Labels.split and Labels.make_training_splits

docs/index.md

@@ -37,11 +37,12 @@ import sleap_io as sio
 labels = sio.load_file("predictions.slp")
 
 # Save to NWB file.
-sio.save_file(labels, "predictions.nwb")
-# Or:
-# labels.save("predictions.nwb")
+labels.save("predictions.nwb")
 ```
 
+**See also:** [`Labels.save`](model.md#sleap_io.Labels.save) and [Formats](formats.md)
+
+
 ### Convert labels to raw arrays
 
 ```py
@@ -60,6 +61,9 @@ n_frames, n_tracks, n_nodes, xy_score = trx.shape
 assert xy_score == 3
 ```
 
+**See also:** [`Labels.numpy`](model.md#sleap_io.Labels.numpy)
+
+
 ### Read video data
 
 ```py
@@ -72,6 +76,10 @@ frame = video[0]
 height, width, channels = frame.shape
 ```
 
+**See also:** [`sio.load_video`](formats.md#sleap_io.load_video) and [`Video`](model.md#sleap_io.Video)
+
+
+
 ### Create labels from raw data
 
 ```py
@@ -107,6 +115,67 @@ labels = sio.Labels(videos=[video], skeletons=[skeleton], labeled_frames=[lf])
 labels.save("labels.slp")
 ```
 
+**See also:** [Model](model.md), [`Labels`](model.md#sleap_io.Labels),
+[`LabeledFrame`](model.md#sleap_io.LabeledFrame),
+[`Instance`](model.md#sleap_io.Instance),
+[`PredictedInstance`](model.md#sleap_io.PredictedInstance),
+[`Skeleton`](model.md#sleap_io.Skeleton), [`Video`](model.md#sleap_io.Video), [`Track`](model.md#sleap_io.Track), [`SuggestionFrame`](model.md#sleap_io.SuggestionFrame)
+
+
+### Fix video paths
+
+```py
+import sleap_io as sio
+
+labels = sio.load_file("labels.v001.slp")
+
+# Fix paths using prefixes.
+labels.replace_filenames(prefix_map={
+    "D:/data/sleap_projects": "/home/user/sleap_projects",
+    "C:/Users/sleaper/Desktop/test": "/home/user/sleap_projects",
+})
+
+labels.save("labels.v002.slp")
+```
+
+**See also:** [`Labels.replace_filenames`](model.md#sleap_io.Labels.replace_filenames)
+
+
+### Save labels with embedded images
+
+```py
+import sleap_io as sio
+
+# Load source labels.
+labels = sio.load_file("labels.v001.slp")
+
+# Save with embedded images for frames with user labeled data and suggested frames.
+labels.save("labels.v001.pkg.slp", embed="user+suggestions")
+```
+
+**See also:** [`Labels.save`](model.md#sleap_io.Labels.save)
+
+
+### Make training/validation/test splits
+
+```py
+import sleap_io as sio
+
+# Load source labels.
+labels = sio.load_file("labels.v001.slp")
+
+# Make splits and export with embedded images.
+labels.make_training_splits(n_train=0.8, n_val=0.1, n_test=0.1, save_dir="split1", seed=42)
+
+# Splits will be saved as self-contained SLP package files with images and labels.
+labels_train = sio.load_file("split1/train.pkg.slp")
+labels_val = sio.load_file("split1/val.pkg.slp")
+labels_test = sio.load_file("split1/test.pkg.slp")
+```
+
+**See also:** [`Labels.make_training_splits`](model.md#sleap_io.Labels.make_training_splits)
+
+
 ## Support
 For technical inquiries specific to this package, please [open an Issue](https://github.com/talmolab/sleap-io/issues)
 with a description of your problem or request.

mkdocs.yml

@@ -144,13 +144,13 @@ extra_css:
   - css/mkdocstrings.css
 
 copyright: >
-  Copyright © 2024 - 2024 Talmo Lab –
+  Copyright © 2022 - 2024 Talmo Lab –
   <a href="#__consent">Change cookie settings</a>
 
 nav:
   - Overview: index.md
   - Changelog: https://github.com/talmolab/sleap-io/releases
   - Core API:
-    - Data model: model.md
-    - Data formats: formats.md
+    - Model: model.md
+    - Formats: formats.md
   - Full API: reference/

sleap_io/io/jabs.py

@@ -169,7 +169,9 @@ def read_labels(
                         instances.append(new_instance)
             frame_label = LabeledFrame(video, frame_idx, instances)
             frames.append(frame_label)
-    return Labels(frames)
+    labels = Labels(frames)
+    labels.provenance["filename"] = labels_path
+    return labels
 
 
 def make_simple_skeleton(name: str, num_points: int) -> Skeleton:

sleap_io/io/labelstudio.py

@@ -43,7 +43,9 @@ def read_labels(
     else:
         assert isinstance(skeleton, Skeleton)
 
-    return parse_tasks(tasks, skeleton)
+    labels = parse_tasks(tasks, skeleton)
+    labels.provenance["filename"] = labels_path
+    return labels
 
 
 def infer_nodes(tasks: List[Dict]) -> Skeleton:

sleap_io/io/main.py

@@ -20,17 +20,29 @@ def load_slp(filename: str) -> Labels:
 
 
 def save_slp(
-    labels: Labels, filename: str, embed: str | list[tuple[Video, int]] | None = None
+    labels: Labels,
+    filename: str,
+    embed: bool | str | list[tuple[Video, int]] | None = None,
 ):
     """Save a SLEAP dataset to a `.slp` file.
 
     Args:
         labels: A SLEAP `Labels` object (see `load_slp`).
         filename: Path to save labels to ending with `.slp`.
-        embed: One of `"user"`, `"suggestions"`, `"user+suggestions"`, `"source"` or
-            list of tuples of `(video, frame_idx)` specifying the frames to embed. If
-            `"source"` is specified, no images will be embedded and the source video
+        embed: Frames to embed in the saved labels file. One of `None`, `True`,
+            `"all"`, `"user"`, `"suggestions"`, `"user+suggestions"`, `"source"` or list
+            of tuples of `(video, frame_idx)`.
+
+            If `None` is specified (the default) and the labels contains embedded
+            frames, those embedded frames will be re-saved to the new file.
+
+            If `True` or `"all"`, all labeled frames and suggested frames will be
+            embedded.
+
+            If `"source"` is specified, no images will be embedded and the source video
             will be restored if available.
+
+            This argument is only valid for the SLP backend.
     """
     return slp.write_labels(filename, labels, embed=embed)
 
@@ -149,7 +161,7 @@ def load_file(
         A `Labels` or `Video` object.
     """
     if isinstance(filename, Path):
-        filename = str(filename)
+        filename = filename.as_posix()
 
     if format is None:
         if filename.endswith(".slp"):

sleap_io/io/nwb.py

@@ -140,6 +140,7 @@ def read_nwb(path: str) -> Labels:
                     LabeledFrame(video=video, frame_idx=frame_idx, instances=insts)
                 )
     labels = Labels(lfs)
+    labels.provenance["filename"] = path
     return labels
 
 

sleap_io/io/slp.py

@@ -329,6 +329,9 @@ def embed_frames(
             to_embed_by_video[video] = []
         to_embed_by_video[video].append(frame_idx)
 
+    for video in to_embed_by_video:
+        to_embed_by_video[video] = np.unique(to_embed_by_video[video])
+
     replaced_videos = {}
     for video, frame_inds in to_embed_by_video.items():
         video_ind = labels.videos.index(video)
@@ -348,25 +351,40 @@ def embed_frames(
 
 
 def embed_videos(
-    labels_path: str, labels: Labels, embed: str | list[tuple[Video, int]]
+    labels_path: str, labels: Labels, embed: bool | str | list[tuple[Video, int]]
 ):
     """Embed videos in a SLEAP labels file.
 
     Args:
         labels_path: A string path to the SLEAP labels file to save.
         labels: A `Labels` object to save.
-        embed: One of `"user"`, `"suggestions"`, `"user+suggestions"`, `"source"` or
-            list of tuples of `(video, frame_idx)` specifying the frames to embed. If
-            `"source"` is specified, no images will be embedded and the source video
+        embed: Frames to embed in the saved labels file. One of `None`, `True`,
+            `"all"`, `"user"`, `"suggestions"`, `"user+suggestions"`, `"source"` or list
+            of tuples of `(video, frame_idx)`.
+
+            If `None` is specified (the default) and the labels contains embedded
+            frames, those embedded frames will be re-saved to the new file.
+
+            If `True` or `"all"`, all labeled frames and suggested frames will be
+            embedded.
+
+            If `"source"` is specified, no images will be embedded and the source video
             will be restored if available.
+
+            This argument is only valid for the SLP backend.
     """
+    if embed == True:
+        embed = "all"
     if embed == "user":
         embed = [(lf.video, lf.frame_idx) for lf in labels.user_labeled_frames]
     elif embed == "suggestions":
         embed = [(sf.video, sf.frame_idx) for sf in labels.suggestions]
     elif embed == "user+suggestions":
         embed = [(lf.video, lf.frame_idx) for lf in labels.user_labeled_frames]
         embed += [(sf.video, sf.frame_idx) for sf in labels.suggestions]
+    elif embed == "all":
+        embed = [(lf.video, lf.frame_idx) for lf in labels]
+        embed += [(sf.video, sf.frame_idx) for sf in labels.suggestions]
     elif embed == "source":
         embed = []
     elif isinstance(embed, list):
@@ -711,6 +729,13 @@ def write_metadata(labels_path: str, labels: Labels):
         "negative_anchors": {},
         "provenance": labels.provenance,
     }
+
+    # Custom encoding.
+    for k in md["provenance"]:
+        if isinstance(md["provenance"][k], Path):
+            # Path -> str
+            md["provenance"][k] = md["provenance"][k].as_posix()
+
     with h5py.File(labels_path, "a") as f:
         grp = f.require_group("metadata")
         grp.attrs["format_id"] = 1.2
@@ -942,7 +967,12 @@ def write_lfs(labels_path: str, labels: Labels):
 
     # Link instances based on from_predicted field.
     for instance_id, from_predicted in to_link:
-        instances[instance_id][5] = inst_to_id[id(from_predicted)]
+        if id(from_predicted) in inst_to_id:
+            instances[instance_id][5] = inst_to_id[id(from_predicted)]
+        else:
+            # Source instance may be missing if predictions were removed from the
+            # labels, in which case, remove the link.
+            instances[instance_id][5] = -1
 
     # Create structured arrays.
     points = np.array([tuple(x) for x in points], dtype=point_dtype)
@@ -1013,23 +1043,35 @@ def read_labels(labels_path: str) -> Labels:
         suggestions=suggestions,
         provenance=provenance,
     )
+    labels.provenance["filename"] = labels_path
 
     return labels
 
 
 def write_labels(
-    labels_path: str, labels: Labels, embed: str | list[tuple[Video, int]] | None = None
+    labels_path: str,
+    labels: Labels,
+    embed: bool | str | list[tuple[Video, int]] | None = None,
 ):
     """Write a SLEAP labels file.
 
     Args:
         labels_path: A string path to the SLEAP labels file to save.
         labels: A `Labels` object to save.
-        embed: One of `"user"`, `"suggestions"`, `"user+suggestions"`, `"source"`,
-            `None` or list of tuples of `(video, frame_idx)` specifying the frames to
-            embed. If `"source"` is specified, no images will be embedded and the source
-            video will be restored if available. If `None` is specified (the default),
-            existing embedded images will be re-embedded.
+        embed: Frames to embed in the saved labels file. One of `None`, `True`,
+            `"all"`, `"user"`, `"suggestions"`, `"user+suggestions"`, `"source"` or list
+            of tuples of `(video, frame_idx)`.
+
+            If `None` is specified (the default) and the labels contains embedded
+            frames, those embedded frames will be re-saved to the new file.
+
+            If `True` or `"all"`, all labeled frames and suggested frames will be
+            embedded.
+
+            If `"source"` is specified, no images will be embedded and the source video
+            will be restored if available.
+
+            This argument is only valid for the SLP backend.
     """
     if Path(labels_path).exists():
         Path(labels_path).unlink()

sleap_io/io/video.py

@@ -538,7 +538,7 @@ def find_embedded(name, obj):
                 self.image_format = ds.attrs["format"]
 
             if "frame_numbers" in ds.parent:
-                frame_numbers = ds.parent["frame_numbers"][:]
+                frame_numbers = ds.parent["frame_numbers"][:].astype(int)
                 self.frame_map = {frame: idx for idx, frame in enumerate(frame_numbers)}
                 self.source_inds = frame_numbers