nerfstudio/nerfstudio/process_data/equirect_utils.py

# Copyright 2022 The Nerfstudio Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Helper utils for processing equirectangular data."""

import os
from pathlib import Path
from typing import Tuple

import cv2
import numpy as np
import torch
from equilib import Equi2Pers
from rich.progress import (
    BarColumn,
    Progress,
    TaskProgressColumn,
    TextColumn,
    TimeRemainingColumn,
)

from nerfstudio.utils.rich_utils import ItersPerSecColumn


def generate_planar_projections_from_equirectangular(
    image_dir: Path,
    planar_image_size: Tuple[int, int],
    samples_per_im: int,
) -> Path:
    """Generate planar projections from an equirectangular image.

    Args:
        image_dir: The directory containing the equirectangular image.
        planar_image_size: The size of the planar projections [width, height].
        samples_per_im: The number of samples to take per image.
    returns:
        The path to the planar projections directory.
    """

    device = torch.device("cuda")

    fov = 120
    yaw_pitch_pairs = []
    if samples_per_im == 8:
        fov = 120
        for i in np.arange(-180, 180, 90):
            yaw_pitch_pairs.append((i, 0))
        for i in np.arange(-180, 180, 180):
            yaw_pitch_pairs.append((i, 45))
        for i in np.arange(-180, 180, 180):
            yaw_pitch_pairs.append((i, -45))
    elif samples_per_im == 14:
        fov = 110
        for i in np.arange(-180, 180, 60):
            yaw_pitch_pairs.append((i, 0))
        for i in np.arange(-180, 180, 90):
            yaw_pitch_pairs.append((i, 45))
        for i in np.arange(-180, 180, 90):
            yaw_pitch_pairs.append((i, -45))

    equi2pers = Equi2Pers(height=planar_image_size[1], width=planar_image_size[0], fov_x=fov, mode="bilinear")
    frame_dir = image_dir
    output_dir = image_dir / "planar_projections"
    output_dir.mkdir(exist_ok=True)
    num_ims = len(os.listdir(frame_dir))
    progress = Progress(
        TextColumn("[bold blue]Generating Planar Images", justify="right"),
        BarColumn(),
        TaskProgressColumn(show_speed=True),
        ItersPerSecColumn(suffix="equirect frames/s"),
        TimeRemainingColumn(elapsed_when_finished=True, compact=True),
    )

    curr_im = 0
    with progress:
        for i in progress.track(os.listdir(frame_dir), description="", total=num_ims):
            if i.lower().endswith((".jpg", ".png", ".jpeg")):
                im = np.array(cv2.imread(os.path.join(frame_dir, i)))
                im = torch.tensor(im, dtype=torch.float32, device=device)
                im = torch.permute(im, (2, 0, 1)) / 255.0
                count = 0
                for u_deg, v_deg in yaw_pitch_pairs:
                    v_rad = torch.pi * v_deg / 180.0
                    u_rad = torch.pi * u_deg / 180.0
                    pers_image = equi2pers(im, rots={"roll": 0, "pitch": v_rad, "yaw": u_rad}) * 255.0
                    pers_image = (pers_image.permute(1, 2, 0)).type(torch.uint8).to("cpu").numpy()
                    cv2.imwrite(f"{output_dir}/{i[:-4]}_{count}.jpg", pers_image)
                    count += 1
            curr_im += 1

    return output_dir


def compute_resolution_from_equirect(image_dir: Path, num_images: int) -> Tuple[int, int]:
    """Compute the resolution of the persepctive projections of equirectangular images
       from the heuristic: num_image * res**2 = orig_height * orig_width.

    Args:
        image_dir: The directory containing the equirectangular images.
    returns:
        The target resolution of the perspective projections.
    """

    for i in os.listdir(image_dir):
        if i.lower().endswith((".jpg", ".png", ".jpeg")):
            im = np.array(cv2.imread(os.path.join(image_dir, i)))
            res_squared = (im.shape[0] * im.shape[1]) / num_images
            return (int(np.sqrt(res_squared)), int(np.sqrt(res_squared)))
    raise ValueError("No images found in the directory.")