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add scripts to generate poses

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Kai-46 2020-10-20 22:54:43 -04:00
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9
README.md
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@ -57,3 +57,12 @@ Plese cite our work if you use the code.
year = {2020},
}
```
## Generating poses with [COLMAP SfM](https://colmap.github.io/)
You can use the scripts inside 'colmap_runner/' to generate camera poses from images with COLMAP SfM.
* Specify 'img_dir' and 'out_dir' in 'colmap_runner/run_colmap.py'.
* Inside 'colmap_runner/', execute command 'python run_colmap.py'.
* After program finishes running, you would see the posed images in the folder 'out_dir/posed_images'.
* Distortion-free images are inside 'out_dir/posed_images/images'.
* Raw COLMAP poses is stored as a json file 'out_dir/posed_images/kai_cameras.json'.
* Normalized camderas is stored in 'out_dir/posed_images/kai_cameras_normalized.json'. See the 'Scene normalization method' in the 'Data' section.

133
colmap_runner/extract_sfm.py Normal file
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@ -0,0 +1,133 @@
from read_write_model import read_model
import numpy as np
import json
import os
from pyquaternion import Quaternion
import trimesh
def parse_tracks(colmap_images, colmap_points3D):
all_tracks = [] # list of dicts; each dict represents a track
all_points = [] # list of all 3D points
view_keypoints = {} # dict of lists; each list represents the triangulated key points of a view
for point3D_id in colmap_points3D:
point3D = colmap_points3D[point3D_id]
image_ids = point3D.image_ids
point2D_idxs = point3D.point2D_idxs
cur_track = {}
cur_track['xyz'] = (point3D.xyz[0], point3D.xyz[1], point3D.xyz[2])
cur_track['err'] = point3D.error.item()
cur_track_len = len(image_ids)
assert (cur_track_len == len(point2D_idxs))
all_points.append(list(cur_track['xyz'] + (cur_track['err'], cur_track_len) + tuple(point3D.rgb)))
pixels = []
for i in range(cur_track_len):
image = colmap_images[image_ids[i]]
img_name = image.name
point2D_idx = point2D_idxs[i]
point2D = image.xys[point2D_idx]
assert (image.point3D_ids[point2D_idx] == point3D_id)
pixels.append((img_name, point2D[0], point2D[1]))
if img_name not in view_keypoints:
view_keypoints[img_name] = [(point2D[0], point2D[1]) + cur_track['xyz'] + (cur_track_len, ), ]
else:
view_keypoints[img_name].append((point2D[0], point2D[1]) + cur_track['xyz'] + (cur_track_len, ))
cur_track['pixels'] = sorted(pixels, key=lambda x: x[0]) # sort pixels by the img_name
all_tracks.append(cur_track)
return all_tracks, all_points, view_keypoints
def parse_camera_dict(colmap_cameras, colmap_images):
camera_dict = {}
for image_id in colmap_images:
image = colmap_images[image_id]
img_name = image.name
cam = colmap_cameras[image.camera_id]
# print(cam)
assert(cam.model == 'PINHOLE')
img_size = [cam.width, cam.height]
params = list(cam.params)
qvec = list(image.qvec)
tvec = list(image.tvec)
# w, h, fx, fy, cx, cy, qvec, tvec
# camera_dict[img_name] = img_size + params + qvec + tvec
camera_dict[img_name] = {}
camera_dict[img_name]['img_size'] = img_size
fx, fy, cx, cy = params
K = np.eye(4)
K[0, 0] = fx
K[1, 1] = fy
K[0, 2] = cx
K[1, 2] = cy
camera_dict[img_name]['K'] = list(K.flatten())
rot = Quaternion(qvec[0], qvec[1], qvec[2], qvec[3]).rotation_matrix
W2C = np.eye(4)
W2C[:3, :3] = rot
W2C[:3, 3] = np.array(tvec)
camera_dict[img_name]['W2C'] = list(W2C.flatten())
return camera_dict
