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320 lines
9.8 KiB
Python
320 lines
9.8 KiB
Python
import numpy as np
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import os, imageio
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########## Slightly modified version of LLFF data loading code
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########## see https://github.com/Fyusion/LLFF for original
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def _minify(basedir, factors=[], resolutions=[]):
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needtoload = False
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for r in factors:
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imgdir = os.path.join(basedir, 'images_{}'.format(r))
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if not os.path.exists(imgdir):
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needtoload = True
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for r in resolutions:
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imgdir = os.path.join(basedir, 'images_{}x{}'.format(r[1], r[0]))
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if not os.path.exists(imgdir):
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needtoload = True
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if not needtoload:
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return
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from shutil import copy
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from subprocess import check_output
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imgdir = os.path.join(basedir, 'images')
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imgs = [os.path.join(imgdir, f) for f in sorted(os.listdir(imgdir))]
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imgs = [f for f in imgs if any([f.endswith(ex) for ex in ['JPG', 'jpg', 'png', 'jpeg', 'PNG']])]
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imgdir_orig = imgdir
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wd = os.getcwd()
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for r in factors + resolutions:
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if isinstance(r, int):
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name = 'images_{}'.format(r)
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resizearg = '{}%'.format(100./r)
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else:
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name = 'images_{}x{}'.format(r[1], r[0])
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resizearg = '{}x{}'.format(r[1], r[0])
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imgdir = os.path.join(basedir, name)
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if os.path.exists(imgdir):
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continue
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print('Minifying', r, basedir)
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os.makedirs(imgdir)
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check_output('cp {}/* {}'.format(imgdir_orig, imgdir), shell=True)
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ext = imgs[0].split('.')[-1]
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args = ' '.join(['mogrify', '-resize', resizearg, '-format', 'png', '*.{}'.format(ext)])
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print(args)
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os.chdir(imgdir)
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check_output(args, shell=True)
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os.chdir(wd)
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if ext != 'png':
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check_output('rm {}/*.{}'.format(imgdir, ext), shell=True)
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print('Removed duplicates')
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print('Done')
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def _load_data(basedir, factor=None, width=None, height=None, load_imgs=True):
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poses_arr = np.load(os.path.join(basedir, 'poses_bounds.npy'))
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poses = poses_arr[:, :-2].reshape([-1, 3, 5]).transpose([1,2,0])
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bds = poses_arr[:, -2:].transpose([1,0])
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img0 = [os.path.join(basedir, 'images', f) for f in sorted(os.listdir(os.path.join(basedir, 'images'))) \
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if f.endswith('JPG') or f.endswith('jpg') or f.endswith('png')][0]
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sh = imageio.imread(img0).shape
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sfx = ''
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if factor is not None:
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sfx = '_{}'.format(factor)
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_minify(basedir, factors=[factor])
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factor = factor
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elif height is not None:
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factor = sh[0] / float(height)
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width = int(sh[1] / factor)
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_minify(basedir, resolutions=[[height, width]])
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sfx = '_{}x{}'.format(width, height)
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elif width is not None:
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factor = sh[1] / float(width)
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height = int(sh[0] / factor)
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_minify(basedir, resolutions=[[height, width]])
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sfx = '_{}x{}'.format(width, height)
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else:
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factor = 1
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imgdir = os.path.join(basedir, 'images' + sfx)
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if not os.path.exists(imgdir):
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print( imgdir, 'does not exist, returning' )
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return
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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')]
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if poses.shape[-1] != len(imgfiles):
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print( 'Mismatch between imgs {} and poses {} !!!!'.format(len(imgfiles), poses.shape[-1]) )
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return
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sh = imageio.imread(imgfiles[0]).shape
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poses[:2, 4, :] = np.array(sh[:2]).reshape([2, 1])
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poses[2, 4, :] = poses[2, 4, :] * 1./factor
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if not load_imgs:
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return poses, bds
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def imread(f):
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if f.endswith('png'):
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return imageio.imread(f, ignoregamma=True)
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else:
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return imageio.imread(f)
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imgs = imgs = [imread(f)[...,:3]/255. for f in imgfiles]
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imgs = np.stack(imgs, -1)
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print('Loaded image data', imgs.shape, poses[:,-1,0])
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return poses, bds, imgs
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def normalize(x):
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return x / np.linalg.norm(x)
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def viewmatrix(z, up, pos):
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vec2 = normalize(z)
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vec1_avg = up
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vec0 = normalize(np.cross(vec1_avg, vec2))
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vec1 = normalize(np.cross(vec2, vec0))
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m = np.stack([vec0, vec1, vec2, pos], 1)
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return m
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def ptstocam(pts, c2w):
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tt = np.matmul(c2w[:3,:3].T, (pts-c2w[:3,3])[...,np.newaxis])[...,0]
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return tt
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def poses_avg(poses):
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hwf = poses[0, :3, -1:]
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center = poses[:, :3, 3].mean(0)
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vec2 = normalize(poses[:, :3, 2].sum(0))
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up = poses[:, :3, 1].sum(0)
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c2w = np.concatenate([viewmatrix(vec2, up, center), hwf], 1)
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return c2w
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def render_path_spiral(c2w, up, rads, focal, zdelta, zrate, rots, N):
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render_poses = []
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rads = np.array(list(rads) + [1.])
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hwf = c2w[:,4:5]
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for theta in np.linspace(0., 2. * np.pi * rots, N+1)[:-1]:
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c = np.dot(c2w[:3,:4], np.array([np.cos(theta), -np.sin(theta), -np.sin(theta*zrate), 1.]) * rads)
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z = normalize(c - np.dot(c2w[:3,:4], np.array([0,0,-focal, 1.])))
