit():
dpg.configure_item('rotate_index', max_value=dpg.get_value('rotate_total'))
dpg.set_value('rotate_index', 0)
self.viewer.set_need_update()
with dpg.group(horizontal=True):
dpg.add_text('Rotate Obj: auto')
dpg.add_checkbox(tag='rotate_auto', callback=self.viewer.set_need_update)
dpg.add_button(tag='roate_reset', label='R', callback=set_rotate_index_limit)
with dpg.group(horizontal=True):
dpg.add_slider_int(tag='rotate_index', callback=self.viewer.set_need_update, width=100, max_value=360)
dpg.add_text('/')
dpg.add_input_int(tag='rotate_total',
step=0,
default_value=360,
min_value=10,
min_clamped=True,
width=50,
callback=set_rotate_index_limit)
with dpg.group(horizontal=True):
dpg.add_text('stage:')
stages = []
for _, stage in reversed(self.net.train_schedule):
if stage != 'canonical' and stage not in stages:
stages.append(stage)
if hasattr(self.net, 'train_schedule'):
dpg.add_combo(
items=stages,
default_value=stages[0] if init_stage is None else init_stage,
tag='stage',
callback=self.viewer.set_need_update
)
def control_show(self):
dpg.add_separator()
with dpg.group(horizontal=True):
dpg.add_text('show')
dpg.add_text('size:')
dpg.add_slider_float(tag='point_size',
min_value=0,
max_value=2.0,
default_value=1.0,
width=100,
callback=self.viewer.set_need_update
)
with dpg.group(horizontal=True):
dpg.add_text('points')
dpg.add_checkbox(tag='show_points', callback=self.viewer.set_need_update)
dpg.add_text('superpoints')
dpg.add_checkbox(tag='show_superpoints', callback=self.viewer.set_need_update)
dpg.add_text('2D')
dpg.add_checkbox(tag='show_sp_2D', callback=self.viewer.set_need_update)
with dpg.group(horizontal=True):
dpg.add_text('point_sp')
dpg.add_checkbox(tag='show_p2sp', callback=self.viewer.set_need_update)
dpg.add_text('skeleton 2D')
dpg.add_checkbox(tag='show_skeleton_2D', callback=self.viewer.set_need_update)
with dpg.group(horizontal=True):
def save_image(sender, app_data):
utils.save_image(app_data['file_path_name'], self.viewer.data)
print('save image to', app_data['file_path_name'])
with dpg.file_dialog(directory_selector=False,
show=False,
callback=save_image,
id="save_file_dialog_id",
default_filename=self.db.scene,
width=700,
height=400):
dpg.add_file_extension(".jpg", color=(150, 255, 150, 255))
dpg.add_file_extension(".png", color=(255, 150, 150, 255))
# dpg.add_button(label='save_image', callback=save_image)
dpg.add_button(label='save_image', callback=lambda: dpg.show_item('save_file_dialog_id'))
def save_video(sender, app_data):
videos = np.stack(self.saved_videos, axis=0)
save_path = Path(app_data['file_path_name'])
utils.save_mp4(save_path, videos)
self.saved_videos = []
dpg.configure_item('save_video', label='save video')
print(f"save videos {videos.shape} to {save_path}")
with dpg.file_dialog(directory_selector=False,
show=False,
callback=save_video,
id="save_video_dialog_id",
default_filename=self.db.scene,
width=700,
height=400):
dpg.add_file_extension(".mp4", color=(150, 255, 150, 255))
def save_video_callback():
if self.is_save_video:
self.is_save_video = False
dpg.show_item('save_video_dialog_id')
dpg.configure_item('save_video', label='save video')
else:
self.is_save_video = True
self.saved_videos = []
dpg.configure_item('save_video', label='save video (0)')
self.viewer.set_need_update()
dpg.add_button(label='save video', tag='save_video', callback=save_video_callback)
dpg.add_input_int(tag='save_video_num', default_value=120, step=0, width=40)
def vary_view(self):
if self.db is None or not dpg.get_value('iterp_view'):
return
idx1, idx2 = dpg.get_value('cam_id_1'), dpg.get_value('cam_id_2')
view_1 = ops_3d.rigid.Rt_to_lie(self.db.Tw2v[idx1])
view_2 = ops_3d.rigid.Rt_to_lie(self.db.Tw2v[idx2])
t = dpg.get_value('view_t')
Tw2v = ops_3d.rigid.lie_to_Rt(view_1 * (1 - t) + view_2 * t).cpu()
Tw2v = ops_3d.convert_coord_system_matrix(Tw2v, self.db.coord_dst, ops_3d.get_coord_system())
self.viewer.set_pose(Tw2v=Tw2v)
self.viewer.set_fovy(math.degrees(self.db.FoV[idx1, 1] if self.db.FoV.ndim == 2 else self.db.FoV[1]))
self.viewer.resize(self.db.image_size[0], self.db.image_size[1])
self.viewer.need_update = True
def control_joint(self):
dpg.add_separator()
with dpg.group(horizontal=True):
dpg.add_text('show sp1')
dpg.add_checkbox(tag='show_sp1', callback=self.viewer.set_need_update)
dpg.add_input_int(tag='sp_1', min_value=0,
max_value=self.net.num_superpoints - 1, width=100, callback=self.viewer.set_need_update)
with dpg.group(horizontal=True):
dpg.add_text('show sp2')
dpg.add_checkbox(tag='show_sp2', callback=self.viewer.set_need_update)
dpg.add_input_int(tag='sp_2',
min_value=0,
max_value=self.net.num_superpoints - 1,
width=100,
callback=self.viewer.set_need_update)
with dpg.group(horizontal=True):
def set_joint():
i = dpg.get_value('joint_idx') % self.net.num_superpoints
if i < 0:
i += self.net.num_superpoints
dpg.set_value('joint_idx', i)
j = self.net.joint_parents[i, 0].item()
if j >= 0:
dpg.set_value('sp_1', i)
dpg.set_value('sp_2', j)
self.viewer.set_need_update()
dpg.add_text('show joint')
dpg.add_checkbox(tag='show_joint', callback=set_joint)
dpg.add_input_int(
tag='joint_idx',
default_value=0,
min_value=0,
max_value=self.net.num_superpoints - 1,
width=100,
callback=set_joint
)
dpg.add_text('', tag='now_joint')
with dpg.group(horizontal=True):
def load_pose(sender, app_data):
filepath = app_data['file_path_name']
self.now_pose = torch.from_numpy(np.loadtxt(filepath, delimiter=',')).to(self.now_pose)
print(f'load pose from {filepath}')
def save_pose(sender, app_data):
filepath = app_data['file_path_name']
np.savetxt(filepath, self.now_pose.cpu().numpy(), delimiter=',')
print(f'save pose to {filepath}')
with dpg.file_dialog(directory_selector=False,
show=False,
callback=load_pose,
id="load_pose_dialog",
default_filename=self.db.scene,
width=700,
height=400):
dpg.add_file_extension(".pose", color=(150, 255, 150, 255))
with dpg.file_dialog(directory_selector=False,
show=False,
callback=save_pose,
id="save_pose_dialog",
default_filename=self.db.scene,
width=700,
height=400):
dpg.add_file_extension(".pose", color=(150, 255, 150, 255))
dpg.add_button(label='load', tag='load_pose', callback=lambda: dpg.show_item('load_pose_dialog'))
dpg.add_button(label='save', tag='save_pose', callback=lambda: dpg.show_item('save_pose_dialog'))
def reset_pose():
self.now_pose.zero_()
self.viewer.set_need_update()
dpg.add_button(label='reset', tag='reset_pose', callback=reset_pose)
with dpg.group(horizontal=True, show=hasattr(self.db, 'times') and self.db.times is not None):
