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Pipeline for custom data #315

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1 change: 1 addition & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -4,4 +4,5 @@ __pycache__
.ipynb_checkpoints
outputs/
datasets/*
query_processing_data/
!datasets/sacre_coeur/
17 changes: 11 additions & 6 deletions hloc/localize_sfm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -71,9 +71,13 @@ def pose_from_cluster(
db_ids: List[int],
features_path: Path,
matches_path: Path,
features_q_path: Path = None,
**kwargs):

kpq = get_keypoints(features_path, qname)
if features_q_path is None:
kpq = get_keypoints(features_path, qname)
else:
kpq = get_keypoints(features_q_path, qname)
kpq += 0.5 # COLMAP coordinates

kp_idx_to_3D = defaultdict(list)
Expand Down Expand Up @@ -122,13 +126,13 @@ def pose_from_cluster(
}
return ret, log


def main(reference_sfm: Union[Path, pycolmap.Reconstruction],
queries: Path,
queries: Union[Path, list],
retrieval: Path,
features: Path,
matches: Path,
results: Path,
features_q: Path = None,
ransac_thresh: int = 12,
covisibility_clustering: bool = False,
prepend_camera_name: bool = False,
Expand All @@ -138,7 +142,8 @@ def main(reference_sfm: Union[Path, pycolmap.Reconstruction],
assert features.exists(), features
assert matches.exists(), matches

queries = parse_image_lists(queries, with_intrinsics=True)
if isinstance(queries, Path):
queries = parse_image_lists(queries, with_intrinsics=True)
retrieval_dict = parse_retrieval(retrieval)

logger.info('Reading the 3D model...')
Expand Down Expand Up @@ -178,7 +183,7 @@ def main(reference_sfm: Union[Path, pycolmap.Reconstruction],
logs_clusters = []
for i, cluster_ids in enumerate(clusters):
ret, log = pose_from_cluster(
localizer, qname, qcam, cluster_ids, features, matches)
localizer, qname, qcam, cluster_ids, features, matches, features_q = features_q)
if ret['success'] and ret['num_inliers'] > best_inliers:
best_cluster = i
best_inliers = ret['num_inliers']
Expand All @@ -194,7 +199,7 @@ def main(reference_sfm: Union[Path, pycolmap.Reconstruction],
}
else:
ret, log = pose_from_cluster(
localizer, qname, qcam, db_ids, features, matches)
localizer, qname, qcam, db_ids, features, matches, features_q = features_q)
if ret['success']:
poses[qname] = (ret['qvec'], ret['tvec'])
else:
Expand Down
29 changes: 29 additions & 0 deletions hloc/pairs_from_retrieval.py
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Expand Up @@ -67,6 +67,35 @@ def pairs_from_score_matrix(scores: torch.Tensor,
return pairs


def save_global_candidates_for_query(
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Why is this function needed? why isn't pairs_from_retrieval.main sufficient?

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You're right, pairs_from_retreival.main is sufficient. I will rewrite my example and push once I get the chance.

db_descriptors,
query_descriptor,
query_image_names,
num_matched,
output_file_path
):
"""
For each image in `query_image_names`, get a list of `num_matched` images from db that are closest to the
query image
"""
db_image_names = list_h5_names(db_descriptors)

device = 'cuda' if torch.cuda.is_available() else 'cpu'
db_desc = get_descriptors(db_image_names, db_descriptors)
query_desc = get_descriptors(query_image_names, query_descriptor)
sim = torch.einsum('id,jd->ij', query_desc.to(device), db_desc.to(device))

invalids = np.array(query_image_names)[:, None] == np.array(db_image_names)[None]
pairs = pairs_from_score_matrix(sim, invalids, num_matched, min_score=0)
pairs = [(query_image_names[i], db_image_names[j]) for i, j in pairs]

logger.info(f'Found {len(pairs)} pairs.')
with open(output_file_path, 'w') as f:
f.write('\n'.join(' '.join([i, j]) for i, j in pairs))

if len(query_image_names) == 1: # If only one image is passed in, eeturn nearest candidates
return [p[1] for p in pairs]

def main(descriptors, output, num_matched,
query_prefix=None, query_list=None,
db_prefix=None, db_list=None, db_model=None, db_descriptors=None):
Expand Down
272 changes: 272 additions & 0 deletions pipeline_custom.ipynb
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@@ -0,0 +1,272 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "1aa2d1ce-7d86-4375-a491-540b56a1df90",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"from pathlib import Path\n",
"\n",
"import numpy as np\n",
"import pycolmap\n",
"\n",
"from hloc import extract_features, pairs_from_covisibility, match_features, triangulation, pairs_from_retrieval, localize_sfm, visualization\n",
"from hloc.utils import viz_3d, io\n",
"from hloc.localize_sfm import QueryLocalizer, pose_from_cluster"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "66d8ac9b-a21f-43df-bd2b-458fba523b5a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"dataset_name = 'LabFront'\n",
"\n",
"dataset = Path(f'datasets/{dataset_name}/')\n",
"image_dir = dataset / 'images'\n",
"colmap_model_path = dataset / 'colmap/sparse/0'\n",
"\n",
"output_dir = dataset / 'hloc_data/'\n",
"# Local features and global descriptor filenames hardocded in feature confs\n",
"sfm_pairs_path = output_dir / 'sfm-pairs-covis20.txt' # Pairs used for SfM reconstruction\n",
"sfm_reconstruction_path = output_dir / 'sfm_reconstruction' # Path to reconstructed SfM\n",
"\n",
"# Query images\n",
"queries_img_dir = dataset / 'query'\n",
"\n",
"# Query data\n",
"query_processing_data_dir = Path(f'query_processing_data/{dataset_name}')\n",
"query_global_matches_path = query_processing_data_dir / 'global_match_pairs.txt'\n",
"query_local_match_path = query_processing_data_dir / 'local_match_data.h5'\n",
"query_results = query_processing_data_dir / 'query_results.txt'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aff8a17a-e45f-4a5a-9fc2-4d96e34edb57",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Feature extraction\n",
"## Extract local features in each data set image using Superpoint\n",
"local_feature_conf = extract_features.confs['superpoint_aachen']\n",
"local_features_path = extract_features.main(\n",
" conf = local_feature_conf,\n",
" image_dir = image_dir,\n",
" export_dir = output_dir\n",
")\n",
"\n",
"## Extract global descriptors from each image using NetVLad\n",
"global_descriptor_conf = extract_features.confs['netvlad']\n",
"global_descriptors_path = extract_features.main(\n",
" conf = global_descriptor_conf,\n",
" image_dir = image_dir,\n",
" export_dir = output_dir\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0224548d-f5b9-448f-a997-5712f0207745",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Create SfM model using the local features just extracted\n",
"\n",
"## Note: There is already an SfM model created using Colmap available. However, that is created using the RootSIFT features.\n",
"## SfM model needs to be created using the new features.\n",
"\n",
"## Create matching pairs:\n",
"## Instead of creating image pairs by exhaustively searching through all possible pairs, we leverage the \n",
"## existing colmap model and form pairs by selecting the top 20 most covisibile neighbors for each image\n",
"pairs_from_covisibility.main(\n",
" model = colmap_model_path,\n",
" output = sfm_pairs_path,\n",
" num_matched = 20\n",
")\n",
"\n",
"## Use the created pairs to match images and store the matching result in a match file\n",
"match_features_conf = match_features.confs['superglue']\n",
"sfm_matches_path = match_features.main(\n",
" conf = match_features_conf,\n",
" pairs = sfm_pairs_path,\n",
" features = local_feature_conf['output'], # This contains the file name where lcoal features are stored\n",
" export_dir = output_dir\n",
")\n",
"\n",
"## Use the matches to reconstruct an SfM model\n",
"reconstruction = triangulation.main(\n",
" sfm_dir = sfm_reconstruction_path,\n",
" reference_model = colmap_model_path,\n",
