Varius bugfixes

add_video_to_db.py

@@ -17,8 +17,7 @@ parser.add_argument('--database', metavar='DB', required=True,
                     help='path to colmap database file, to get the image ids right')
 
 
-def add_to_db(db_path, metadata_path, frame_list_path, **env):
-    metadata = pd.read_csv(metadata_path)
+def add_to_db(db_path, metadata, frame_list_path, **env):
     database = db.COLMAPDatabase.connect(db_path)
 
     frame_list = []
@@ -62,7 +61,7 @@ def get_frame_without_features(db_path):
 
 def main():
     args = parser.parse_args()
-    add_to_db(args.database, args.metadata, args.frame_list)
+    add_to_db(args.database, pd.read_csv(args.metadata), args.frame_list)
 
     return
 

cli_utils.py

@@ -20,7 +20,7 @@ def add_main_options(parser):
     main_parser.add_argument('--show_steps', action="store_true")
     main_parser.add_argument('--add_new_videos', action="store_true",
                              help="If selected, will skip first 6 steps to directly register videos without mapping")
-    main_parser.add_argument('--generate_groundtruth_for_individual_images', action="store_true",
+    main_parser.add_argument('--generate_groundtruth_for_individual_images', '--gt_images', action="store_true",
                              help="If selected, will generate Ground truth for individual images as well as videos")
     main_parser.add_argument('--save_space', action="store_true")
     main_parser.add_argument('-v', '--verbose', action="count", default=0)

convert_dataset.py

@@ -42,12 +42,10 @@ def save_intrinsics(cameras, images, output_dir, output_width=None, downscale=No
             yaml.dump(camera_dict, f, default_flow_style=False)
 
     if len(cameras) == 1:
-        print("bonjour")
         cam = cameras[list(cameras.keys())[0]]
         save_cam(cam, output_dir / "intrinsics.txt", output_dir / "camera.yaml")
 
     else:
-        print("au revoir")
         for _, img in images.items():
             cam = cameras[img.camera_id]
 
@@ -210,7 +208,7 @@ def convert_dataset(final_model, depth_dir, images_root_folder, occ_dir,
         metadata = metadata.set_index("db_id", drop=False).sort_values("time")
         framerate = metadata["framerate"].values[0]
         # image_df = image_df.reindex(metadata.index)
-        images_list = metadata["image_path"]
+        images_list = metadata["image_path"].values
     else:
         assert images_list is not None
         framerate = None

main_pipeline.py

@@ -273,15 +273,15 @@ def main():
                         video_index=j+1,
                         step_index=i,
                         num_videos=len(env["videos_to_localize"]),
-                        metadata=video_env["metadata"],
+                        metadata_path=video_env["metadata_path"],
                         **video_env["output_env"], **env)
         if env["generate_groundtruth_for_individual_images"]:
             by_folder = pi.group_pics_by_folder(env["individual_pictures"])
-        for folder, pic_list in by_folder.items():
-            generate_GT_individual_pictures(input_colmap_model=env["georef_full_recon"],
-                                            individual_pictures=pic_list,
-                                            relpath=folder,
-                                            step_index=i, **env)
+            for folder, pic_list in by_folder.items():
+                generate_GT_individual_pictures(input_colmap_model=env["georef_full_recon"],
+                                                individual_pictures_list=pic_list,
+                                                relpath=folder,
+                                                step_index=i, **env)
 
 
 if __name__ == '__main__':

picture_localization.py

@@ -95,7 +95,7 @@ def main():
         by_folder = pi.group_pics_by_folder(env["individual_pictures"])
         for folder, pic_list in by_folder.items():
             generate_GT_individual_pictures(input_colmap_model=env["georef_full_recon"],
-                                            individual_pictures=pic_list,
+                                            individual_pictures_list=pic_list,
                                             relpath=folder,
                                             step_index=i, **env)
 

video_localization.py

@@ -15,7 +15,7 @@ import meshlab_xml_writer as mxw
 def is_video_in_model(video_name, colmap_model, metadata):
 
     mapped_images_ids = read_images_binary(colmap_model/"images.bin").keys()
-    video_image_ids = pd.read_csv(metadata)["db_id"]
+    video_image_ids = metadata["db_id"]
     return sum(video_image_ids.isin(mapped_images_ids)) > 0
 
 
@@ -273,8 +273,8 @@ def generate_GT(video_name, raw_output_folder, images_root_folder, video_frames_
     i_pv += 1
     print_step_pv(i_pv, "Creating Ground truth data with ETH3D")
 
-    eth3d.create_ground_truth(final_mlp, final_model, raw_output_folder,
-                              final_occlusions, final_splats)
+    # eth3d.create_ground_truth(final_mlp, final_model, raw_output_folder,
+    #                           final_occlusions, final_splats)
     viz_folder.makedirs_p()
     kitti_format_folder.makedirs_p()
 
@@ -286,7 +286,7 @@ def generate_GT(video_name, raw_output_folder, images_root_folder, video_frames_
                        images_root_folder,
                        raw_output_folder / "occlusion_depth" / video_name.stem,
                        kitti_format_folder, viz_folder,
-                       metadata, interpolated_frames,
+                       metadata=metadata, interpolated_frames=interpolated_frames,
                        visualization=True, video=True, downscale=4, threads=8, **env)
     if filter_models:
         interpolated_frames_list.copy(kitti_format_folder)
@@ -296,7 +296,7 @@ def generate_GT(video_name, raw_output_folder, images_root_folder, video_frames_
     return
 
 
-def generate_GT_individual_pictures(colmap_img_root, individual_pictures, raw_output_folder,
+def generate_GT_individual_pictures(colmap_img_root, individual_pictures_list, raw_output_folder,
                                     converted_output_folder, input_colmap_model,
                                     aligned_mlp, relpath,
                                     occlusion_ply, splats_ply,
@@ -315,7 +315,7 @@ def generate_GT_individual_pictures(colmap_img_root, individual_pictures, raw_ou
         return
     i_pv = 1
     print_step_pv(i_pv, "Copy individual images to output dataset {}".format(raw_output_folder))
-    for p in individual_pictures:
+    for p in individual_pictures_list:
         output_path = raw_output_folder / "images" / p
         output_path.parent.makedirs_p()
         (colmap_img_root / p).copy(output_path)
@@ -326,7 +326,7 @@ def generate_GT_individual_pictures(colmap_img_root, individual_pictures, raw_ou
     pictures_colmap_model.makedirs_p()
     epfm.extract_pictures(input=input_colmap_model,
                           output=pictures_colmap_model,
-                          picture_list=individual_pictures,
+                          picture_list=individual_pictures_list,
                           output_format=".txt")
 
     i_pv += 1
@@ -348,7 +348,7 @@ def generate_GT_individual_pictures(colmap_img_root, individual_pictures, raw_ou
                        raw_output_folder / "images",
                        occlusion_depth_folder,
                        kitti_format_folder, viz_folder,
-                       images_list=individual_pictures,
+                       images_list=individual_pictures_list,
                        visualization=True, video=False, downscale=4, threads=8, **env)
     if save_space:
         (raw_output_folder / "occlusion_depth" / "individual_pictures").rmtree_p()