replace str with pathlib.Path

deruyter92 · Jul 15, 2026 · May 29, 2026 · May 29, 2026 · May 29, 2026 · May 29, 2026
commit 2403b72329277bf40d273bc975e1a64debcdbfef
diff --git a/deeplabcut/create_project/new.py b/deeplabcut/create_project/new.py
@@ -154,7 +154,7 @@ def create_new_project(
     # Create project and sub-directories
     if not DEBUG and project_path.exists():
         print(f'Project "{project_path}" already exists!')
-        return os.path.join(str(project_path), "config.yaml")
+        return str(project_path / "config.yaml")
     video_path = project_path / "videos"
     data_path = project_path / "labeled-data"
     shuffles_path = project_path / "training-datasets"
@@ -304,7 +304,7 @@ def create_new_project(
     cfg_file["alphavalue"] = 0.7  # for plots transparency of markers
     cfg_file["colormap"] = "rainbow"  # for plots type of colormap
 
-    projconfigfile = os.path.join(str(project_path), "config.yaml")
+    projconfigfile = project_path / "config.yaml"
     # Write dictionary to yaml  config file
     auxiliaryfunctions.write_config(projconfigfile, cfg_file)
 

diff --git a/deeplabcut/create_project/new_3d.py b/deeplabcut/create_project/new_3d.py
@@ -10,7 +10,6 @@
 #
 
 
-import os
 from pathlib import Path
 
 from deeplabcut import DEBUG
@@ -122,7 +121,7 @@ def create_new_project_3d(project, experimenter, num_cameras=2, working_director
         cfg_file_3d.insert(len(cfg_file_3d), str("shuffle_camera-" + str(i + 1)), 1)
         cfg_file_3d.insert(len(cfg_file_3d), str("trainingsetindex_camera-" + str(i + 1)), 0)
 
-    projconfigfile = os.path.join(str(project_path), "config.yaml")
+    projconfigfile = project_path / "config.yaml"
     auxiliaryfunctions.write_config_3d(projconfigfile, cfg_file_3d)
 
     print('Generated "{}"'.format(project_path / "config.yaml"))

diff --git a/deeplabcut/generate_training_dataset/trainingsetmanipulation.py b/deeplabcut/generate_training_dataset/trainingsetmanipulation.py
@@ -139,14 +139,14 @@ def dropduplicatesinannotatinfiles(config):
 
     for folder in folders:
         try:
-            fn = os.path.join(str(folder), "CollectedData_" + cfg["scorer"] + ".h5")
+            fn = folder / ("CollectedData_" + cfg["scorer"] + ".h5")
             DC = pd.read_hdf(fn)
             numimages = len(DC.index)
             DC = DC[~DC.index.duplicated(keep="first")]
             if len(DC.index) < numimages:
                 print("Dropped", numimages - len(DC.index))
                 DC.to_hdf(fn, key="df_with_missing", mode="w")
-                DC.to_csv(os.path.join(str(folder), "CollectedData_" + cfg["scorer"] + ".csv"))
+                DC.to_csv(folder / ("CollectedData_" + cfg["scorer"] + ".csv"))
 
         except FileNotFoundError:
             print("Attention:", folder, "does not appear to have labeled data!")
@@ -168,7 +168,7 @@ def dropannotationfileentriesduetodeletedimages(config):
     folders = [Path(config).parent / "labeled-data" / Path(i) for i in video_names]
 
     for folder in folders:
-        fn = os.path.join(str(folder), "CollectedData_" + cfg["scorer"] + ".h5")
+        fn = folder / ("CollectedData_" + cfg["scorer"] + ".h5")
         try:
             DC = pd.read_hdf(fn)
         except FileNotFoundError:
@@ -184,7 +184,7 @@ def dropannotationfileentriesduetodeletedimages(config):
                 dropped = True
         if dropped:
             DC.to_hdf(fn, key="df_with_missing", mode="w")
-            DC.to_csv(os.path.join(str(folder), "CollectedData_" + cfg["scorer"] + ".csv"))
+            DC.to_csv(folder / ("CollectedData_" + cfg["scorer"] + ".csv"))
 
