load_llff.py

import numpy as np
import os, imageio
import torch


########## Slightly modified version of LLFF data loading code
##########  see https://github.com/Fyusion/LLFF for original

# downsample
def _minify(basedir, factors=[], resolutions=[]):  # basedir: ./data/nerf_llff_data/fern
    needtoload = False
    for r in factors:
        imgdir = os.path.join(basedir, 'images_{}'.format(r))
        if not os.path.exists(imgdir):
            needtoload = True
    for r in resolutions:
        imgdir = os.path.join(basedir, 'images_{}x{}'.format(r[1], r[0]))
        if not os.path.exists(imgdir):
            needtoload = True
    if not needtoload:
        return

    from shutil import copy
    from subprocess import check_output

    imgdir = os.path.join(basedir, 'images')
    imgs = [os.path.join(imgdir, f) for f in sorted(os.listdir(imgdir))]
    imgs = [f for f in imgs if any([f.endswith(ex) for ex in ['JPG', 'jpg', 'png', 'jpeg', 'PNG']])]
    imgdir_orig = imgdir

    wd = os.getcwd()

    for r in factors + resolutions:
        if isinstance(r, int):
            name = 'images_{}'.format(r)
            resizearg = '{}%'.format(100. / r)
        else:
            name = 'images_{}x{}'.format(r[1], r[0])
            resizearg = '{}x{}'.format(r[1], r[0])
        imgdir = os.path.join(basedir, name)
        if os.path.exists(imgdir):
            continue

        print('Minifying', r, basedir)

        os.makedirs(imgdir)
        check_output('cp {}/* {}'.format(imgdir_orig, imgdir), shell=True)

        ext = imgs[0].split('.')[-1]
        args = ' '.join(['mogrify', '-resize', resizearg, '-format', 'png', '*.{}'.format(ext)])
        print(args)
        os.chdir(imgdir)
        check_output(args, shell=True)
        os.chdir(wd)

        if ext != 'png':
            check_output('rm {}/*.{}'.format(imgdir, ext), shell=True)
            print('Removed duplicates')
        print('Done')


def _load_data(basedir, factor=None, width=None, height=None, load_imgs=True):

    poses_arr = np.load(os.path.join(basedir, 'poses_bounds.npy'))

    poses = poses_arr[:, :-2].reshape([-1, 3, 5]).transpose([1, 2, 0])
    bds = poses_arr[:, -2:].transpose([1, 0])

    img0 = [os.path.join(basedir, 'images', f) for f in sorted(os.listdir(os.path.join(basedir, 'images'))) \
            if f.endswith('JPG') or f.endswith('jpg') or f.endswith('png')][0]
    sh = imageio.imread(img0).shape

    sfx = ''

    if factor is not None:
        sfx = '_{}'.format(factor)
        _minify(basedir, factors=[factor])
        factor = factor
    elif height is not None:
        factor = sh[0] / float(height)
        width = int(sh[1] / factor)
        _minify(basedir, resolutions=[[height, width]])
        sfx = '_{}x{}'.format(width, height)
    elif width is not None:
        factor = sh[1] / float(width)
        height = int(sh[0] / factor)
        _minify(basedir, resolutions=[[height, width]])
        sfx = '_{}x{}'.format(width, height)
    else:
        factor = 1

    imgdir = os.path.join(basedir, 'images' + sfx)
    if not os.path.exists(imgdir):
        print(imgdir, 'does not exist, returning')
        return

    imgfiles = [os.path.join(imgdir, f) for f in sorted(os.listdir(imgdir)) if
                f.endswith('JPG') or f.endswith('jpg') or f.endswith('png')]

    if poses.shape[-1] != len(imgfiles):
        print( 'Mismatch between imgs {} and poses {} !!!!'.format(len(imgfiles), poses.shape[-1]) )
        # return

    sh = imageio.imread(imgfiles[0]).shape
    poses[:2, 4, :] = np.array(sh[:2]).reshape([2, 1])
    poses[2, 4, :] = poses[2, 4, :] * 1. / factor

    if not load_imgs:
        return poses, bds

    def imread(f):
        if f.endswith('png'):
            return imageio.imread(f, ignoregamma=True)
        else:
            return imageio.imread(f)

    imgs = [imread(f)[..., :3] / 255. for f in imgfiles]


