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| import os | |
| import cv2 | |
| import time | |
| import tqdm | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| import rembg | |
| from cam_utils import orbit_camera, OrbitCamera | |
| from gs_renderer_4d import Renderer, MiniCam | |
| from grid_put import mipmap_linear_grid_put_2d | |
| import imageio | |
| import copy | |
| 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.seed = 888 | |
| 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.guidance_svd = None | |
| self.enable_sd = False | |
| self.enable_zero123 = False | |
| self.enable_svd = False | |
| # renderer | |
| self.renderer = Renderer(self.opt, 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 | |
| self.input_img_list = None | |
| self.input_mask_list = None | |
| self.input_img_torch_list = None | |
| self.input_mask_torch_list = None | |
| # 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: # True | |
| self.load_input(self.opt.input) # load imgs, if has bg, then rm bg; or just load imgs | |
| # override prompt from cmdline | |
| if self.opt.prompt is not None: # None | |
| self.prompt = self.opt.prompt | |
| # override if provide a checkpoint | |
| if self.opt.load is not None: # not None | |
| self.renderer.initialize(self.opt.load) | |
| # self.renderer.gaussians.load_model(opt.outdir, opt.save_path) | |
| else: | |
| # initialize gaussians to a blob | |
| self.renderer.initialize(num_pts=self.opt.num_pts) | |
| self.seed_everything() | |
| def seed_everything(self): | |
| try: | |
| seed = int(self.seed) | |
| except: | |
| seed = np.random.randint(0, 1000000) | |
| print(f'Seed: {seed:d}') | |
| 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 | |
| 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, | |
| ) | |
| self.enable_sd = self.opt.lambda_sd > 0 | |
| self.enable_zero123 = self.opt.lambda_zero123 > 0 | |
| self.enable_svd = self.opt.lambda_svd > 0 and self.input_img is not None | |
| # lazy load guidance model | |
| 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!") | |
| if self.guidance_zero123 is None and self.enable_zero123: | |
| print(f"[INFO] loading zero123...") | |
| from guidance.zero123_utils import Zero123 | |
| if self.opt.stable_zero123: | |
| self.guidance_zero123 = Zero123(self.device, model_key='ashawkey/stable-zero123-diffusers') | |
| else: | |
| self.guidance_zero123 = Zero123(self.device, model_key='ashawkey/zero123-xl-diffusers') | |
| print(f"[INFO] loaded zero123!") | |
| if self.guidance_svd is None and self.enable_svd: # False | |
| print(f"[INFO] loading SVD...") | |
| from guidance.svd_utils import StableVideoDiffusion | |
| self.guidance_svd = StableVideoDiffusion(self.device) | |
| print(f"[INFO] loaded SVD!") | |
| # 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) | |
| if self.input_img_list is not None: | |
| self.input_img_torch_list = [torch.from_numpy(input_img).permute(2, 0, 1).unsqueeze(0).to(self.device) for input_img in self.input_img_list] | |
| self.input_img_torch_list = [F.interpolate(input_img_torch, (self.opt.ref_size, self.opt.ref_size), mode="bilinear", align_corners=False) for input_img_torch in self.input_img_torch_list] | |
| self.input_mask_torch_list = [torch.from_numpy(input_mask).permute(2, 0, 1).unsqueeze(0).to(self.device) for input_mask in self.input_mask_list] | |
| self.input_mask_torch_list = [F.interpolate(input_mask_torch, (self.opt.ref_size, self.opt.ref_size), mode="bilinear", align_corners=False) for input_mask_torch in self.input_mask_torch_list] | |
| # prepare embeddings | |
| with torch.no_grad(): | |
| if self.enable_sd: | |
| if self.opt.imagedream: | |
| img_pos_list, img_neg_list, ip_pos_list, ip_neg_list, emb_pos_list, emb_neg_list = [], [], [], [], [], [] | |
| for _ in range(self.opt.n_views): | |
| for input_img_torch in self.input_img_torch_list: | |
| img_pos, img_neg, ip_pos, ip_neg, emb_pos, emb_neg = self.guidance_sd.get_image_text_embeds(input_img_torch, [self.prompt], [self.negative_prompt]) | |
| img_pos_list.append(img_pos) | |
| img_neg_list.append(img_neg) | |
| ip_pos_list.append(ip_pos) | |
| ip_neg_list.append(ip_neg) | |
| emb_pos_list.append(emb_pos) | |
| emb_neg_list.append(emb_neg) | |