def extract_all_to_dir(sparse_dir, out_dir, ext='.bin'):
if not os.path.exists(out_dir):
os.mkdir(out_dir)
camera_dict_file = os.path.join(out_dir, 'kai_cameras.json')
xyz_file = os.path.join(out_dir, 'kai_points.txt')
track_file = os.path.join(out_dir, 'kai_tracks.json')
keypoints_file = os.path.join(out_dir, 'kai_keypoints.json')
colmap_cameras, colmap_images, colmap_points3D = read_model(sparse_dir, ext)
camera_dict = parse_camera_dict(colmap_cameras, colmap_images)
with open(camera_dict_file, 'w') as fp:
json.dump(camera_dict, fp, indent=2, sort_keys=True)
all_tracks, all_points, view_keypoints = parse_tracks(colmap_images, colmap_points3D)
all_points = np.array(all_points)
np.savetxt(xyz_file, all_points, header='# format: x, y, z, reproj_err, track_len, color(RGB)', fmt='%.6f')
mesh = trimesh.Trimesh(vertices=all_points[:, :3].astype(np.float32),
vertex_colors=all_points[:, -3:].astype(np.uint8))
mesh.export(os.path.join(out_dir, 'kai_points.ply'))
with open(track_file, 'w') as fp:
json.dump(all_tracks, fp)
with open(keypoints_file, 'w') as fp:
json.dump(view_keypoints, fp)
if __name__ == '__main__':
mvs_dir = '/home/zhangka2/sg_render/run_mvs/scan114_train_5/colmap_mvs/mvs'
sparse_dir = os.path.join(mvs_dir, 'sparse')
out_dir = os.path.join(mvs_dir, 'sparse_inspect')
extract_all_to_dir(sparse_dir, out_dir)
xyz_file = os.path.join(out_dir, 'kai_points.txt')
reproj_errs = np.loadtxt(xyz_file)[:, 3]
with open(os.path.join(out_dir, 'stats.txt'), 'w') as fp:
fp.write('reprojection errors (px) in SfM:\n')
fp.write(' percentile value\n')
for a in [50, 70, 90, 99]:
fp.write(' {} {:.3f}\n'.format(a, np.percentile(reproj_errs, a)))
print('reprojection errors (px) in SfM:')
print(' percentile value')
for a in [50, 70, 90, 99]:
print(' {} {:.3f}'.format(a, np.percentile(reproj_errs, a)))

57
colmap_runner/normalize_cam_dict.py Normal file
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@ -0,0 +1,57 @@
import numpy as np
import json
import copy
def get_tf_cams(cam_dict, target_radius=1.):
cam_centers = []
for im_name in cam_dict:
W2C = np.array(cam_dict[im_name]['W2C']).reshape((4, 4))
C2W = np.linalg.inv(W2C)
cam_centers.append(C2W[:3, 3:4])
def get_center_and_diag(cam_centers):
cam_centers = np.hstack(cam_centers)
avg_cam_center = np.mean(cam_centers, axis=1, keepdims=True)
center = avg_cam_center
dist = np.linalg.norm(cam_centers - center, axis=0, keepdims=True)
diagonal = np.max(dist)
return center.flatten(), diagonal
center, diagonal = get_center_and_diag(cam_centers)
radius = diagonal / 2. * 1.1
translate = -center
scale = target_radius / radius
return translate, scale
def normalize_cam_dict(in_cam_dict_file, out_cam_dict_file, target_radius=1.):
with open(in_cam_dict_file) as fp:
in_cam_dict = json.load(fp)
translate, scale = get_tf_cams(in_cam_dict, target_radius=target_radius)
def transform_pose(W2C, translate, scale):
C2W = np.linalg.inv(W2C)
cam_center = C2W[:3, 3]
cam_center = (cam_center + translate) * scale
C2W[:3, 3] = cam_center
return np.linalg.inv(C2W)
out_cam_dict = copy.deepcopy(in_cam_dict)
for img_name in out_cam_dict:
W2C = np.array(out_cam_dict[img_name]['W2C']).reshape((4, 4))
W2C = transform_pose(W2C, translate, scale)
assert(np.isclose(np.linalg.det(W2C[:3, :3]), 1.))
out_cam_dict[img_name]['W2C'] = list(W2C.flatten())
with open(out_cam_dict_file, 'w') as fp:
json.dump(out_cam_dict, fp, indent=2, sort_keys=True)
if __name__ == '__main__':
in_cam_dict_file = ''
out_cam_dict_file = ''
normalize_cam_dict(in_cam_dict_file, out_cam_dict_file, target_radius=1.)

