import json import os import re import shutil import cv2 import imageio import matplotlib.pyplot as plt import numpy as np import torch import trimesh import wandb from matplotlib import cm from matplotlib.colors import LinearSegmentedColormap from PIL import Image, ImageDraw from pytorch_lightning.loggers import WandbLogger from threestudio.models.mesh import Mesh from threestudio.utils.typing import * class SaverMixin: _save_dir: Optional[str] = None _wandb_logger: Optional[WandbLogger] = None def set_save_dir(self, save_dir: str): self._save_dir = save_dir def get_save_dir(self): if self._save_dir is None: raise ValueError("Save dir is not set") return self._save_dir def convert_data(self, data): if data is None: return None elif isinstance(data, np.ndarray): return data elif isinstance(data, torch.Tensor): return data.detach().cpu().numpy() elif isinstance(data, list): return [self.convert_data(d) for d in data] elif isinstance(data, dict): return {k: self.convert_data(v) for k, v in data.items()} else: raise TypeError( "Data must be in type numpy.ndarray, torch.Tensor, list or dict, getting", type(data), ) def get_save_path(self, filename): save_path = os.path.join(self.get_save_dir(), filename) os.makedirs(os.path.dirname(save_path), exist_ok=True) return save_path def create_loggers(self, cfg_loggers: DictConfig) -> None: if "wandb" in cfg_loggers.keys() and cfg_loggers.wandb.enable: self._wandb_logger = WandbLogger( project=cfg_loggers.wandb.project, name=cfg_loggers.wandb.name ) def get_loggers(self) -> List: if self._wandb_logger: return [self._wandb_logger] else: return [] DEFAULT_RGB_KWARGS = {"data_format": "HWC", "data_range": (0, 1)} DEFAULT_UV_KWARGS = { "data_format": "HWC", "data_range": (0, 1), "cmap": "checkerboard", } DEFAULT_GRAYSCALE_KWARGS = {"data_range": None, "cmap": "jet"} DEFAULT_GRID_KWARGS = {"align": "max"} def get_rgb_image_(self, img, data_format, data_range, rgba=False): img = self.convert_data(img) assert data_format in ["CHW", "HWC"] if data_format == "CHW": img = img.transpose(1, 2, 0) if img.dtype != np.uint8: img = img.clip(min=data_range[0], max=data_range[1]) img = ( (img - data_range[0]) / (data_range[1] - data_range[0]) * 255.0 ).astype(np.uint8) nc = 4 if rgba else 3 imgs = [img[..., start : start + nc] for start in range(0, img.shape[-1], nc)] imgs = [ img_ if img_.shape[-1] == nc else np.concatenate( [ img_, np.zeros( (img_.shape[0], img_.shape[1], nc - img_.shape[2]), dtype=img_.dtype, ), ], axis=-1, ) for img_ in imgs ] img = np.concatenate(imgs, axis=1) if rgba: img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA) else: img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) return img def _save_rgb_image( self, filename, img, data_format, data_range, name: Optional[str] = None, step: Optional[int] = None, ): img = self.get_rgb_image_(img, data_format, data_range) cv2.imwrite(filename, img) if name and self._wandb_logger: wandb.log( { name: wandb.Image(self.get_save_path(filename)), "trainer/global_step": step, } ) def save_rgb_image( self, filename, img, data_format=DEFAULT_RGB_KWARGS["data_format"], data_range=DEFAULT_RGB_KWARGS["data_range"], name: Optional[str] = None, step: Optional[int] = None, ) -> str: save_path = self.get_save_path(filename) self._save_rgb_image(save_path, img, data_format, data_range, name, step) return save_path def get_uv_image_(self, img, data_format, data_range, cmap): img = self.convert_data(img) assert data_format in ["CHW", "HWC"] if data_format == "CHW": img = img.transpose(1, 2, 0) img = img.clip(min=data_range[0], max=data_range[1]) img = (img - data_range[0]) / (data_range[1] - data_range[0]) assert cmap in ["checkerboard", "color"] if cmap == "checkerboard": n_grid = 64 mask = (img * n_grid).astype(int) mask = (mask[..., 0] + mask[..., 1]) % 2 == 0 img = np.ones((img.shape[0], img.shape[1], 3), dtype=np.uint8) * 255 img[mask] = np.array([255, 0, 255], dtype=np.uint8) img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) elif cmap == "color": img_ = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8) img_[..., 0] = (img[..., 0] * 255).astype(np.uint8) img_[..., 1] = (img[..., 1] * 255).astype(np.uint8) img_ = cv2.cvtColor(img_, cv2.COLOR_RGB2BGR) img = img_ return img def save_uv_image( self, filename, img, data_format=DEFAULT_UV_KWARGS["data_format"], data_range=DEFAULT_UV_KWARGS["data_range"], cmap=DEFAULT_UV_KWARGS["cmap"], ) -> str: save_path = self.get_save_path(filename) img = self.get_uv_image_(img, data_format, data_range, cmap) cv2.imwrite(save_path, img) return save_path def get_grayscale_image_(self, img, data_range, cmap): img = self.convert_data(img) img = np.nan_to_num(img) if data_range is None: img = (img - img.min()) / (img.max() - img.min()) else: img = img.clip(data_range[0], data_range[1]) img = (img - data_range[0]) / (data_range[1] - data_range[0]) assert cmap in [None, "jet", "magma", "spectral"] if cmap == None: img = (img * 255.0).astype(np.uint8) img = np.repeat(img[..., None], 3, axis=2) elif cmap == "jet": img = (img * 255.0).astype(np.uint8) img = cv2.applyColorMap(img, cv2.COLORMAP_JET) elif cmap == "magma": img = 1.0 - img base = cm.get_cmap("magma") num_bins = 256 colormap = LinearSegmentedColormap.from_list( f"{base.name}{num_bins}", base(np.linspace(0, 1, num_bins)), num_bins )(np.linspace(0, 1, num_bins))[:, :3] a = np.floor(img * 255.0) b = (a + 1).clip(max=255.0) f = img * 255.0 - a a = a.astype(np.uint16).clip(0, 255) b = b.astype(np.uint16).clip(0, 255) img = colormap[a] + (colormap[b] - colormap[a]) * f[..., None] img = (img * 255.0).astype(np.uint8) elif cmap == "spectral": colormap = plt.get_cmap("Spectral") def blend_rgba(image): image = image[..., :3] * image[..., -1:] + ( 1.0 - image[..., -1:] ) # blend A to RGB return image img = colormap(img) img = blend_rgba(img) img = (img * 255).astype(np.uint8) img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) return img def _save_grayscale_image( self, filename, img, data_range, cmap, name: Optional[str] = None, step: Optional[int] = None, ): img = self.get_grayscale_image_(img, data_range, cmap) cv2.imwrite(filename, img) if name and self._wandb_logger: wandb.log( { name: wandb.Image(self.get_save_path(filename)), "trainer/global_step": step, } ) def save_grayscale_image( self, filename, img, data_range=DEFAULT_GRAYSCALE_KWARGS["data_range"], cmap=DEFAULT_GRAYSCALE_KWARGS["cmap"], name: Optional[str] = None, step: Optional[int] = None, ) -> str: save_path = self.get_save_path(filename) self._save_grayscale_image(save_path, img, data_range, cmap, name, step) return save_path def get_image_grid_(self, imgs, align): if isinstance(imgs[0], list): return np.concatenate( [self.get_image_grid_(row, align) for row in imgs], axis=0 ) cols = [] for col in imgs: assert col["type"] in ["rgb", "uv", "grayscale"] if col["type"] == "rgb": rgb_kwargs = self.DEFAULT_RGB_KWARGS.copy() rgb_kwargs.update(col["kwargs"]) cols.append(self.get_rgb_image_(col["img"], **rgb_kwargs)) elif col["type"] == "uv": uv_kwargs = self.DEFAULT_UV_KWARGS.copy() uv_kwargs.update(col["kwargs"]) cols.append(self.get_uv_image_(col["img"], **uv_kwargs)) elif col["type"] == "grayscale": grayscale_kwargs = self.DEFAULT_GRAYSCALE_KWARGS.copy() grayscale_kwargs.update(col["kwargs"]) cols.append(self.get_grayscale_image_(col["img"], **grayscale_kwargs)) if align == "max": h = max([col.shape[0] for col in cols]) w = max([col.shape[1] for col in cols]) elif align == "min": h = min([col.shape[0] for col in cols]) w = min([col.shape[1] for col in cols]) elif isinstance(align, int): h = align w = align elif ( isinstance(align, tuple) and isinstance(align[0], int) and isinstance(align[1], int) ): h, w = align else: raise ValueError( f"Unsupported image grid align: {align}, should be min, max, int or (int, int)" ) for i in range(len(cols)): if cols[i].shape[0] != h or cols[i].shape[1] != w: cols[i] = cv2.resize(cols[i], (w, h), interpolation=cv2.INTER_LINEAR) return np.concatenate(cols, axis=1) def save_image_grid( self, filename, imgs, align=DEFAULT_GRID_KWARGS["align"], name: Optional[str] = None, step: Optional[int] = None, texts: Optional[List[float]] = None, ): save_path = self.get_save_path(filename) img = self.get_image_grid_(imgs, align=align) if texts is not None: img = Image.fromarray(img) draw = ImageDraw.Draw(img) black, white = (0, 0, 0), (255, 255, 255) for i, text in enumerate(texts): draw.text((2, (img.size[1] // len(texts)) * i + 1), f"{text}", white) draw.text((0, (img.size[1] // len(texts)) * i + 1), f"{text}", white) draw.text((2, (img.size[1] // len(texts)) * i - 1), f"{text}", white) draw.text((0, (img.size[1] // len(texts)) * i - 1), f"{text}", white) draw.text((1, (img.size[1] // len(texts)) * i), f"{text}", black) img = np.asarray(img) cv2.imwrite(save_path, img) if name and self._wandb_logger: wandb.log({name: wandb.Image(save_path), "trainer/global_step": step}) return save_path def save_image(self, filename, img) -> str: save_path = self.get_save_path(filename) img = self.convert_data(img) assert img.dtype == np.uint8 or img.dtype == np.uint16 if img.ndim == 3 and img.shape[-1] == 3: img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) elif img.ndim == 3 and img.shape[-1] == 4: img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA) cv2.imwrite(save_path, img) return save_path def save_cubemap(self, filename, img, data_range=(0, 1), rgba=False) -> str: save_path = self.get_save_path(filename) img = self.convert_data(img) assert img.ndim == 4 and img.shape[0] == 6 and img.shape[1] == img.shape[2] imgs_full = [] for start in range(0, img.shape[-1], 3): img_ = img[..., start : start + 3] img_ = np.stack( [ self.get_rgb_image_(img_[i], "HWC", data_range, rgba=rgba) for i in range(img_.shape[0]) ], axis=0, ) size = img_.shape[1] placeholder = np.zeros((size, size, 3), dtype=np.float32) img_full = np.concatenate( [ np.concatenate( [placeholder, img_[2], placeholder, placeholder], axis=1 ), np.concatenate([img_[1], img_[4], img_[0], img_[5]], axis=1), np.concatenate( [placeholder, img_[3], placeholder, placeholder], axis=1 ), ], axis=0, ) imgs_full.append(img_full) imgs_full = np.concatenate(imgs_full, axis=1) cv2.imwrite(save_path, imgs_full) return save_path def save_data(self, filename, data) -> str: data = self.convert_data(data) if isinstance(data, dict): if not filename.endswith(".npz"): filename += ".npz" save_path = self.get_save_path(filename) np.savez(save_path, **data) else: if not filename.endswith(".npy"): filename += ".npy" save_path = self.get_save_path(filename) np.save(save_path, data) return save_path def save_state_dict(self, filename, data) -> str: save_path = self.get_save_path(filename) torch.save(data, save_path) return save_path def save_img_sequence( self, filename, img_dir, matcher, save_format="mp4", fps=30, name: Optional[str] = None, step: Optional[int] = None, ) -> str: assert save_format in ["gif", "mp4"] if not filename.endswith(save_format): filename += f".{save_format}" save_path = self.get_save_path(filename) matcher = re.compile(matcher) img_dir = os.path.join(self.get_save_dir(), img_dir) imgs = [] for f in os.listdir(img_dir): if matcher.search(f): imgs.append(f) imgs = sorted(imgs, key=lambda f: int(matcher.search(f).groups()[0])) imgs = [cv2.imread(os.path.join(img_dir, f)) for f in imgs] if save_format == "gif": imgs = [cv2.cvtColor(i, cv2.COLOR_BGR2RGB) for i in imgs] imageio.mimsave(save_path, imgs, fps=fps, palettesize=256) elif save_format == "mp4": imgs = [cv2.cvtColor(i, cv2.COLOR_BGR2RGB) for i in imgs] imageio.mimsave(save_path, imgs, fps=fps) if name and self._wandb_logger: wandb.log( { name: wandb.Video(save_path, format="mp4"), "trainer/global_step": step, } ) return save_path def save_mesh(self, filename, v_pos, t_pos_idx, v_tex=None, t_tex_idx=None) -> str: save_path = self.get_save_path(filename) v_pos = self.convert_data(v_pos) t_pos_idx = self.convert_data(t_pos_idx) mesh = trimesh.Trimesh(vertices=v_pos, faces=t_pos_idx) mesh.export(save_path) return save_path def save_obj( self, filename: str, mesh: Mesh, save_mat: bool = False, save_normal: bool = False, save_uv: bool = False, save_vertex_color: bool = False, map_Kd: Optional[Float[Tensor, "H W 3"]] = None, map_Ks: Optional[Float[Tensor, "H W 3"]] = None, map_Bump: Optional[Float[Tensor, "H W 3"]] = None, map_Pm: Optional[Float[Tensor, "H W 1"]] = None, map_Pr: Optional[Float[Tensor, "H W 1"]] = None, map_format: str = "jpg", ) -> List[str]: save_paths: List[str] = [] if not filename.endswith(".obj"): filename += ".obj" v_pos, t_pos_idx = self.convert_data(mesh.v_pos), self.convert_data( mesh.t_pos_idx ) v_nrm, v_tex, t_tex_idx, v_rgb = None, None, None, None if save_normal: v_nrm = self.convert_data(mesh.v_nrm) if save_uv: v_tex, t_tex_idx = self.convert_data(mesh.v_tex), self.convert_data( mesh.t_tex_idx ) if save_vertex_color: v_rgb = self.convert_data(mesh.v_rgb) matname, mtllib = None, None if save_mat: matname = "default" mtl_filename = filename.replace(".obj", ".mtl") mtllib = os.path.basename(mtl_filename) mtl_save_paths = self._save_mtl( mtl_filename, matname, map_Kd=self.convert_data(map_Kd), map_Ks=self.convert_data(map_Ks), map_Bump=self.convert_data(map_Bump), map_Pm=self.convert_data(map_Pm), map_Pr=self.convert_data(map_Pr), map_format=map_format, ) save_paths += mtl_save_paths obj_save_path = self._save_obj( filename, v_pos, t_pos_idx, v_nrm=v_nrm, v_tex=v_tex, t_tex_idx=t_tex_idx, v_rgb=v_rgb, matname=matname, mtllib=mtllib, ) save_paths.append(obj_save_path) return save_paths def _save_obj( self, filename, v_pos, t_pos_idx, v_nrm=None, v_tex=None, t_tex_idx=None, v_rgb=None, matname=None, mtllib=None, ) -> str: obj_str = "" if matname is not None: obj_str += f"mtllib {mtllib}\n" obj_str += f"g object\n" obj_str += f"usemtl {matname}\n" for i in range(len(v_pos)): obj_str += f"v {v_pos[i][0]} {v_pos[i][1]} {v_pos[i][2]}" if v_rgb is not None: obj_str += f" {v_rgb[i][0]} {v_rgb[i][1]} {v_rgb[i][2]}" obj_str += "\n" if v_nrm is not None: for v in v_nrm: obj_str += f"vn {v[0]} {v[1]} {v[2]}\n" if v_tex is not None: for v in v_tex: obj_str += f"vt {v[0]} {1.0 - v[1]}\n" for i in range(len(t_pos_idx)): obj_str += "f" for j in range(3): obj_str += f" {t_pos_idx[i][j] + 1}/" if v_tex is not None: obj_str += f"{t_tex_idx[i][j] + 1}" obj_str += "/" if v_nrm is not None: obj_str += f"{t_pos_idx[i][j] + 1}" obj_str += "\n" save_path = self.get_save_path(filename) with open(save_path, "w") as f: f.write(obj_str) return save_path def _save_mtl( self, filename, matname, Ka=(0.0, 0.0, 0.0), Kd=(1.0, 1.0, 1.0), Ks=(0.0, 0.0, 0.0), map_Kd=None, map_Ks=None, map_Bump=None, map_Pm=None, map_Pr=None, map_format="jpg", step: Optional[int] = None, ) -> List[str]: mtl_save_path = self.get_save_path(filename) save_paths = [mtl_save_path] mtl_str = f"newmtl {matname}\n" mtl_str += f"Ka {Ka[0]} {Ka[1]} {Ka[2]}\n" if map_Kd is not None: map_Kd_save_path = os.path.join( os.path.dirname(mtl_save_path), f"texture_kd.{map_format}" ) mtl_str += f"map_Kd texture_kd.{map_format}\n" self._save_rgb_image( map_Kd_save_path, map_Kd, data_format="HWC", data_range=(0, 1), name=f"{matname}_Kd", step=step, ) save_paths.append(map_Kd_save_path) else: mtl_str += f"Kd {Kd[0]} {Kd[1]} {Kd[2]}\n" if map_Ks is not None: map_Ks_save_path = os.path.join( os.path.dirname(mtl_save_path), f"texture_ks.{map_format}" ) mtl_str += f"map_Ks texture_ks.{map_format}\n" self._save_rgb_image( map_Ks_save_path, map_Ks, data_format="HWC", data_range=(0, 1), name=f"{matname}_Ks", step=step, ) save_paths.append(map_Ks_save_path) else: mtl_str += f"Ks {Ks[0]} {Ks[1]} {Ks[2]}\n" if map_Bump is not None: map_Bump_save_path = os.path.join( os.path.dirname(mtl_save_path), f"texture_nrm.{map_format}" ) mtl_str += f"map_Bump texture_nrm.{map_format}\n" self._save_rgb_image( map_Bump_save_path, map_Bump, data_format="HWC", data_range=(0, 1), name=f"{matname}_Bump", step=step, ) save_paths.append(map_Bump_save_path) if map_Pm is not None: map_Pm_save_path = os.path.join( os.path.dirname(mtl_save_path), f"texture_metallic.{map_format}" ) mtl_str += f"map_Pm texture_metallic.{map_format}\n" self._save_grayscale_image( map_Pm_save_path, map_Pm, data_range=(0, 1), cmap=None, name=f"{matname}_refl", step=step, ) save_paths.append(map_Pm_save_path) if map_Pr is not None: map_Pr_save_path = os.path.join( os.path.dirname(mtl_save_path), f"texture_roughness.{map_format}" ) mtl_str += f"map_Pr texture_roughness.{map_format}\n" self._save_grayscale_image( map_Pr_save_path, map_Pr, data_range=(0, 1), cmap=None, name=f"{matname}_Ns", step=step, ) save_paths.append(map_Pr_save_path) with open(self.get_save_path(filename), "w") as f: f.write(mtl_str) return save_paths def save_file(self, filename, src_path) -> str: save_path = self.get_save_path(filename) shutil.copyfile(src_path, save_path) return save_path def save_json(self, filename, payload) -> str: save_path = self.get_save_path(filename) with open(save_path, "w") as f: f.write(json.dumps(payload)) return save_path