| self.guidance_sd.image_embeddings['pos'] = torch.cat(img_pos_list, 0) | |
| self.guidance_sd.image_embeddings['neg'] = torch.cat(img_pos_list, 0) | |
| self.guidance_sd.image_embeddings['ip_img'] = torch.cat(ip_pos_list, 0) | |
| self.guidance_sd.image_embeddings['neg_ip_img'] = torch.cat(ip_neg_list, 0) | |
| self.guidance_sd.embeddings['pos'] = torch.cat(emb_pos_list, 0) | |
| self.guidance_sd.embeddings['neg'] = torch.cat(emb_neg_list, 0) | |
| else: | |
| self.guidance_sd.get_text_embeds([self.prompt], [self.negative_prompt]) | |
| if self.enable_zero123: | |
| c_list, v_list = [], [] | |
| for _ in range(self.opt.n_views): | |
| for input_img_torch in self.input_img_torch_list: | |
| c, v = self.guidance_zero123.get_img_embeds(input_img_torch) | |
| c_list.append(c) | |
| v_list.append(v) | |
| self.guidance_zero123.embeddings = [torch.cat(c_list, 0), torch.cat(v_list, 0)] | |
| if self.enable_svd: | |
| self.guidance_svd.get_img_embeds(self.input_img) | |
| 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): # 1 | |
| self.step += 1 # self.step starts from 0 | |
| step_ratio = min(1, self.step / self.opt.iters) # 1, step / 500 | |
| # update lr | |
| self.renderer.gaussians.update_learning_rate(self.step) | |
| loss = 0 | |
| self.renderer.prepare_render() | |
| ### known view | |
| if not self.opt.imagedream: | |
| for b_idx in range(self.opt.batch_size): | |
| cur_cam = copy.deepcopy(self.fixed_cam) | |
| cur_cam.time = b_idx | |
| 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_list[b_idx]) / self.opt.batch_size | |
| # 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_list[b_idx]) / self.opt.batch_size | |
| ### novel view (manual batch) | |
| render_resolution = 128 if step_ratio < 0.3 else (256 if step_ratio < 0.6 else 512) | |
| # render_resolution = 512 | |
| images = [] | |
| poses = [] | |
| vers, hors, radii = [], [], [] | |
| # avoid too large elevation (> 80 or < -80), and make sure it always cover [-30, 30] | |
| 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) | |
| for _ in range(self.opt.n_views): | |
| for b_idx 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) | |
| poses.append(pose) | |
| cur_cam = MiniCam(pose, render_resolution, render_resolution, self.cam.fovy, self.cam.fovx, self.cam.near, self.cam.far, time=b_idx) | |
| bg_color = torch.tensor([1, 1, 1] if np.random.rand() > self.opt.invert_bg_prob else [0, 0, 0], dtype=torch.float32, device="cuda") | |
| out = self.renderer.render(cur_cam, bg_color=bg_color) | |
| image = out["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1] | |
| images.append(image) | |
| # enable mvdream training | |
| if self.opt.mvdream or self.opt.imagedream: # False | |
| for view_i in range(1, 4): | |
| pose_i = orbit_camera(self.opt.elevation + ver, hor + 90 * view_i, self.opt.radius + radius) | |
| 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="cuda") | |
| out_i = self.renderer.render(cur_cam_i, bg_color=bg_color) | |
| image = out_i["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1] | |
| images.append(image) | |
| images = torch.cat(images, dim=0) | |
| poses = torch.from_numpy(np.stack(poses, axis=0)).to(self.device) | |
| # guidance loss | |
| if self.enable_sd: | |
| if self.opt.mvdream or self.opt.imagedream: | |
| loss = loss + self.opt.lambda_sd * self.guidance_sd.train_step(images, poses, step_ratio) | |
| else: | |
| 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) / (self.opt.batch_size * self.opt.n_views) | |
| if self.enable_svd: | |
| loss = loss + self.opt.lambda_svd * self.guidance_svd.train_step(images, 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 | |
| def load_input(self, file): | |
| if self.opt.data_mode == 'c4d': | |
| file_list = [os.path.join(file, f'{x * self.opt.downsample_rate}.png') for x in range(self.opt.batch_size)] | |
| elif self.opt.data_mode == 'svd': | |
| # file_list = [file.replace('.png', f'_frames/{x* self.opt.downsample_rate:03d}_rgba.png') for x in range(self.opt.batch_size)] | |
| # file_list = [x if os.path.exists(x) else (x.replace('_rgba.png', '.png')) for x in file_list] | |
| file_list = [file.replace('.png', f'_frames/{x* self.opt.downsample_rate:03d}.png') for x in range(self.opt.batch_size)] | |