    # imgs = [imread(f)[:400, :400, :3] / 255. for f in imgfiles]


    imgs = np.stack(imgs, -1)

    print('Loaded image data', imgs.shape, poses[:, -1, 0])

    return poses, bds, imgs


def normalize(x):
    return x / np.linalg.norm(x)


def viewmatrix(z, up, pos):
    vec2 = normalize(z)
    vec1_avg = up
    vec0 = normalize(np.cross(vec1_avg, vec2))
    vec1 = normalize(np.cross(vec2, vec0))
    m = np.stack([vec0, vec1, vec2, pos], 1)
    return m


def ptstocam(pts, c2w):
    tt = np.matmul(c2w[:3, :3].T, (pts - c2w[:3, 3])[..., np.newaxis])[..., 0]
    return tt


def poses_avg(poses):
    hwf = poses[0, :3, -1:]

    center = poses[:, :3, 3].mean(0)
    vec2 = normalize(poses[:, :3, 2].sum(0))
    up = poses[:, :3, 1].sum(0)
    c2w = np.concatenate([viewmatrix(vec2, up, center), hwf], 1)

    return c2w


def render_path_spiral(c2w, up, rads, focal, zdelta, zrate, rots, N):
    render_poses = []
    rads = np.array(list(rads) + [1.])
    hwf = c2w[:, 4:5]

    for theta in np.linspace(0., 2. * np.pi * rots, N + 1)[:-1]:
        c = np.dot(c2w[:3, :4], np.array([np.cos(theta), -np.sin(theta), -np.sin(theta * zrate), 1.]) * rads)
        z = normalize(c - np.dot(c2w[:3, :4], np.array([0, 0, -focal, 1.])))
        render_poses.append(np.concatenate([viewmatrix(z, up, c), hwf], 1))
    return render_poses


def recenter_poses(poses):
    poses_ = poses + 0
    bottom = np.reshape([0, 0, 0, 1.], [1, 4])
    c2w = poses_avg(poses)
    c2w = np.concatenate([c2w[:3, :4], bottom], -2)
    bottom = np.tile(np.reshape(bottom, [1, 1, 4]), [poses.shape[0], 1, 1])
    poses = np.concatenate([poses[:, :3, :4], bottom], -2)
    poses = np.linalg.inv(c2w) @ poses
    poses_[:, :3, :4] = poses[:, :3, :4]
    poses = poses_
    return poses


#####################


def spherify_poses(poses, bds):
    p34_to_44 = lambda p: np.concatenate([p, np.tile(np.reshape(np.eye(4)[-1, :], [1, 1, 4]), [p.shape[0], 1, 1])], 1)

    rays_d = poses[:, :3, 2:3]
    rays_o = poses[:, :3, 3:4]

    def min_line_dist(rays_o, rays_d):
        A_i = np.eye(3) - rays_d * np.transpose(rays_d, [0, 2, 1])
        b_i = -A_i @ rays_o
        pt_mindist = np.squeeze(-np.linalg.inv((np.transpose(A_i, [0, 2, 1]) @ A_i).mean(0)) @ (b_i).mean(0))
        return pt_mindist

    pt_mindist = min_line_dist(rays_o, rays_d)

    center = pt_mindist
    up = (poses[:, :3, 3] - center).mean(0)

    vec0 = normalize(up)
    vec1 = normalize(np.cross([.1, .2, .3], vec0))
    vec2 = normalize(np.cross(vec0, vec1))
    pos = center
    c2w = np.stack([vec1, vec2, vec0, pos], 1)

    poses_reset = np.linalg.inv(p34_to_44(c2w[None])) @ p34_to_44(poses[:, :3, :4])

    rad = np.sqrt(np.mean(np.sum(np.square(poses_reset[:, :3, 3]), -1)))

    sc = 1. / rad
    poses_reset[:, :3, 3] *= sc
    bds *= sc
    rad *= sc

    centroid = np.mean(poses_reset[:, :3, 3], 0)
    zh = centroid[2]
    radcircle = np.sqrt(rad ** 2 - zh ** 2)
    new_poses = []

    for th in np.linspace(0., 2. * np.pi, 120):
        camorigin = np.array([radcircle * np.cos(th), radcircle * np.sin(th), zh])
        up = np.array([0, 0, -1.])