483
colmap_runner/read_write_model.py Executable file
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@ -0,0 +1,483 @@
# 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-demuc-dot-de)
import os
import sys
import collections
import numpy as np
import struct
import argparse
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])
CAMERA_MODEL_NAMES = dict([(camera_model.model_name, 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 write_next_bytes(fid, data, format_char_sequence, endian_character="<"):
"""pack and write to a binary file.
:param fid:
:param data: data to send, if multiple elements are sent at the same time,
they should be encapsuled either in a list or a tuple
:param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
should be the same length as the data list or tuple
:param endian_character: Any of {@, =, <, >, !}
"""
if isinstance(data, (list, tuple)):
bytes = struct.pack(endian_character + format_char_sequence, *data)
else:
bytes = struct.pack(endian_character + format_char_sequence, data)
fid.write(bytes)
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 write_cameras_text(cameras, path):
"""
see: src/base/reconstruction.cc
void Reconstruction::WriteCamerasText(const std::string& path)
void Reconstruction::ReadCamerasText(const std::string& path)
"""
HEADER = '# Camera list with one line of data per camera:\n'
'# CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]\n'
'# Number of cameras: {}\n'.format(len(cameras))
with open(path, "w") as fid:
fid.write(HEADER)
for _, cam in cameras.items():
to_write = [cam.id, cam.model, cam.width, cam.height, *cam.params]
line = " ".join([str(elem) for elem in to_write])
fid.write(line + "\n")
def write_cameras_binary(cameras, path_to_model_file):
"""
see: src/base/reconstruction.cc
void Reconstruction::WriteCamerasBinary(const std::string& path)
void Reconstruction::ReadCamerasBinary(const std::string& path)
"""
with open(path_to_model_file, "wb") as fid:
write_next_bytes(fid, len(cameras), "Q")
for _, cam in cameras.items():
model_id = CAMERA_MODEL_NAMES[cam.model].model_id
camera_properties = [cam.id,
model_id,
cam.width,
cam.height]
write_next_bytes(fid, camera_properties, "iiQQ")
for p in cam.params:
write_next_bytes(fid, float(p), "d")
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 write_images_text(images, path):
"""
see: src/base/reconstruction.cc
void Reconstruction::ReadImagesText(const std::string& path)
void Reconstruction::WriteImagesText(const std::string& path)
"""
if len(images) == 0:
mean_observations = 0
else:
mean_observations = sum((len(img.point3D_ids) for _, img in images.items()))/len(images)
HEADER = '# Image list with two lines of data per image:\n'
'# IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME\n'
'# POINTS2D[] as (X, Y, POINT3D_ID)\n'
'# Number of images: {}, mean observations per image: {}\n'.format(len(images), mean_observations)
with open(path, "w") as fid:
fid.write(HEADER)
for _, img in images.items():
image_header = [img.id, *img.qvec, *img.tvec, img.camera_id, img.name]
first_line = " ".join(map(str, image_header))
fid.write(first_line + "\n")
points_strings = []
for xy, point3D_id in zip(img.xys, img.point3D_ids):
points_strings.append(" ".join(map(str, [*xy, point3D_id])))
fid.write(" ".join(points_strings) + "\n")
def write_images_binary(images, path_to_model_file):
"""
see: src/base/reconstruction.cc
void Reconstruction::ReadImagesBinary(const std::string& path)
void Reconstruction::WriteImagesBinary(const std::string& path)
"""
with open(path_to_model_file, "wb") as fid:
write_next_bytes(fid, len(images), "Q")
for _, img in images.items():
write_next_bytes(fid, img.id, "i")
write_next_bytes(fid, img.qvec.tolist(), "dddd")
write_next_bytes(fid, img.tvec.tolist(), "ddd")
write_next_bytes(fid, img.camera_id, "i")
for char in img.name:
write_next_bytes(fid, char.encode("utf-8"), "c")
write_next_bytes(fid, b"\x00", "c")
write_next_bytes(fid, len(img.point3D_ids), "Q")
for xy, p3d_id in zip(img.xys, img.point3D_ids):
write_next_bytes(fid, [*xy, p3d_id], "ddq")
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 write_points3D_text(points3D, path):
"""
see: src/base/reconstruction.cc
void Reconstruction::ReadPoints3DText(const std::string& path)
void Reconstruction::WritePoints3DText(const std::string& path)