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render_poses.append(np.concatenate([viewmatrix(z, up, c), hwf], 1))
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return render_poses
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def recenter_poses(poses):
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poses_ = poses+0
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bottom = np.reshape([0,0,0,1.], [1,4])
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c2w = poses_avg(poses)
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c2w = np.concatenate([c2w[:3,:4], bottom], -2)
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bottom = np.tile(np.reshape(bottom, [1,1,4]), [poses.shape[0],1,1])
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poses = np.concatenate([poses[:,:3,:4], bottom], -2)
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poses = np.linalg.inv(c2w) @ poses
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poses_[:,:3,:4] = poses[:,:3,:4]
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poses = poses_
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return poses
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#####################
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def spherify_poses(poses, bds):
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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)
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rays_d = poses[:,:3,2:3]
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rays_o = poses[:,:3,3:4]
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def min_line_dist(rays_o, rays_d):
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A_i = np.eye(3) - rays_d * np.transpose(rays_d, [0,2,1])
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b_i = -A_i @ rays_o
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pt_mindist = np.squeeze(-np.linalg.inv((np.transpose(A_i, [0,2,1]) @ A_i).mean(0)) @ (b_i).mean(0))
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return pt_mindist
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pt_mindist = min_line_dist(rays_o, rays_d)
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center = pt_mindist
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up = (poses[:,:3,3] - center).mean(0)
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vec0 = normalize(up)
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vec1 = normalize(np.cross([.1,.2,.3], vec0))
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vec2 = normalize(np.cross(vec0, vec1))
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pos = center
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c2w = np.stack([vec1, vec2, vec0, pos], 1)
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poses_reset = np.linalg.inv(p34_to_44(c2w[None])) @ p34_to_44(poses[:,:3,:4])
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rad = np.sqrt(np.mean(np.sum(np.square(poses_reset[:,:3,3]), -1)))
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sc = 1./rad
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poses_reset[:,:3,3] *= sc
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bds *= sc
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rad *= sc
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centroid = np.mean(poses_reset[:,:3,3], 0)
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zh = centroid[2]
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radcircle = np.sqrt(rad**2-zh**2)
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new_poses = []
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for th in np.linspace(0.,2.*np.pi, 120):
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camorigin = np.array([radcircle * np.cos(th), radcircle * np.sin(th), zh])
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up = np.array([0,0,-1.])
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vec2 = normalize(camorigin)
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vec0 = normalize(np.cross(vec2, up))
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vec1 = normalize(np.cross(vec2, vec0))
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pos = camorigin
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p = np.stack([vec0, vec1, vec2, pos], 1)
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new_poses.append(p)
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new_poses = np.stack(new_poses, 0)
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new_poses = np.concatenate([new_poses, np.broadcast_to(poses[0,:3,-1:], new_poses[:,:3,-1:].shape)], -1)
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poses_reset = np.concatenate([poses_reset[:,:3,:4], np.broadcast_to(poses[0,:3,-1:], poses_reset[:,:3,-1:].shape)], -1)
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return poses_reset, new_poses, bds
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def load_llff_data(basedir, factor=8, recenter=True, bd_factor=.75, spherify=False, path_zflat=False):
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poses, bds, imgs = _load_data(basedir, factor=factor) # factor=8 downsamples original imgs by 8x
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print('Loaded', basedir, bds.min(), bds.max())
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# Correct rotation matrix ordering and move variable dim to axis 0
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poses = np.concatenate([poses[:, 1:2, :], -poses[:, 0:1, :], poses[:, 2:, :]], 1)
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poses = np.moveaxis(poses, -1, 0).astype(np.float32)
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imgs = np.moveaxis(imgs, -1, 0).astype(np.float32)
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images = imgs
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bds = np.moveaxis(bds, -1, 0).astype(np.float32)
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# Rescale if bd_factor is provided
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sc = 1. if bd_factor is None else 1./(bds.min() * bd_factor)
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poses[:,:3,3] *= sc
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bds *= sc
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if recenter:
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poses = recenter_poses(poses)
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if spherify:
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poses, render_poses, bds = spherify_poses(poses, bds)
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else:
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c2w = poses_avg(poses)
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print('recentered', c2w.shape)
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print(c2w[:3,:4])
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## Get spiral
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# Get average pose
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up = normalize(poses[:, :3, 1].sum(0))
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# Find a reasonable "focus depth" for this dataset
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close_depth, inf_depth = bds.min()*.9, bds.max()*5.
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dt = .75
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mean_dz = 1./(((1.-dt)/close_depth + dt/inf_depth))
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focal = mean_dz
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# Get radii for spiral path
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shrink_factor = .8
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zdelta = close_depth * .2
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tt = poses[:,:3,3] # ptstocam(poses[:3,3,:].T, c2w).T
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rads = np.percentile(np.abs(tt), 90, 0)
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c2w_path = c2w
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N_views = 120
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N_rots = 2
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if path_zflat:
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# zloc = np.percentile(tt, 10, 0)[2]
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zloc = -close_depth * .1
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c2w_path[:3,3] = c2w_path[:3,3] + zloc * c2w_path[:3,2]
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rads[2] = 0.
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N_rots = 1
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N_views/=2
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# Generate poses for spiral path
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render_poses = render_path_spiral(c2w_path, up, rads, focal, zdelta, zrate=.5, rots=N_rots, N=N_views)
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render_poses = np.array(render_poses).astype(np.float32)
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c2w = poses_avg(poses)
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print('Data:')
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print(poses.shape, images.shape, bds.shape)
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dists = np.sum(np.square(c2w[:3,3] - poses[:,:3,3]), -1)
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i_test = np.argmin(dists)
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print('HOLDOUT view is', i_test)
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images = images.astype(np.float32)
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poses = poses.astype(np.float32)
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return images, poses, bds, render_poses, i_test
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