[calib] add read colmap
This commit is contained in:
QingShuai@zju3dv
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530dde403c
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232
apps/calibration/align_colmap_ground.py
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232
apps/calibration/align_colmap_ground.py
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# 这个脚本用于对colmap的相机标定结果，寻找地面与场景中心
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# 方法：
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# 1. 使用棋盘格
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# 2. 估计点云里的地面
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import os
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from os.path import join
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from easymocap.annotator.file_utils import save_json
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from easymocap.mytools.debug_utils import myerror, run_cmd, mywarn, log
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from easymocap.mytools.camera_utils import read_cameras, write_camera
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from easymocap.mytools import read_json
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from easymocap.mytools import batch_triangulate, projectN3, Undistort
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import numpy as np
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import cv2
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from apps.calibration.calib_extri import solvePnP
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def guess_ground(pcdname):
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pcd = o3d.io.read_point_cloud(pcdname)
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def compute_rel(R_src, T_src, R_tgt, T_tgt):
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R_rel = R_src.T @ R_tgt
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T_rel = R_src.T @ (T_tgt - T_src)
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return R_rel, T_rel
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def triangulate(cameras, areas):
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Ps, k2ds = [], []
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for cam, _, k2d, k3d in areas:
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k2d = Undistort.points(k2d, cameras[cam]['K'], cameras[cam]['dist'])
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P = cameras[cam]['K'] @ np.hstack([cameras[cam]['R'], cameras[cam]['T']])
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Ps.append(P)
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k2ds.append(k2d)
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Ps = np.stack(Ps)
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k2ds = np.stack(k2ds)
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k3d = batch_triangulate(k2ds, Ps)
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return k3d
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def best_fit_transform(A, B):
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'''
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Calculates the least-squares best-fit transform that maps corresponding points A to B in m spatial dimensions
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Input:
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A: Nxm numpy array of corresponding points
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B: Nxm numpy array of corresponding points
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Returns:
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T: (m+1)x(m+1) homogeneous transformation matrix that maps A on to B
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R: mxm rotation matrix
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t: mx1 translation vector
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'''
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assert A.shape == B.shape
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# get number of dimensions
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m = A.shape[1]
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# translate points to their centroids
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centroid_A = np.mean(A, axis=0)
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centroid_B = np.mean(B, axis=0)
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AA = A - centroid_A
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BB = B - centroid_B
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# rotation matrix
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H = np.dot(AA.T, BB)
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U, S, Vt = np.linalg.svd(H)
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R = np.dot(Vt.T, U.T)
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# special reflection case
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if np.linalg.det(R) < 0:
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Vt[m-1,:] *= -1
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R = np.dot(Vt.T, U.T)
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# translation
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t = centroid_B.T - np.dot(R,centroid_A.T)
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return R, t
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def align_by_chessboard(cameras, path):
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camnames = sorted(os.listdir(join(path, 'chessboard')))
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areas = []
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for ic, cam in enumerate(camnames):
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imagename = join(path, 'images', cam, '000000.jpg')
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chessname = join(path, 'chessboard', cam, '000000.json')
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data = read_json(chessname)
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k3d = np.array(data['keypoints3d'], dtype=np.float32)
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k2d = np.array(data['keypoints2d'], dtype=np.float32)
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# TODO
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# pattern = (11, 8)
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if 'pattern' in data.keys():
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pattern = data['pattern']
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else:
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pattern = None
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img = cv2.imread(imagename)
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if args.scale2d is not None:
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k2d[:, :2] *= args.scale2d
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img = cv2.resize(img, None, fx=args.scale2d, fy=args.scale2d)
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if args.origin is not None:
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cameras[args.prefix+cam] = cameras.pop(args.origin+cam.replace('VID_', '0000'))
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cam = args.prefix + cam
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if cam not in cameras.keys():
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myerror('camera {} not found in {}'.format(cam, cameras.keys()))
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continue
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cameras[cam]['shape'] = img.shape[:2]
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if k2d[:, -1].sum() < 1:
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continue
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# calculate the area of the chessboard
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mask = np.zeros_like(img[:, :, 0])
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k2d_int = np.round(k2d[:, :2]).astype(int)
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if pattern is not None:
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cv2.fillPoly(mask, [k2d_int[[0, pattern[0]-1, -1, -pattern[0]]]], 1)
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else:
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cv2.fillPoly(mask, [k2d_int[[0, 1, 2, 3, 0]]], 1)
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area = mask.sum()
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print(cam, area)
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areas.append([cam, area, k2d, k3d])
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areas.sort(key=lambda x: -x[1])
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best_cam, area, k2d, k3d = areas[0]
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# 先解决尺度问题
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ref_point_id = np.linalg.norm(k3d - k3d[:1], axis=-1).argmax()
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k3d_pre = triangulate(cameras, areas)
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length_gt = np.linalg.norm(k3d[0, :3] - k3d[ref_point_id, :3])
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length = np.linalg.norm(k3d_pre[0, :3] - k3d_pre[ref_point_id, :3])
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log('gt diag={:.3f}, est diag={:.3f}, scale={:.3f}'.format(length_gt, length, length_gt/length))
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scale_colmap = length_gt / length
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for cam, camera in cameras.items():
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camera['T'] *= scale_colmap
