| import numpy as np | |
| from ..database import BaseDatabase | |
| # from utils.base_utils import pose_inverse, project_points | |
| def compute_nearest_camera_indices(database, que_ids, ref_ids=None): | |
| if ref_ids is None: ref_ids = que_ids | |
| ref_poses = [database.get_pose(ref_id) for ref_id in ref_ids] | |
| ref_cam_pts = np.asarray([-pose[:, :3].T @ pose[:, 3] for pose in ref_poses]) | |
| que_poses = [database.get_pose(que_id) for que_id in que_ids] | |
| que_cam_pts = np.asarray([-pose[:, :3].T @ pose[:, 3] for pose in que_poses]) | |
| dists = np.linalg.norm(ref_cam_pts[None, :, :] - que_cam_pts[:, None, :], 2, 2) | |
| dists_idx = np.argsort(dists, 1) | |
| return dists_idx | |
| def select_working_views(ref_poses, que_poses, work_num, exclude_self=False): | |
| ref_cam_pts = np.asarray([-pose[:, :3].T @ pose[:, 3] for pose in ref_poses]) | |
| render_cam_pts = np.asarray([-pose[:, :3].T @ pose[:, 3] for pose in que_poses]) | |
| dists = np.linalg.norm(ref_cam_pts[None, :, :] - render_cam_pts[:, None, :], 2, 2) # qn,rfn | |
| ids = np.argsort(dists) | |
| if exclude_self: | |
| ids = ids[:, 1:work_num+1] | |
| else: | |
| ids = ids[:, :work_num] | |
| return ids | |
| # def select_working_views_by_overlap(ref_poses, ref_Ks, ref_size, que_pose, que_K, que_size, que_depth_ranges, work_num, plane_num=8): | |
| # near, far = que_depth_ranges[0], que_depth_ranges[1] | |
| # depth_vals = np.linspace(near, far, plane_num) # dn | |
| # depth_vals = depth_vals[None,None,:,None] # 1,1,dn,1 | |
| # qh, qw = que_size | |
| # dn = plane_num | |
| # num = 32 | |
| # coords2d = np.stack(np.meshgrid(np.linspace(0,qw-1,num),np.linspace(0,qh-1,num)),-1)[:,:,None,:] # qh,qw,1,2 | |
| # pts = np.concatenate([np.tile(depth_vals,[num,num,1,1]), np.tile(coords2d, [1,1,dn,1])],-1) # qh,qw,dn,3 | |
| # pts = pts.reshape([num*num*dn, 3]) | |
| # pts[:,:2] *= pts[:,2:] | |
| # | |
| # que_pose_inv = pose_inverse(que_pose) # 3,4 | |
| # que_K_inv = np.linalg.inv(que_K) | |
| # RK = que_pose_inv[:,:3] @ que_K_inv | |
| # t= que_pose_inv[:,3:] | |
| # pts = pts @ RK.T + t.T # in world coordinate [pn,3] | |
| # | |
| # rfn = ref_poses.shape[0] | |
| # ref_h, ref_w = ref_size | |
| # | |
| # def get_valid_mask(pts2d, depth, h, w): | |
| # valid_mask = (pts2d[:, 0] < w) & (pts2d[:, 1] < h) & (pts2d[:, 0] >= 0) & (pts2d[:, 1] >= 0) & (depth > 0) | |
| # return valid_mask | |
| # | |
| # global_visibility=[np.mean(get_valid_mask(*project_points(pts, ref_poses[rfi], ref_Ks[rfi]), ref_h, ref_w)) for rfi in range(rfn)] | |
| # | |
| # # all points are invisible | |
| # cur_pts = pts | |
| # invisible_mask = np.ones(cur_pts.shape[0],dtype=np.bool) | |
| # | |
| # cur_ref_ids = [rfi for rfi in range(rfn)] | |
| # resulted_ref_ids = [] | |
| # for wi in range(min(work_num, rfn)): | |
| # cur_pts = cur_pts[invisible_mask] | |
| # if cur_pts.shape[0]/pts.shape[0]>=0.02: | |
| # # select by cur visibility | |
| # cur_visibility = [np.mean(get_valid_mask(*project_points(cur_pts, ref_poses[rfi], ref_Ks[rfi]), ref_h, ref_w)) for rfi in cur_ref_ids] | |
| # else: | |
| # # select by global visibility | |
| # cur_visibility = [global_visibility[rfi] for rfi in cur_ref_ids] | |
| # | |
| # max_ref_index = np.argmax(np.asarray(cur_visibility)) | |
| # max_ref_id = cur_ref_ids[max_ref_index] | |
| # resulted_ref_ids.append(max_ref_id) | |
| # cur_ref_ids.remove(max_ref_id) | |
| # | |
| # # update invisible mask | |
| # invisible_mask = ~get_valid_mask(*project_points(cur_pts, ref_poses[max_ref_id], ref_Ks[max_ref_id]), ref_h, ref_w) | |
| # return resulted_ref_ids | |
| def select_working_views_db(database: BaseDatabase, ref_ids, que_poses, work_num, exclude_self=False): | |
| ref_ids = database.get_img_ids() if ref_ids is None else ref_ids | |
| ref_poses = [database.get_pose(img_id) for img_id in ref_ids] | |
| ref_ids = np.asarray(ref_ids) | |
| ref_poses = np.asarray(ref_poses) | |
| indices = select_working_views(ref_poses, que_poses, work_num, exclude_self) | |
| return ref_ids[indices] # qn,wn |