Better reprs and QOL

sleap_io/io/main.py

@@ -123,7 +123,10 @@ def load_video(filename: str, **kwargs) -> Video:
     """Load a video file.
 
     Args:
-        filename: Path to a video file.
+        filename: The filename(s) of the video. Supported extensions: "mp4", "avi",
+            "mov", "mj2", "mkv", "h5", "hdf5", "slp", "png", "jpg", "jpeg", "tif",
+            "tiff", "bmp". If the filename is a list, a list of image filenames are
+            expected. If filename is a folder, it will be searched for images.
 
     Returns:
         A `Video` object.

sleap_io/model/instance.py

@@ -265,6 +265,13 @@ def __len__(self) -> int:
         """Return the number of points in the instance."""
         return len(self.points)
 
+    def __repr__(self) -> str:
+        """Return a readable representation of the instance."""
+        pts = self.numpy().tolist()
+        track = f'"{self.track.name}"' if self.track is not None else self.track
+
+        return f"Instance(points={pts}, track={track})"
+
     @property
     def n_visible(self) -> int:
         """Return the number of visible points in the instance."""
@@ -327,6 +334,22 @@ class PredictedInstance(Instance):
     score: float = 0.0
     tracking_score: Optional[float] = 0
 
+    def __repr__(self) -> str:
+        """Return a readable representation of the instance."""
+        pts = self.numpy().tolist()
+        track = f'"{self.track.name}"' if self.track is not None else self.track
+
+        score = str(self.score) if self.score is None else f"{self.score:.2f}"
+        tracking_score = (
+            str(self.tracking_score)
+            if self.tracking_score is None
+            else f"{self.tracking_score:.2f}"
+        )
+        return (
+            f"PredictedInstance(points={pts}, track={track}, "
+            f"score={score}, tracking_score={tracking_score})"
+        )
+
     @classmethod
     def from_numpy(  # type: ignore[override]
         cls,

sleap_io/model/labels.py

@@ -23,7 +23,7 @@
 from attrs import define, field
 from typing import Union, Optional, Any
 import numpy as np
-
+from pathlib import Path
 from sleap_io.model.skeleton import Skeleton
 
 
@@ -57,6 +57,14 @@
 
     def __attrs_post_init__(self):
         """Append videos, skeletons, and tracks seen in `labeled_frames` to `Labels`."""
+        self.update()
+
+    def update(self):
+        """Update data structures based on contents.
+
+        This function will update the list of skeletons, videos and tracks from the
+        labeled frames, instances and suggestions.
+        """
         for lf in self.labeled_frames:
             if lf.video not in self.videos:
                 self.videos.append(lf.video)
@@ -68,7 +76,13 @@
                 if inst.track is not None and inst.track not in self.tracks:
                     self.tracks.append(inst.track)
 
-    def __getitem__(self, key: int) -> list[LabeledFrame] | LabeledFrame:
+        for sf in self.suggestions:
+            if sf.video not in self.videos:
+                self.videos.append(sf.video)
+
+    def __getitem__(
+        self, key: int | slice | list[int] | np.ndarray | tuple[Video, int]
+    ) -> list[LabeledFrame] | LabeledFrame:
         """Return one or more labeled frames based on indexing criteria."""
         if type(key) == int:
             return self.labeled_frames[key]
@@ -111,14 +125,58 @@
             f"labeled_frames={len(self.labeled_frames)}, "
             f"videos={len(self.videos)}, "
             f"skeletons={len(self.skeletons)}, "
-            f"tracks={len(self.tracks)}"
+            f"tracks={len(self.tracks)}, "
+            f"suggestions={len(self.suggestions)}"
             ")"
         )
 
     def __str__(self) -> str:
         """Return a readable representation of the labels."""
         return self.__repr__()
 