" image_dir = image_dir,\n",
" pairs = sfm_pairs_path,\n",
" features = local_features_path,\n",
" matches = sfm_matches_path\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "696ab530-9124-4fd5-939a-95fdc7a694be",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Visualize the reconstruction\n",
"\n",
"fig = viz_3d.init_figure()\n",
"viz_3d.plot_reconstruction(fig, reconstruction, color='rgba(255,0,0,0.5)', name=\"mapping\", points_rgb=True)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b1123ae8-d2a7-4185-b689-082e2bc776dd",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Localize a new Image\n",
"\n",
"query_image_name = 'query_2.png'\n",
"\n",
"# Extarct local features and global descriptor for the new image\n",
"query_local_features_path = extract_features.main(\n",
" conf = local_feature_conf,\n",
" image_dir = queries_img_dir,\n",
" export_dir = query_processing_data_dir,\n",
" image_list = [query_image_name]\n",
")\n",
"\n",
"query_global_descriptor_path = extract_features.main(\n",
" conf = global_descriptor_conf,\n",
" image_dir = queries_img_dir,\n",
" export_dir = query_processing_data_dir,\n",
" image_list = [query_image_name]\n",
")\n",
"\n",
"## Use global descriptor matching to get candidate matches\n",
"nearest_candidate_images = pairs_from_retrieval.save_global_candidates_for_query(\n",
" db_descriptors = global_descriptors_path,\n",
" query_descriptor = query_global_descriptor_path,\n",
" query_image_names = [query_image_name],\n",
" num_matched = 10,\n",
" output_file_path = query_global_matches_path\n",
")\n",
"\n",
"## Match the query image against the candidate pairs from above\n",
"match_features.match_from_paths(\n",
" conf = match_features_conf,\n",
" pairs_path = query_global_matches_path,\n",
" match_path = query_local_match_path,\n",
" feature_path_q = query_local_features_path,\n",
" feature_path_ref = local_features_path\n",
")\n",
"\n",
"## Now we have global candidate and thier mathces. We use this, along with SfM reconstruction to localize the image.\n",
"\n",
"camera = pycolmap.infer_camera_from_image(queries_img_dir / query_image_name)\n",
"ref_ids = [reconstruction.find_image_with_name(r).image_id for r in nearest_candidate_images]\n",
"conf = {\n",
" 'estimation': {'ransac': {'max_error': 12}},\n",
" 'refinement': {'refine_focal_length': True, 'refine_extra_params': True},\n",
"}\n",
"localizer = QueryLocalizer(reconstruction, conf)\n",
"ret, log = pose_from_cluster(\n",
" localizer = localizer, \n",
" qname = query_image_name, \n",
" query_camera = camera, \n",
" db_ids = ref_ids, \n",
" features_path = local_features_path, \n",
" matches_path = query_local_match_path,\n",
" features_q_path = query_local_features_path\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca011477-2bf8-4d56-871d-0a53de76d952",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# visualization.visualize_loc_from_log(\n",
"# image_dir = queries_img_dir, \n",
"# query_name = query_image_name, \n",
"# loc = log, \n",
"# reconstruction = reconstruction, \n",
"# db_image_dir = image_dir\n",
"# )\n",
"\n",
"fig = viz_3d.init_figure()\n",
"viz_3d.plot_reconstruction(fig, reconstruction, color='rgba(255,0,0,0.5)', points_rgb=True)\n",
"pose = pycolmap.Image(tvec=ret['tvec'], qvec=ret['qvec'])\n",
"viz_3d.plot_camera_colmap(fig, pose, camera, color='rgba(0,255,0,0.5)', name=query_image_name, fill=True)\n",
"# visualize 2D-3D correspodences\n",
"inl_3d = np.array([reconstruction.points3D[pid].xyz for pid in np.array(log['points3D_ids'])[ret['inliers']]])\n",
"viz_3d.plot_points(fig, inl_3d, color=\"lime\", ps=1, name=query_image_name)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b9ead3d-2098-4efa-b0e6-d388954e59bc",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3bead830-aedc-4893-b8fb-abc48841ab34",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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