step1x3d_geometry/utils/saving.py

import json
import os
import re
import shutil

import cv2
import imageio
import matplotlib.pyplot as plt
import numpy as np
import torch
import torchvision.utils as vutils
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 step1x3d_geometry.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_image_vutils(self, filename, img) -> str:
        save_path = self.get_save_path(filename)
        vutils.save_image(img, save_path)
        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_file(self, filename, src_path) -> str:
        save_path = self.get_save_path(filename)
        shutil.copyfile(src_path, save_path)
        return save_path

    def save_txt(self, filename, comment) -> str:
        save_path = self.get_save_path(filename)
        with open(save_path, "w") as f:
            f.write(comment)
        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