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#!/usr/bin/env python3
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# visloc script with support for coarse to fine
# --------------------------------------------------------
import os
import numpy as np
import random
import torch
import torchvision.transforms as tvf
import argparse
from tqdm import tqdm
from PIL import Image
import math
from mast3r.model import AsymmetricMASt3R
from mast3r.fast_nn import fast_reciprocal_NNs
from mast3r.utils.coarse_to_fine import select_pairs_of_crops, crop_slice
from mast3r.utils.collate import cat_collate, cat_collate_fn_map
from mast3r.utils.misc import mkdir_for
from mast3r.datasets.utils.cropping import crop_to_homography
import mast3r.utils.path_to_dust3r # noqa
from dust3r.inference import inference, loss_of_one_batch
from dust3r.utils.geometry import geotrf, colmap_to_opencv_intrinsics, opencv_to_colmap_intrinsics
from dust3r.datasets.utils.transforms import ImgNorm
from dust3r_visloc.datasets import *
from dust3r_visloc.localization import run_pnp
from dust3r_visloc.evaluation import get_pose_error, aggregate_stats, export_results
from dust3r_visloc.datasets.utils import get_HW_resolution, rescale_points3d
def get_args_parser():
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", type=str, required=True, help="visloc dataset to eval")
parser_weights = parser.add_mutually_exclusive_group(required=True)
parser_weights.add_argument("--weights", type=str, help="path to the model weights", default=None)
parser_weights.add_argument("--model_name", type=str, help="name of the model weights",
choices=["MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric"])
parser.add_argument("--confidence_threshold", type=float, default=1.001,
help="confidence values higher than threshold are invalid")
parser.add_argument('--pixel_tol', default=5, type=int)
parser.add_argument("--coarse_to_fine", action='store_true', default=False,
help="do the matching from coarse to fine")
parser.add_argument("--max_image_size", type=int, default=None,
help="max image size for the fine resolution")
parser.add_argument("--c2f_crop_with_homography", action='store_true', default=False,
help="when using coarse to fine, crop with homographies to keep cx, cy centered")
parser.add_argument("--device", type=str, default='cuda', help="pytorch device")
parser.add_argument("--pnp_mode", type=str, default="cv2", choices=['cv2', 'poselib', 'pycolmap'],
help="pnp lib to use")
parser_reproj = parser.add_mutually_exclusive_group()
parser_reproj.add_argument("--reprojection_error", type=float, default=5.0, help="pnp reprojection error")
parser_reproj.add_argument("--reprojection_error_diag_ratio", type=float, default=None,
help="pnp reprojection error as a ratio of the diagonal of the image")
parser.add_argument("--max_batch_size", type=int, default=48,
help="max batch size for inference on crops when using coarse to fine")
parser.add_argument("--pnp_max_points", type=int, default=100_000, help="pnp maximum number of points kept")
parser.add_argument("--viz_matches", type=int, default=0, help="debug matches")
parser.add_argument("--output_dir", type=str, default=None, help="output path")
parser.add_argument("--output_label", type=str, default='', help="prefix for results files")
return parser
@torch.no_grad()
def coarse_matching(query_view, map_view, model, device, pixel_tol, fast_nn_params):
# prepare batch
imgs = []
for idx, img in enumerate([query_view['rgb_rescaled'], map_view['rgb_rescaled']]):
imgs.append(dict(img=img.unsqueeze(0), true_shape=np.int32([img.shape[1:]]),
idx=idx, instance=str(idx)))
output = inference([tuple(imgs)], model, device, batch_size=1, verbose=False)
pred1, pred2 = output['pred1'], output['pred2']
conf_list = [pred1['desc_conf'].squeeze(0).cpu().numpy(), pred2['desc_conf'].squeeze(0).cpu().numpy()]
desc_list = [pred1['desc'].squeeze(0).detach(), pred2['desc'].squeeze(0).detach()]
# find 2D-2D matches between the two images
PQ, PM = desc_list[0], desc_list[1]
if len(PQ) == 0 or len(PM) == 0:
return [], [], [], []
if pixel_tol == 0:
matches_im_map, matches_im_query = fast_reciprocal_NNs(PM, PQ, subsample_or_initxy1=8, **fast_nn_params)
HM, WM = map_view['rgb_rescaled'].shape[1:]
HQ, WQ = query_view['rgb_rescaled'].shape[1:]
# ignore small border around the edge
valid_matches_map = (matches_im_map[:, 0] >= 3) & (matches_im_map[:, 0] < WM - 3) & (
matches_im_map[:, 1] >= 3) & (matches_im_map[:, 1] < HM - 3)
valid_matches_query = (matches_im_query[:, 0] >= 3) & (matches_im_query[:, 0] < WQ - 3) & (
matches_im_query[:, 1] >= 3) & (matches_im_query[:, 1] < HQ - 3)
valid_matches = valid_matches_map & valid_matches_query
matches_im_map = matches_im_map[valid_matches]
matches_im_query = matches_im_query[valid_matches]
valid_pts3d = []
matches_confs = []
else:
yM, xM = torch.where(map_view['valid_rescaled'])
matches_im_map, matches_im_query = fast_reciprocal_NNs(PM, PQ, (xM, yM), pixel_tol=pixel_tol, **fast_nn_params)
valid_pts3d = map_view['pts3d_rescaled'].cpu().numpy()[matches_im_map[:, 1], matches_im_map[:, 0]]
matches_confs = np.minimum(
conf_list[1][matches_im_map[:, 1], matches_im_map[:, 0]],
conf_list[0][matches_im_query[:, 1], matches_im_query[:, 0]]
)
# from cv2 to colmap
matches_im_query = matches_im_query.astype(np.float64)
matches_im_map = matches_im_map.astype(np.float64)
matches_im_query[:, 0] += 0.5
matches_im_query[:, 1] += 0.5
matches_im_map[:, 0] += 0.5
matches_im_map[:, 1] += 0.5
# rescale coordinates
matches_im_query = geotrf(query_view['to_orig'], matches_im_query, norm=True)
matches_im_map = geotrf(map_view['to_orig'], matches_im_map, norm=True)
# from colmap back to cv2
matches_im_query[:, 0] -= 0.5
matches_im_query[:, 1] -= 0.5
matches_im_map[:, 0] -= 0.5
matches_im_map[:, 1] -= 0.5
return valid_pts3d, matches_im_query, matches_im_map, matches_confs
@torch.no_grad()
def crops_inference(pairs, model, device, batch_size=48, verbose=True):
assert len(pairs) == 2, "Error, data should be a tuple of dicts containing the batch of image pairs"
# Forward a possibly big bunch of data, by blocks of batch_size
B = pairs[0]['img'].shape[0]
if B < batch_size:
return loss_of_one_batch(pairs, model, None, device=device, symmetrize_batch=False)
preds = []
for ii in range(0, B, batch_size):
sel = slice(ii, ii + min(B - ii, batch_size))
temp_data = [{}, {}]
for di in [0, 1]:
temp_data[di] = {kk: pairs[di][kk][sel]
for kk in pairs[di].keys() if pairs[di][kk] is not None} # copy chunk for forward
preds.append(loss_of_one_batch(temp_data, model,
None, device=device, symmetrize_batch=False)) # sequential forward
# Merge all preds
return cat_collate(preds, collate_fn_map=cat_collate_fn_map)
@torch.no_grad()
def fine_matching(query_views, map_views, model, device, max_batch_size, pixel_tol, fast_nn_params):
assert pixel_tol > 0
output = crops_inference([query_views, map_views],
model, device, batch_size=max_batch_size, verbose=False)