"""
if len(points3D) == 0:
mean_track_length = 0
else:
mean_track_length = sum((len(pt.image_ids) for _, pt in points3D.items()))/len(points3D)
HEADER = '# 3D point list with one line of data per point:\n'
'# POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[] as (IMAGE_ID, POINT2D_IDX)\n'
'# Number of points: {}, mean track length: {}\n'.format(len(points3D), mean_track_length)
with open(path, "w") as fid:
fid.write(HEADER)
for _, pt in points3D.items():
point_header = [pt.id, *pt.xyz, *pt.rgb, pt.error]
fid.write(" ".join(map(str, point_header)) + " ")
track_strings = []
for image_id, point2D in zip(pt.image_ids, pt.point2D_idxs):
track_strings.append(" ".join(map(str, [image_id, point2D])))
fid.write(" ".join(track_strings) + "\n")
def write_points3d_binary(points3D, 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, "wb") as fid:
write_next_bytes(fid, len(points3D), "Q")
for _, pt in points3D.items():
write_next_bytes(fid, pt.id, "Q")
write_next_bytes(fid, pt.xyz.tolist(), "ddd")
write_next_bytes(fid, pt.rgb.tolist(), "BBB")
write_next_bytes(fid, pt.error, "d")
track_length = pt.image_ids.shape[0]
write_next_bytes(fid, track_length, "Q")
for image_id, point2D_id in zip(pt.image_ids, pt.point2D_idxs):
write_next_bytes(fid, [image_id, point2D_id], "ii")
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 write_model(cameras, images, points3D, path, ext):
if ext == ".txt":
write_cameras_text(cameras, os.path.join(path, "cameras" + ext))
write_images_text(images, os.path.join(path, "images" + ext))
write_points3D_text(points3D, os.path.join(path, "points3D") + ext)
else:
write_cameras_binary(cameras, os.path.join(path, "cameras" + ext))
write_images_binary(images, os.path.join(path, "images" + ext))
write_points3d_binary(points3D, 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():
parser = argparse.ArgumentParser(description='Read and write COLMAP binary and text models')
parser.add_argument('input_model', help='path to input model folder')
parser.add_argument('input_format', choices=['.bin', '.txt'],
help='input model format')
parser.add_argument('--output_model', metavar='PATH',
help='path to output model folder')
parser.add_argument('--output_format', choices=['.bin', '.txt'],
help='outut model format', default='.txt')
args = parser.parse_args()
cameras, images, points3D = read_model(path=args.input_model, ext=args.input_format)
print("num_cameras:", len(cameras))
print("num_images:", len(images))
print("num_points3D:", len(points3D))
if args.output_model is not None:
write_model(cameras, images, points3D, path=args.output_model, ext=args.output_format)
if __name__ == "__main__":
main()

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import os
import subprocess
from extract_sfm import extract_all_to_dir
from normalize_cam_dict import normalize_cam_dict
#########################################################################
# Note: configure the colmap_bin to the colmap executable on your machine
#########################################################################
def bash_run(cmd):
colmap_bin = '/home/zhangka2/code/colmap/build/__install__/bin/colmap'
cmd = colmap_bin + ' ' + cmd
print('\nRunning cmd: ', cmd)
subprocess.check_call(['/bin/bash', '-c', cmd])
gpu_index = '-1'
def run_sift_matching(img_dir, db_file, remove_exist=False):
print('Running sift matching...')
if remove_exist and os.path.exists(db_file):
os.remove(db_file) # otherwise colmap will skip sift matching
# feature extraction
# if there's no attached display, cannot use feature extractor with GPU
cmd = ' feature_extractor --database_path {} \
--image_path {} \
--ImageReader.single_camera 1 \
--ImageReader.camera_model SIMPLE_RADIAL \
--SiftExtraction.max_image_size 5000 \
--SiftExtraction.estimate_affine_shape 0 \
--SiftExtraction.domain_size_pooling 1 \
--SiftExtraction.use_gpu 1 \
--SiftExtraction.max_num_features 16384 \
--SiftExtraction.gpu_index {}'.format(db_file, img_dir, gpu_index)
bash_run(cmd)
# feature matching
cmd = ' exhaustive_matcher --database_path {} \
--SiftMatching.guided_matching 1 \
--SiftMatching.use_gpu 1 \
--SiftMatching.max_num_matches 65536 \
--SiftMatching.max_error 3 \
--SiftMatching.gpu_index {}'.format(db_file, gpu_index)
bash_run(cmd)
def run_sfm(img_dir, db_file, out_dir):
print('Running SfM...')
