Allow csv and text file support on sleap track

sleap/nn/inference.py

@@ -5285,8 +5285,8 @@ def _make_provider_from_cli(args: argparse.Namespace) -> Tuple[Provider, str]:
         args: Parsed CLI namespace.
 
     Returns:
-        A tuple of `(provider, data_path)` with the data `Provider` and path to the data
-        that was specified in the args.
+        `(provider_list, data_path_list, output_path_list)` with the data `Provider`, path to the data
+        that was specified in the args, and list out output paths if a csv file was inputed.
     """
 
     # Figure out which input path to use.
@@ -5299,72 +5299,96 @@ def _make_provider_from_cli(args: argparse.Namespace) -> Tuple[Provider, str]:
         )
 
     data_path_obj = Path(data_path)
+
+    # Set output_path_list to None as a default to return later
+    output_path_list = None
 
     # Check that input value is valid
     if not data_path_obj.exists():
         raise ValueError("Path to data_path does not exist")
+
+    elif data_path_obj.suffix.lower() == ".csv":
+        try:
+            # Read the CSV file
+            df = pandas.read_csv(data_path)
+
+            # Check if the 'data_path' and 'output_path' columns exist
+            if "data_path" in df.columns:
+                raw_data_path_list = df["data_path"].tolist()
+            else:
+                print("Column 'data_path' does not exist in data_path csv file.")
+            if "output_path" in df.columns:
+                output_path_list = df["output_path"].tolist()
+
+        except FileNotFoundError as e:
+            raise ValueError(f"CSV file not found: {data_path}") from e
+        except pandas.errors.EmptyDataError as e:
+           raise ValueError(f"CSV file is empty: {data_path}") from e
+        except pandas.errors.ParserError as e:
+            raise ValueError(f"Error parsing CSV file: {data_path}") from e
 
     # Check for multiple video inputs
     # Compile file(s) into a list for later itteration
-    if data_path_obj.is_dir():
-        data_path_list = []
+    elif data_path_obj.is_dir():
+        raw_data_path_list = []
         for file_path in data_path_obj.iterdir():
             if file_path.is_file():
-                data_path_list.append(Path(file_path))
+                raw_data_path_list.append(Path(file_path))
+
     elif data_path_obj.is_file():
-        data_path_list = [data_path_obj]
+        raw_data_path_list = [data_path_obj]
 
     # Provider list to accomodate multiple video inputs
-    output_provider_list = []
-    output_data_path_list = []
-    for file_path in data_path_list:
+    provider_list = []
+    data_path_list = []
+    for file_path in raw_data_path_list:
         # Create a provider for each file
-        if file_path.as_posix().endswith(".slp") and len(data_path_list) > 1:
+        if file_path.as_posix().endswith(".slp") and len(raw_data_path_list) > 1:
             print(f"slp file skipped: {file_path.as_posix()}")
 
         elif file_path.as_posix().endswith(".slp"):
             labels = sleap.load_file(file_path.as_posix())
 
             if args.only_labeled_frames:
-                output_provider_list.append(
+                provider_list.append(
                     LabelsReader.from_user_labeled_frames(labels)
                 )
             elif args.only_suggested_frames:
-                output_provider_list.append(
+                provider_list.append(
                     LabelsReader.from_unlabeled_suggestions(labels)
                 )
             elif getattr(args, "video.index") != "":
-                output_provider_list.append(
+                provider_list.append(
                     VideoReader(
                         video=labels.videos[int(getattr(args, "video.index"))],
                         example_indices=frame_list(args.frames),
                     )
                 )
             else:
-                output_provider_list.append(LabelsReader(labels))
+                provider_list.append(LabelsReader(labels))
 
-            output_data_path_list.append(file_path)
+            data_path_list.append(file_path)
 
         else:
             try:
                 video_kwargs = dict(
                     dataset=vars(args).get("video.dataset"),
                     input_format=vars(args).get("video.input_format"),
                 )
-                output_provider_list.append(
+                provider_list.append(
                     VideoReader.from_filepath(
                         filename=file_path.as_posix(),
                         example_indices=frame_list(args.frames),
                         **video_kwargs,
                     )
                 )
                 print(f"Video: {file_path.as_posix()}")
-                output_data_path_list.append(file_path)
+                data_path_list.append(file_path)
                 # TODO: Clean this up.
             except Exception:
                 print(f"Error reading file: {file_path.as_posix()}")
 
-    return output_provider_list, output_data_path_list
+    return provider_list, data_path_list, output_path_list
 
 
 def _make_predictor_from_cli(args: argparse.Namespace) -> Predictor:
@@ -5496,10 +5520,12 @@ def main(args: Optional[list] = None):
     print()
 
     # Setup data loader.
-    provider_list, data_path_list = _make_provider_from_cli(args)
-
-    output_path = args.output
+    provider_list, data_path_list, output_path_list = _make_provider_from_cli(args)
 
+# if output_path has not been extracted from a csv file yet
+    if output_path_list is None:
+        output_path = args.output
+
     # check if output_path is valid before running inference
     if (
         output_path is not None
@@ -5520,7 +5546,7 @@ def main(args: Optional[list] = None):
     if args.models is not None:
 
         # Run inference on all files inputed
-        for data_path, provider in zip(data_path_list, provider_list):
+        for i, (data_path, provider) in enumerate(zip(data_path_list, provider_list)):
             # Setup models.
             data_path_obj = Path(data_path)
             predictor = _make_predictor_from_cli(args)
@@ -5531,11 +5557,17 @@ def main(args: Optional[list] = None):
 
             # if output path was not provided, create an output path
             if output_path is None:
-                output_path = f"{data_path.as_posix()}.predictions.slp"
-                output_path_obj = Path(output_path)
+                output_path = data_path + ".predictions.slp"
+                # if output path was not provided, create an output path
+                if output_path_list is not None:
+                    output_path = output_path_list[i]
+
+                elif output_path is None:
+                    output_path = f"{data_path.as_posix()}.predictions.slp"
+                    output_path_obj = Path(output_path)
 
-            else:
-                output_path_obj = Path(output_path)
+                else:
+                    output_path_obj = Path(output_path)
 
                 # if output_path was provided and multiple inputs were provided, create a directory to store outputs
                 if len(data_path_list) > 1: