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colmap2mvsnet.py
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colmap2mvsnet.py
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#!/usr/bin/env python
"""
Copyright 2019, Jingyang Zhang and Yao Yao, HKUST. Model reading is provided by COLMAP.
Preprocess script.
"""
from __future__ import print_function
import collections
import struct
import numpy as np
import multiprocessing as mp
from functools import partial
import os
import argparse
import shutil
import cv2
#============================ read_model.py ============================#
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 calc_score(inputs, images, points3d, extrinsic, args):
i, j = inputs
id_i = images[i+1].point3D_ids
id_j = images[j+1].point3D_ids
id_intersect = [it for it in id_i if it in id_j]
cam_center_i = -np.matmul(extrinsic[i+1][:3, :3].transpose(), extrinsic[i+1][:3, 3:4])[:, 0]
cam_center_j = -np.matmul(extrinsic[j+1][:3, :3].transpose(), extrinsic[j+1][:3, 3:4])[:, 0]
score = 0
for pid in id_intersect:
if pid == -1:
continue
p = points3d[pid].xyz
theta = (180 / np.pi) * np.arccos(np.dot(cam_center_i - p, cam_center_j - p) / np.linalg.norm(cam_center_i - p) / np.linalg.norm(cam_center_j - p))
score += np.exp(-(theta - args.theta0) * (theta - args.theta0) / (2 * (args.sigma1 if theta <= args.theta0 else args.sigma2) ** 2))
return i, j, score
def processing_single_scene(args):
image_dir = os.path.join(args.dense_folder, 'images')
model_dir = os.path.join(args.dense_folder, 'sparse')
cam_dir = os.path.join(args.save_folder, 'cams')
image_converted_dir = os.path.join(args.save_folder, 'images_post')
if os.path.exists(image_converted_dir):
print("remove:{}".format(image_converted_dir))
shutil.rmtree(image_converted_dir)
os.makedirs(image_converted_dir)
if os.path.exists(cam_dir):
print("remove:{}".format(cam_dir))
shutil.rmtree(cam_dir)
cameras, images, points3d = read_model(model_dir, args.model_ext)
num_images = len(list(images.items()))
param_type = {
'SIMPLE_PINHOLE': ['f', 'cx', 'cy'],
'PINHOLE': ['fx', 'fy', 'cx', 'cy'],
'SIMPLE_RADIAL': ['f', 'cx', 'cy', 'k'],
'SIMPLE_RADIAL_FISHEYE': ['f', 'cx', 'cy', 'k'],
'RADIAL': ['f', 'cx', 'cy', 'k1', 'k2'],
'RADIAL_FISHEYE': ['f', 'cx', 'cy', 'k1', 'k2'],
'OPENCV': ['fx', 'fy', 'cx', 'cy', 'k1', 'k2', 'p1', 'p2'],
'OPENCV_FISHEYE': ['fx', 'fy', 'cx', 'cy', 'k1', 'k2', 'k3', 'k4'],
'FULL_OPENCV': ['fx', 'fy', 'cx', 'cy', 'k1', 'k2', 'p1', 'p2', 'k3', 'k4', 'k5', 'k6'],
'FOV': ['fx', 'fy', 'cx', 'cy', 'omega'],
'THIN_PRISM_FISHEYE': ['fx', 'fy', 'cx', 'cy', 'k1', 'k2', 'p1', 'p2', 'k3', 'k4', 'sx1', 'sy1']
}
# intrinsic
intrinsic = {}
for camera_id, cam in cameras.items():
params_dict = {key: value for key, value in zip(param_type[cam.model], cam.params)}
if 'f' in param_type[cam.model]:
params_dict['fx'] = params_dict['f']
params_dict['fy'] = params_dict['f']
i = np.array([
[params_dict['fx'], 0, params_dict['cx']],
[0, params_dict['fy'], params_dict['cy']],
[0, 0, 1]
])
intrinsic[camera_id] = i
print('intrinsic\n', intrinsic, end='\n\n')
new_images = {}
for i, image_id in enumerate(sorted(images.keys())):
new_images[i+1] = images[image_id]
images = new_images
# extrinsic
extrinsic = {}
for image_id, image in images.items():
e = np.zeros((4, 4))
e[:3, :3] = qvec2rotmat(image.qvec)
e[:3, 3] = image.tvec
e[3, 3] = 1
extrinsic[image_id] = e
print('extrinsic[1]\n', extrinsic[1], end='\n\n')
# depth range and interval
depth_ranges = {}
for i in range(num_images):
zs = []
for p3d_id in images[i+1].point3D_ids:
if p3d_id == -1:
continue
transformed = np.matmul(extrinsic[i+1], [points3d[p3d_id].xyz[0], points3d[p3d_id].xyz[1], points3d[p3d_id].xyz[2], 1])
zs.append(np.asscalar(transformed[2]))
zs_sorted = sorted(zs)
# relaxed depth range
max_ratio = 0.1
min_ratio = 0.03
