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#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
import numpy as np
import collections
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"])
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])
CAMERA_MODEL_NAMES = dict([(camera_model.model_name, camera_model)
for camera_model in CAMERA_MODELS])
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
class Image(BaseImage):
def qvec2rotmat(self):
return qvec2rotmat(self.qvec)
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_points3D_text(path):
"""
see: src/base/reconstruction.cc
void Reconstruction::ReadPoints3DText(const std::string& path)
void Reconstruction::WritePoints3DText(const std::string& path)
"""
xyzs = None
rgbs = None
errors = None
num_points = 0
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] != "#":
num_points += 1
xyzs = np.empty((num_points, 3))
rgbs = np.empty((num_points, 3))
errors = np.empty((num_points, 1))
count = 0
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()
xyz = np.array(tuple(map(float, elems[1:4])))
rgb = np.array(tuple(map(int, elems[4:7])))
error = np.array(float(elems[7]))
xyzs[count] = xyz
rgbs[count] = rgb
errors[count] = error
count += 1
return xyzs, rgbs, errors
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)
"""
with open(path_to_model_file, "rb") as fid:
num_points = read_next_bytes(fid, 8, "Q")[0]
xyzs = np.empty((num_points, 3))
rgbs = np.empty((num_points, 3))
errors = np.empty((num_points, 1))
for p_id in range(num_points):
binary_point_line_properties = read_next_bytes(
fid, num_bytes=43, format_char_sequence="QdddBBBd")
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)
xyzs[p_id] = xyz
rgbs[p_id] = rgb
errors[p_id] = error
return xyzs, rgbs, errors
def read_intrinsics_text(path):
"""
Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py
"""
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]
assert model == "PINHOLE", "While the loader support other types, the rest of the code assumes PINHOLE"
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_extrinsics_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 _ 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_intrinsics_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 _ 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_extrinsics_text(path):
"""
Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_write_model.py
"""
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_colmap_bin_array(path):
"""
Taken from https://github.com/colmap/colmap/blob/dev/scripts/python/read_dense.py
:param path: path to the colmap binary file.
:return: nd array with the floating point values in the value
"""
with open(path, "rb") as fid:
width, height, channels = np.genfromtxt(fid, delimiter="&", max_rows=1,
usecols=(0, 1, 2), dtype=int)
fid.seek(0)
num_delimiter = 0
byte = fid.read(1)
while True:
if byte == b"&":
num_delimiter += 1
if num_delimiter >= 3:
break
byte = fid.read(1)
array = np.fromfile(fid, np.float32)
array = array.reshape((width, height, channels), order="F")
return np.transpose(array, (1, 0, 2)).squeeze()
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