update convert and demo_sfm

convert_bin_to_text.py

@@ -0,0 +1,8 @@
+import pycolmap
+
+reconstruction = pycolmap.Reconstruction("outputs/demo/sfm")
+print(reconstruction.summary())
+
+# convert .bin file to .txt file
+reconstruction.write_text("outputs/demo/sfm")  # text format
+reconstruction.export_PLY("outputs/demo/sfm/rec.ply")  # PLY format

demo_sfm.py

@@ -8,3 +8,35 @@
     pairs_from_retrieval,
 )
 
+# define path
+images = Path("datasets/South-Building/images/")
+
+outputs = Path("outputs/sfm_south/")
+sfm_pairs = outputs / "pairs-netvlad.txt"
+sfm_dir = outputs / "sfm_superpoint+superglue"
+
+retrieval_conf = extract_features.confs["netvlad"]
+feature_conf = extract_features.confs["superpoint_aachen"]
+matcher_conf = match_features.confs["superglue"]
+
+# find image pairs via image retrieval
+# We extract global descriptors with NetVLAD and find for each image the most similar ones.
+
+# netvlad process each image and output a 4096 dim global descriptor vector
+retrieval_path = extract_features.main(retrieval_conf, images, outputs)
+pairs_from_retrieval.main(retrieval_path, sfm_pairs, num_matched=5)
+
+# extract and match local features
+feature_path = extract_features.main(feature_conf, images, outputs)
+match_path = match_features.main(
+    matcher_conf, sfm_pairs, feature_conf["output"], outputs
+)
+
+# run colmap on the features and matches
+model = reconstruction.main(sfm_dir, images, sfm_pairs, feature_path, match_path)
+
+# model.write_text("./")  # text format
+model.export_PLY(outputs / "scene.ply")  # PLY format
+
+# visualize
+visualization.visualize_sfm_2d(model, images, color_by="visibility", n=5)