 
 def dropimagesduetolackofannotation(config):
@@ -203,15 +203,15 @@ def dropimagesduetolackofannotation(config):
     folders = [Path(config).parent / "labeled-data" / Path(i) for i in video_names]
 
     for folder in folders:
-        h5file = os.path.join(str(folder), "CollectedData_" + cfg["scorer"] + ".h5")
+        h5file = folder / ("CollectedData_" + cfg["scorer"] + ".h5")
         try:
             DC = pd.read_hdf(h5file)
         except FileNotFoundError:
             print("Attention:", folder, "does not appear to have labeled data!")
             continue
         conversioncode.guarantee_multiindex_rows(DC)
         annotatedimages = [fn[-1] for fn in DC.index]
-        imagelist = [fns for fns in os.listdir(str(folder)) if ".png" in fns]
+        imagelist = [fns for fns in os.listdir(folder) if ".png" in fns]
         print("Annotated images: ", len(annotatedimages), " In folder:", len(imagelist))
         for imagename in imagelist:
             if imagename in annotatedimages:
@@ -250,7 +250,7 @@ def dropunlabeledframes(config):
     folders = [Path(config).parent / "labeled-data" / Path(i) for i in video_names]
 
     for folder in folders:
-        h5file = os.path.join(str(folder), "CollectedData_" + cfg["scorer"] + ".h5")
+        h5file = folder / ("CollectedData_" + cfg["scorer"] + ".h5")
         try:
             DC = pd.read_hdf(h5file)
         except FileNotFoundError:
@@ -262,7 +262,7 @@ def dropunlabeledframes(config):
         dropped = before_len - after_len
         if dropped:
             DC.to_hdf(h5file, key="df_with_missing", mode="w")
-            DC.to_csv(os.path.join(str(folder), "CollectedData_" + cfg["scorer"] + ".csv"))
+            DC.to_csv(folder / ("CollectedData_" + cfg["scorer"] + ".csv"))
 
             print("Dropped ", dropped, "entries in ", folder)
 
@@ -327,11 +327,11 @@ def check_labels(
     videos = cfg["video_sets"].keys()
     video_names = [_robust_path_split(video)[1] for video in videos]
 
-    folders = [os.path.join(cfg["project_path"], "labeled-data", str(Path(i))) for i in video_names]
+    folders = [Path(cfg["project_path"]) / "labeled-data" / Path(i) for i in video_names]
     print("Creating images with labels by {}.".format(cfg["scorer"]))
     for folder in folders:
         try:
-            DataCombined = pd.read_hdf(os.path.join(str(folder), "CollectedData_" + cfg["scorer"] + ".h5"))
+            DataCombined = pd.read_hdf(folder / ("CollectedData_" + cfg["scorer"] + ".h5"))
             conversioncode.guarantee_multiindex_rows(DataCombined)
             if cfg.get("multianimalproject", False):
                 color_by = "individual" if visualizeindividuals else "bodypart"

diff --git a/deeplabcut/gui/displays/selected_shuffle_display.py b/deeplabcut/gui/displays/selected_shuffle_display.py
@@ -117,7 +117,7 @@ def _set_text_error(self, error: str) -> None:
         self.pose_cfg = None
 
     def _read_pose_config(self, pose_cfg_path: Path) -> None:
-        pose_cfg = auxiliaryfunctions.read_plainconfig(str(pose_cfg_path))
+        pose_cfg = auxiliaryfunctions.read_plainconfig(pose_cfg_path)
 
         self._engine = Engine.PYTORCH if "pytorch" in pose_cfg_path.stem.lower() else Engine.TF
         self._net_type = pose_cfg.get("net_type", "UNKNOWN")

diff --git a/deeplabcut/pose_estimation_pytorch/apis/tracklets.py b/deeplabcut/pose_estimation_pytorch/apis/tracklets.py
@@ -84,7 +84,7 @@ def convert_detections2tracklets(
     )
     model_dir = Path(cfg["project_path"]) / rel_model_dir
     path_test_config = model_dir / "test" / "pose_cfg.yaml"
-    dlc_cfg = auxiliaryfunctions.read_plainconfig(str(path_test_config))
+    dlc_cfg = auxiliaryfunctions.read_plainconfig(path_test_config)
 
     if "multi-animal" not in dlc_cfg["dataset_type"]:
         raise ValueError("This function is only required for multianimal projects!")

