[BREAKING] Move the COLMAP .bin files to the top level SfM directory (#76)

* Move the COLMAP .bin files to the top level SfM directory
* Update the notebooks
* Propagate the changes to the new pipelines
* Remove the Aachen v1.1 notebook again

Author: skydes

hloc/pipelines/4Seasons/localize.py

@@ -63,7 +63,7 @@
 ffile = extract_features.main(fconf, seq_images, output_dir)
 mfile = match_features.main(mconf, loc_pairs, fconf['output'], output_dir)
 localize_sfm.main(
-    ref_sfm / 'model', query_list, loc_pairs, ffile, mfile, results_path)
+    ref_sfm, query_list, loc_pairs, ffile, mfile, results_path)
 
 # Convert the absolute poses to relative poses with the reference frames.
 submission_dir.mkdir(exist_ok=True)

hloc/pipelines/7Scenes/create_gt_sfm.py

@@ -120,7 +120,7 @@ def correct_sfm_with_gt_depth(sfm_path, depth_folder_path, output_path):
     outputs = Path('outputs/7Scenes')
 
     for scene in SCENES:
-        sfm_path = outputs / scene / 'sfm_superpoint+superglue/model'
+        sfm_path = outputs / scene / 'sfm_superpoint+superglue'
         depth_path = dataset / f'depth/7scenes_{scene}/train/depth'
-        output_path = outputs / scene / 'sfm_superpoint+superglue+depth/model'
+        output_path = outputs / scene / 'sfm_superpoint+superglue+depth'
         correct_sfm_with_gt_depth(sfm_path, depth_path, output_path)

hloc/pipelines/7Scenes/pipeline.py

@@ -56,15 +56,14 @@ def run_scene(images, gt_dir, retrieval, outputs, results, num_covis,
     if use_dense_depth:
         assert depth_dir is not None
         ref_sfm_fix = outputs / 'sfm_superpoint+superglue+depth'
-        correct_sfm_with_gt_depth(
-            ref_sfm / 'model', depth_dir, ref_sfm_fix / 'model')
+        correct_sfm_with_gt_depth(ref_sfm, depth_dir, ref_sfm_fix)
         ref_sfm = ref_sfm_fix
 
     loc_matches = match_features.main(
         matcher_conf, retrieval, feature_conf['output'], outputs)
 
     localize_sfm.main(
-        ref_sfm / 'model',
+        ref_sfm,
         query_list,
         retrieval,
         features,

hloc/pipelines/Aachen/pipeline.py

@@ -62,12 +62,12 @@
 global_descriptors = extract_features.main(retrieval_conf, images, outputs)
 pairs_from_retrieval.main(
     global_descriptors, loc_pairs, args.num_loc,
-    query_prefix='query', db_model=reference_sfm / 'model')
+    query_prefix='query', db_model=reference_sfm)
 loc_matches = match_features.main(
     matcher_conf, loc_pairs, feature_conf['output'], outputs)
 
 localize_sfm.main(
-    reference_sfm / 'model',
+    reference_sfm,
     dataset / 'queries/*_time_queries_with_intrinsics.txt',
     loc_pairs,
     features,

hloc/pipelines/Aachen_v1_1/pipeline.py

@@ -56,12 +56,12 @@
 global_descriptors = extract_features.main(retrieval_conf, images, outputs)
 pairs_from_retrieval.main(
     global_descriptors, loc_pairs, args.num_loc,
-    query_prefix='query', db_model=reference_sfm / 'model')
+    query_prefix='query', db_model=reference_sfm)
 loc_matches = match_features.main(
     matcher_conf, loc_pairs, feature_conf['output'], outputs)
 
 localize_sfm.main(
-    reference_sfm / 'model',
+    reference_sfm,
     dataset / 'queries/*_time_queries_with_intrinsics.txt',
     loc_pairs,
     features,

hloc/pipelines/CMU/pipeline.py

@@ -66,15 +66,15 @@ def run_slice(slice_, root, outputs, num_covis, num_loc):
         retrieval_conf, query_images, outputs)
     pairs_from_retrieval.main(
         global_descriptors, loc_pairs, num_loc,
-        query_list=query_list, db_model=ref_sfm / 'model')
+        query_list=query_list, db_model=ref_sfm)
 
     features = extract_features.main(
         feature_conf, query_images, outputs, as_half=True)
     loc_matches = match_features.main(
         matcher_conf, loc_pairs, feature_conf['output'], outputs)
 
     localize_sfm.main(
-        ref_sfm / 'model',
+        ref_sfm,
         dataset / 'queries/*_time_queries_with_intrinsics.txt',
         loc_pairs,
         features,

hloc/pipelines/Cambridge/pipeline.py

@@ -68,7 +68,7 @@ def run_scene(images, gt_dir, outputs, results, num_covis, num_loc):
         matcher_conf, loc_pairs, feature_conf['output'], outputs)
 
