🎉 initial rewrite and documentation for the poses module
parent
b454ef2c2f
commit
2b686d4fc5
@ -0,0 +1,2 @@
|
||||
Contains the COLMAP wrappers and other utils to extract, read and write camera
|
||||
poses from and to different formats.
|
||||
@ -0,0 +1,312 @@
|
||||
# Copyright (c) 2018, ETH Zurich and UNC Chapel Hill.
|
||||
# All rights reserved.
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions are met:
|
||||
#
|
||||
# * Redistributions of source code must retain the above copyright
|
||||
# notice, this list of conditions and the following disclaimer.
|
||||
#
|
||||
# * Redistributions in binary form must reproduce the above copyright
|
||||
# notice, this list of conditions and the following disclaimer in the
|
||||
# documentation and/or other materials provided with the distribution.
|
||||
#
|
||||
# * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of
|
||||
# its contributors may be used to endorse or promote products derived
|
||||
# from this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
||||
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
|
||||
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
|
||||
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
|
||||
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
|
||||
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
|
||||
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
|
||||
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||||
# POSSIBILITY OF SUCH DAMAGE.
|
||||
#
|
||||
# Author: Johannes L. Schoenberger (jsch at inf.ethz.ch)
|
||||
|
||||
import os
|
||||
import sys
|
||||
import collections
|
||||
import numpy as np
|
||||
import struct
|
||||
|
||||
|
||||
CameraModel = collections.namedtuple(
|
||||
"CameraModel", ["model_id", "model_name", "num_params"])
|
||||
Camera = collections.namedtuple(
|
||||
"Camera", ["id", "model", "width", "height", "params"])
|
||||
BaseImage = collections.namedtuple(
|
||||
"Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"])
|
||||
Point3D = collections.namedtuple(
|
||||
"Point3D", ["id", "xyz", "rgb", "error", "image_ids", "point2D_idxs"])
|
||||
|
||||
class Image(BaseImage):
|
||||
def qvec2rotmat(self):
|
||||
return qvec2rotmat(self.qvec)
|
||||
|
||||
|
||||
CAMERA_MODELS = {
|
||||
CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3),
|
||||
CameraModel(model_id=1, model_name="PINHOLE", num_params=4),
|
||||
CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4),
|
||||
CameraModel(model_id=3, model_name="RADIAL", num_params=5),
|
||||
CameraModel(model_id=4, model_name="OPENCV", num_params=8),
|
||||
CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8),
|
||||
CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12),
|
||||
CameraModel(model_id=7, model_name="FOV", num_params=5),
|
||||
CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4),
|
||||
CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5),
|
||||
CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12)
|
||||
}
|
||||
CAMERA_MODEL_IDS = dict([(camera_model.model_id, camera_model) \
|
||||
for camera_model in CAMERA_MODELS])
|
||||
|
||||
|
||||
def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"):
|
||||
"""Read and unpack the next bytes from a binary file.
|
||||
:param fid:
|
||||
:param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc.
|
||||
:param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
|
||||
:param endian_character: Any of {@, =, <, >, !}
|
||||