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k3d_pre = triangulate(cameras, areas)
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length = np.linalg.norm(k3d_pre[0, :3] - k3d_pre[-1, :3])
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log('gt diag={:.3f}, est diag={:.3f}, scale={:.3f}'.format(length_gt, length, length_gt/length))
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# 计算相机相对于棋盘格的RT
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if False:
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for cam, _, k2d, k3d in areas:
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K, dist = cameras[cam]['K'], cameras[cam]['dist']
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R, T = cameras[cam]['R'], cameras[cam]['T']
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err, rvec, tvec, kpts_repro = solvePnP(k3d, k2d, K, dist, flag=cv2.SOLVEPNP_ITERATIVE)
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# 不同视角的计算的相对变换应该是一致的
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R_tgt = cv2.Rodrigues(rvec)[0]
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T_tgt = tvec.reshape(3, 1)
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R_rel, T_rel = compute_rel(R, T, R_tgt, T_tgt)
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break
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else:
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# 使用估计的棋盘格坐标与实际的棋盘格坐标
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X = k3d_pre[:, :3]
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X_gt = k3d[:, :3]
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R_rel, T_rel = best_fit_transform(X_gt, X)
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# 从棋盘格坐标系映射到colmap坐标系
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T_rel = T_rel.reshape(3, 1)
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centers = []
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for cam, camera in cameras.items():
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camera.pop('Rvec')
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R_old, T_old = camera['R'], camera['T']
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R_new = R_old @ R_rel
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T_new = T_old + R_old @ T_rel
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camera['R'] = R_new
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camera['T'] = T_new
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center = - camera['R'].T @ camera['T']
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centers.append(center)
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print('{}: ({:6.3f}, {:.3f}, {:.3f})'.format(cam, *np.round(center.T[0], 3)))
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# 使用棋盘格估计一下尺度
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k3d_pre = triangulate(cameras, areas)
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length = np.linalg.norm(k3d_pre[0, :3] - k3d_pre[ref_point_id, :3])
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log('{} {} {}'.format(length_gt, length, length_gt/length))
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log(k3d_pre)
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transform = np.eye(4)
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transform[:3, :3] = R_rel
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transform[:3, 3:] = T_rel
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return cameras, scale_colmap, np.linalg.inv(transform)
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# for 3D points X,
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# in origin world: \Pi(RX + T) = x
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# in new world: \Pi(R'Y+T') = x
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# , where X = R_pY + T_p
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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('path', type=str)
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parser.add_argument('out', type=str)
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parser.add_argument('--plane_by_chessboard', type=str, default=None)
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parser.add_argument('--plane_by_point', type=str, default=None)
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parser.add_argument('--num', type=int, default=1)
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parser.add_argument('--scale2d', type=float, default=None)
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parser.add_argument('--prefix', type=str, default='')
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parser.add_argument('--origin', type=str, default=None)
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parser.add_argument('--guess_plane', action='store_true')
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parser.add_argument('--noshow', action='store_true')
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parser.add_argument('--debug', action='store_true')
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args = parser.parse_args()
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if not os.path.exists(join(args.path, 'intri.yml')):
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assert os.path.exists(join(args.path, 'cameras.bin')), os.listdir(args.path)
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cmd = f'python3 apps/calibration/read_colmap.py {args.path} .bin'
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run_cmd(cmd)
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cameras = read_cameras(args.path)
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if args.plane_by_point is not None:
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# 读入点云
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import ipdb; ipdb.set_trace()
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if args.plane_by_chessboard is not None:
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cameras, scale, transform = align_by_chessboard(cameras, args.plane_by_chessboard)
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if not args.noshow:
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import open3d as o3d
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sparse_name = join(args.path, 'sparse.ply')
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dense_name = join(args.path, '..', '..', 'dense', 'fused.ply')
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if os.path.exists(dense_name):
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pcd = o3d.io.read_point_cloud(dense_name)
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else:
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pcd = o3d.io.read_point_cloud(sparse_name)
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save_json(join(args.out, 'transform.json'), {'scale': scale, 'transform': transform.tolist()})
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points = np.asarray(pcd.points)
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# TODO: read correspondence of points3D and points2D
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points_new = (scale*points) @ transform[:3, :3].T + transform[:3, 3:].T
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pcd.points = o3d.utility.Vector3dVector(points_new)
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o3d.io.write_point_cloud(join(args.out, 'sparse_aligned.ply'), pcd)
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grids = []
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center = o3d.geometry.TriangleMesh.create_coordinate_frame(
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size=1, origin=[0, 0, 0])
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grids.append(center)
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for cam, camera in cameras.items():
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center = - camera['R'].T @ camera['T']
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center = o3d.geometry.TriangleMesh.create_coordinate_frame(
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size=0.5, origin=[center[0, 0], center[1, 0], center[2, 0]])
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if cam.startswith(args.prefix):
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center.paint_uniform_color([1, 0, 1])
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center.rotate(camera['R'].T)
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grids.append(center)
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o3d.visualization.draw_geometries([pcd] + grids)
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write_camera(cameras, args.out)
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if args.prefix is not None:
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cameras_ = {}
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for key, camera in cameras.items():
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if args.prefix not in key:
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continue
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cameras_[key.replace(args.prefix, '')] = camera
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os.makedirs(join(args.out, args.prefix), exist_ok=True)
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write_camera(cameras_, join(args.out, args.prefix))
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532
apps/calibration/read_colmap.py
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532
apps/calibration/read_colmap.py
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# Copyright (c) 2018, ETH Zurich and UNC Chapel Hill.