     localize_sfm.main(
-        ref_sfm / 'model',
+        ref_sfm,
         query_list,
         loc_pairs,
         features,

hloc/pipelines/RobotCar/pipeline.py

@@ -91,12 +91,12 @@ def generate_query_list(dataset, image_dir, path):
 # TODO: do per location and per camera
 pairs_from_retrieval.main(
     global_descriptors, loc_pairs, args.num_loc,
-    query_prefix='query', db_model=reference_sfm / 'model')
+    query_prefix='query', db_model=reference_sfm)
 loc_matches = match_features.main(
     matcher_conf, loc_pairs, feature_conf['output'], outputs)
 
 localize_sfm.main(
-    reference_sfm / 'model',
+    reference_sfm,
     Path(str(query_list).format(condition='*')),
     loc_pairs,
     features,

hloc/reconstruction.py

@@ -60,23 +60,24 @@ def get_image_ids(database_path):
     return images
 
 
-def run_reconstruction(colmap_path, model_path, database_path, image_dir,
+def run_reconstruction(colmap_path, sfm_dir, database_path, image_dir,
                        min_num_matches=None):
     logging.info('Running the 3D reconstruction...')
-    model_path.mkdir(exist_ok=True)
+    models_path = sfm_dir / 'models'
+    models_path.mkdir(exist_ok=True, parents=True)
 
     cmd = [
         str(colmap_path), 'mapper',
         '--database_path', str(database_path),
         '--image_path', str(image_dir),
-        '--output_path', str(model_path),
+        '--output_path', str(models_path),
         '--Mapper.num_threads', str(min(multiprocessing.cpu_count(), 16))]
     if min_num_matches:
         cmd += ['--Mapper.min_num_matches', str(min_num_matches)]
     logging.info(' '.join(cmd))
     subprocess.run(cmd, check=True)
 
-    models = list(model_path.iterdir())
+    models = list(models_path.iterdir())
     if len(models) == 0:
         logging.error('Could not reconstruct any model!')
         return False
@@ -91,7 +92,7 @@ def run_reconstruction(colmap_path, model_path, database_path, image_dir,
             largest_model_num_images = num_images
     assert largest_model_num_images > 0
     logging.info(f'Largest model is #{largest_model.name} '
-                 'with {largest_model_num_images} images.')
+                 f'with {largest_model_num_images} images.')
 
     stats_raw = subprocess.check_output(
         [str(colmap_path), 'model_analyzer',
@@ -112,6 +113,9 @@ def run_reconstruction(colmap_path, model_path, database_path, image_dir,
         elif stat.startswith("Mean reprojection error"):
             stats['mean_reproj_error'] = float(stat.split()[-1][:-2])
 
+    for filename in ['images.bin', 'cameras.bin', 'points3D.bin']:
+        shutil.move(str(largest_model / filename), str(sfm_dir))
+
     return stats
 
 
@@ -126,8 +130,6 @@ def main(sfm_dir, image_dir, pairs, features, matches,
 
     sfm_dir.mkdir(parents=True, exist_ok=True)
     database = sfm_dir / 'database.db'
-    models = sfm_dir / 'models'
-    models.mkdir(exist_ok=True)
 
     create_empty_db(database)
     import_images(
@@ -139,7 +141,7 @@ def main(sfm_dir, image_dir, pairs, features, matches,
     if not skip_geometric_verification:
         geometric_verification(colmap_path, database, pairs)
     stats = run_reconstruction(
-        colmap_path, models, database, image_dir, min_num_matches)
+        colmap_path, sfm_dir, database, image_dir, min_num_matches)
     stats['num_input_images'] = len(image_ids)
     logging.info(f'Statistics:\n{pprint.pformat(stats)}')
 

hloc/triangulation.py

@@ -168,8 +168,6 @@ def main(sfm_dir, reference_sfm_model, image_dir, pairs, features, matches,
 
     sfm_dir.mkdir(parents=True, exist_ok=True)
     database = sfm_dir / 'database.db'
-    model = sfm_dir / 'model'
-    model.mkdir(exist_ok=True)
     empty_model = sfm_dir / 'empty'
 
     create_empty_model(reference_sfm_model, empty_model)
@@ -180,7 +178,7 @@ def main(sfm_dir, reference_sfm_model, image_dir, pairs, features, matches,
     if not skip_geometric_verification:
         geometric_verification(colmap_path, database, pairs)
     stats = run_triangulation(
-        colmap_path, model, database, image_dir, empty_model)
+        colmap_path, sfm_dir, database, image_dir, empty_model)
 
     logging.info(f'Statistics:\n{pprint.pformat(stats)}')
     shutil.rmtree(empty_model)