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| # Copyright (C) 2024-present Naver Corporation. All rights reserved. | |
| # Licensed under CC BY-NC-SA 4.0 (non-commercial use only). | |
| # | |
| # -------------------------------------------------------- | |
| # evaluation utilities | |
| # -------------------------------------------------------- | |
| import collections | |
| import os | |
| import numpy as np | |
| import quaternion | |
| import roma | |
| import torch | |
| def aggregate_stats(info_str, pose_errors, angular_errors): | |
| stats = collections.Counter() | |
| median_pos_error = np.median(pose_errors) | |
| median_angular_error = np.median(angular_errors) | |
| out_str = f"{info_str}: {len(pose_errors)} images - {median_pos_error=}, {median_angular_error=}" | |
| for trl_thr, ang_thr in [(0.1, 1), (0.25, 2), (0.5, 5), (5, 10)]: | |
| for pose_error, angular_error in zip(pose_errors, angular_errors): | |
| correct_for_this_threshold = (pose_error < trl_thr) and ( | |
| angular_error < ang_thr | |
| ) | |
| stats[trl_thr, ang_thr] += correct_for_this_threshold | |
| stats = { | |
| f"acc@{key[0]:g}m,{key[1]}deg": 100 * val / len(pose_errors) | |
| for key, val in stats.items() | |
| } | |
| for metric, perf in stats.items(): | |
| out_str += f" - {metric:12s}={float(perf):.3f}" | |
| return out_str | |
| def get_pose_error(pr_camtoworld, gt_cam_to_world): | |
| abs_transl_error = torch.linalg.norm( | |
| torch.tensor(pr_camtoworld[:3, 3]) - torch.tensor(gt_cam_to_world[:3, 3]) | |
| ) | |
| abs_angular_error = ( | |
| roma.rotmat_geodesic_distance( | |
| torch.tensor(pr_camtoworld[:3, :3]), torch.tensor(gt_cam_to_world[:3, :3]) | |
| ) | |
| * 180 | |
| / np.pi | |
| ) | |
| return abs_transl_error, abs_angular_error | |
| def export_results(output_dir, xp_label, query_names, poses_pred): | |
| if output_dir is not None: | |
| os.makedirs(output_dir, exist_ok=True) | |
| lines = "" | |
| lines_ltvl = "" | |
| for query_name, pr_querycam_to_world in zip(query_names, poses_pred): | |
| if pr_querycam_to_world is None: | |
| pr_world_to_querycam = np.eye(4) | |
| else: | |
| pr_world_to_querycam = np.linalg.inv(pr_querycam_to_world) | |
| query_shortname = os.path.basename(query_name) | |
| pr_world_to_querycam_q = quaternion.from_rotation_matrix( | |
| pr_world_to_querycam[:3, :3] | |
| ) | |
| pr_world_to_querycam_t = pr_world_to_querycam[:3, 3] | |
| line_pose = ( | |
| quaternion.as_float_array(pr_world_to_querycam_q).tolist() | |
| + pr_world_to_querycam_t.flatten().tolist() | |
| ) | |
| line_content = [query_name] + line_pose | |
| lines += " ".join(str(v) for v in line_content) + "\n" | |
| line_content_ltvl = [query_shortname] + line_pose | |
| lines_ltvl += " ".join(str(v) for v in line_content_ltvl) + "\n" | |
| with open(os.path.join(output_dir, xp_label + "_results.txt"), "wt") as f: | |
| f.write(lines) | |
| with open(os.path.join(output_dir, xp_label + "_ltvl.txt"), "wt") as f: | |
| f.write(lines_ltvl) | |