cmd = ' mapper \
--database_path {} \
--image_path {} \
--output_path {} \
--Mapper.tri_min_angle 3.0 \
--Mapper.filter_min_tri_angle 3.0'.format(db_file, img_dir, out_dir)
bash_run(cmd)
def prepare_mvs(img_dir, sparse_dir, mvs_dir):
print('Preparing for MVS...')
cmd = ' image_undistorter \
--image_path {} \
--input_path {} \
--output_path {} \
--output_type COLMAP \
--max_image_size 2000'.format(img_dir, sparse_dir, mvs_dir)
bash_run(cmd)
def run_photometric_mvs(mvs_dir, window_radius):
print('Running photometric MVS...')
cmd = ' patch_match_stereo --workspace_path {} \
--PatchMatchStereo.window_radius {} \
--PatchMatchStereo.min_triangulation_angle 3.0 \
--PatchMatchStereo.filter 1 \
--PatchMatchStereo.geom_consistency 1 \
--PatchMatchStereo.gpu_index={} \
--PatchMatchStereo.num_samples 15 \
--PatchMatchStereo.num_iterations 12'.format(mvs_dir,
window_radius, gpu_index)
bash_run(cmd)
def run_fuse(mvs_dir, out_ply):
print('Running depth fusion...')
cmd = ' stereo_fusion --workspace_path {} \
--output_path {} \
--input_type geometric'.format(mvs_dir, out_ply)
bash_run(cmd)
def run_possion_mesher(in_ply, out_ply, trim):
print('Running possion mesher...')
cmd = ' poisson_mesher \
--input_path {} \
--output_path {} \
--PoissonMeshing.trim {}'.format(in_ply, out_ply, trim)
bash_run(cmd)
def main(img_dir, out_dir, run_mvs=False):
os.makedirs(out_dir, exist_ok=True)
#### run sfm
sfm_dir = os.path.join(out_dir, 'sfm')
os.makedirs(sfm_dir, exist_ok=True)
img_dir_link = os.path.join(sfm_dir, 'images')
if os.path.exists(img_dir_link):
os.remove(img_dir_link)
os.symlink(img_dir, img_dir_link)
db_file = os.path.join(sfm_dir, 'database.db')
run_sift_matching(img_dir, db_file, remove_exist=False)
sparse_dir = os.path.join(sfm_dir, 'sparse')
os.makedirs(sparse_dir, exist_ok=True)
run_sfm(img_dir, db_file, sparse_dir)
# undistort images
mvs_dir = os.path.join(out_dir, 'mvs')
os.makedirs(mvs_dir, exist_ok=True)
prepare_mvs(img_dir, sparse_dir, mvs_dir)
# extract camera parameters and undistorted images
os.makedirs(os.path.join(out_dir, 'posed_images'), exist_ok=True)
extract_all_to_dir(os.path.join(mvs_dir, 'sparse'), os.path.join(out_dir, 'posed_images'))
undistorted_img_dir = os.path.join(mvs_dir, 'images')
posed_img_dir_link = os.path.join(out_dir, 'posed_images/images')
if os.path.exists(posed_img_dir_link):
os.remove(posed_img_dir_link)
os.symlink(undistorted_img_dir, posed_img_dir_link)
# normalize average camera center to origin, and put all cameras inside the unit sphere
normalize_cam_dict(os.path.join(out_dir, 'posed_images/kai_cameras.json'),
os.path.join(out_dir, 'posed_images/kai_cameras_normalized.json'))
if run_mvs:
# run mvs
run_photometric_mvs(mvs_dir, window_radius=7)
out_ply = os.path.join(out_dir, 'mvs/fused.ply')
run_fuse(mvs_dir, out_ply)
out_mesh_ply = os.path.join(out_dir, 'mvs/meshed_trim_3.ply')
run_possion_mesher(out_ply, out_mesh_ply, trim=3)
if __name__ == '__main__':
img_dir = ''
out_dir = ''
run_mvs = False
main(img_dir, out_dir, run_mvs=run_mvs)