num_max = max(5, int(len(zs) * max_ratio))
num_min = max(1, int(len(zs) * min_ratio))
depth_min = 1.0 * sum(zs_sorted[:num_min]) / len(zs_sorted[:num_min])
depth_max = 1.0 * sum(zs_sorted[-num_max:]) / len(zs_sorted[-num_max:])
# print("new", depth_min, depth_max, zs_sorted[-10:])
# depth_min = zs_sorted[int(len(zs) * .01)]
# depth_max = zs_sorted[int(len(zs) * .99)]
# print("old", depth_min, depth_max, zs_sorted[:10], zs_sorted[-10:])
# determine depth number by inverse depth setting, see supplementary material
if args.max_d == 0:
image_int = intrinsic[images[i+1].camera_id]
image_ext = extrinsic[i+1]
image_r = image_ext[0:3, 0:3]
image_t = image_ext[0:3, 3]
p1 = [image_int[0, 2], image_int[1, 2], 1]
p2 = [image_int[0, 2] + 1, image_int[1, 2], 1]
P1 = np.matmul(np.linalg.inv(image_int), p1) * depth_min
P1 = np.matmul(np.linalg.inv(image_r), (P1 - image_t))
P2 = np.matmul(np.linalg.inv(image_int), p2) * depth_min
P2 = np.matmul(np.linalg.inv(image_r), (P2 - image_t))
depth_num = (1 / depth_min - 1 / depth_max) / (1 / depth_min - 1 / (depth_min + np.linalg.norm(P2 - P1)))
else:
depth_num = args.max_d
depth_interval = (depth_max - depth_min) / (depth_num - 1) / args.interval_scale
depth_ranges[i+1] = (depth_min, depth_interval, depth_num, depth_max)
print('depth_ranges[1]\n', depth_ranges[1], end='\n\n')
# view selection
score = np.zeros((len(images), len(images)))
queue = []
for i in range(len(images)):
for j in range(i + 1, len(images)):
queue.append((i, j))
p = mp.Pool(processes=mp.cpu_count())
func = partial(calc_score, images=images, points3d=points3d, args=args, extrinsic=extrinsic)
result = p.map(func, queue)
for i, j, s in result:
score[i, j] = s
score[j, i] = s
view_sel = []
for i in range(len(images)):
sorted_score = np.argsort(score[i])[::-1]
view_sel.append([(k, score[i, k]) for k in sorted_score[:10]])
print('view_sel[0]\n', view_sel[0], end='\n\n')
# write
try:
os.makedirs(cam_dir)
except os.error:
print(cam_dir + ' already exist.')
for i in range(num_images):
with open(os.path.join(cam_dir, '%08d_cam.txt' % i), 'w') as f:
f.write('extrinsic\n')
for j in range(4):
for k in range(4):
f.write(str(extrinsic[i+1][j, k]) + ' ')
f.write('\n')
f.write('\nintrinsic\n')
for j in range(3):
for k in range(3):
f.write(str(intrinsic[images[i+1].camera_id][j, k]) + ' ')
f.write('\n')
f.write('\n%f %f %f %f\n' % (depth_ranges[i+1][0], depth_ranges[i+1][1], depth_ranges[i+1][2], depth_ranges[i+1][3]))
with open(os.path.join(args.save_folder, 'pair.txt'), 'w') as f:
f.write('%d\n' % len(images))
for i, sorted_score in enumerate(view_sel):
f.write('%d\n%d ' % (i, len(sorted_score)))
for image_id, s in sorted_score:
f.write('%d %f ' % (image_id, s))
f.write('\n')
#convert to jpg
for i in range(num_images):
img_path = os.path.join(image_dir, images[i + 1].name)
if not img_path.endswith(".jpg"):
img = cv2.imread(img_path)
cv2.imwrite(os.path.join(image_converted_dir, '%08d.jpg' % i), img)
else:
shutil.copyfile(os.path.join(image_dir, images[i+1].name), os.path.join(image_converted_dir, '%08d.jpg' % i))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Convert colmap camera')
parser.add_argument('--dense_folder', required=True, type=str, help='dense_folder.')
parser.add_argument('--save_folder', required=True, type=str, help='save_folder.')
parser.add_argument('--max_d', type=int, default=192)
parser.add_argument('--interval_scale', type=float, default=1)
parser.add_argument('--theta0', type=float, default=5)
parser.add_argument('--sigma1', type=float, default=1)
parser.add_argument('--sigma2', type=float, default=10)
parser.add_argument('--model_ext', type=str, default=".bin", choices=[".txt", ".bin"], help='sparse model ext')
args = parser.parse_args()
os.makedirs(os.path.join(args.save_folder), exist_ok=True)
processing_single_scene(args)