diff --git a/deeplabcut/pose_estimation_tensorflow/core/evaluate.py b/deeplabcut/pose_estimation_tensorflow/core/evaluate.py
@@ -682,7 +682,7 @@ def evaluate_network(
             cfg, comparisonbodyparts
         )
         # Make folder for evaluation
-        auxiliaryfunctions.attempt_to_make_folder(str(cfg["project_path"] + "/evaluation-results/"))
+        auxiliaryfunctions.attempt_to_make_folder(Path(cfg["project_path"]) / "evaluation-results")
         for shuffle in Shuffles:
             for trainFraction in TrainingFractions:
                 ##################################################

diff --git a/deeplabcut/pose_estimation_tensorflow/core/evaluate_multianimal.py b/deeplabcut/pose_estimation_tensorflow/core/evaluate_multianimal.py
@@ -153,7 +153,7 @@ def evaluate_multianimal_full(
     all_bpts = np.asarray(len(cfg["individuals"]) * cfg["multianimalbodyparts"] + cfg["uniquebodyparts"])
     colors = visualization.get_cmap(len(comparisonbodyparts), name=cfg["colormap"])
     # Make folder for evaluation
-    auxiliaryfunctions.attempt_to_make_folder(str(cfg["project_path"] + "/evaluation-results/"))
+    auxiliaryfunctions.attempt_to_make_folder(Path(cfg["project_path"]) / "evaluation-results")
     for shuffle in Shuffles:
         for trainFraction in TrainingFractions:
             ##################################################

diff --git a/deeplabcut/pose_estimation_tensorflow/export.py b/deeplabcut/pose_estimation_tensorflow/export.py
@@ -276,12 +276,12 @@ def export_model(
 
     ### set up export directory
 
-    export_dir = os.path.normpath(cfg["project_path"] + "/" + "exported-models")
+    export_dir = Path(cfg["project_path"]) / "exported-models"
     if not os.path.isdir(export_dir):
         os.mkdir(export_dir)
 
     sub_dir_name = f"DLC_{cfg['Task']}_{dlc_cfg['net_type']}_iteration-{cfg['iteration']}_shuffle-{shuffle}"
-    full_export_dir = os.path.normpath(export_dir + "/" + sub_dir_name)
+    full_export_dir = export_dir / sub_dir_name
 
     if os.path.isdir(full_export_dir):
         if not overwrite:

diff --git a/deeplabcut/pose_estimation_tensorflow/visualizemaps.py b/deeplabcut/pose_estimation_tensorflow/visualizemaps.py
@@ -9,6 +9,7 @@
 # Licensed under GNU Lesser General Public License v3.0
 #
 import os
+from pathlib import Path
 
 import matplotlib.pyplot as plt
 from skimage.transform import resize
@@ -61,7 +62,6 @@ def extract_maps(
     If you want to extract the data for image 0 and 103 (of the training set) for model trained with shuffle 0.
     >>> deeplabcut.extract_maps(configfile,0,Indices=[0,103])
     """
-    from pathlib import Path
 
     import numpy as np
     import pandas as pd
@@ -114,7 +114,7 @@ def extract_maps(
     )
 
     # Make folder for evaluation
-    auxiliaryfunctions.attempt_to_make_folder(str(cfg["project_path"] + "/evaluation-results/"))
+    auxiliaryfunctions.attempt_to_make_folder(Path(cfg["project_path"]) / "evaluation-results")
 