| else: | |
| raise NotImplementedError | |
| self.input_img_list, self.input_mask_list = [], [] | |
| for file in file_list: | |
| # 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) | |
| # cv2.imwrite(file.replace('.png', '_rgba.png'), img) | |
| 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() | |
| self.input_img_list.append(input_img) | |
| self.input_mask_list.append(input_mask) | |
| def save_model(self, mode='geo', texture_size=1024, interp=1): | |
| os.makedirs(self.opt.outdir, exist_ok=True) | |
| if mode == 'geo': | |
| path = f'logs/{opt.save_path}_mesh_{t:03d}.ply' | |
| mesh = self.renderer.gaussians.extract_mesh_t(path, self.opt.density_thresh, t=t) | |
| mesh.write_ply(path) | |
| elif mode == 'geo+tex': | |
| from mesh import Mesh, safe_normalize | |
| os.makedirs(os.path.join(self.opt.outdir, self.opt.save_path+'_meshes'), exist_ok=True) | |
| for t in range(self.opt.batch_size): | |
| path = os.path.join(self.opt.outdir, self.opt.save_path+'_meshes', f'{t:03d}.obj') | |
| mesh = self.renderer.gaussians.extract_mesh_t(path, self.opt.density_thresh, t=t) | |
| # 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) | |
| 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, | |
| time=t | |
| ) | |
| cur_out = self.renderer.render(cur_cam) | |
| rgbs = cur_out["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1] | |
| # 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, | |
| ) | |
| 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) | |
| elif mode == 'frames': | |
| os.makedirs(os.path.join(self.opt.outdir, self.opt.save_path+'_frames'), exist_ok=True) | |
| for t in range(self.opt.batch_size * interp): | |
| tt = t / interp | |
| path = os.path.join(self.opt.outdir, self.opt.save_path+'_frames', f'{t:03d}.ply') | |
| self.renderer.gaussians.save_frame_ply(path, tt) | |
| else: | |
| path = os.path.join(self.opt.outdir, self.opt.save_path + '_4d_model.ply') | |
| self.renderer.gaussians.save_ply(path) | |
| self.renderer.gaussians.save_deformation(self.opt.outdir, self.opt.save_path) | |
| print(f"[INFO] save model to {path}.") | |
| # no gui mode | |
| def train(self, iters=500, ui=False): | |
| if self.gui: | |
| from visualizer.visergui import ViserViewer | |
| self.viser_gui = ViserViewer(device="cuda", viewer_port=8080) | |
| if iters > 0: | |
| self.prepare_train() | |
| if self.gui: | |
| self.viser_gui.set_renderer(self.renderer, self.fixed_cam) | |
| for i in tqdm.trange(iters): | |
| self.train_step() | |
| if self.gui: | |
| self.viser_gui.update() | |
| if self.opt.mesh_format == 'frames': | |
| self.save_model(mode='frames', interp=4) | |
| elif self.opt.mesh_format == 'obj': | |
| self.save_model(mode='geo+tex') | |
| if self.opt.save_model: | |
| self.save_model(mode='model') | |
| # render eval | |
| image_list =[] | |
| nframes = self.opt.batch_size * 7 + 15 * 7 | |
| hor = 180 | |
| delta_hor = 45 / 15 | |
| delta_time = 1 | |
| for i in range(8): | |
| time = 0 | |
| for j in range(self.opt.batch_size + 15): | |
| pose = orbit_camera(self.opt.elevation, hor-180, self.opt.radius) | |
| cur_cam = MiniCam( | |
| pose, | |
| 512, | |
| 512, | |
| self.cam.fovy, | |
| self.cam.fovx, | |
| self.cam.near, | |
| self.cam.far, | |
| time=time | |
| ) | |
| with torch.no_grad(): | |
| outputs = self.renderer.render(cur_cam) | |
| out = outputs["image"].cpu().detach().numpy().astype(np.float32) | |
| out = np.transpose(out, (1, 2, 0)) | |
| out = np.uint8(out*255) | |
| image_list.append(out) | |
| time = (time + delta_time) % self.opt.batch_size | |
| if j >= self.opt.batch_size: | |
| hor = (hor+delta_hor) % 360 | |
| imageio.mimwrite(f'vis_data/{opt.save_path}.mp4', image_list, fps=7) | |
| if self.gui: | |
| while True: | |
| self.viser_gui.update() | |
| 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)) | |
| opt.save_path = os.path.splitext(os.path.basename(opt.input))[0] if opt.save_path == '' else opt.save_path | |
| # auto find mesh from stage 1 | |
| opt.load = os.path.join(opt.outdir, opt.save_path + '_model.ply') | |
| gui = GUI(opt) | |
| gui.train(opt.iters) | |
| # python main_4d.py --config configs/4d_low.yaml input=data/CONSISTENT4D_DATA/in-the-wild/blooming_rose |