:return: Tuple of read and unpacked values.
|
||||
"""
|
||||
data = fid.read(num_bytes)
|
||||
return struct.unpack(endian_character + format_char_sequence, data)
|
||||
|
||||
|
||||
def read_cameras_text(path):
|
||||
"""
|
||||
see: src/base/reconstruction.cc
|
||||
void Reconstruction::WriteCamerasText(const std::string& path)
|
||||
void Reconstruction::ReadCamerasText(const std::string& path)
|
||||
"""
|
||||
cameras = {}
|
||||
with open(path, "r") as fid:
|
||||
while True:
|
||||
line = fid.readline()
|
||||
if not line:
|
||||
break
|
||||
line = line.strip()
|
||||
if len(line) > 0 and line[0] != "#":
|
||||
elems = line.split()
|
||||
camera_id = int(elems[0])
|
||||
model = elems[1]
|
||||
width = int(elems[2])
|
||||
height = int(elems[3])
|
||||
params = np.array(tuple(map(float, elems[4:])))
|
||||
cameras[camera_id] = Camera(id=camera_id, model=model,
|
||||
width=width, height=height,
|
||||
params=params)
|
||||
return cameras
|
||||
|
||||
|
||||
def read_cameras_binary(path_to_model_file):
|
||||
"""
|
||||
see: src/base/reconstruction.cc
|
||||
void Reconstruction::WriteCamerasBinary(const std::string& path)
|
||||
void Reconstruction::ReadCamerasBinary(const std::string& path)
|
||||
"""
|
||||
cameras = {}
|
||||
with open(path_to_model_file, "rb") as fid:
|
||||
num_cameras = read_next_bytes(fid, 8, "Q")[0]
|
||||
for camera_line_index in range(num_cameras):
|
||||
camera_properties = read_next_bytes(
|
||||
fid, num_bytes=24, format_char_sequence="iiQQ")
|
||||
camera_id = camera_properties[0]
|
||||
model_id = camera_properties[1]
|
||||
model_name = CAMERA_MODEL_IDS[camera_properties[1]].model_name
|
||||
width = camera_properties[2]
|
||||
height = camera_properties[3]
|
||||
num_params = CAMERA_MODEL_IDS[model_id].num_params
|
||||
params = read_next_bytes(fid, num_bytes=8*num_params,
|
||||
format_char_sequence="d"*num_params)
|
||||
cameras[camera_id] = Camera(id=camera_id,
|
||||
model=model_name,
|
||||
width=width,
|
||||
height=height,
|
||||
params=np.array(params))
|
||||
assert len(cameras) == num_cameras
|
||||
return cameras
|
||||
|
||||
|
||||
def read_images_text(path):
|
||||
"""
|
||||
see: src/base/reconstruction.cc
|
||||
void Reconstruction::ReadImagesText(const std::string& path)
|
||||
void Reconstruction::WriteImagesText(const std::string& path)
|
||||
"""
|
||||
images = {}
|
||||
with open(path, "r") as fid:
|
||||
while True:
|
||||
line = fid.readline()
|
||||
if not line:
|
||||
break
|
||||
line = line.strip()
|
||||
if len(line) > 0 and line[0] != "#":
|
||||
elems = line.split()
|
||||
image_id = int(elems[0])
|
||||
qvec = np.array(tuple(map(float, elems[1:5])))
|
||||
tvec = np.array(tuple(map(float, elems[5:8])))
|
||||
camera_id = int(elems[8])
|
||||
image_name = elems[9]
|
||||
elems = fid.readline().split()
|
||||
xys = np.column_stack([tuple(map(float, elems[0::3])),
|
||||
tuple(map(float, elems[1::3]))])
|
||||
point3D_ids = np.array(tuple(map(int, elems[2::3])))
|
||||
images[image_id] = Image(
|
||||
id=image_id, qvec=qvec, tvec=tvec,
|
||||
camera_id=camera_id, name=image_name,
|
||||
xys=xys, point3D_ids=point3D_ids)
|
||||
return images
|
||||
|
||||
|
||||
def read_images_binary(path_to_model_file):
|
||||
"""
|
||||
see: src/base/reconstruction.cc
|
||||
void Reconstruction::ReadImagesBinary(const std::string& path)
|
||||
void Reconstruction::WriteImagesBinary(const std::string& path)
|
||||
"""
|
||||
images = {}
|
||||
with open(path_to_model_file, "rb") as fid:
|
||||
num_reg_images = read_next_bytes(fid, 8, "Q")[0]
|
||||
for image_index in range(num_reg_images):
|
||||
binary_image_properties = read_next_bytes(
|
||||
fid, num_bytes=64, format_char_sequence="idddddddi")
|
||||
image_id = binary_image_properties[0]
|
||||
qvec = np.array(binary_image_properties[1:5])
|
||||
tvec = np.array(binary_image_properties[5:8])
|
||||
camera_id = binary_image_properties[8]
|
||||
image_name = ""
|
||||
current_char = read_next_bytes(fid, 1, "c")[0]
|
||||
while current_char != b"\x00": # look for the ASCII 0 entry
|
||||
image_name += current_char.decode("utf-8")
|
||||
current_char = read_next_bytes(fid, 1, "c")[0]
|
||||
num_points2D = read_next_bytes(fid, num_bytes=8,
|
||||
format_char_sequence="Q")[0]
|
||||
x_y_id_s = read_next_bytes(fid, num_bytes=24*num_points2D,
|
||||
format_char_sequence="ddq"*num_points2D)
|
||||
xys = np.column_stack([tuple(map(float, x_y_id_s[0::3])),
|
||||
tuple(map(float, x_y_id_s[1::3]))])
|
||||
point3D_ids = np.array(tuple(map(int, x_y_id_s[2::3])))
|
||||
images[image_id] = Image(
|
||||
id=image_id, qvec=qvec, tvec=tvec,
|
||||
camera_id=camera_id, name=image_name,
|
||||
xys=xys, point3D_ids=point3D_ids)
|
||||
return images
|
||||
|
||||
|
||||
def read_points3D_text(path):
|
||||
"""
|
||||
see: src/base/reconstruction.cc
|
||||
void Reconstruction::ReadPoints3DText(const std::string& path)
|
||||
void Reconstruction::WritePoints3DText(const std::string& path)
|
||||
"""
|
||||
points3D = {}
|
||||
with open(path, "r") as fid:
|
||||
while True:
|
||||
line = fid.readline()
|
||||
if not line:
|
||||
break
|
||||
line = line.strip()
|
||||
if len(line) > 0 and line[0] != "#":
|
||||
elems = line.split()
|
||||
point3D_id = int(elems[0])
|
||||
xyz = np.array(tuple(map(float, elems[1:4])))
|
||||
rgb = np.array(tuple(map(int, elems[4:7])))
|
||||
error = float(elems[7])
|
||||
image_ids = np.array(tuple(map(int, elems[8::2])))
|
||||
point2D_idxs = np.array(tuple(map(int, elems[9::2])))
|
||||
points3D[point3D_id] = Point3D(id=point3D_id, xyz=xyz, rgb=rgb,
|
||||
error=error, image_ids=image_ids,
|
||||
point2D_idxs=point2D_idxs)
|
||||
return points3D
|
||||
|
||||
|
||||
def read_points3d_binary(path_to_model_file):
|
||||
"""
|
||||
see: src/base/reconstruction.cc
|
||||
void Reconstruction::ReadPoints3DBinary(const std::string& path)
|
||||
void Reconstruction::WritePoints3DBinary(const std::string& path)
|
||||
"""
|
||||
points3D = {}
|
||||
with open(path_to_model_file, "rb") as fid:
|
||||
num_points = read_next_bytes(fid, 8, "Q")[0]
|
||||
for point_line_index in range(num_points):
|
||||
binary_point_line_properties = read_next_bytes(
|
||||
fid, num_bytes=43, format_char_sequence="QdddBBBd")
|
||||
point3D_id = binary_point_line_properties[0]
|
||||
xyz = np.array(binary_point_line_properties[1:4])
|
||||
rgb = np.array(binary_point_line_properties[4:7])
|
||||
error = np.array(binary_point_line_properties[7])
|
||||
track_length = read_next_bytes(
|
||||
fid, num_bytes=8, format_char_sequence="Q")[0]
|
||||
track_elems = read_next_bytes(
|
||||
fid, num_bytes=8*track_length,
|
||||
format_char_sequence="ii"*track_length)
|
||||
image_ids = np.array(tuple(map(int, track_elems[0::2])))
|
||||
point2D_idxs = np.array(tuple(map(int, track_elems[1::2])))
|
||||
points3D[point3D_id] = Point3D(
|
||||
id=point3D_id, xyz=xyz, rgb=rgb,
|
||||
error=error, image_ids=image_ids,
|
||||
point2D_idxs=point2D_idxs)
|
||||
return points3D
|
||||
|
||||
|
||||
def read_model(path, ext):
|
||||
if ext == ".txt":
|
||||
cameras = read_cameras_text(os.path.join(path, "cameras" + ext))
|
||||
images = read_images_text(os.path.join(path, "images" + ext))
|
||||
points3D = read_points3D_text(os.path.join(path, "points3D") + ext)
|
||||
else:
|
||||
cameras = read_cameras_binary(os.path.join(path, "cameras" + ext))
|
||||
images = read_images_binary(os.path.join(path, "images" + ext))
|
||||
points3D = read_points3d_binary(os.path.join(path, "points3D") + ext)
|
||||
return cameras, images, points3D
|
||||
|
||||
|
||||
def qvec2rotmat(qvec):
|
||||
return np.array([
|
||||
[1 - 2 * qvec[2]**2 - 2 * qvec[3]**2,
|
||||
2 * qvec[1] * qvec[2] - 2 * qvec[0] * qvec[3],
|
||||
2 * qvec[3] * qvec[1] + 2 * qvec[0] * qvec[2]],
|
||||
[2 * qvec[1] * qvec[2] + 2 * qvec[0] * qvec[3],
|
||||
1 - 2 * qvec[1]**2 - 2 * qvec[3]**2,
|
||||
2 * qvec[2] * qvec[3] - 2 * qvec[0] * qvec[1]],
|
||||
[2 * qvec[3] * qvec[1] - 2 * qvec[0] * qvec[2],
|
||||
2 * qvec[2] * qvec[3] + 2 * qvec[0] * qvec[1],
|
||||
1 - 2 * qvec[1]**2 - 2 * qvec[2]**2]])
|
||||
|
||||
|
||||
def rotmat2qvec(R):
|
||||
Rxx, Ryx, Rzx, Rxy, Ryy, Rzy, Rxz, Ryz, Rzz = R.flat
|
||||
K = np.array([
|
||||
[Rxx - Ryy - Rzz, 0, 0, 0],
|
||||
[Ryx + Rxy, Ryy - Rxx - Rzz, 0, 0],
|
||||
[Rzx + Rxz, Rzy + Ryz, Rzz - Rxx - Ryy, 0],
|
||||
[Ryz - Rzy, Rzx - Rxz, Rxy - Ryx, Rxx + Ryy + Rzz]]) / 3.0
|
||||
eigvals, eigvecs = np.linalg.eigh(K)
|
||||
qvec = eigvecs[[3, 0, 1, 2], np.argmax(eigvals)]
|
||||
if qvec[0] < 0:
|
||||
qvec *= -1
|
||||
return qvec
|
||||
|
||||
|
||||
def main():
|
||||
if len(sys.argv) != 3:
|
||||
print("Usage: python read_model.py path/to/model/folder [.txt,.bin]")
|
||||
return
|
||||
|
||||
cameras, images, points3D = read_model(path=sys.argv[1], ext=sys.argv[2])
|
||||
|
||||
print("num_cameras:", len(cameras))
|
||||
print("num_images:", len(images))
|
||||
print("num_points3D:", len(points3D))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -0,0 +1,71 @@
|
||||
import os
|
||||
import subprocess
|
||||
import logging
|
||||
|
||||
# $ DATASET_PATH=/path/to/dataset
|
||||
|
||||
# $ colmap feature_extractor \
|
||||
# --database_path $DATASET_PATH/database.db \
|
||||
# --image_path $DATASET_PATH/images
|
||||
|
||||
# $ colmap exhaustive_matcher \
|
||||
# --database_path $DATASET_PATH/database.db
|
||||
|
||||
# $ mkdir $DATASET_PATH/sparse
|
||||
|
||||
# $ colmap mapper \
|
||||
# --database_path $DATASET_PATH/database.db \
|
||||
# --image_path $DATASET_PATH/images \
|
||||
# --output_path $DATASET_PATH/sparse
|
||||
|
||||
# $ mkdir $DATASET_PATH/dense
|
||||
|
||||
|
||||
def run_colmap(basedir, match_type):
|
||||
"""Simple wrapper for COLMAP"""
|
||||
|
||||
logfile_name = os.path.join(basedir, 'colmap_output.txt')
|
||||
logfile = open(logfile_name, 'w')
|
||||
|
||||
feature_extractor_args = [
|
||||
'colmap', 'feature_extractor',
|
||||
'--database_path', os.path.join(basedir, 'database.db'),
|
||||
'--image_path', os.path.join(basedir, 'images'),
|
||||
'--ImageReader.single_camera', '1',
|
||||
# '--SiftExtraction.use_gpu', '0',
|
||||
]
|
||||
logging.debug("Extracting features.")
|
||||
feat_output = subprocess.check_output(feature_extractor_args, universal_newlines=True)
|
||||
logfile.write(feat_output)
|
||||
|
||||
exhaustive_matcher_args = [
|
||||
'colmap', match_type,
|
||||
'--database_path', os.path.join(basedir, 'database.db'),
|
||||
]
|
||||
|
||||
logging.debug("Matching features.")
|
||||
match_output = subprocess.check_output(exhaustive_matcher_args, universal_newlines=True)
|
||||
logfile.write(match_output)
|
||||
|
||||
p = os.path.join(basedir, 'sparse')
|
||||
if not os.path.exists(p):
|
||||
os.makedirs(p)
|
||||
|
||||
mapper_args = [
|
||||
'colmap', 'mapper',
|
||||
'--database_path', os.path.join(basedir, 'database.db'),
|
||||
'--image_path', os.path.join(basedir, 'images'),
|
||||
'--output_path', os.path.join(basedir, 'sparse'), # --export_path changed to --output_path in colmap 3.6
|
||||
'--Mapper.num_threads', '16',
|
||||
'--Mapper.init_min_tri_angle', '4',
|
||||
'--Mapper.multiple_models', '0',
|
||||
'--Mapper.extract_colors', '0',
|
||||
]
|
||||
|
||||
logging.debug("Creating sparse map")
|
||||
map_output = subprocess.check_output(mapper_args, universal_newlines=True)
|
||||
logfile.write(map_output)
|
||||
logfile.close()
|
||||
|
||||
logging.info("Finished running COLMAP. Logfile written in %s",
|
||||
logfile_name)
|
||||
@ -0,0 +1,138 @@
|
||||
import os
|
||||
import logging
|
||||
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
|
||||
|
||||
|
||||
def gen_poses(basedir: str, match_type: str='exhaustive') -> None:
|
||||
"""
|
||||
Geneate or retreive camera poses.
|
||||
|
||||
Retrieve the cameras either by generating using COLMAP or reading them if
|
||||
they already have been written.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
basedir: str
|
||||
The path of the directory either containing raw images or the
|
||||
arborescence of a COLMAP directory if camera poses have already been
|
||||
computed
|
||||
match_type: {'exhaustive', 'sequential'}
|
||||
The type of match COLMAP should perform if it needs to be run.
|
||||
Defaults to 'exhaustive'.
|
||||
"""
|
||||
|
||||
files_needed = ["cameras.bin", "images.bin", "points3D.bin"]
|
||||
|
||||
path_to_sparse = os.path.join(basedir, "sparse/0")
|
||||
if os.path.exists(path_to_sparse):
|
||||
existing_files = os.listdir(path_to_sparse)
|
||||
else:
|
||||
existing_files = []
|
||||
|
||||
if not all([f in existing_files for f in files_needed]):
|
||||
logging.info("Running COLMAP")
|
||||
run_colmap(basedir, match_type)
|
||||
else:
|
||||
logging.info("Files genreated by COLMAP found. Skipping running COLMAP.")
|
||||
|
||||
logging.debug("Loading COLMAP data")
|
||||
poses, points_3d, perm = load_colmap_data(basedir)
|
||||
|
||||
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.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
basedir: str
|
||||
The path of the directory which contains the COLMAP arborescence
|
||||
|
||||
Returns
|
||||
-------
|
||||
poses: numpy.ndarray
|
||||
List of poses for each image.
|
||||
pts3d:
|
||||
List of 3D points for each image.
|
||||
perm:
|
||||
Index list ordered by name of images.
|
||||
Should be [1, 2, ..., N] if well ordered.
|
||||
"""
|
||||
# append prefix
|
||||
basedir = os.path.join(basedir, "sparse/0/")
|
||||
|
||||
# read cameras data
|
||||
cameras_file = os.path.join(basedir, "cameras.bin")
|
||||
cameras_data = read_model.read_cameras_binary(cameras_file)
|
||||
logging.debug(f"Loading camera model from {cameras_file}")
|
||||
|
||||
# extract intrinsic values for the camera
|
||||
# asumption is made that it is unique
|
||||
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])
|
||||
logging.debug(f"Number of cameras: {len(cameras_data)}")
|
||||
|
||||
# read images data
|
||||
images_file = os.path.join(basedir, "images.bin")
|
||||
images_data = read_model.read_images_binary(images_file)
|
||||
|
||||
w2c_mats = []
|
||||
bottom = np.array([0., 0., 0., 1.]).reshape([1, 4])
|
||||
|
||||
# sort by name
|
||||
names = [images_data[k].name for k in images_data]
|
||||
perm = np.argsort(names)
|
||||
# equivalent to a range since dict is index by number from 1 to N
|
||||
|
||||
# create camera matrix
|
||||
for k in images_data:
|
||||
im = images_data[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)
|
||||
|
||||
# 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)
|
||||
|
||||
return poses, pts3d, perm
|
||||
|
||||
def save_poses(basedir, poses, pts3d, perm) -> None:
|
||||
"""
|
||||
Save the COLMAP data in a `.npy` format.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
basedir: str
|
||||
The path of the directory in which to save the data.
|
||||
Data will be saved in `basedir/poses_bounds.npy`.
|
||||
poses:
|
||||
The list of poses
|
||||
pts3d:
|
||||
The list of 3d points
|
||||
perm:
|
||||
The sorted index of the array
|
||||
"""
|
||||
|
||||
# TODO
|
||||
Loading…
Reference in New Issue