     Maps = {}
     for trainFraction in TrainingFractions:

diff --git a/deeplabcut/utils/auxfun_multianimal.py b/deeplabcut/utils/auxfun_multianimal.py
@@ -228,7 +228,7 @@ def returnlabelingdata(config):
         print("Do you want to get the data for folder:", folder, "?")
         askuser = input("yes/no")
         if askuser == "y" or askuser == "yes" or askuser == "Ja" or askuser == "ha":  # multilanguage support :)
-            fn = os.path.join(str(folder), "CollectedData_" + cfg["scorer"] + ".h5")
+            fn = folder / ("CollectedData_" + cfg["scorer"] + ".h5")
             Data = pd.read_hdf(fn)
             return Data
 
@@ -292,8 +292,8 @@ def convert2_maDLC(config, userfeedback=True, forceindividual=None):
             askuser = "yes"
 
         if askuser == "y" or askuser == "yes" or askuser == "Ja" or askuser == "ha":  # multilanguage support :)
-            fn = os.path.join(str(folder), "CollectedData_" + cfg["scorer"])
-            Data = pd.read_hdf(fn + ".h5")
+            fn = folder / ("CollectedData_" + cfg["scorer"])
+            Data = pd.read_hdf(str(fn) + ".h5")
             conversioncode.guarantee_multiindex_rows(Data)
             imindex = Data.index
 
@@ -380,8 +380,8 @@ def convert_single2multiplelegacyAM(config, userfeedback=True, target=None):
             askuser = "yes"
 
         if askuser == "y" or askuser == "yes" or askuser == "Ja" or askuser == "ha":  # multilanguage support :)
-            fn = os.path.join(str(folder), "CollectedData_" + cfg["scorer"])
-            Data = pd.read_hdf(fn + ".h5")
+            fn = folder / ("CollectedData_" + cfg["scorer"])
+            Data = pd.read_hdf(str(fn) + ".h5")
             conversioncode.guarantee_multiindex_rows(Data)
             imindex = Data.index
 
@@ -544,7 +544,7 @@ def check_inferencecfg_sanity(cfg, inferencecfg):
 def read_inferencecfg(path_inference_config, cfg):
     """Load inferencecfg or initialize it."""
     try:
-        inferencecfg = auxiliaryfunctions.read_plainconfig(str(path_inference_config))
+        inferencecfg = auxiliaryfunctions.read_plainconfig(path_inference_config)
     except FileNotFoundError:
         inferencecfg = form_default_inferencecfg(cfg)
         auxiliaryfunctions.write_plainconfig(str(path_inference_config), dict(inferencecfg))

diff --git a/deeplabcut/utils/conversioncode.py b/deeplabcut/utils/conversioncode.py
@@ -68,7 +68,7 @@ def convertcsv2h5(config, userfeedback=True, scorer=None):
                 askuser = "yes"
 
             if askuser in ("y", "yes", "Ja", "ha", "oui"):  # multilanguage support :)
-                fn = os.path.join(str(folder), "CollectedData_" + cfg["scorer"] + ".csv")
+                fn = folder / ("CollectedData_" + cfg["scorer"] + ".csv")
                 # Determine whether the data are single- or multi-animal without loading into memory
                 # simply by checking whether 'individuals' is in the second line of the CSV.
                 with open(fn) as datafile:

diff --git a/deeplabcut/utils/make_labeled_video.py b/deeplabcut/utils/make_labeled_video.py
@@ -670,7 +670,7 @@ def create_labeled_video(
         )
         model_config_path = Path(config).parent / model_folder / "train" / Engine.PYTORCH.pose_cfg_name
         if model_config_path.exists():
-            model_config = auxiliaryfunctions.read_plainconfig(str(model_config_path))
+            model_config = auxiliaryfunctions.read_plainconfig(model_config_path)
             if model_config["train_settings"].get("weight_init", {}).get("memory_replay", False):
                 superanimal_name = model_config["train_settings"]["weight_init"]["dataset"]
             if bboxes_pcutoff is None: