wheels/dust3r/visloc.py

#!/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).
#
# --------------------------------------------------------
# Simple visloc script
# --------------------------------------------------------
import numpy as np
import random
import argparse
from tqdm import tqdm
import math

from dust3r.inference import inference
from dust3r.model import AsymmetricCroCo3DStereo
from dust3r.utils.geometry import find_reciprocal_matches, xy_grid, geotrf

from dust3r_visloc.datasets import *
from dust3r_visloc.localization import run_pnp
from dust3r_visloc.evaluation import get_pose_error, aggregate_stats, export_results


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=["DUSt3R_ViTLarge_BaseDecoder_512_dpt",
                                         "DUSt3R_ViTLarge_BaseDecoder_512_linear",
                                         "DUSt3R_ViTLarge_BaseDecoder_224_linear"])
    parser.add_argument("--confidence_threshold", type=float, default=3.0,
                        help="confidence values higher than threshold are invalid")
    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("--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


if __name__ == '__main__':
    parser = get_args_parser()
    args = parser.parse_args()
    conf_thr = args.confidence_threshold
    device = args.device
    pnp_mode = args.pnp_mode
    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 = AsymmetricCroCo3DStereo.from_pretrained(weights_path).to(args.device)

    dataset = eval(args.dataset)
    dataset.set_resolution(model)

    query_names = []
    poses_pred = []
    pose_errors = []
    angular_errors = []
    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 = []
        for map_view in map_views:
            # 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']
            confidence_masks = [pred1['conf'].squeeze(0) >= conf_thr,
                                (pred2['conf'].squeeze(0) >= conf_thr) & map_view['valid_rescaled']]
            pts3d = [pred1['pts3d'].squeeze(0), pred2['pts3d_in_other_view'].squeeze(0)]

            # find 2D-2D matches between the two images
            pts2d_list, pts3d_list = [], []
            for i in range(2):
                conf_i = confidence_masks[i].cpu().numpy()
                true_shape_i = imgs[i]['true_shape'][0]
                pts2d_list.append(xy_grid(true_shape_i[1], true_shape_i[0])[conf_i])
                pts3d_list.append(pts3d[i].detach().cpu().numpy()[conf_i])

            PQ, PM = pts3d_list[0], pts3d_list[1]
            if len(PQ) == 0 or len(PM) == 0:
                continue
            reciprocal_in_PM, nnM_in_PQ, num_matches = find_reciprocal_matches(PQ, PM)
            if viz_matches > 0:
                print(f'found {num_matches} matches')
            matches_im1 = pts2d_list[1][reciprocal_in_PM]
            matches_im0 = pts2d_list[0][nnM_in_PQ][reciprocal_in_PM]
            valid_pts3d = map_view['pts3d_rescaled'][matches_im1[:, 1], matches_im1[:, 0]]

            # from cv2 to colmap
            matches_im0 = matches_im0.astype(np.float64)
            matches_im1 = matches_im1.astype(np.float64)
            matches_im0[:, 0] += 0.5
            matches_im0[:, 1] += 0.5
            matches_im1[:, 0] += 0.5
            matches_im1[:, 1] += 0.5
            # rescale coordinates
            matches_im0 = geotrf(query_view['to_orig'], matches_im0, norm=True)
            matches_im1 = geotrf(query_view['to_orig'], matches_im1, norm=True)
            # from colmap back to cv2
            matches_im0[:, 0] -= 0.5
            matches_im0[:, 1] -= 0.5
            matches_im1[:, 0] -= 0.5
            matches_im1[:, 1] -= 0.5

            # visualize a few matches
            if viz_matches > 0:
                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_im0, viz_matches_im1 = matches_im0[match_idx_to_viz], 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_im0[i].T, viz_matches_im1[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.cpu().numpy())
                query_pts2d.append(matches_im0)

        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 = f'tol_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)