pred1, pred2 = output['pred1'], output['pred2']
descs1 = pred1['desc'].clone()
descs2 = pred2['desc'].clone()
confs1 = pred1['desc_conf'].clone()
confs2 = pred2['desc_conf'].clone()
# Compute matches
valid_pts3d, matches_im_map, matches_im_query, matches_confs = [], [], [], []
for ppi, (pp1, pp2, cc11, cc21) in enumerate(zip(descs1, descs2, confs1, confs2)):
valid_ppi = map_views['valid'][ppi]
pts3d_ppi = map_views['pts3d'][ppi].cpu().numpy()
conf_list_ppi = [cc11.cpu().numpy(), cc21.cpu().numpy()]
y_ppi, x_ppi = torch.where(valid_ppi)
matches_im_map_ppi, matches_im_query_ppi = fast_reciprocal_NNs(pp2, pp1, (x_ppi, y_ppi),
pixel_tol=pixel_tol, **fast_nn_params)
valid_pts3d_ppi = pts3d_ppi[matches_im_map_ppi[:, 1], matches_im_map_ppi[:, 0]]
matches_confs_ppi = np.minimum(
conf_list_ppi[1][matches_im_map_ppi[:, 1], matches_im_map_ppi[:, 0]],
conf_list_ppi[0][matches_im_query_ppi[:, 1], matches_im_query_ppi[:, 0]]
)
# inverse operation where we uncrop pixel coordinates
matches_im_map_ppi = geotrf(map_views['to_orig'][ppi].cpu().numpy(), matches_im_map_ppi.copy(), norm=True)
matches_im_query_ppi = geotrf(query_views['to_orig'][ppi].cpu().numpy(), matches_im_query_ppi.copy(), norm=True)
matches_im_map.append(matches_im_map_ppi)
matches_im_query.append(matches_im_query_ppi)
valid_pts3d.append(valid_pts3d_ppi)
matches_confs.append(matches_confs_ppi)
if len(valid_pts3d) == 0:
return [], [], [], []
matches_im_map = np.concatenate(matches_im_map, axis=0)
matches_im_query = np.concatenate(matches_im_query, axis=0)
valid_pts3d = np.concatenate(valid_pts3d, axis=0)
matches_confs = np.concatenate(matches_confs, axis=0)
return valid_pts3d, matches_im_query, matches_im_map, matches_confs
def crop(img, mask, pts3d, crop, intrinsics=None):
out_cropped_img = img.clone()
if mask is not None:
out_cropped_mask = mask.clone()
else:
out_cropped_mask = None
if pts3d is not None:
out_cropped_pts3d = pts3d.clone()
else:
out_cropped_pts3d = None
to_orig = torch.eye(3, device=img.device)
# If intrinsics available, crop and apply rectifying homography. Otherwise, just crop
if intrinsics is not None:
K_old = intrinsics
imsize, K_new, R, H = crop_to_homography(K_old, crop)
# apply homography to image
H /= H[2, 2]
homo8 = H.ravel().tolist()[:8]
# From float tensor to uint8 PIL Image
pilim = Image.fromarray((255 * (img + 1.) / 2).to(torch.uint8).numpy())
pilout_cropped_img = pilim.transform(imsize, Image.Transform.PERSPECTIVE,
homo8, resample=Image.Resampling.BICUBIC)
# From uint8 PIL Image to float tensor
out_cropped_img = 2. * torch.tensor(np.array(pilout_cropped_img)).to(img) / 255. - 1.
if out_cropped_mask is not None:
pilmask = Image.fromarray((255 * out_cropped_mask).to(torch.uint8).numpy())
pilout_cropped_mask = pilmask.transform(
imsize, Image.Transform.PERSPECTIVE, homo8, resample=Image.Resampling.NEAREST)
out_cropped_mask = torch.from_numpy(np.array(pilout_cropped_mask) > 0).to(out_cropped_mask.dtype)
if out_cropped_pts3d is not None:
out_cropped_pts3d = out_cropped_pts3d.numpy()
out_cropped_X = np.array(Image.fromarray(out_cropped_pts3d[:, :, 0]).transform(imsize,
Image.Transform.PERSPECTIVE,
homo8,
resample=Image.Resampling.NEAREST))
out_cropped_Y = np.array(Image.fromarray(out_cropped_pts3d[:, :, 1]).transform(imsize,
Image.Transform.PERSPECTIVE,
homo8,
resample=Image.Resampling.NEAREST))
out_cropped_Z = np.array(Image.fromarray(out_cropped_pts3d[:, :, 2]).transform(imsize,
Image.Transform.PERSPECTIVE,
