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main.py
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main.py
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import os
# os.environ["CUDA_VISIBLE_DEVICES"] = ""
import cv2
import time
import tqdm
import numpy as np
import dearpygui.dearpygui as dpg
import torch
import torch.nn.functional as F
import rembg
import common.global_constant
from cam_utils import orbit_camera, OrbitCamera
from gs_renderer import Renderer, MiniCam
from grid_put import mipmap_linear_grid_put_2d
from mesh import Mesh, safe_normalize
# import nvdiffrast.torch as dr
class GUI:
def __init__(self, opt):
self.opt = opt # shared with the trainer's opt to support in-place modification of rendering parameters.
self.gui = opt.gui # enable gui
self.W = opt.W
self.H = opt.H
self.cam = OrbitCamera(opt.W, opt.H, r=opt.radius, fovy=opt.fovy)
self.fixed_cam = None
self.fixed_cam_list = []
self.mode = "image"
self.seed = "random"
# only for gui
self.buffer_image = np.ones((self.W, self.H, 3), dtype=np.float32)
self.need_update = True # update buffer_image
# models
self.device = torch.device(common.global_constant.GPU.DEVICE)
self.bg_remover = None
self.guidance_sd = None
self.guidance_zero123 = None
self.enable_sd = False
self.enable_zero123 = False
# renderer
self.renderer = Renderer(sh_degree=self.opt.sh_degree)
self.gaussian_scale_factor = 1
# input image
self.input_img = None
self.input_mask = None
self.input_img_torch = None
self.input_mask_torch = None
# {{ only for GUI
self.overlay_input_img = False
self.overlay_input_img_ratio = 0.5
# }}
self.input_img_list = []
self.input_mask_list = []
self.input_img_torch_list = []
self.input_mask_torch_list = []
# input text
self.prompt = ""
self.negative_prompt = ""
self.prompt_list = []
self.negative_prompt_list = []
# training stuff
self.training = False
self.optimizer = None
self.step = 0
self.train_steps = 1 # steps per rendering loop
# load input data from cmdline
if self.opt.input is not None:
self.load_input(self.opt.input)
if self.opt.input_files is not None:
input_files = self.opt.input_files.split(',')
self.load_input_files(input_files)
else:
if self.input_img is not None:
self.input_img_list.append(self.input_img)
self.input_mask_list.append(self.input_mask)
# print(self.input_img, self.input_mask)
# print(self.input_img_list, self.input_mask_list, self.prompt_list)
# override prompt from cmdline
if self.opt.prompt is not None:
self.prompt = self.opt.prompt
if self.opt.negative_prompt is not None:
self.negative_prompt = self.opt.negative_prompt
print('prompt:', self.prompt, '; negative_prompt:', self.negative_prompt)
# exit(0)
# override if provide a checkpoint
print('load:', self.opt.load)
if self.opt.load is not None:
self.renderer.initialize(self.opt.load)
else:
# initialize gaussians to a blob
self.renderer.initialize(num_pts=self.opt.num_pts)
if self.gui:
dpg.create_context()
self.register_dpg()
self.test_step()
def __del__(self):
if self.gui:
dpg.destroy_context()
def seed_everything(self):
try:
seed = int(self.seed)
except:
seed = np.random.randint(0, 1000000)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
self.last_seed = seed
def prepare_train(self, is_load_guidance=True):
print('prepare_train ... is_load_guidance:', is_load_guidance)
self.step = 0
# setup training
self.renderer.gaussians.training_setup(self.opt)
# do not do progressive sh-level
self.renderer.gaussians.active_sh_degree = self.renderer.gaussians.max_sh_degree
self.optimizer = self.renderer.gaussians.optimizer
# default camera
if self.opt.mvdream or self.opt.imagedream:
# the second view is the front view for mvdream/imagedream.
pose = orbit_camera(self.opt.elevation, 90, self.opt.radius)
else:
pose = orbit_camera(self.opt.elevation, 0, self.opt.radius)
self.fixed_cam = MiniCam(
pose,
self.opt.ref_size,
self.opt.ref_size,
self.cam.fovy,
self.cam.fovx,
self.cam.near,
self.cam.far,
)
# TODO: self.fixed_cam_list
if self.opt.input_camera_pose is not None:
input_camera_poses = self.opt.input_camera_pose.split(',')
for pose in input_camera_poses:
pose = float(pose)
print('pose: %s' % pose)
if self.opt.mvdream or self.opt.imagedream:
# the second view is the front view for mvdream/imagedream.
pose = orbit_camera(self.opt.elevation, 90 + pose, self.opt.radius)
else:
pose = orbit_camera(self.opt.elevation, pose, self.opt.radius)
self.fixed_cam_list.append(MiniCam(
pose,
self.opt.ref_size,
self.opt.ref_size,
self.cam.fovy,
self.cam.fovx,
self.cam.near,
self.cam.far,
))
else:
self.fixed_cam_list.append(self.fixed_cam)
self.enable_sd = self.opt.lambda_sd > 0 and self.prompt != ""
print('lambda_sd:', self.opt.lambda_sd)
print('enable_sd:', self.enable_sd)
self.enable_zero123 = self.opt.lambda_zero123 > 0 and (
self.input_img is not None or len(self.input_img_list) > 0)
print('lambda_zero123:', self.opt.lambda_zero123)
print('enable_zero123:', self.enable_zero123)
# lazy load guidance model
print('guidance_sd:', self.guidance_sd)
if not is_load_guidance:
return
if self.guidance_sd is None and self.enable_sd:
if self.opt.mvdream:
print(f"[INFO] loading MVDream...")
from guidance.mvdream_utils import MVDream
self.guidance_sd = MVDream(self.device)
print(f"[INFO] loaded MVDream!")
elif self.opt.imagedream:
print(f"[INFO] loading ImageDream...")
from guidance.imagedream_utils import ImageDream
self.guidance_sd = ImageDream(self.device)
print(f"[INFO] loaded ImageDream!")
else:
print(f"[INFO] loading SD...")
from guidance.sd_utils import StableDiffusion
self.guidance_sd = StableDiffusion(self.device)
print(f"[INFO] loaded SD!")
print('guidance_zero123:', self.guidance_zero123)
if self.guidance_zero123 is None and self.enable_zero123:
print(f"[INFO] loading zero123...")
model_key = None
from guidance.zero123_utils import Zero123
if self.opt.stable_zero123:
model_key = 'ashawkey/stable-zero123-diffusers'
self.guidance_zero123 = Zero123(self.device, model_key=model_key)
else:
model_key = 'ashawkey/zero123-xl-diffusers'
self.guidance_zero123 = Zero123(self.device, model_key=model_key)
print(f"[INFO] loaded zero123!", model_key)
# input image
'''
if self.input_img is not None:
self.input_img_torch = torch.from_numpy(self.input_img).permute(2, 0, 1).unsqueeze(0).to(self.device)
self.input_img_torch = F.interpolate(self.input_img_torch, (self.opt.ref_size, self.opt.ref_size),
mode="bilinear", align_corners=False)
self.input_mask_torch = torch.from_numpy(self.input_mask).permute(2, 0, 1).unsqueeze(0).to(self.device)
self.input_mask_torch = F.interpolate(self.input_mask_torch, (self.opt.ref_size, self.opt.ref_size),
mode="bilinear", align_corners=False)
'''
# TODO: self.input_img_torch_list input_mask_torch_list
print('input_img_list:', len(self.input_img_list))
for img_idx in range(len(self.input_img_list)):
input_img_torch = torch.from_numpy(self.input_img_list[img_idx]).permute(2, 0, 1).unsqueeze(0).to(self.device)
input_img_torch = F.interpolate(input_img_torch, (self.opt.ref_size, self.opt.ref_size),
mode="bilinear", align_corners=False)
self.input_img_torch_list.append(input_img_torch)
input_mask_torch = torch.from_numpy(self.input_mask_list[img_idx]).permute(2, 0, 1).unsqueeze(0).to(self.device)
input_mask_torch = F.interpolate(input_mask_torch, (self.opt.ref_size, self.opt.ref_size),
mode="bilinear", align_corners=False)
self.input_mask_torch_list.append(input_mask_torch)
# prepare embeddings
with torch.no_grad():
# TODO: self.input_img_torch_list just use first image calc embedding because utils train_step not handle multi
# prompts negative_prompts support multi?but encode_text in utils not support?
# support multi image embedding in future? necessary?
input_img_torch = self.input_img_torch_list[0] if (len(self.input_img_torch_list) > 0) else None
if self.enable_sd:
if self.opt.imagedream: # NOTICE: mvdream only use text input, imagedream both?
self.guidance_sd.get_image_text_embeds(input_img_torch, [self.prompt], [self.negative_prompt])
else: # only use text input
self.guidance_sd.get_text_embeds([self.prompt], [self.negative_prompt])
if self.enable_zero123: # only use image input
self.guidance_zero123.get_img_embeds(input_img_torch)
def train_step(self):
starter = torch.cuda.Event(enable_timing=True)
ender = torch.cuda.Event(enable_timing=True)
starter.record()
for step in range(self.train_steps): # 1??
print('step:', step)
self.step += 1
step_ratio = min(1, self.step / self.opt.iters)
# update lr
self.renderer.gaussians.update_learning_rate(self.step)
loss = 0
### known view has ground truth
if not self.opt.imagedream: # NOTICE: imagedream not use as ground truth?
print('known view ground truth loss...')
for idx_image in range(len(self.input_img_torch_list)):
# TODO: set cur_cam
#cur_cam = self.fixed_cam
cur_cam = self.fixed_cam_list[idx_image]
out = self.renderer.render(cur_cam)
# rgb loss
image = out["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1]
mse_loss_rgb = F.mse_loss(image, self.input_img_torch_list[idx_image])
print('mse_loss_rgb:', mse_loss_rgb)
lambda_loss_rgb = 10000
loss = loss + lambda_loss_rgb * (step_ratio if self.opt.warmup_rgb_loss else 1) * mse_loss_rgb
# mask loss
mask = out["alpha"].unsqueeze(0) # [1, 1, H, W] in [0, 1]
mse_loss_alpha = F.mse_loss(mask, self.input_mask_torch_list[idx_image])
print('mse_loss_alpha:', mse_loss_alpha)
lambda_loss_alpha = 1000
loss = loss + lambda_loss_alpha * (step_ratio if self.opt.warmup_rgb_loss else 1) * mse_loss_alpha
### novel view (manual batch) no ground truth so use guidance loss
print('novel view guidance loss...')
render_resolution = 128 if step_ratio < 0.3 else (256 if step_ratio < 0.6 else 512)
images = []
poses = []
vers, hors, radii = [], [], []
# avoid too large elevation (> 80 or < -80), and make sure it always cover [min_ver, max_ver]
min_ver = max(min(self.opt.min_ver, self.opt.min_ver - self.opt.elevation), -80 - self.opt.elevation)
max_ver = min(max(self.opt.max_ver, self.opt.max_ver - self.opt.elevation), 80 - self.opt.elevation)
print('batch_size:', self.opt.batch_size)
for batch_i in range(self.opt.batch_size):
print('batch_i:', batch_i)
# render random view
ver = np.random.randint(min_ver, max_ver)
hor = np.random.randint(-180, 180)
radius = 0
vers.append(ver)
hors.append(hor)
radii.append(radius)
pose = orbit_camera(self.opt.elevation + ver, hor, self.opt.radius + radius)
# print('pose:', pose.shape)
# print('pose after orbit_camera', pose)
poses.append(pose)
cur_cam = MiniCam(pose, render_resolution, render_resolution, self.cam.fovy, self.cam.fovx,
self.cam.near, self.cam.far)
# print('pose after MiniCam', pose)
bg_color = torch.tensor([1, 1, 1] if np.random.rand() > self.opt.invert_bg_prob else [0, 0, 0],
dtype=torch.float32, device=common.global_constant.GPU.DEVICE)
print('render ...', batch_i)
out = self.renderer.render(cur_cam, bg_color=bg_color)
print('render finish!', batch_i)
# print('pose after render', pose)
# print(out.keys())
# print('image:', out["image"].shape)
# print('image:', out["image"]) #crash!
image = out["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1]
print('image:', image.shape)
# print('image:', image)
images.append(image) # current random view
# enable mvdream training
if self.opt.mvdream or self.opt.imagedream: # other views: rotate horizontally based random view
print('mvdream or imagedream...')
for view_i in range(1, 4): # 3 views each rotate 90deg
pose_i = orbit_camera(self.opt.elevation + ver, hor + 90 * view_i, self.opt.radius + radius)
print('pose_i:', pose_i.shape)
poses.append(pose_i)
cur_cam_i = MiniCam(pose_i, render_resolution, render_resolution, self.cam.fovy, self.cam.fovx,
self.cam.near, self.cam.far)
# bg_color = torch.tensor([0.5, 0.5, 0.5], dtype=torch.float32, device=common.global_constant.GPU.DEVICE)
out_i = self.renderer.render(cur_cam_i, bg_color=bg_color)
# print('out_i:', out_i)
image = out_i["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1]
print('image:', image.shape)
images.append(image)
print('mvdream or imagedream finish!')
print('batch all finish!', len(images))
images = torch.cat(images, dim=0)
print('images finish!', images.shape, 'poses:', len(poses))
# print('images:', images)
# poses1 = torch.ones(1, 4, 4)
# print('pose1 after from_numpy', poses1, poses1.dtype)
# poses = torch.from_numpy(np.stack(poses, axis=0)).to(self.device)
poses = np.stack(poses, axis=0)
# print('pose after stack', poses, poses.dtype) #crash!!
# poses = torch.from_numpy(np.ascontiguousarray(poses))
# poses = torch.from_numpy(poses.copy())
poses = torch.from_numpy(poses)
# print('pose shape after from_numpy', poses.shape)
# poses = np.ones_like(poses)
# print('pose after from_numpy', poses)
# poses = torch.from_numpy(poses)
# poses = torch.ones_like(poses)
# poses = torch.ones(1, 4, 4)
# print('pose after from_numpy', poses, poses.dtype)
# print('pose1 after from_numpy', poses1, poses1.dtype)
# poses2 = torch.ones(1, 4, 4)
# print('pose2 after from_numpy', poses2, poses2.dtype)
# print('device:', self.device)
# poses2 = poses2.to(self.device)
# print('pose2 after to', poses2)
# print('poses to:', self.device)
poses = poses.to(self.device)
# print('pose after to', poses)
# import kiui
# print(hor, ver)
# kiui.vis.plot_image(images)
# guidance loss
print('guidance loss start')
# TODO:
# all has embeddings which inited in prepare_train should handle multi images or texts
# mvdream:text imagedream image&text sd:text zero123:image
# only zero123 has cam_embeddings for novel views
if self.enable_sd:
print('guidance_sd.train_step...')
if self.opt.mvdream or self.opt.imagedream:
loss = loss + self.opt.lambda_sd * self.guidance_sd.train_step(images, poses,
step_ratio=step_ratio if self.opt.anneal_timestep else None)
else:
loss = loss + self.opt.lambda_sd * self.guidance_sd.train_step(images,
step_ratio=step_ratio if self.opt.anneal_timestep else None)
print('guidance_sd.train_step finish!')
if self.enable_zero123:
print('guidance_zero123.train_step ...')
loss = loss + self.opt.lambda_zero123 * self.guidance_zero123.train_step(images, vers, hors, radii,
step_ratio=step_ratio if self.opt.anneal_timestep else None,
default_elevation=self.opt.elevation)
print('guidance_zero123.train_step finish!')
# optimize step
print('optimize step ...')
loss.backward()
self.optimizer.step()
self.optimizer.zero_grad()
print('optimize step finish!')
# densify and prune
# No training functions
if self.step >= self.opt.density_start_iter and self.step <= self.opt.density_end_iter:
print('densify and prune')
#print(out)
#print(out["visibility_filter"])
viewspace_point_tensor, visibility_filter, radii = out["viewspace_points"], out["visibility_filter"], \
out["radii"]
self.renderer.gaussians.max_radii2D[visibility_filter] = torch.max(
self.renderer.gaussians.max_radii2D[visibility_filter], radii[visibility_filter])
self.renderer.gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter)
if self.step % self.opt.densification_interval == 0:
self.renderer.gaussians.densify_and_prune(self.opt.densify_grad_threshold, min_opacity=0.01,
extent=4, max_screen_size=1)
if self.step % self.opt.opacity_reset_interval == 0:
self.renderer.gaussians.reset_opacity()
print('record...')
ender.record()
print('record finish!')
torch.cuda.synchronize()
t = starter.elapsed_time(ender)
self.need_update = True
if self.gui:
dpg.set_value("_log_train_time", f"{t:.4f}ms")
dpg.set_value(
"_log_train_log",
f"step = {self.step: 5d} (+{self.train_steps: 2d}) loss = {loss.item():.4f}",
)
# dynamic train steps (no need for now)
# max allowed train time per-frame is 500 ms
# full_t = t / self.train_steps * 16
# train_steps = min(16, max(4, int(16 * 500 / full_t)))
# if train_steps > self.train_steps * 1.2 or train_steps < self.train_steps * 0.8:
# self.train_steps = train_steps
@torch.no_grad()
def test_step(self):
# ignore if no need to update
if not self.need_update:
return
starter = torch.cuda.Event(enable_timing=True)
ender = torch.cuda.Event(enable_timing=True)
starter.record()
# should update image
if self.need_update:
# render image
cur_cam = MiniCam(
self.cam.pose,
self.W,
self.H,
self.cam.fovy,
self.cam.fovx,
self.cam.near,
self.cam.far,
)
out = self.renderer.render(cur_cam, self.gaussian_scale_factor)
buffer_image = out[self.mode] # [3, H, W]
if self.mode in ['depth', 'alpha']:
buffer_image = buffer_image.repeat(3, 1, 1)
if self.mode == 'depth':
buffer_image = (buffer_image - buffer_image.min()) / (
buffer_image.max() - buffer_image.min() + 1e-20)
buffer_image = F.interpolate(
buffer_image.unsqueeze(0),
size=(self.H, self.W),
mode="bilinear",
align_corners=False,
).squeeze(0)
self.buffer_image = (
buffer_image.permute(1, 2, 0)
.contiguous()
.clamp(0, 1)
.contiguous()
.detach()
.cpu()
.numpy()
)
# display input_image
if self.overlay_input_img and self.input_img is not None:
self.buffer_image = (
self.buffer_image * (1 - self.overlay_input_img_ratio)
+ self.input_img * self.overlay_input_img_ratio
)
self.need_update = False
ender.record()
torch.cuda.synchronize()
t = starter.elapsed_time(ender)
if self.gui:
dpg.set_value("_log_infer_time", f"{t:.4f}ms ({int(1000 / t)} FPS)")
dpg.set_value(
"_texture", self.buffer_image
) # buffer must be contiguous, else seg fault!
def load_input(self, file):
print('file:', file)
self.input_img, self.input_mask = self.load_image(file)
# print(self.input_img, self.input_mask)
# exit(0)
self.prompt = self.load_prompt(file)
print('prompt：', self.prompt)
def load_input_files(self, files):
for file in files:
print('file:', file)
input_img, input_mask = self.load_image(file)
self.input_img_list.append(input_img)
self.input_mask_list.append(input_mask)
prompt = self.load_prompt(file)
print('prompt：', prompt)
self.prompt_list.append(prompt)
def load_image(self, file):
# load image
print(f'[INFO] load image from {file} ...')
img = cv2.imread(file, cv2.IMREAD_UNCHANGED)
print('img shape:', img.shape)
if img.shape[-1] == 3:
if self.bg_remover is None:
self.bg_remover = rembg.new_session()
img = rembg.remove(img, session=self.bg_remover)
img = cv2.resize(img, (self.W, self.H), interpolation=cv2.INTER_AREA)
img = img.astype(np.float32) / 255.0
input_mask = img[..., 3:]
# white bg
input_img = img[..., :3] * input_mask + (1 - input_mask)
# bgr to rgb
input_img = input_img[..., ::-1].copy()
return input_img, input_mask
def load_prompt(self, file):
# load prompt
prompt = ""
file_prompt = file.replace("_rgba.png", "_caption.txt")
if os.path.exists(file_prompt):
print(f'[INFO] load prompt from {file_prompt}...')
with open(file_prompt, "r") as f:
prompt = f.read().strip()
return prompt
@torch.no_grad()
def save_model(self, mode='geo', texture_size=1024):
os.makedirs(self.opt.outdir, exist_ok=True)
if mode == 'geo':
print(f"[INFO] geo extract_mesh ...")
path = os.path.join(self.opt.outdir, self.opt.save_path + '_mesh.ply')
mesh = self.renderer.gaussians.extract_mesh(path, self.opt.density_thresh)
print('write_ply...')
mesh.write_ply(path)
elif mode == 'geo+tex':
path = os.path.join(self.opt.outdir, self.opt.save_path + '_mesh.' + self.opt.mesh_format)
print(f"[INFO] geo+tex extract_mesh ...")
mesh = self.renderer.gaussians.extract_mesh(path, self.opt.density_thresh)
# perform texture extraction
print(f"[INFO] unwrap uv...")
h = w = texture_size
mesh.auto_uv()
mesh.auto_normal()
print("mesh:", mesh)
# TODO: dump everything use another python file call nvidia!
albedo = torch.zeros((h, w, 3), device=self.device, dtype=torch.float32)
cnt = torch.zeros((h, w, 1), device=self.device, dtype=torch.float32)
# self.prepare_train() # tmp fix for not loading 0123
# vers = [0]
# hors = [0]
vers = [0] * 8 + [-45] * 8 + [45] * 8 + [-89.9, 89.9]
hors = [0, 45, -45, 90, -90, 135, -135, 180] * 3 + [0, 0]
render_resolution = 512
print('loading nvdiffrast...')
import nvdiffrast.torch as dr
if not self.opt.force_cuda_rast and (not self.opt.gui or os.name == 'nt'):
print('RasterizeGLContext')
glctx = dr.RasterizeGLContext()
else:
print('RasterizeCudaContext')
glctx = dr.RasterizeCudaContext()
for ver, hor in zip(vers, hors):
print('ver, hor:', ver, hor)
# render image
print('orbit_camera...')
pose = orbit_camera(ver, hor, self.cam.radius)
print('MiniCam...')
cur_cam = MiniCam(
pose,
render_resolution,
render_resolution,
self.cam.fovy,
self.cam.fovx,
self.cam.near,
self.cam.far,
)
print('render cur_cam...')
cur_out = self.renderer.render(cur_cam)
print('render cur_cam finish!')
rgbs = cur_out["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1]
# enhance texture quality with zero123 [not working well]
# if self.opt.guidance_model == 'zero123':
# rgbs = self.guidance.refine(rgbs, [ver], [hor], [0])
# import kiui
# kiui.vis.plot_image(rgbs)
# get coordinate in texture image
pose = torch.from_numpy(pose.astype(np.float32)).to(self.device)
proj = torch.from_numpy(self.cam.perspective.astype(np.float32)).to(self.device)
print('v_cam matmul...')
v_cam = torch.matmul(F.pad(mesh.v, pad=(0, 1), mode='constant', value=1.0),
torch.inverse(pose).T).float().unsqueeze(0)
print('v_clip matmul...')
v_clip = v_cam @ proj.T
print('dr rasterize...')
# only this use subprocess call nvidia!! notice glctx not in the loop!
rast, rast_db = dr.rasterize(glctx, v_clip, mesh.f, (render_resolution, render_resolution))
print('dr interpolate 1...')
depth, _ = dr.interpolate(-v_cam[..., [2]], rast, mesh.f) # [1, H, W, 1]
depth = depth.squeeze(0) # [H, W, 1] #Notice: return value not used???
alpha = (rast[0, ..., 3:] > 0).float()
print('dr interpolate 2...')
uvs, _ = dr.interpolate(mesh.vt.unsqueeze(0), rast, mesh.ft) # [1, 512, 512, 2] in [0, 1]
# use normal to produce a back-project mask
print('dr interpolate 3...')
normal, _ = dr.interpolate(mesh.vn.unsqueeze(0).contiguous(), rast, mesh.fn)
normal = safe_normalize(normal[0])
# rotated normal (where [0, 0, 1] always faces camera)
print('rot_normal...')
rot_normal = normal @ pose[:3, :3]
viewcos = rot_normal[..., [2]]
mask = (alpha > 0) & (viewcos > 0.5) # [H, W, 1]
mask = mask.view(-1)
uvs = uvs.view(-1, 2).clamp(0, 1)[mask]
rgbs = rgbs.view(3, -1).permute(1, 0)[mask].contiguous()
# update texture image
print('mipmap_linear_grid_put_2d...')
cur_albedo, cur_cnt = mipmap_linear_grid_put_2d(
h, w,
uvs[..., [1, 0]] * 2 - 1,
rgbs,
min_resolution=256,
return_count=True,
)
# albedo += cur_albedo
# cnt += cur_cnt
mask = cnt.squeeze(-1) < 0.1
albedo[mask] += cur_albedo[mask]
cnt[mask] += cur_cnt[mask]
mask = cnt.squeeze(-1) > 0
albedo[mask] = albedo[mask] / cnt[mask].repeat(1, 3)
mask = mask.view(h, w)
albedo = albedo.detach().cpu().numpy()
mask = mask.detach().cpu().numpy()
# dilate texture
from sklearn.neighbors import NearestNeighbors
from scipy.ndimage import binary_dilation, binary_erosion
print('binary_dilation...')
inpaint_region = binary_dilation(mask, iterations=32)
inpaint_region[mask] = 0
search_region = mask.copy()
print('binary_erosion...')
not_search_region = binary_erosion(search_region, iterations=3)
search_region[not_search_region] = 0
search_coords = np.stack(np.nonzero(search_region), axis=-1)
inpaint_coords = np.stack(np.nonzero(inpaint_region), axis=-1)
knn = NearestNeighbors(n_neighbors=1, algorithm="kd_tree").fit(
search_coords
)
print('kneighbors...')
_, indices = knn.kneighbors(inpaint_coords)
albedo[tuple(inpaint_coords.T)] = albedo[tuple(search_coords[indices[:, 0]].T)]
mesh.albedo = torch.from_numpy(albedo).to(self.device)
mesh.write(path)
else:
path = os.path.join(self.opt.outdir, self.opt.save_path + '_model.ply')
print('save_ply...')
self.renderer.gaussians.save_ply(path)
print(f"[INFO] save model to {path} .")
def register_dpg(self):
### register texture
with dpg.texture_registry(show=False):
dpg.add_raw_texture(
self.W,
self.H,
self.buffer_image,
format=dpg.mvFormat_Float_rgb,
tag="_texture",
)
### register window
# the rendered image, as the primary window
with dpg.window(
tag="_primary_window",
width=self.W,
height=self.H,
pos=[0, 0],
no_move=True,
no_title_bar=True,
no_scrollbar=True,
):
# add the texture
dpg.add_image("_texture")
# dpg.set_primary_window("_primary_window", True)
# control window
with dpg.window(
label="Control",
tag="_control_window",
width=600,
height=self.H,
pos=[self.W, 0],
no_move=True,
no_title_bar=True,
):
# button theme
with dpg.theme() as theme_button:
with dpg.theme_component(dpg.mvButton):
dpg.add_theme_color(dpg.mvThemeCol_Button, (23, 3, 18))
dpg.add_theme_color(dpg.mvThemeCol_ButtonHovered, (51, 3, 47))
dpg.add_theme_color(dpg.mvThemeCol_ButtonActive, (83, 18, 83))
dpg.add_theme_style(dpg.mvStyleVar_FrameRounding, 5)
dpg.add_theme_style(dpg.mvStyleVar_FramePadding, 3, 3)
# timer stuff
with dpg.group(horizontal=True):
dpg.add_text("Infer time: ")
dpg.add_text("no data", tag="_log_infer_time")
def callback_setattr(sender, app_data, user_data):
setattr(self, user_data, app_data)
# init stuff
with dpg.collapsing_header(label="Initialize", default_open=True):
# seed stuff
def callback_set_seed(sender, app_data):
self.seed = app_data
self.seed_everything()
dpg.add_input_text(
label="seed",
default_value=self.seed,
on_enter=True,
callback=callback_set_seed,
)
# input stuff
def callback_select_input(sender, app_data):
# only one item
for k, v in app_data["selections"].items():
dpg.set_value("_log_input", k)
self.load_input(v)
self.need_update = True
with dpg.file_dialog(
directory_selector=False,
show=False,
callback=callback_select_input,
file_count=1,
tag="file_dialog_tag",
width=700,
height=400,
):
dpg.add_file_extension("Images{.jpg,.jpeg,.png}")
with dpg.group(horizontal=True):
dpg.add_button(
label="input",
callback=lambda: dpg.show_item("file_dialog_tag"),
)
dpg.add_text("", tag="_log_input")
# overlay stuff
with dpg.group(horizontal=True):
def callback_toggle_overlay_input_img(sender, app_data):
self.overlay_input_img = not self.overlay_input_img
self.need_update = True
dpg.add_checkbox(
label="overlay image",
default_value=self.overlay_input_img,
callback=callback_toggle_overlay_input_img,
)
def callback_set_overlay_input_img_ratio(sender, app_data):
self.overlay_input_img_ratio = app_data
self.need_update = True
dpg.add_slider_float(
label="ratio",
min_value=0,
max_value=1,
format="%.1f",
default_value=self.overlay_input_img_ratio,
callback=callback_set_overlay_input_img_ratio,
)
# prompt stuff
dpg.add_input_text(
label="prompt",
default_value=self.prompt,
callback=callback_setattr,
user_data="prompt",
)
dpg.add_input_text(
label="negative",
default_value=self.negative_prompt,
callback=callback_setattr,
user_data="negative_prompt",
)
# save current model
with dpg.group(horizontal=True):
dpg.add_text("Save: ")
def callback_save(sender, app_data, user_data):
self.save_model(mode=user_data)
dpg.add_button(
label="model",
tag="_button_save_model",
callback=callback_save,
user_data='model',
)
dpg.bind_item_theme("_button_save_model", theme_button)
dpg.add_button(
label="geo",
tag="_button_save_mesh",
callback=callback_save,
user_data='geo',
)
dpg.bind_item_theme("_button_save_mesh", theme_button)
dpg.add_button(
label="geo+tex",
tag="_button_save_mesh_with_tex",
callback=callback_save,
user_data='geo+tex',
)
dpg.bind_item_theme("_button_save_mesh_with_tex", theme_button)
dpg.add_input_text(
label="",
default_value=self.opt.save_path,
callback=callback_setattr,
user_data="save_path",
)
# training stuff
with dpg.collapsing_header(label="Train", default_open=True):
# lr and train button
with dpg.group(horizontal=True):
dpg.add_text("Train: ")
def callback_train(sender, app_data):
if self.training:
self.training = False
dpg.configure_item("_button_train", label="start")
else:
self.prepare_train()
self.training = True
dpg.configure_item("_button_train", label="stop")
# dpg.add_button(
# label="init", tag="_button_init", callback=self.prepare_train
# )
# dpg.bind_item_theme("_button_init", theme_button)
dpg.add_button(
label="start", tag="_button_train", callback=callback_train
)
dpg.bind_item_theme("_button_train", theme_button)
with dpg.group(horizontal=True):
dpg.add_text("", tag="_log_train_time")
dpg.add_text("", tag="_log_train_log")
# rendering options
with dpg.collapsing_header(label="Rendering", default_open=True):
# mode combo
def callback_change_mode(sender, app_data):
self.mode = app_data
self.need_update = True
dpg.add_combo(
("image", "depth", "alpha"),
label="mode",
default_value=self.mode,
callback=callback_change_mode,
)
# fov slider
def callback_set_fovy(sender, app_data):
self.cam.fovy = np.deg2rad(app_data)
self.need_update = True
dpg.add_slider_int(
label="FoV (vertical)",
min_value=1,
max_value=120,
format="%d deg",
default_value=np.rad2deg(self.cam.fovy),
callback=callback_set_fovy,
)
def callback_set_gaussian_scale(sender, app_data):
self.gaussian_scale_factor = app_data
self.need_update = True
dpg.add_slider_float(
label="gaussian scale",
min_value=0,
max_value=1,
format="%.2f",
default_value=self.gaussian_scale_factor,
callback=callback_set_gaussian_scale,
)
### register camera handler
def callback_camera_drag_rotate_or_draw_mask(sender, app_data):
if not dpg.is_item_focused("_primary_window"):
return