Remove dependencies on hloc

app.py

@@ -17,7 +17,6 @@ from functools import partial
 import matplotlib.pyplot as plt
 import shutil
 from util.utils import seed_all_random_engines
-from util.match_extraction import extract_match
 from util.load_img_folder import load_and_preprocess_images
 from util.geometry_guided_sampling import geometry_guided_sampling
 from pytorch3d.vis.plotly_vis import get_camera_wireframe
@@ -196,25 +195,8 @@ def estimate_images_pose(image_folder, mode) -> None:
     start_time = time.time()
 
     # Perform match extraction
-    if cfg.GGS.enable:
-        # Optional TODO: remove the keypoints outside the cropped region?
-
-        kp1, kp2, i12 = extract_match(image_folder, image_info)
-
-        keys = ["kp1", "kp2", "i12", "img_shape"]
-        values = [kp1, kp2, i12, images.shape]
-        matches_dict = dict(zip(keys, values))
-
-        cfg.GGS.pose_encoding_type = cfg.MODEL.pose_encoding_type
-        GGS_cfg = OmegaConf.to_container(cfg.GGS)
-
-        cond_fn = partial(
-            geometry_guided_sampling, matches_dict=matches_dict, GGS_cfg=GGS_cfg
-        )
-        print("=====> Sampling with GGS <=====")
-    else:
-        cond_fn = None
-        print("=====> Sampling without GGS <=====")
+    cond_fn = None
+    print("=====> Sampling without GGS <=====")
 
     # Forward
     with torch.no_grad():

util/match_extraction.py

@@ -1,175 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import os
-import shutil
-import tempfile
-from pathlib import Path
-
-import numpy as np
-import pycolmap
-from typing import Optional, List, Dict, Any
-from hloc import (
-    extract_features,
-    logger,
-    match_features,
-    pairs_from_exhaustive,
-)
-from hloc.triangulation import (
-    import_features,
-    import_matches,
-    estimation_and_geometric_verification,
-    parse_option_args,
-    OutputCapture,
-)
-from hloc.utils.database import (
-    COLMAPDatabase,
-    image_ids_to_pair_id,
-    pair_id_to_image_ids,
-)
-from hloc.reconstruction import create_empty_db, import_images, get_image_ids
-
-
-def extract_match(image_folder_path: str, image_info: Dict):
-    # Now only supports SPSG
-    with tempfile.TemporaryDirectory() as tmpdir:
-        tmp_mapping = os.path.join(tmpdir, "mapping")
-        os.makedirs(tmp_mapping)
-        for filename in os.listdir(image_folder_path):
-            if filename.lower().endswith(
-                (".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff")
-            ):
-                shutil.copy(
-                    os.path.join(image_folder_path, filename),
-                    os.path.join(tmp_mapping, filename),
-                )
-        matches, keypoints = run_hloc(tmpdir)
-
-    # From the format of colmap to PyTorch3D
-    kp1, kp2, i12 = colmap_keypoint_to_pytorch3d(matches, keypoints, image_info)
-
-    return kp1, kp2, i12
-
-
-def colmap_keypoint_to_pytorch3d(matches, keypoints, image_info):
-    kp1, kp2, i12 = [], [], []
-    bbox_xyxy, scale = image_info["bboxes_xyxy"], image_info["resized_scales"]
-
-    for idx in keypoints:
-        # coordinate change from COLMAP to OpenCV
-        cur_keypoint = keypoints[idx] - 0.5
-
-        # go to the coordiante after cropping
-        # use idx - 1 here because the COLMAP format starts from 1 instead of 0
-        cur_keypoint = cur_keypoint - [
-            bbox_xyxy[idx - 1][0],
-            bbox_xyxy[idx - 1][1],
-        ]
-        cur_keypoint = cur_keypoint * scale[idx - 1]
-        keypoints[idx] = cur_keypoint
-
-    for (r_idx, q_idx), pair_match in matches.items():
-        if pair_match is not None:
-            kp1.append(keypoints[r_idx][pair_match[:, 0]])
-            kp2.append(keypoints[q_idx][pair_match[:, 1]])
-
-            i12_pair = np.array([[r_idx - 1, q_idx - 1]])
-            i12.append(np.repeat(i12_pair, len(pair_match), axis=0))
-
-    if kp1:
-        kp1, kp2, i12 = map(np.concatenate, (kp1, kp2, i12), (0, 0, 0))
-    else:
-        kp1 = kp2 = i12 = None
-
-    return kp1, kp2, i12
-
-
-def run_hloc(output_dir: str):
-    # learned from
-    # https://github.com/cvg/Hierarchical-Localization/blob/master/pipeline_SfM.ipynb
-
-    images = Path(output_dir)
-    outputs = Path(os.path.join(output_dir, "output"))
-    sfm_pairs = outputs / "pairs-sfm.txt"
-    sfm_dir = outputs / "sfm"
-    features = outputs / "features.h5"
-    matches = outputs / "matches.h5"
-
-    feature_conf = extract_features.confs[
-        "superpoint_inloc"
-    ]  # or superpoint_max
-    matcher_conf = match_features.confs["superpoint+lightglue"]
-
-    references = [
-        p.relative_to(images).as_posix()
-        for p in (images / "mapping/").iterdir()
-    ]
-
-    extract_features.main(
-        feature_conf, images, image_list=references, feature_path=features
-    )
-    pairs_from_exhaustive.main(sfm_pairs, image_list=references)
-    match_features.main(
-        matcher_conf, sfm_pairs, features=features, matches=matches
-    )
-
-    matches, keypoints = compute_matches_and_keypoints(
-        sfm_dir, images, sfm_pairs, features, matches, image_list=references
-    )
-
-    return matches, keypoints
-
-
-def compute_matches_and_keypoints(
-    sfm_dir: Path,
-    image_dir: Path,
-    pairs: Path,
-    features: Path,
-    matches: Path,
-    camera_mode: pycolmap.CameraMode = pycolmap.CameraMode.AUTO,
-    verbose: bool = False,
-    min_match_score: Optional[float] = None,
-    image_list: Optional[List[str]] = None,
-    image_options: Optional[Dict[str, Any]] = None,
-) -> pycolmap.Reconstruction:
-    # learned from
-    # https://github.com/cvg/Hierarchical-Localization/blob/master/hloc/reconstruction.py
-
-    sfm_dir.mkdir(parents=True, exist_ok=True)
-    database = sfm_dir / "database.db"
-
-    create_empty_db(database)
-    import_images(image_dir, database, camera_mode, image_list, image_options)
-    image_ids = get_image_ids(database)
-    import_features(image_ids, database, features)
-    import_matches(image_ids, database, pairs, matches, min_match_score)
-    estimation_and_geometric_verification(database, pairs, verbose)
-
-    db = COLMAPDatabase.connect(database)
-
-    matches = dict(
-        (
-            pair_id_to_image_ids(pair_id),
-            _blob_to_array_safe(data, np.uint32, (-1, 2)),
-        )
-        for pair_id, data in db.execute("SELECT pair_id, data FROM matches")
-    )
-
-    keypoints = dict(
-        (image_id, _blob_to_array_safe(data, np.float32, (-1, 2)))
-        for image_id, data in db.execute("SELECT image_id, data FROM keypoints")
-    )
-
-    db.close()
-
-    return matches, keypoints
-
-
-def _blob_to_array_safe(blob, dtype, shape=(-1,)):
-    if blob is not None:
-        return np.fromstring(blob, dtype=dtype).reshape(*shape)
-    else:
-        return blob