colmap的相机类型可以参考models.h文件。

主要有以下几种相机类型：

SimplePinhole：
内参格式：f, cx, cy
实际用的时候：fx=fy=f

Pinhole:
内参格式：fx, fy, cx, cy

其他可以自行查看models.h文件。

内参存放在images.bin, 外参存放在cameras.bin中

def read_colmap(path):
    try:
        cameras_extrinsic_file = os.path.join(path, "sparse/0", "images.bin")
        cameras_intrinsic_file = os.path.join(path, "sparse/0", "cameras.bin")
        cam_extrinsics = read_extrinsics_binary(cameras_extrinsic_file)
        cam_intrinsics = read_intrinsics_binary(cameras_intrinsic_file)
    except:
        cameras_extrinsic_file = os.path.join(path, "sparse/0", "images.txt")
        cameras_intrinsic_file = os.path.join(path, "sparse/0", "cameras.txt")
        cam_extrinsics = read_extrinsics_text(cameras_extrinsic_file)
        cam_intrinsics = read_intrinsics_text(cameras_intrinsic_file)

extr = cam_extrinsics[key]
intr = cam_intrinsics[extr.camera_id]

R = qvec2rotmat(extr.qvec)
t = np.array(extr.tvec)

#world to camera
T = np.zeros((4, 4))
T[:3, :3] = R
T[:3, 3] = t
T[3, 3] = 1.0

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]])

如果是SimplePinhole,
那么，fx = fy = intr[0], cx = intr[1], cy = intr[2]
其他类推。