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# * Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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#
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# * Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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#
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# * Neither the name of ETH Zurich and UNC Chapel Hill nor the names of
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# its contributors may be used to endorse or promote products derived
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# from this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
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# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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# POSSIBILITY OF SUCH DAMAGE.
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#
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# Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de)
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import os
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import sys
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import collections
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import numpy as np
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import struct
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import cv2
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CameraModel = collections.namedtuple(
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"CameraModel", ["model_id", "model_name", "num_params"])
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Camera = collections.namedtuple(
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"Camera", ["id", "model", "width", "height", "params"])
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BaseImage = collections.namedtuple(
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"Image", ["id", "qvec", "tvec", "camera_id", "name", "xys", "point3D_ids"])
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Point3D = collections.namedtuple(
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"Point3D", ["id", "xyz", "rgb", "error", "image_ids", "point2D_idxs"])
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class Image(BaseImage):
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def qvec2rotmat(self):
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return qvec2rotmat(self.qvec)
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CAMERA_MODELS = {
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CameraModel(model_id=0, model_name="SIMPLE_PINHOLE", num_params=3),
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CameraModel(model_id=1, model_name="PINHOLE", num_params=4),
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CameraModel(model_id=2, model_name="SIMPLE_RADIAL", num_params=4),
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CameraModel(model_id=3, model_name="RADIAL", num_params=5),
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CameraModel(model_id=4, model_name="OPENCV", num_params=8),
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CameraModel(model_id=5, model_name="OPENCV_FISHEYE", num_params=8),
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CameraModel(model_id=6, model_name="FULL_OPENCV", num_params=12),
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CameraModel(model_id=7, model_name="FOV", num_params=5),
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CameraModel(model_id=8, model_name="SIMPLE_RADIAL_FISHEYE", num_params=4),
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CameraModel(model_id=9, model_name="RADIAL_FISHEYE", num_params=5),
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CameraModel(model_id=10, model_name="THIN_PRISM_FISHEYE", num_params=12)
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}
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CAMERA_MODEL_IDS = dict([(camera_model.model_id, camera_model) \
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for camera_model in CAMERA_MODELS])
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CAMERA_MODEL_NAMES = dict([(camera_model.model_name, camera_model)
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for camera_model in CAMERA_MODELS])
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def read_next_bytes(fid, num_bytes, format_char_sequence, endian_character="<"):
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"""Read and unpack the next bytes from a binary file.
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:param fid:
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:param num_bytes: Sum of combination of {2, 4, 8}, e.g. 2, 6, 16, 30, etc.
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:param format_char_sequence: List of {c, e, f, d, h, H, i, I, l, L, q, Q}.