homo8,
resample=Image.Resampling.NEAREST))
out_cropped_pts3d = torch.from_numpy(np.stack([out_cropped_X, out_cropped_Y, out_cropped_Z], axis=-1))
to_orig = torch.tensor(H, device=img.device)
else:
out_cropped_img = img[crop_slice(crop)]
if out_cropped_mask is not None:
out_cropped_mask = out_cropped_mask[crop_slice(crop)]
if out_cropped_pts3d is not None:
out_cropped_pts3d = out_cropped_pts3d[crop_slice(crop)]
to_orig[:2, -1] = torch.tensor(crop[:2])
return out_cropped_img, out_cropped_mask, out_cropped_pts3d, to_orig
def resize_image_to_max(max_image_size, rgb, K):
W, H = rgb.size
if max_image_size and max(W, H) > max_image_size:
islandscape = (W >= H)
if islandscape:
WMax = max_image_size
HMax = int(H * (WMax / W))
else:
HMax = max_image_size
WMax = int(W * (HMax / H))
resize_op = tvf.Compose([ImgNorm, tvf.Resize(size=[HMax, WMax])])
rgb_tensor = resize_op(rgb).permute(1, 2, 0)
to_orig_max = np.array([[W / WMax, 0, 0],
[0, H / HMax, 0],
[0, 0, 1]])
to_resize_max = np.array([[WMax / W, 0, 0],
[0, HMax / H, 0],
[0, 0, 1]])
# Generate new camera parameters
new_K = opencv_to_colmap_intrinsics(K)
new_K[0, :] *= WMax / W
new_K[1, :] *= HMax / H
new_K = colmap_to_opencv_intrinsics(new_K)
else:
rgb_tensor = ImgNorm(rgb).permute(1, 2, 0)
to_orig_max = np.eye(3)
to_resize_max = np.eye(3)
HMax, WMax = H, W
new_K = K
return rgb_tensor, new_K, to_orig_max, to_resize_max, (HMax, WMax)
if __name__ == '__main__':
parser = get_args_parser()
args = parser.parse_args()
conf_thr = args.confidence_threshold
device = args.device
pnp_mode = args.pnp_mode
assert args.pixel_tol > 0
reprojection_error = args.reprojection_error
reprojection_error_diag_ratio = args.reprojection_error_diag_ratio
pnp_max_points = args.pnp_max_points
viz_matches = args.viz_matches
if args.weights is not None:
weights_path = args.weights
else:
weights_path = "naver/" + args.model_name
model = AsymmetricMASt3R.from_pretrained(weights_path).to(args.device)
fast_nn_params = dict(device=device, dist='dot', block_size=2**13)
dataset = eval(args.dataset)
dataset.set_resolution(model)
query_names = []
poses_pred = []
pose_errors = []
angular_errors = []
params_str = f'tol_{args.pixel_tol}' + ("_c2f" if args.coarse_to_fine else '')
if args.max_image_size is not None:
params_str = params_str + f'_{args.max_image_size}'
if args.coarse_to_fine and args.c2f_crop_with_homography:
params_str = params_str + '_with_homography'
for idx in tqdm(range(len(dataset))):
views = dataset[(idx)] # 0 is the query
query_view = views[0]
map_views = views[1:]
query_names.append(query_view['image_name'])
query_pts2d = []
query_pts3d = []
maxdim = max(model.patch_embed.img_size)
query_rgb_tensor, query_K, query_to_orig_max, query_to_resize_max, (HQ, WQ) = resize_image_to_max(
args.max_image_size, query_view['rgb'], query_view['intrinsics'])
# pairs of crops have the same resolution
query_resolution = get_HW_resolution(HQ, WQ, maxdim=maxdim, patchsize=model.patch_embed.patch_size)
for map_view in map_views:
if args.output_dir is not None:
cache_file = os.path.join(args.output_dir, 'matches', params_str,
query_view['image_name'], map_view['image_name'] + '.npz')
else:
cache_file = None
if cache_file is not None and os.path.isfile(cache_file):
matches = np.load(cache_file)
valid_pts3d = matches['valid_pts3d']
matches_im_query = matches['matches_im_query']
matches_im_map = matches['matches_im_map']
matches_conf = matches['matches_conf']
else:
# coarse matching
if args.coarse_to_fine and (maxdim < max(WQ, HQ)):
# use all points
_, coarse_matches_im0, coarse_matches_im1, _ = coarse_matching(query_view, map_view, model, device,
0, fast_nn_params)
# visualize a few matches
if viz_matches > 0:
num_matches = coarse_matches_im1.shape[0]
print(f'found {num_matches} matches')
viz_imgs = [np.array(query_view['rgb']), np.array(map_view['rgb'])]
from matplotlib import pyplot as pl
n_viz = viz_matches
match_idx_to_viz = np.round(np.linspace(0, num_matches - 1, n_viz)).astype(int)
viz_matches_im_query = coarse_matches_im0[match_idx_to_viz]
viz_matches_im_map = coarse_matches_im1[match_idx_to_viz]
H0, W0, H1, W1 = *viz_imgs[0].shape[:2], *viz_imgs[1].shape[:2]
img0 = np.pad(viz_imgs[0], ((0, max(H1 - H0, 0)), (0, 0), (0, 0)),
'constant', constant_values=0)
img1 = np.pad(viz_imgs[1], ((0, max(H0 - H1, 0)), (0, 0), (0, 0)),
'constant', constant_values=0)
img = np.concatenate((img0, img1), axis=1)
pl.figure()
pl.imshow(img)
cmap = pl.get_cmap('jet')
for i in range(n_viz):
(x0, y0), (x1, y1) = viz_matches_im_query[i].T, viz_matches_im_map[i].T
pl.plot([x0, x1 + W0], [y0, y1], '-+',
color=cmap(i / (n_viz - 1)), scalex=False, scaley=False)
pl.show(block=True)
valid_all = map_view['valid']
pts3d = map_view['pts3d']
WM_full, HM_full = map_view['rgb'].size
map_rgb_tensor, map_K, map_to_orig_max, map_to_resize_max, (HM, WM) = resize_image_to_max(
args.max_image_size, map_view['rgb'], map_view['intrinsics'])
if WM_full != WM or HM_full != HM:
y_full, x_full = torch.where(valid_all)
pos2d_cv2 = torch.stack([x_full, y_full], dim=-1).cpu().numpy().astype(np.float64)
sparse_pts3d = pts3d[y_full, x_full].cpu().numpy()
_, _, pts3d_max, valid_max = rescale_points3d(
pos2d_cv2, sparse_pts3d, map_to_resize_max, HM, WM)
pts3d = torch.from_numpy(pts3d_max)
valid_all = torch.from_numpy(valid_max)
coarse_matches_im0 = geotrf(query_to_resize_max, coarse_matches_im0, norm=True)
coarse_matches_im1 = geotrf(map_to_resize_max, coarse_matches_im1, norm=True)
crops1, crops2 = [], []
crops_v1, crops_p1 = [], []
to_orig1, to_orig2 = [], []
map_resolution = get_HW_resolution(HM, WM, maxdim=maxdim, patchsize=model.patch_embed.patch_size)
for crop_q, crop_b, pair_tag in select_pairs_of_crops(map_rgb_tensor,
query_rgb_tensor,
coarse_matches_im1,
coarse_matches_im0,
maxdim=maxdim,
overlap=.5,
forced_resolution=[map_resolution,
query_resolution]):
# Per crop processing
if not args.c2f_crop_with_homography:
map_K = None
query_K = None
c1, v1, p1, trf1 = crop(map_rgb_tensor, valid_all, pts3d, crop_q, map_K)
c2, _, _, trf2 = crop(query_rgb_tensor, None, None, crop_b, query_K)
crops1.append(c1)
crops2.append(c2)
crops_v1.append(v1)
crops_p1.append(p1)
to_orig1.append(trf1)
to_orig2.append(trf2)
if len(crops1) == 0 or len(crops2) == 0:
valid_pts3d, matches_im_query, matches_im_map, matches_conf = [], [], [], []
else:
crops1, crops2 = torch.stack(crops1), torch.stack(crops2)
if len(crops1.shape) == 3:
crops1, crops2 = crops1[None], crops2[None]
crops_v1 = torch.stack(crops_v1)
crops_p1 = torch.stack(crops_p1)
to_orig1, to_orig2 = torch.stack(to_orig1), torch.stack(to_orig2)
map_crop_view = dict(img=crops1.permute(0, 3, 1, 2),
instance=['1' for _ in range(crops1.shape[0])],
valid=crops_v1, pts3d=crops_p1,
to_orig=to_orig1)
query_crop_view = dict(img=crops2.permute(0, 3, 1, 2),
instance=['2' for _ in range(crops2.shape[0])],
to_orig=to_orig2)
# Inference and Matching
valid_pts3d, matches_im_query, matches_im_map, matches_conf = fine_matching(query_crop_view,