+    def append(self, lf: LabeledFrame, update: bool = True):
+        """Append a labeled frame to the labels.
+
+        Args:
+            lf: A labeled frame to add to the labels.
+            update: If `True` (the default), update list of videos, tracks and
+                skeletons from the contents.
+        """
+        self.labeled_frames.append(lf)
+
+        if update:
+            if lf.video not in self.videos:
+                self.videos.append(lf.video)
+
+            for inst in lf:
+                if inst.skeleton not in self.skeletons:
+                    self.skeletons.append(inst.skeleton)
+
+                if inst.track is not None and inst.track not in self.tracks:
+                    self.tracks.append(inst.track)
+
+    def extend(self, lfs: list[LabeledFrame], update: bool = True):
+        """Append a labeled frame to the labels.
+
+        Args:
+            lfs: A list of labeled frames to add to the labels.
+            update: If `True` (the default), update list of videos, tracks and
+                skeletons from the contents.
+        """
+        self.labeled_frames.extend(lfs)
+
+        if update:
+            for lf in lfs:
+                if lf.video not in self.videos:
+                    self.videos.append(lf.video)
+
+                for inst in lf:
+                    if inst.skeleton not in self.skeletons:
+                        self.skeletons.append(inst.skeleton)
+
+                    if inst.track is not None and inst.track not in self.tracks:
+                        self.tracks.append(inst.track)
+
     def numpy(
         self,
         video: Optional[Union[Video, int]] = None,
@@ -417,3 +475,79 @@
         for sf in self.suggestions:
             if sf.video in video_map:
                 sf.video = video_map[sf.video]
+
+    def replace_filenames(
+        self,
+        new_filenames: list[str | Path] | None = None,
+        filename_map: dict[str | Path, str | Path] | None = None,
+        prefix_map: dict[str | Path, str | Path] | None = None,
+    ):
+        """Replace video filenames.
+
+        Args:
+            new_filenames: List of new filenames. Must have the same length as the
+                number of videos in the labels.
+            filename_map: Dictionary mapping old filenames (keys) to new filenames
+                (values).
+            prefix_map: Dictonary mapping old prefixes (keys) to new prefixes (values).
+
+        Notes:
+            Only one of the argument types can be provided.
+        """
+        n = 0
+        if new_filenames is not None:
+            n += 1
+        if filename_map is not None:
+            n += 1
+        if prefix_map is not None:
+            n += 1
+        if n != 1:
+            raise ValueError(
+                "Exactly one input method must be provided to replace filenames."
+            )
+
+        if new_filenames is not None:
+            if len(self.videos) != len(new_filenames):
+                raise ValueError(
+                    f"Number of new filenames ({len(new_filenames)}) does not match "
+                    f"the number of videos ({len(self.videos)})."
+                )
+
+            for video, new_filename in zip(self.videos, new_filenames):
+                video.replace_filename(new_filename)
+
+        elif filename_map is not None:
+            for video in self.videos:
+                for old_fn, new_fn in filename_map.items():
+                    if type(video.filename) == list:
+                        new_fns = []
+                        for fn in video.filename:
+                            if Path(fn) == Path(old_fn):
+                                new_fns.append(new_fn)
+                            else:
+                                new_fns.append(fn)
+                        video.replace_filename(new_fns)
+                    else:
+                        if Path(video.filename) == Path(old_fn):
+                            video.replace_filename(new_fn)
+
+        elif prefix_map is not None:
+            for video in self.videos:
+                for old_prefix, new_prefix in prefix_map.items():
+                    old_prefix, new_prefix = Path(old_prefix), Path(new_prefix)
+
+                    if type(video.filename) == list:
+                        new_fns = []
+                        for fn in video.filename:
+                            fn = Path(fn)
+                            if fn.as_posix().startswith(old_prefix.as_posix()):
+                                new_fns.append(new_prefix / fn.relative_to(old_prefix))
+                            else:
+                                new_fns.append(fn)
+                        video.replace_filename(new_fns)
+                    else:
+                        fn = Path(video.filename)
+                        if fn.as_posix().startswith(old_prefix.as_posix()):
+                            video.replace_filename(
+                                new_prefix / fn.relative_to(old_prefix)
+                            )

sleap_io/model/skeleton.py

@@ -165,6 +165,11 @@ def edge_inds(self) -> list[Tuple[int, int]]:
             for edge in self.edges
         ]
 
