Adding support for more .nwb training data to SLEAP

load_test.ipynb

@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import sleap_io as sio\n",
+    "import pynwb\n",
+    "import numpy as np\n",
+    "from numpy.testing import assert_equal\n",
+    "from sleap_io import save_file\n",
+    "from sleap_io.model.skeleton import Node, Edge, Symmetry, Skeleton\n",
+    "from sleap_io.model.instance import *\n",
+    "from pynwb.image import ImageSeries\n",
+    "from ndx_pose import (\n",
+    "    PoseEstimation,\n",
+    "    PoseEstimationSeries,\n",
+    "    TrainingFrame,\n",
+    "    TrainingFrames,\n",
+    "    PoseTraining,\n",
+    "    SourceVideos,\n",
+    ")\n",
+    "from sleap_io.io.nwb import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Labels(labeled_frames=1, videos=1, skeletons=1, tracks=0, suggestions=0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels_original = sio.load_slp(\"tests/data/slp/minimal_instance.pkg.slp\")\n",
+    "skel = Skeleton([Node(\"A\"), Node(\"B\")])\n",
+    "skel.name = \"name\"\n",
+    "skel.edges = [Edge(Node(\"A\"), Node(\"B\"))]\n",
+    "slp_skeleton_to_nwb(skel)\n",
+    "\n",
+    "inst = Instance({\"A\": [0, 1], \"B\": [2, 3]}, skeleton=Skeleton([\"A\", \"B\"]))\n",
+    "inst.skeleton.name = \"name\"\n",
+    "inst.skeleton.edges = [Edge(Node(\"A\"), Node(\"B\"))]\n",
+    "instance_to_skeleton_instance(inst)\n",
+    "\n",
+    "pose = labels_to_pose_training(labels_original)\n",
+    "labels = pose_training_to_labels(pose)\n",
+    "print(labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'tests/data/slp/minimal_instance.pkg.slp'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m labels_original \u001b[38;5;241m=\u001b[39m sio\u001b[38;5;241m.\u001b[39mload_slp(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtests/data/slp/minimal_instance.pkg.slp\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mlabels_original\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mminimal_instance.pkg.nwb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnwb_training\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m labels_loaded \u001b[38;5;241m=\u001b[39m sio\u001b[38;5;241m.\u001b[39mload_nwb(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtests/data/slp/minimal_instance.pkg.nwb\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(labels_loaded)\n",
+      "File \u001b[0;32m~/salk/io_fork/sleap_io/model/labels.py:372\u001b[0m, in \u001b[0;36mLabels.save\u001b[0;34m(self, filename, format, embed, **kwargs)\u001b[0m\n\u001b[1;32m    348\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Save labels to file in specified format.\u001b[39;00m\n\u001b[1;32m    349\u001b[0m \n\u001b[1;32m    350\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    368\u001b[0m \u001b[38;5;124;03m        This argument is only valid for the SLP backend.\u001b[39;00m\n\u001b[1;32m    369\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    370\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msleap_io\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m save_file\n\u001b[0;32m--> 372\u001b[0m \u001b[43msave_file\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43membed\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/salk/io_fork/sleap_io/io/main.py:235\u001b[0m, in \u001b[0;36msave_file\u001b[0;34m(labels, filename, format, **kwargs)\u001b[0m\n\u001b[1;32m    233\u001b[0m     save_slp(labels, filename, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    234\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnwb\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnwb_training\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnwb_predictions\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 235\u001b[0m     \u001b[43msave_nwb\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    236\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabelstudio\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    237\u001b[0m     save_labelstudio(labels, filename, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m~/salk/io_fork/sleap_io/io/main.py:86\u001b[0m, in \u001b[0;36msave_nwb\u001b[0;34m(labels, filename, as_training, append, **kwargs)\u001b[0m\n\u001b[1;32m     84\u001b[0m     nwb\u001b[38;5;241m.