map_crop_view,
model, device,
args.max_batch_size,
args.pixel_tol,
fast_nn_params)
matches_im_query = geotrf(query_to_orig_max, matches_im_query, norm=True)
matches_im_map = geotrf(map_to_orig_max, matches_im_map, norm=True)
else:
# use only valid 2d points
valid_pts3d, matches_im_query, matches_im_map, matches_conf = coarse_matching(query_view, map_view,
model, device,
args.pixel_tol,
fast_nn_params)
if cache_file is not None:
mkdir_for(cache_file)
np.savez(cache_file, valid_pts3d=valid_pts3d, matches_im_query=matches_im_query,
matches_im_map=matches_im_map, matches_conf=matches_conf)
# apply conf
if len(matches_conf) > 0:
mask = matches_conf >= conf_thr
valid_pts3d = valid_pts3d[mask]
matches_im_query = matches_im_query[mask]
matches_im_map = matches_im_map[mask]
matches_conf = matches_conf[mask]
# visualize a few matches
if viz_matches > 0:
num_matches = matches_im_map.shape[0]
print(f'found {num_matches} matches')
viz_imgs = [np.array(query_view['rgb']), np.array(map_view['rgb'])]
from matplotlib import pyplot as pl
n_viz = viz_matches
match_idx_to_viz = np.round(np.linspace(0, num_matches - 1, n_viz)).astype(int)
viz_matches_im_query = matches_im_query[match_idx_to_viz]
viz_matches_im_map = matches_im_map[match_idx_to_viz]
H0, W0, H1, W1 = *viz_imgs[0].shape[:2], *viz_imgs[1].shape[:2]
img0 = np.pad(viz_imgs[0], ((0, max(H1 - H0, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)
img1 = np.pad(viz_imgs[1], ((0, max(H0 - H1, 0)), (0, 0), (0, 0)), 'constant', constant_values=0)
img = np.concatenate((img0, img1), axis=1)
pl.figure()
pl.imshow(img)
cmap = pl.get_cmap('jet')
for i in range(n_viz):
(x0, y0), (x1, y1) = viz_matches_im_query[i].T, viz_matches_im_map[i].T
pl.plot([x0, x1 + W0], [y0, y1], '-+', color=cmap(i / (n_viz - 1)), scalex=False, scaley=False)
pl.show(block=True)
if len(valid_pts3d) == 0:
pass
else:
query_pts3d.append(valid_pts3d)
query_pts2d.append(matches_im_query)
if len(query_pts2d) == 0:
success = False
pr_querycam_to_world = None
else:
query_pts2d = np.concatenate(query_pts2d, axis=0).astype(np.float32)
query_pts3d = np.concatenate(query_pts3d, axis=0)
if len(query_pts2d) > pnp_max_points:
idxs = random.sample(range(len(query_pts2d)), pnp_max_points)
query_pts3d = query_pts3d[idxs]
query_pts2d = query_pts2d[idxs]
W, H = query_view['rgb'].size
if reprojection_error_diag_ratio is not None:
reprojection_error_img = reprojection_error_diag_ratio * math.sqrt(W**2 + H**2)
else:
reprojection_error_img = reprojection_error
success, pr_querycam_to_world = run_pnp(query_pts2d, query_pts3d,
query_view['intrinsics'], query_view['distortion'],
pnp_mode, reprojection_error_img, img_size=[W, H])
if not success:
abs_transl_error = float('inf')
abs_angular_error = float('inf')
else:
abs_transl_error, abs_angular_error = get_pose_error(pr_querycam_to_world, query_view['cam_to_world'])
pose_errors.append(abs_transl_error)
angular_errors.append(abs_angular_error)
poses_pred.append(pr_querycam_to_world)
xp_label = params_str + f'_conf_{conf_thr}'
if args.output_label:
xp_label = args.output_label + "_" + xp_label
if reprojection_error_diag_ratio is not None:
xp_label = xp_label + f'_reproj_diag_{reprojection_error_diag_ratio}'
else:
xp_label = xp_label + f'_reproj_err_{reprojection_error}'
export_results(args.output_dir, xp_label, query_names, poses_pred)
out_string = aggregate_stats(f'{args.dataset}', pose_errors, angular_errors)
print(out_string)