dpg.add_slider_float(label='t', tag='time_pose', max_value=1.0, callback=self.viewer.set_need_update)
def vary_pose():
self.is_vary_pose = not self.is_vary_pose
if dpg.get_value('save_video'):
self.is_save_video = True
self.saved_videos = []
dpg.configure_item('save_video', label='(0)')
dpg.add_button(label='A', callback=vary_pose)
with dpg.group(horizontal=True):
dpg.add_checkbox(label='enable', tag='joint_rot', callback=self.viewer.set_need_update)
def set_pose():
scale = math.radians(dpg.get_value('joint_rot_scale'))
R = ops_3d.rotate(
dpg.get_value('joint_rot_x') * scale,
dpg.get_value('joint_rot_y') * scale,
dpg.get_value('joint_rot_z') * scale,
device=self.device
)
jid = dpg.get_value('joint_idx') % self.net.num_superpoints
self.now_pose[jid] += ops_3d.rotation.R_to_lie(R)
print(f'set joint {jid}')
dpg.set_value('joint_rot_x', 0)
dpg.set_value('joint_rot_y', 0)
dpg.set_value('joint_rot_z', 0)
self.viewer.set_need_update()
dpg.add_button(label='set', tag='set_pose', callback=set_pose)
for name in ['x', 'y', 'z']:
dpg.add_slider_float(
label=f'{name}',
tag=f'joint_rot_{name}',
min_value=-1,
max_value=1,
callback=self.viewer.set_need_update
)
dpg.add_slider_float(
label=f'scale',
tag=f'joint_rot_scale',
min_value=0,
max_value=360,
default_value=45,
callback=self.viewer.set_need_update
)
# dpg.add_checkbox(label='joint stage', tag='stage_joint', callback=self.viewer.set_need_update)
def options(self):
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str, default='results/SP_GS/joint/jumpingjacks/best.pth')
parser.add_argument('-i', '--load', type=str, default='./exps/sp_gs/dnerf.yaml')
parser.add_argument('-s', '--scene', type=str, default=None)
parser.add_argument('--stage', type=str, default=None)
parser.add_argument('--split', default='train')
args = parser.parse_args()
return args
def build_dataset_and_net(self):
args = self.options()
pth_path = Path(args.load)
assert args.load and pth_path.exists(), f"must load a model/checkpoint"
cfg_path = args.config
# scene = Path(pth_path).parts[-2]
scene = args.scene
if scene is None:
for scene in D_NERF_SCENES + ZJU_SCENES + WIM_SCENES:
if scene in Path(pth_path).parts:
break
print('scene:', scene)
assert scene is not None
pth = torch.load(pth_path, map_location='cpu')
if 'model' in pth: # checkpoints
pth = pth['model']
parser = argparse.ArgumentParser()
my_ext.config.options(parser)
options(parser)
datasets.options(parser)
my_ext.my_logger.options(parser)
cfg = my_ext.config.make(
[f'-c={cfg_path}', f'--scene={scene}', '--no-log'], ignore_unknown=True, ignore_warning=True, parser=parser
)
print(cfg)
db: datasets.DNerfDataset.DNeRFDataset = datasets.make(cfg, 'train') # noqa
print(db)
net = make(cfg) # type: SuperpointSkeletonGaussianSplatting # noqa
# print(net)
net.set_from_dataset(db)
net.load_state_dict(pth, strict=False)
print(net)
print(f'There are {net.points.shape[0]} Gaussians')
print(f'There are {net.num_superpoints} superpoints')
print(f'There are {net.num_frames} frames in train dataset')
print(f'Root is {net.joint_root}')
net.eval()
net.cuda()
if args.split != 'train':
db: datasets.MyTreeSegDataset.MyTreeSegDataset = datasets.make(cfg, args.split) # noqa
print(db)
return net, db, args.stage
@torch.no_grad()
def rendering(self, Tw2v, fovy, size):
Tw2v = Tw2v.cuda()
if dpg.get_value('official_rasterizer'):
