otthorn/nerf_homemade
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity

Absolute import and linting

Browse source
This commit is contained in:
otthorn 2021-03-25 11:15:19 +01:00
parent 2f991920e6
commit f6debcb679
1 changed files with 33 additions and 24 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

39
poses/pose_utils.py
View file

@ -4,13 +4,14 @@ import typing
import numpy as np
from colmap_wrapper import run_colmap
import colmap_read_model as read_model
from nerf_homemade.poses.colmap_wrapper import run_colmap
import nerf_homemade.poses.colmap_read_model as read_model
FORMAT = "%(asctime)s %(levelname)s \t %(message)s"
logging.basicConfig(format=FORMAT, level=logging.DEBUG)
def gen_poses(basedir: str, match_type: str='exhaustive') -> None:
def gen_poses(basedir: str, match_type: str = 'exhaustive') -> None:
"""
Geneate or retreive camera poses.
@ -40,7 +41,8 @@ def gen_poses(basedir: str, match_type: str='exhaustive') -> None:
logging.info("Running COLMAP")
run_colmap(basedir, match_type)
else:
logging.info("Files genreated by COLMAP found. Skipping running COLMAP.")
logging.info(
"Files genreated by COLMAP found. Skipping running COLMAP.")
logging.debug("Loading COLMAP data")
poses, points_3d, perm = load_colmap_data(basedir)
@ -48,6 +50,7 @@ def gen_poses(basedir: str, match_type: str='exhaustive') -> None:
logging.debug("Saving COLMAP data to npy")
save_poses(basedir, poses, points_3d, perm)
def load_colmap_data(basedir: str) -> (np.ndarray, dict, np.ndarray):
"""
Load data from a COLMAP arborescence.
@ -80,7 +83,7 @@ def load_colmap_data(basedir: str) -> (np.ndarray, dict, np.ndarray):
camera_h = cameras_data[1].height
camera_w = cameras_data[1].width
camera_f = cameras_data[1].params[0]
hwf = np.array([camera_h, camera_w, camera_f]).reshape([3,1])
hwf = np.array([camera_h, camera_w, camera_f]).reshape([3, 1])
logging.debug(f"Number of cameras: {len(cameras_data)}")
# read images data
@ -99,25 +102,28 @@ def load_colmap_data(basedir: str) -> (np.ndarray, dict, np.ndarray):
for k in images_data:
im = images_data[k]
R = im.qvec2rotmat()
t = im.tvec.reshape([3,1])
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 = c2w_mats[:, :3, :4].transpose([1, 2, 0])
poses = np.concatenate(
[poses, np.tile(hwf[..., np.newaxis], [1, 1, poses.shape[-1]])], 1)
# read 3d points data
pts3d_file = os.path.join(basedir, "points3D.bin")
pts3d = read_model.read_points3d_binary(pts3d_file)
# 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)
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) -> None:
"""
Save the COLMAP data in a `.npy` format.
@ -142,7 +148,8 @@ def save_poses(basedir, poses, pts3d, perm) -> None:
cams = [0] * poses.shape[-1]
for ind in pts3d[k].image_ids:
if len(cams) < ind - 1:
logging.error("The correct camera poses for current points cannot be accessed")
logging.error(
"The correct camera poses for current points cannot be accessed")
return
cams[ind - 1] = 1
vis_arr.append(cams)
@ -151,19 +158,21 @@ def save_poses(basedir, poses, pts3d, perm) -> None:
vis_arr = np.array(vis_arr)
logging.info(f"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)
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]
logging.info(f"Depths stats - min: {valid_z.min()} max: {valid_z.max()}
mean: {valid_z.mean()}")
logging.info(
f"Depths stats - min: {valid_z.min()} max: {valid_z.max()} mean: {valid_z.mean()}")
save_arr = []
for i in perm:
vis = vis_arr[:, i]
zs = zvals[:, i]
zs = zs[vis==1]
zs = zs[vis == 1]
close_depth, inf_depth = np.percentile(zs, .1), np.percentile(zs, 99.9)
save_arr.append(np.concatenate([poses[..., i].ravel(), np.array([close_depth, inf_depth])], 0))
save_arr.append(np.concatenate(
[poses[..., i].ravel(), np.array([close_depth, inf_depth])], 0))
save_arr = np.array(save_arr)
save_path = os.path.join(basedir, "poses_bounds.npy")