        vec2 = normalize(camorigin)
        vec0 = normalize(np.cross(vec2, up))
        vec1 = normalize(np.cross(vec2, vec0))
        pos = camorigin
        p = np.stack([vec0, vec1, vec2, pos], 1)

        new_poses.append(p)

    new_poses = np.stack(new_poses, 0)

    new_poses = np.concatenate([new_poses, np.broadcast_to(poses[0, :3, -1:], new_poses[:, :3, -1:].shape)], -1)
    poses_reset = np.concatenate(
        [poses_reset[:, :3, :4], np.broadcast_to(poses[0, :3, -1:], poses_reset[:, :3, -1:].shape)], -1)

    return poses_reset, new_poses, bds


def downsample_images(tensor, n):
    B, H, W, C = tensor.shape
    tensor_reshaped = tensor.reshape(B, H//n, n, W, C)
    downsampled_tensor = tensor_reshaped.mean(dim=2)
    return downsampled_tensor




def load_llff_data_real(basedir, factor=8, recenter=True, bd_factor=.75, spherify=False, path_zflat=False):
    

    poses, bds, imgs = _load_data(basedir, factor=factor) # factor=8 downsamples original imgs by 8x
    print('Loaded', basedir, bds.min(), bds.max())
    
    # Correct rotation matrix ordering and move variable dim to axis 0
    poses = np.concatenate([poses[:, 1:2, :], -poses[:, 0:1, :], poses[:, 2:, :]], 1)
    poses = np.moveaxis(poses, -1, 0).astype(np.float32)
    imgs = np.moveaxis(imgs, -1, 0).astype(np.float32)
    images = imgs
    bds = np.moveaxis(bds, -1, 0).astype(np.float32)
    
    # Rescale if bd_factor is provided
    sc = 1. if bd_factor is None else 1./(bds.min() * bd_factor)
    poses[:,:3,3] *= sc
    bds *= sc
    
    if recenter:
        poses = recenter_poses(poses)
        
    if spherify:
        poses, render_poses, bds = spherify_poses(poses, bds)

    else:
        
        c2w = poses_avg(poses)
        print('recentered', c2w.shape)
        print(c2w[:3,:4])

        ## Get spiral
        # Get average pose
        up = normalize(poses[:, :3, 1].sum(0))

        # Find a reasonable "focus depth" for this dataset
        close_depth, inf_depth = bds.min()*.9, bds.max()*5.
        dt = .75
        mean_dz = 1./(((1.-dt)/close_depth + dt/inf_depth))
        focal = mean_dz

        # Get radii for spiral path
        shrink_factor = .8
        zdelta = close_depth * .2
        tt = poses[:,:3,3] # ptstocam(poses[:3,3,:].T, c2w).T
        rads = np.percentile(np.abs(tt), 50, 0)
        c2w_path = c2w
        N_views = 120
        N_rots = 2
        if path_zflat:
#             zloc = np.percentile(tt, 10, 0)[2]
            zloc = -close_depth * .1
            c2w_path[:3,3] = c2w_path[:3,3] + zloc * c2w_path[:3,2]
            rads[2] = 0.
            N_rots = 1
            N_views/=2

        # Generate poses for spiral path
        render_poses = render_path_spiral(c2w_path, up, rads, focal, zdelta, zrate=.5, rots=N_rots, N=N_views)
        
        
    render_poses = np.array(render_poses).astype(np.float32)
    render_poses = torch.Tensor(render_poses)

    c2w = poses_avg(poses)
    print('Data:')
    print(poses.shape, images.shape, bds.shape)
    
    dists = np.sum(np.square(c2w[:3,3] - poses[:,:3,3]), -1)
    i_test = np.argmin(dists)
    print('HOLDOUT view is', i_test)
    
    images = images.astype(np.float32)
    poses = poses.astype(np.float32)

    return images, poses, bds, render_poses, i_test