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poses_util.py
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poses_util.py
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import numpy as np
import os
import sys
import imageio
import skimage.transform
from colmap_wrapper import run_colmap
import colmap_read_model as read_model
def load_colmap_data(realdir):
camerasfile = os.path.join(realdir, 'sparse/0/cameras.bin')
camdata = read_model.read_cameras_binary(camerasfile)
# cam = camdata[camdata.keys()[0]]
list_of_keys = list(camdata.keys())
cam = camdata[list_of_keys[0]]
print( 'Cameras', len(cam))
h, w = cam.height, cam.width
# w, h, f = factor * w, factor * h, factor * f
imagesfile = os.path.join(realdir, 'sparse/0/images.bin')
imdata = read_model.read_images_binary(imagesfile)
w2c_mats = []
bottom = np.array([0,0,0,1.]).reshape([1,4])
hwf = np.stack([
np.array([h, w, camdata[imdata[x].camera_id].params[0]])[:, None]
for x in imdata
], -1)
names = [imdata[k].name for k in imdata]
print( 'Images #', len(names))
perm = np.argsort(names)
for k in imdata:
im = imdata[k]
R = im.qvec2rotmat()
t = im.tvec.reshape([3,1])
m = np.concatenate([np.concatenate([R, t], 1), bottom], 0)
w2c_mats.append(m)
w2c_mats = np.stack(w2c_mats, 0)
c2w_mats = np.linalg.inv(w2c_mats)
poses = c2w_mats[:, :3, :4].transpose([1,2,0])
# poses = np.concatenate([poses, np.tile(hwf[..., np.newaxis], [1,1,poses.shape[-1]])], 1)
poses = np.concatenate([poses, hwf], 1)
points3dfile = os.path.join(realdir, 'sparse/0/points3D.bin')
pts3d = read_model.read_points3d_binary(points3dfile)
# must switch to [-u, r, -t] from [r, -u, t], NOT [r, u, -t]
poses = np.concatenate([poses[:, 1:2, :], poses[:, 0:1, :], -poses[:, 2:3, :], poses[:, 3:4, :], poses[:, 4:5, :]], 1)
return poses, pts3d, perm
def save_poses(basedir, poses, pts3d, perm):
pts_arr = []
vis_arr = []
for k in pts3d:
pts_arr.append(pts3d[k].xyz)
cams = [0] * poses.shape[-1]
for ind in pts3d[k].image_ids:
if len(cams) < ind - 1:
print('ERROR: the correct camera poses for current points cannot be accessed')
return
cams[ind-1] = 1
vis_arr.append(cams)
pts_arr = np.array(pts_arr)
vis_arr = np.array(vis_arr)
print( 'Points', pts_arr.shape, 'Visibility', vis_arr.shape )
zvals = np.sum(-(pts_arr[:, np.newaxis, :].transpose([2,0,1]) - poses[:3, 3:4, :]) * poses[:3, 2:3, :], 0)
valid_z = zvals[vis_arr==1]
print( 'Depth stats', valid_z.min(), valid_z.max(), valid_z.mean() )
save_arr = []
for i in perm:
vis = vis_arr[:, i]
zs = zvals[:, i]
zs = zs[vis==1]
close_depth, inf_depth = np.percentile(zs, 1), np.percentile(zs, 99.9)
# print( i, close_depth, inf_depth )
save_arr.append(np.concatenate([poses[..., i].ravel(), np.array([close_depth, inf_depth])], 0))
save_arr = np.array(save_arr)
np.save(os.path.join(basedir, 'poses_bounds.npy'), save_arr)
def minify(basedir, factors=[], resolutions=[]):
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(int(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
# imgs = [imageio.imread(f, ignoregamma=True)[...,:3]/255. for f in imgfiles]
def imread(f):
if f.endswith('png'):
return imageio.imread(f, ignoregamma=True)
else:
return imageio.imread(f)
imgs = imgs = [imread(f)[...,: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 gen_poses(basedir, match_type, factors=None):
files_needed = ['{}.bin'.format(f) for f in ['cameras', 'images', 'points3D']]
if os.path.exists(os.path.join(basedir, 'sparse/0')):
files_had = os.listdir(os.path.join(basedir, 'sparse/0'))
else:
files_had = []
if not all([f in files_had for f in files_needed]):
print( 'Need to run COLMAP' )
run_colmap(basedir, match_type)
else:
print('Don\'t need to run COLMAP')
print( 'Post-colmap')
poses, pts3d, perm = load_colmap_data(basedir)
save_poses(basedir, poses, pts3d, perm)
if factors is not None:
print( 'Factors:', factors)
minify(basedir, factors)
print( 'Done with imgs2poses' )
return True