def load_colmap_data(realdir):
camerasfile = os.path.join(realdir, 'sparse/0/cameras.bin')
camdata = read_model.read_cameras_binary(camerasfile)
# cam = camdata[camdata.keys()[0]]
list_of_keys = list(camdata.keys())
cam = camdata[list_of_keys[0]]
print('Cameras', len(cam))
h, w, f = cam.height, cam.width, cam.params[0]
# w, h, f = factor * w, factor * h, factor * f
hwf = np.array([h, w, f]).reshape([3, 1])
imagesfile = os.path.join(realdir, 'sparse/0/images.bin')
imdata = read_model.read_images_binary(imagesfile)
# for key in range(1, max(imdata.keys())):
# if key not in imdata:
# print(f'Missing {key}')
w2c_mats = []
bottom = np.array([0, 0, 0, 1.]).reshape([1, 4])
ids = [imdata[k].id for k in imdata]
print('Images #', len(ids))
perm = np.argsort(ids)
for k in imdata:
im = imdata[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)
points3dfile = os.path.join(realdir, 'sparse/0/points3D.bin')
pts3d = read_model.read_points3d_binary(points3dfile)
# 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 load_colmap_data(realdir):
camerasfile = os.path.join(realdir, 'dense/sparse/cameras.bin')
camdata = read_model.read_cameras_binary(camerasfile)
# cam = camdata[camdata.keys()[0]]
list_of_keys = list(camdata.keys())
cam = camdata[list_of_keys[0]]
h, w, fx, fy, cx, cy = cam.height, cam.width, cam.params[0], cam.params[
1], cam.params[2], cam.params[3]
# w, h, f = factor * w, factor * h, factor * f
hwf_cxcy = np.array([h, w, fx, fy, cx, cy]).reshape([6, 1])
imagesfile = os.path.join(realdir, 'dense/sparse/images.bin')
imdata = read_model.read_images_binary(imagesfile)
w2c_mats = []
bottom = np.array([0, 0, 0, 1.]).reshape([1, 4])
names = [imdata[k].name for k in imdata]
print('Images #', len(names))
perm = np.argsort(names)
for k in imdata:
im = imdata[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)
points3dfile = os.path.join(realdir, 'dense/sparse/points3D.bin')
pts3d = read_model.read_points3d_binary(points3dfile)
# 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, :]
], 1)
return poses, pts3d, perm, hwf_cxcy
np.linspace(0, 2 * np.pi, num=num_poses + 1)[:num_poses, None])
rotmats = sR.from_rotvec(rotvecs).as_matrix()
transforms = np.eye(4)[None].repeat(num_poses, axis=0)
transforms[:, :3, :3] = rotmats
circular_c2ws = transforms @ c2w
return circular_c2ws
def focal_from_fov(fov, s):
return (s / 2) / np.tan(fov / 2)
realdir = '../nerf-scenes/bushes2/'
points3dfile = os.path.join(realdir, 'sparse/0/points3D.bin')
pts3d = read_model.read_points3d_binary(points3dfile)
camerasfile = os.path.join(realdir, 'sparse/0/cameras.bin')
camdata = read_model.read_cameras_binary(camerasfile)
list_of_keys = list(camdata.keys())
cam = camdata[list_of_keys[0]]
h, w, f = cam.height, cam.width, cam.params[0]
hwf = np.array([h, w, f]).reshape([3, 1])
imagesfile = os.path.join(realdir, 'sparse/0/images.bin')
imdata = read_model.read_images_binary(imagesfile)
w2c_mats = []
bottom = np.array([0, 0, 0, 1.]).reshape([1, 4])