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:param endian_character: Any of {@, =, <, >, !}
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||||||
|
: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 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_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 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 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 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_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")
|
||||||
|
|
||||||
|
|
||||||
|
class FileStorage(object):
|
||||||
|
def __init__(self, filename, isWrite=False):
|
||||||
|
version = cv2.__version__
|
||||||
|
self.version = int(version.split('.')[0])
|
||||||
|
if isWrite:
|
||||||
|
self.fs = cv2.FileStorage(filename, cv2.FILE_STORAGE_WRITE)
|
||||||
|
else:
|
||||||
|
self.fs = cv2.FileStorage(filename, cv2.FILE_STORAGE_READ)
|
||||||
|
|
||||||
|
def __del__(self):
|
||||||
|
cv2.FileStorage.release(self.fs)
|
||||||
|
|
||||||
|
def write(self, key, value, dt='mat'):
|
||||||
|
if dt == 'mat':
|
||||||
|
cv2.FileStorage.write(self.fs, key, value)
|
||||||
|
elif dt == 'list':
|
||||||
|
if self.version == 4: # 4.4
|
||||||
|
# self.fs.write(key, '[')
|
||||||
|
# for elem in value:
|
||||||
|
# self.fs.write('none', elem)
|
||||||
|
# self.fs.write('none', ']')
|
||||||
|
# import ipdb; ipdb.set_trace()
|
||||||
|
self.fs.startWriteStruct(key, cv2.FileNode_SEQ)
|
||||||
|
for elem in value:
|
||||||
|
self.fs.write('', elem)
|
||||||
|
self.fs.endWriteStruct()
|
||||||
|
else: # 3.4
|
||||||
|
self.fs.write(key, '[')
|
||||||
|
for elem in value:
|
||||||
|
self.fs.write('none', elem)
|
||||||
|
self.fs.write('none', ']')
|
||||||
|
|
||||||
|
def main():
|
||||||
|
if len(sys.argv) != 3:
|
||||||
|
print("Usage: python read_model.py path/to/model/folder [.txt,.bin]")
|
||||||
|
return
|
||||||
|
|
||||||
|
cameras, images, points3D = read_model(path=sys.argv[1], ext=sys.argv[2])
|
||||||
|
import cv2
|
||||||
|
cameras_out = {}
|
||||||
|
for key in cameras.keys():
|
||||||
|
p = cameras[key].params
|
||||||
|
if cameras[key].model == 'SIMPLE_RADIAL':
|
||||||
|
f, cx, cy, k = p
|
||||||
|
K = np.array([f, 0, cx, 0, f, cy, 0, 0, 1]).reshape(3, 3)
|
||||||
|
dist = np.array([[k, 0, 0, 0, 0]])
|
||||||
|
elif cameras[key].model == 'PINHOLE':
|
||||||
|
fx, fy, cx, cy = p
|
||||||
|
K = np.array([fx, 0, cx, 0, fy, cy, 0, 0, 1]).reshape(3, 3)
|
||||||
|
dist = np.array([[0, 0, 0, 0, 0]])
|
||||||
|
else:
|
||||||
|
K = np.array([[p[0], 0, p[2], 0, p[1], p[3], 0, 0, 1]]).reshape(3, 3)
|
||||||
|
dist = np.array([[p[4], p[5], p[6], p[7], 0.]])
|
||||||
|
cameras_out[key] = {'K': K, 'dist': dist}
|
||||||
|
mapkey = {}
|
||||||
|
cameras_new = {}
|
||||||
|
for key, val in images.items():
|
||||||
|
cam = cameras_out[val.camera_id].copy()
|
||||||
|
t = val.tvec.reshape(3, 1)
|
||||||
|
R = qvec2rotmat(val.qvec)
|
||||||
|
cam['R'] = R
|
||||||
|
cam['Rvec'] = cv2.Rodrigues(R)[0]
|
||||||
|
cam['T'] = t
|
||||||
|
# mapkey[val.name.split('.')[0]] = val.camera_id
|
||||||
|
|
||||||
|
cameras_new[val.name.split('.')[0]] = cam
|
||||||
|
# cameras_new[val.name.split('.')[0].split('/')[0]] = cam
|
||||||
|
keys = sorted(list(cameras_new.keys()))
|
||||||
|
cameras_new = {key:cameras_new[key] for key in keys}
|
||||||
|
print("num_cameras: {}/{}".format(len(cameras), len(cameras_new)))
|
||||||
|
print("num_images:", len(images))
|
||||||
|
print("num_points3D:", len(points3D))
|
||||||
|
if len(points3D) > 0:
|
||||||
|
keys = list(points3D.keys())
|
||||||
|
xyz = np.stack([points3D[k].xyz for k in keys])
|
||||||
|
rgb = np.stack([points3D[k].rgb for k in keys])
|
||||||
|
import open3d as o3d
|
||||||
|
pcd = o3d.geometry.PointCloud()
|
||||||
|
pcd.points = o3d.utility.Vector3dVector(xyz)
|
||||||
|
pcd.colors = o3d.utility.Vector3dVector(rgb/255.)
|
||||||
|
from os.path import join
|
||||||
|
pcdname = join(sys.argv[1], 'sparse.ply')
|
||||||
|
o3d.io.write_point_cloud(pcdname, pcd)
|
||||||
|
if False:
|
||||||
|
o3d.visualization.draw_geometries([pcd])
|
||||||
|
from easymocap.mytools.camera_utils import write_camera
|
||||||
|
write_camera(cameras_new, sys.argv[1])
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
Loading…
Reference in New Issue
Block a user