import os 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 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 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.mode = "image" self.seed = "random" 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("cuda") 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.gaussain_scale_factor = 1 # input image self.input_img = None self.input_mask = None self.input_img_torch = None self.input_mask_torch = None self.overlay_input_img = False self.overlay_input_img_ratio = 0.5 # input text self.prompt = "" self.negative_prompt = "" # 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) # override prompt from cmdline if self.opt.prompt is not None: self.prompt = self.opt.prompt # override if provide a checkpoint 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): 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 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, ) self.enable_sd = self.opt.lambda_sd > 0 and self.prompt != "" self.enable_zero123 = self.opt.lambda_zero123 > 0 and self.input_img is not None # lazy load guidance model if self.guidance_sd is None and self.enable_sd: print(f"[INFO] loading SD...") from guidance.sd_utils import StableDiffusion self.guidance_sd = StableDiffusion(self.device) print(f"[INFO] loaded SD!") if self.guidance_zero123 is None and self.enable_zero123: print(f"[INFO] loading zero123...") from guidance.zero123_utils import Zero123 self.guidance_zero123 = Zero123(self.device) print(f"[INFO] loaded zero123!") # 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) # prepare embeddings with torch.no_grad(): if self.enable_sd: self.guidance_sd.get_text_embeds([self.prompt], [self.negative_prompt]) if self.enable_zero123: self.guidance_zero123.get_img_embeds(self.input_img_torch) def train_step(self): starter = torch.cuda.Event(enable_timing=True) ender = torch.cuda.Event(enable_timing=True) starter.record() for _ in range(self.train_steps): 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 if self.input_img_torch is not None: cur_cam = self.fixed_cam out = self.renderer.render(cur_cam) # rgb loss image = out["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1] loss = loss + 10000 * step_ratio * F.mse_loss(image, self.input_img_torch) # mask loss mask = out["alpha"].unsqueeze(0) # [1, 1, H, W] in [0, 1] loss = loss + 1000 * step_ratio * F.mse_loss(mask, self.input_mask_torch) ### novel view (manual batch) render_resolution = 128 if step_ratio < 0.3 else (256 if step_ratio < 0.6 else 512) images = [] vers, hors, radii = [], [], [] # avoid too large elevation (> 80 or < -80), and make sure it always cover [-30, 30] min_ver = max(min(-30, -30 - self.opt.elevation), -80 - self.opt.elevation) max_ver = min(max(30, 30 - self.opt.elevation), 80 - self.opt.elevation) for _ in range(self.opt.batch_size): # 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) cur_cam = MiniCam( pose, render_resolution, render_resolution, self.cam.fovy, self.cam.fovx, self.cam.near, self.cam.far, ) invert_bg_color = np.random.rand() > self.opt.invert_bg_prob out = self.renderer.render(cur_cam, invert_bg_color=invert_bg_color) image = out["image"].unsqueeze(0)# [1, 3, H, W] in [0, 1] images.append(image) images = torch.cat(images, dim=0) # import kiui # kiui.lo(hor, ver) # kiui.vis.plot_image(image) # guidance loss if self.enable_sd: loss = loss + self.opt.lambda_sd * self.guidance_sd.train_step(images, step_ratio) if self.enable_zero123: loss = loss + self.opt.lambda_zero123 * self.guidance_zero123.train_step(images, vers, hors, radii, step_ratio) # optimize step loss.backward() self.optimizer.step() self.optimizer.zero_grad() # densify and prune if self.step >= self.opt.density_start_iter and self.step <= self.opt.density_end_iter: 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: # size_threshold = 20 if self.step > self.opt.opacity_reset_interval else None self.renderer.gaussians.densify_and_prune(self.opt.densify_grad_threshold, min_opacity=0.01, extent=0.5, max_screen_size=1) if self.step % self.opt.opacity_reset_interval == 0: self.renderer.gaussians.reset_opacity() ender.record() 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.gaussain_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): # load image print(f'[INFO] load image from {file}...') img = cv2.imread(file, cv2.IMREAD_UNCHANGED) 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 self.input_mask = img[..., 3:] # white bg self.input_img = img[..., :3] * self.input_mask + (1 - self.input_mask) # bgr to rgb self.input_img = self.input_img[..., ::-1].copy() # load 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: self.prompt = f.read().strip() @torch.no_grad() def save_model(self, mode='geo', texture_size=1024): os.makedirs(self.opt.outdir, exist_ok=True) if mode == 'geo': path = os.path.join(self.opt.outdir, self.opt.save_path + '_mesh.ply') mesh = self.renderer.gaussians.extract_mesh(path, self.opt.density_thresh) mesh.write_ply(path) elif mode == 'geo+tex': path = os.path.join(self.opt.outdir, self.opt.save_path + '_mesh.' + self.opt.mesh_format) 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() 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 import nvdiffrast.torch as dr if not self.opt.force_cuda_rast and (not self.opt.gui or os.name == 'nt'): glctx = dr.RasterizeGLContext() else: glctx = dr.RasterizeCudaContext() for ver, hor in zip(vers, hors): # render image pose = orbit_camera(ver, hor, self.cam.radius) cur_cam = MiniCam( pose, render_resolution, render_resolution, self.cam.fovy, self.cam.fovx, self.cam.near, self.cam.far, ) cur_out = self.renderer.render(cur_cam) 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) v_cam = torch.matmul(F.pad(mesh.v, pad=(0, 1), mode='constant', value=1.0), torch.inverse(pose).T).float().unsqueeze(0) v_clip = v_cam @ proj.T rast, rast_db = dr.rasterize(glctx, v_clip, mesh.f, (render_resolution, render_resolution)) depth, _ = dr.interpolate(-v_cam[..., [2]], rast, mesh.f) # [1, H, W, 1] depth = depth.squeeze(0) # [H, W, 1] alpha = (rast[0, ..., 3:] > 0).float() 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 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) 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 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 inpaint_region = binary_dilation(mask, iterations=32) inpaint_region[mask] = 0 search_region = mask.copy() 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 ) _, 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') 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_gaussain_scale(sender, app_data): self.gaussain_scale_factor = app_data self.need_update = True dpg.add_slider_float( label="gaussain scale", min_value=0, max_value=1, format="%.2f", default_value=self.gaussain_scale_factor, callback=callback_set_gaussain_scale, ) ### register camera handler def callback_camera_drag_rotate_or_draw_mask(sender, app_data): if not dpg.is_item_focused("_primary_window"): return dx = app_data[1] dy = app_data[2] self.cam.orbit(dx, dy) self.need_update = True def callback_camera_wheel_scale(sender, app_data): if not dpg.is_item_focused("_primary_window"): return delta = app_data self.cam.scale(delta) self.need_update = True def callback_camera_drag_pan(sender, app_data): if not dpg.is_item_focused("_primary_window"): return dx = app_data[1] dy = app_data[2] self.cam.pan(dx, dy) self.need_update = True def callback_set_mouse_loc(sender, app_data): if not dpg.is_item_focused("_primary_window"): return # just the pixel coordinate in image self.mouse_loc = np.array(app_data) with dpg.handler_registry(): # for camera moving dpg.add_mouse_drag_handler( button=dpg.mvMouseButton_Left, callback=callback_camera_drag_rotate_or_draw_mask, ) dpg.add_mouse_wheel_handler(callback=callback_camera_wheel_scale) dpg.add_mouse_drag_handler( button=dpg.mvMouseButton_Middle, callback=callback_camera_drag_pan ) dpg.create_viewport( title="Gaussian3D", width=self.W + 600, height=self.H + (45 if os.name == "nt" else 0), resizable=False, ) ### global theme with dpg.theme() as theme_no_padding: with dpg.theme_component(dpg.mvAll): # set all padding to 0 to avoid scroll bar dpg.add_theme_style( dpg.mvStyleVar_WindowPadding, 0, 0, category=dpg.mvThemeCat_Core ) dpg.add_theme_style( dpg.mvStyleVar_FramePadding, 0, 0, category=dpg.mvThemeCat_Core ) dpg.add_theme_style( dpg.mvStyleVar_CellPadding, 0, 0, category=dpg.mvThemeCat_Core ) dpg.bind_item_theme("_primary_window", theme_no_padding) dpg.setup_dearpygui() ### register a larger font # get it from: https://github.com/lxgw/LxgwWenKai/releases/download/v1.300/LXGWWenKai-Regular.ttf if os.path.exists("LXGWWenKai-Regular.ttf"): with dpg.font_registry(): with dpg.font("LXGWWenKai-Regular.ttf", 18) as default_font: dpg.bind_font(default_font) # dpg.show_metrics() dpg.show_viewport() def render(self): assert self.gui while dpg.is_dearpygui_running(): # update texture every frame if self.training: self.train_step() self.test_step() dpg.render_dearpygui_frame() # no gui mode def train(self, iters=500): if iters > 0: self.prepare_train() for i in tqdm.trange(iters): self.train_step() # do a last prune self.renderer.gaussians.prune(min_opacity=0.01, extent=1, max_screen_size=1) # save self.save_model(mode='model') self.save_model(mode='geo+tex') if __name__ == "__main__": import argparse from omegaconf import OmegaConf parser = argparse.ArgumentParser() parser.add_argument("--config", required=True, help="path to the yaml config file") args, extras = parser.parse_known_args() # override default config from cli opt = OmegaConf.merge(OmegaConf.load(args.config), OmegaConf.from_cli(extras)) gui = GUI(opt) if opt.gui: gui.render() else: gui.train(opt.iters)