+    @property
+    def edge_names(self) -> list[str, str]:
+        """Edge names as a list of 2-tuples with string node names."""
+        return [(edge.source.name, edge.destination.name) for edge in self.edges]
+
     @property
     def flipped_node_inds(self) -> list[int]:
         """Returns node indices that should be switched when horizontally flipping."""
@@ -183,6 +188,11 @@ def __len__(self) -> int:
         """Return the number of nodes in the skeleton."""
         return len(self.nodes)
 
+    def __repr__(self) -> str:
+        """Return a readable representation of the skeleton."""
+        nodes = ", ".join([f'"{node}"' for node in self.node_names])
+        return "Skeleton(" f"nodes=[{nodes}], " f"edges={self.edge_inds}" ")"
+
     def index(self, node: Node | str) -> int:
         """Return the index of a node specified as a `Node` or string name."""
         if type(node) == str:

sleap_io/model/video.py

@@ -8,7 +8,7 @@
 import attrs
 from typing import Tuple, Optional, Optional
 import numpy as np
-from sleap_io.io.video import VideoBackend, MediaVideo, HDF5Video
+from sleap_io.io.video import VideoBackend, MediaVideo, HDF5Video, ImageVideo
 from pathlib import Path
 
 
@@ -23,7 +23,10 @@
     backend appropriately.
 
     Attributes:
-        filename: The filename(s) of the video.
+        filename: The filename(s) of the video. Supported extensions: "mp4", "avi",
+            "mov", "mj2", "mkv", "h5", "hdf5", "slp", "png", "jpg", "jpeg", "tif",
+            "tiff", "bmp". If the filename is a list, a list of image filenames are
+            expected. If filename is a folder, it will be searched for images.
         backend: An object that implements the basic methods for reading and
             manipulating frames of a specific video type.
         backend_metadata: A dictionary of metadata specific to the backend. This is
@@ -45,7 +48,12 @@
     backend_metadata: dict[str, any] = attrs.field(factory=dict)
     source_video: Optional[Video] = None
 
-    EXTS = MediaVideo.EXTS + HDF5Video.EXTS
+    EXTS = MediaVideo.EXTS + HDF5Video.EXTS + ImageVideo.EXTS
+
+    def __attrs_post_init__(self):
+        """Post init syntactic sugar."""
+        if self.backend is None and self.exists():
+            self.open()
 
     def __attrs_post_init__(self):
         """Post init syntactic sugar."""
@@ -65,7 +73,10 @@
         """Create a Video from a filename.
 
         Args:
-            filename: Path to video file(s).
+            filename: The filename(s) of the video. Supported extensions: "mp4", "avi",
+                "mov", "mj2", "mkv", "h5", "hdf5", "slp", "png", "jpg", "jpeg", "tif",
+                "tiff", "bmp". If the filename is a list, a list of image filenames are
+                expected. If filename is a folder, it will be searched for images.
             dataset: Name of dataset in HDF5 file.
             grayscale: Whether to force grayscale. If None, autodetect on first frame
                 load.

tests/model/test_instance.py

@@ -89,6 +89,10 @@ def test_instance():
     inst = Instance({"A": [0, 1], "B": [2, 3]}, skeleton=Skeleton(["A", "B"]))
     assert_equal(inst.numpy(), [[0, 1], [2, 3]])
     assert type(inst["A"]) == Point
+    assert str(inst) == "Instance(points=[[0.0, 1.0], [2.0, 3.0]], track=None)"
+
+    inst.track = Track("trk")
+    assert str(inst) == 'Instance(points=[[0.0, 1.0], [2.0, 3.0]], track="trk")'
 
     inst = Instance({"A": [0, 1]}, skeleton=Skeleton(["A", "B"]))
     assert_equal(inst.numpy(), [[0, 1], [np.nan, np.nan]])
@@ -155,3 +159,8 @@ def test_predicted_instance():
     assert inst[0].score == 0.4
     assert inst[1].score == 0.5
     assert inst.score == 0.6
+
+    assert (
+        str(inst) == "PredictedInstance(points=[[0.0, 1.0], [2.0, 3.0]], track=None, "
+        "score=0.60, tracking_score=None)"
+    )