Tw2v = ops_3d.convert_coord_system(
Tw2v, ops_3d.get_coord_system(), self.db.coord_dst if self.db is not None else 'opencv')
Tv2c = ops_3d.opencv.perspective(fovy, size=self.db.image_size).cuda()
else:
Tv2c = ops_3d.perspective(fovy, size=self.db.image_size).cuda()
Tw2c = Tv2c @ Tw2v
Tv2w = torch.inverse(Tw2v)
t = torch.tensor([dpg.get_value('time')]).cuda()
info = {
'Tw2c': Tw2c,
'Tw2v': Tw2v,
'campos': Tv2w[:3, 3],
'size': self.db.image_size,
'index': self.image_index,
'fov_xy': (ops_3d.fovx_to_fovy(fovy, size[1] / size[0]), fovy),
}
if dpg.get_value('official_rasterizer'):
from diff_gaussian_rasterization import GaussianRasterizationSettings as Settings
raster_settings = Settings(
image_width=info['size'][0],
image_height=info['size'][1],
tanfovx=math.tan(0.5 * info['fov_xy'][0]),
tanfovy=math.tan(0.5 * info['fov_xy'][1]),
scale_modifier=1.0,
viewmatrix=info['Tw2v'].view(4, 4).transpose(-1, -2),
projmatrix=info['Tw2c'].view(4, 4).transpose(-1, -2),
sh_degree=self.net.max_sh_degree,
campos=info['campos'],
prefiltered=False,
debug=False,
bg=torch.full((3,), 1. if dpg.get_value('bg_white') else 0.).cuda()
)
gs_rasterizer = render_gs_offical
else:
from networks.renderer.gaussian_render import GaussianRasterizationSettings, render
raster_settings = GaussianRasterizationSettings(
image_width=info['size'][0],
image_height=info['size'][1],
tanfovx=math.tan(0.5 * info['fov_xy'][0]),
tanfovy=math.tan(0.5 * info['fov_xy'][1]),
scale_modifier=1.0,
viewmatrix=info['Tw2v'].view(4, 4), # .transpose(-1, -2),
projmatrix=info['Tw2c'].view(4, 4), # .transpose(-1, -2),
sh_degree=self.net.max_sh_degree,
campos=info['campos'],
prefiltered=False,
debug=False,
detach_other_extra=False
)
gs_rasterizer = render
kwargs = {}
if hasattr(self.net, 'train_schedule'):
stage = kwargs['stage'] = dpg.get_value('stage')
else:
stage = None
if 'sk' in stage:
kwargs['sk_r_delta'] = self.now_pose * dpg.get_value('time_pose')
if dpg.get_value('joint_rot'):
scale = math.radians(dpg.get_value('joint_rot_scale'))
R = ops_3d.rotate(
dpg.get_value('joint_rot_x') * scale,
dpg.get_value('joint_rot_y') * scale,
dpg.get_value('joint_rot_z') * scale,
device=self.device
)
jid = dpg.get_value('joint_idx') % self.net.num_superpoints
if jid != dpg.get_value('joint_idx'):
dpg.set_value('joint_idx', jid)
kwargs['sk_r_delta'][jid] += ops_3d.rotation.R_to_lie(R)
rotate_angle = 2.0 * torch.pi * dpg.get_value('rotate_index') / dpg.get_value('rotate_total')
R = ops_3d.rotate_z(rotate_angle).cuda()
net_out = self.net(t=t, campos=info['campos'], **kwargs)
device = torch.device('cuda')
M = getattr(self.net, 'num_superpoints', 0)
if self.net.sk_is_init and hasattr(self.net, 'sk_W'):
W = self.net.sk_W
else:
index = torch.arange(self.net.sp_knn.shape[0], device=device)[:, None].expand_as(self.net.sp_knn)
W = torch.zeros((len(self.net.points), M), device=device)
W[index, self.net.sp_knn] = self.net.sp_weights
net_out['points'] = ops_3d.xfm(net_out['points'], R)
net_out['rotations'] = ops_3d.quaternion.mul(ops_3d.rotation.R_to_quaternion(R)[None], net_out['rotations'])
net_out['colors'] = ops_3d.SH_to_RGB(
net_out.pop('sh_features'), net_out['points'], info['campos'], self.net.active_sh_degree, clamp=True)
point_scale = 10 ** ((dpg.get_value('point_size') * 0.5 - 1) * 2) # [1e-2, 1.]
if dpg.get_value('show_points'):
net_out['scales'] = torch.full_like(net_out['scales'], self.mean_point_scale * point_scale).float()
sp1 = dpg.get_value('sp_1') % self.net.num_superpoints
sp2 = dpg.get_value('sp_2') % self.net.num_superpoints
if dpg.get_value('show_p2sp'):
net_out['colors'] = torch.sum(self.sp_colors * W[..., None], dim=1)
sp_xyz = self.xfm_superpoints(self.net.sp_points, net_out)
sp_xyz = ops_3d.apply(sp_xyz, R)
if dpg.get_value('show_superpoints'):
net_out = self.add_gaussians(
net_out,
points=sp_xyz,
colors=self.sp_colors,
scales=self.mean_point_scale * point_scale * 5.,
replace=not dpg.get_value('show_points')
)
elif dpg.get_value('show_sp1') or dpg.get_value('show_sp2'):
w = 0
if dpg.get_value('show_sp1'):
w = W[:, sp1:sp1 + 1]
if dpg.get_value('show_sp2') and not (dpg.get_value('show_sp1') and sp1 == sp2):
w = w + W[:, sp2:sp2 + 1]
net_out['opacity'] *= w
if dpg.get_value('show_joint'):
joints = []
if self.net.sk_is_init:
joints.append(self.xfm_superpoints(ops_3d.apply(self.net.sp_points[sp1], R), net_out, sp1))
joints.append(self.xfm_superpoints(ops_3d.apply(self.net.sp_points[sp2], R), net_out, sp2))
else:
joints.append(self.xfm_superpoints(ops_3d.apply(self.net.joint_pos[sp1][sp2], R), net_out, sp1))
joints.append(self.xfm_superpoints(ops_3d.apply(self.net.joint_pos[sp2][sp1], R), net_out, sp2))
net_out = self.add_gaussians(
net_out,
points=torch.stack(joints),
colors=self.joint_color,
scales=self.mean_point_scale * point_scale * 10.0
)
# p2sp = torch.cat([p2sp, p2sp.new_tensor([sp2, sp1])], dim=0)
# if dpg.get_value('joint_rot'):
# R = ops_3d.rotate(
# dpg.get_value('joint_rot_x') * torch.pi * 2.,
# dpg.get_value('joint_rot_y') * torch.pi * 2.,
# dpg.get_value('joint_rot_z') * torch.pi * 2.,
# device=self.device
# )
# mask = p2sp == sp1
# net_out['points'][mask] = ops_3d.xfm(net_out['points'][mask] - joint1, R) + joint1
net_out = self.add_lines(net_out, sp_xyz[sp1:sp1 + 1], sp_xyz[sp2:sp2 + 1])
elif dpg.get_value('show_joint'):
mask = self.net.joint_parents[:, 0] >= 0
if self.net.sk_is_init and 'sk' not in stage:
joint = self.xfm_superpoints(self.net.sp_points, net_out)
elif self.net.joint_is_init:
ja = torch.arange(M, device=self.device)[mask]
jb = self.net.joint_parents[:, 0][mask]
# joint = self.net.joint_pos[ja, jb]
joint = self.xfm_superpoints(self.net.joint_pos[ja, jb], net_out, jb)
# joint2 = self.xfm_superpoints(self.net.joint_pos[ja, jb], net_out, ja)
# joint = (joint + joint2) * 0.5
else:
joint = None
if joint is not None:
joint = ops_3d.apply(joint, R)
net_out = self.add_gaussians(
net_out,
points=joint,
colors=joint.new_tensor([1., 0, 0.]),
scales=self.mean_point_scale * point_scale * 10.0
)
# root = torch.nonzero(torch.logical_not(mask)).item()
root = self.net.joint_root.item()
jb = self.net.joint_parents[:, 0]
jb = jb[jb >= 0]
jb = torch.where(jb.ge(root), jb - 1, jb)
# net_out = self.add_lines(net_out, joint, joint[jb])
# print(net_out['points'])
render_out_f = gs_rasterizer(**{k: v for k, v in net_out.items() if not k.startswith('_')},
raster_settings=raster_settings)
images = torch.permute(render_out_f['images'], (1, 2, 0)).contiguous()
# background = torch.rand_like(images)
# background: Tensor = None
# if background is not None:
# images = images + (1 - render_out_f['opacity'][..., None]) * background.squeeze(0)
cmp_GT = dpg.get_value('cmp_GT')
if cmp_GT != 'no':
gt_img = self.db.get_image(dpg.get_value('img_id'))[..., :3].to(images.device)
if gt_img.dtype == torch.uint8:
gt_img = gt_img.float() / 255.
if cmp_GT == 'GT':
images = gt_img
elif cmp_GT == 'blend':
images = torch.lerp(images, gt_img, 0.5)
else:
images[:] = torch.abs(images - gt_img) # .mean(dim=-1, keepdim=True)
self.now_joints = None
images = self.draw_skeleton(images, stage, net_out, R, Tw2c, size, M)
if dpg.get_value('show_sp_2D'):
images = self.draw_superpoints_2D(images, stage, net_out, R, Tw2c, size, M)
return images
def xfm_superpoints(self, points, net_out, mask=None, stage='static'):
if '_sk_tr' in net_out:
T = SE3.exp(net_out['_sk_tr'])
elif '_spT' in net_out:
T = SE3.InitFromVec(net_out['_spT'])
elif '_skT' in net_out:
T = SE3.InitFromVec(net_out['_skT'])
elif '_sp_tr' in net_out:
T = SE3.exp(net_out['_sp_tr'])
else:
T = None
if T is not None and mask is not None:
T = T[mask]
if T is None:
if stage != 'static':
print('sp points is not transformed!!')
else:
points = T.act(points)
return points
def draw_superpoints_2D(self, images, stage, net_out, R, Tw2c, size, M):
sp_points = self.net.sp_points
if stage != 'static':
sp_points = self.xfm_superpoints(sp_points, net_out)
sp_points = ops_3d.xfm(sp_points, R)
sp_points = ops_3d.xfm(sp_points, Tw2c, homo=True)
sp_points = ((sp_points[:, :2] / sp_points[:, -1:] + 1) * sp_points.new_tensor(size) - 1) * 0.5
sp_points = sp_points.cpu().numpy().astype(np.int32)
images = np.ascontiguousarray(utils.as_np_image(images))
for j in range(M):
images = cv2.circle(images, sp_points[j].tolist(), radius=3, color=(0, 0, 255), thickness=-1)
return images
def draw_skeleton(self, images, stage, net_out, R, Tw2c, size, M):
if not dpg.get_value('show_skeleton_2D'):
return images
if not self.net.sp_is_init:
return images
images = utils.as_np_image(images)
a, b, mask = self.net.joint_pair
if self.net.sk_is_init:
joint = self.net.sp_points
else:
joint = self.net.sp_points.clone()
joint[mask] = self.net.joint_pos[a, b]
if stage != 'static':
joint = self.xfm_superpoints(joint, net_out)
joint = ops_3d.apply(joint, R)
joint = ops_3d.apply(joint, Tw2c, homo=True)
joint = ((joint[:, :2] / joint[:, -1:] + 1) * joint.new_tensor(size) - 1) * 0.5
joint = joint.cpu().numpy().astype(np.int32)
self.now_joints = joint
images = np.ascontiguousarray(images)
for j in range(M):
if mask[j]:
c = (0, 255, 0)
if dpg.get_value('show_joint'):
if j == dpg.get_value('joint_idx'):
c = (255, 255, 0)
else:
if str(j) == dpg.get_value('now_joint'):
c = (255, 255, 0)
images = cv2.circle(images, joint[j].tolist(), radius=3, color=c, thickness=-1)
for j in range(len(a)):
images = cv2.line(images, joint[a[j]], joint[b[j]], color=(255, 0, 0), thickness=1)
return images
def add_lines(self, net_out, line_p1: Tensor, line_p2: Tensor, line_width=1.0):
points = (line_p1 + line_p2) / 2
scales = torch.ones_like(points) * line_width * self.mean_point_scale
dist = torch.pairwise_distance(line_p1, line_p2)
scales[:, 0] = dist / 6
if torch.all(line_p1 == line_p2):
rotations = None
else:
v = line_p2 - points
rotations = ops_3d.rotation.direction_vector_to_quaternion(v.new_tensor([[1, 0, 0]]), v)
rotations = ops_3d.quaternion.normalize(rotations)
return self.add_gaussians(net_out, points, points.new_tensor([0., 1., 0.]), scales, rotations)
def add_gaussians(self, net_out, points, colors, scales, rotations=None, opacity=None, replace=False):
P = points.shape[0]
colors = colors.view(-1, 3).expand(P, 3)
if isinstance(scales, Tensor):
scales = scales.view(-1, scales.shape[-1] if scales.ndim > 0 else 1).expand(P, 3)
else:
scales = net_out['scales'].new_tensor([scales]).view(-1, 1).expand(P, 3)
if rotations is None:
rotations = net_out['rotations'].new_zeros([P, 4])
rotations[:, -1] = 1.
else:
rotations = rotations.view(-1, 4).expand(P, 4)
if opacity is None:
opacity = net_out['opacity'].new_ones([P, 1])
else:
opacity = opacity.view(-1, 1).expand(P, 1)
if replace:
net_out.update({
'points': points,
'colors': colors,
'scales': scales,
'rotations': rotations,
'opacity': opacity
})
else:
net_out['points'] = torch.cat([net_out['points'], points], dim=0)
net_out['colors'] = torch.cat([net_out['colors'], colors], dim=0)
net_out['scales'] = torch.cat([net_out['scales'], scales], dim=0)
net_out['rotations'] = torch.cat([net_out['rotations'], rotations], dim=0)
net_out['opacity'] = torch.cat([net_out['opacity'], opacity], dim=0)
return net_out
def run(self):
last_size = None
while dpg.is_dearpygui_running():
dpg.render_dearpygui_frame()
if self.is_vary_time:
if self.is_save_video:
t = len(self.saved_videos) / dpg.get_value('save_video_num')
else:
t = dpg.get_value('time')
t = t + 0.01
if t > 1:
t = 0.
dpg.set_value('time', t)
self.viewer.need_update = True
if self.is_vary_pose:
if self.is_save_video:
t = len(self.saved_videos) / dpg.get_value('save_video_num')
else:
t = dpg.get_value('time_pose')
t = t + 0.01
if t > 1:
t = 0.
dpg.set_value('time_pose', t)
self.viewer.need_update = True
if self.is_vary_view:
if self.is_save_video:
t = len(self.saved_videos) / dpg.get_value('save_video_num')
else:
t = dpg.get_value('view_t')
t = t + 1. / dpg.get_value('view_speed')
if t > 1:
t = 0.
dpg.set_value('view_t', t)
self.vary_view()
if dpg.get_value('rotate_auto'):
if self.is_save_video:
rotate_index = len(self.saved_videos) / dpg.get_value('save_video_num')
rotate_index = int(rotate_index * dpg.get_value('rotate_total'))
else:
rotate_index = dpg.get_value('rotate_index') + 1
if rotate_index >= dpg.get_value('rotate_total'):
rotate_index = 0
dpg.set_value('rotate_index', rotate_index)
self.viewer.need_update = True
if self.viewer.need_update:
dpg.set_value('eye_x', self.viewer.eye[0].item())
dpg.set_value('eye_y', self.viewer.eye[1].item())
dpg.set_value('eye_z', self.viewer.eye[2].item())
dpg.set_value('at_x', self.viewer.at[0].item())
dpg.set_value('at_y', self.viewer.at[1].item())
dpg.set_value('at_z', self.viewer.at[2].item())
self.viewer.update()
now_size = self.viewer.size
if last_size != now_size:
dpg.configure_item('control', pos=(dpg.get_item_width(self.viewer.win_tag), 0))
dpg.set_viewport_width(dpg.get_item_width(self.viewer.win_tag) + dpg.get_item_width('control'))
dpg.set_viewport_height(dpg.get_item_height(self.viewer.win_tag))
last_size = now_size
dpg.configure_item('control', label=f"FPS: {dpg.get_frame_rate()}")
if self.is_save_video and len(self.saved_videos) < dpg.get_value('save_video_num'):
self.saved_videos.append(utils.as_np_image(self.viewer.data).copy())
dpg.configure_item('save_video', label=f"save video({len(self.saved_videos)})")
dpg.destroy_context()
def callback_mouse_click(self, sender, app_data):
if dpg.is_item_clicked(self.viewer.image_tag):
if self.now_joints is not None:
x, y = self.viewer.get_mouse_pos()
dist = np.linalg.norm(self.now_joints - np.array([x, y]), axis=-1)
nearest = np.argmin(dist).item()
if dist[nearest] < 10:
dpg.set_value('joint_idx', nearest)
dpg.set_value('sp_1', nearest)
dpg.set_value('sp_2', self.net.joint_parents[nearest, 0].item())
self.viewer.set_need_update()
def callback_mouse_hover(self, sender, app_data):
if dpg.is_item_hovered(self.viewer.image_tag):
old = dpg.get_value('now_joint')
if self.now_joints is not None:
x, y = self.viewer.get_mouse_pos()
dist = np.linalg.norm(self.now_joints - np.array([x, y]), axis=-1)
nearest = np.argmin(dist)
dpg.set_value('now_joint', f'{nearest}' if dist[nearest] < 10 else '')
else:
dpg.set_value('now_joint', '')
if old != dpg.get_value('now_joint'):
self.viewer.set_need_update()
def callback_keypress(self, sender, app_data):
pass
if __name__ == '__main__':
SP_GS_GUI().run()