\u001b[39mappend_nwb(labels, filename, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     85\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m     \u001b[43mnwb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_nwb\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/salk/io_fork/sleap_io/io/nwb.py:408\u001b[0m, in \u001b[0;36mwrite_nwb\u001b[0;34m(labels, nwbfile_path, nwb_file_kwargs, pose_estimation_metadata)\u001b[0m\n\u001b[1;32m    401\u001b[0m nwb_file_kwargs\u001b[38;5;241m.\u001b[39mupdate(\n\u001b[1;32m    402\u001b[0m     session_description\u001b[38;5;241m=\u001b[39msession_description,\n\u001b[1;32m    403\u001b[0m     session_start_time\u001b[38;5;241m=\u001b[39msession_start_time,\n\u001b[1;32m    404\u001b[0m     identifier\u001b[38;5;241m=\u001b[39midentifier,\n\u001b[1;32m    405\u001b[0m )\n\u001b[1;32m    407\u001b[0m nwbfile \u001b[38;5;241m=\u001b[39m NWBFile(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mnwb_file_kwargs)\n\u001b[0;32m--> 408\u001b[0m nwbfile \u001b[38;5;241m=\u001b[39m \u001b[43mappend_nwb_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnwbfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpose_estimation_metadata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    410\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m NWBHDF5IO(\u001b[38;5;28mstr\u001b[39m(nwbfile_path), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m io:\n\u001b[1;32m    411\u001b[0m     io\u001b[38;5;241m.\u001b[39mwrite(nwbfile)\n",
+      "File \u001b[0;32m~/salk/io_fork/sleap_io/io/nwb.py:491\u001b[0m, in \u001b[0;36mappend_nwb_data\u001b[0;34m(labels, nwbfile, pose_estimation_metadata)\u001b[0m\n\u001b[1;32m    487\u001b[0m default_metadata\u001b[38;5;241m.\u001b[39mupdate(pose_estimation_metadata)\n\u001b[1;32m    489\u001b[0m \u001b[38;5;66;03m# For every track in that video create a PoseEstimation container\u001b[39;00m\n\u001b[1;32m    490\u001b[0m name_of_tracks_in_video \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 491\u001b[0m     \u001b[43mlabels_data_df\u001b[49m\u001b[43m[\u001b[49m\u001b[43mvideo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilename\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    492\u001b[0m     \u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_level_values(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrack_name\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    493\u001b[0m     \u001b[38;5;241m.\u001b[39munique()\n\u001b[1;32m    494\u001b[0m )\n\u001b[1;32m    496\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m track_name \u001b[38;5;129;01min\u001b[39;00m name_of_tracks_in_video:\n\u001b[1;32m    497\u001b[0m     pose_estimation_container \u001b[38;5;241m=\u001b[39m build_pose_estimation_container_for_track(\n\u001b[1;32m    498\u001b[0m         labels_data_df,\n\u001b[1;32m    499\u001b[0m         labels,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    502\u001b[0m         default_metadata,\n\u001b[1;32m    503\u001b[0m     )\n",
+      "File \u001b[0;32m~/mambaforge3/envs/io_dev/lib/python3.12/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
+      "File \u001b[0;32m~/mambaforge3/envs/io_dev/lib/python3.12/site-packages/pandas/core/indexes/range.py:417\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m    416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Hashable):\n\u001b[0;32m--> 417\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n\u001b[1;32m    418\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n\u001b[1;32m    419\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'tests/data/slp/minimal_instance.pkg.slp'"
+     ]
+    }
+   ],
+   "source": [
+    "labels_original = sio.load_slp(\"tests/data/slp/minimal_instance.pkg.slp\")\n",
+    "labels_original.save(\"minimal_instance.pkg.nwb\", format=\"nwb_training\")\n",
+    "\n",
+    "labels_loaded = sio.load_nwb(\"tests/data/slp/minimal_instance.pkg.nwb\")\n",
+    "print(labels_loaded)\n",
+    "assert len(labels_original.labeled_frames) == len(labels_loaded.labeled_frames)\n",
+    "assert len(labels_original.videos) == len(labels_loaded.videos)\n",
+    "assert len(labels_original.skeletons) == len(labels_loaded.skeletons)\n",
+    "assert len(labels_original.tracks) == len(labels_loaded.tracks)\n",
+    "assert len(labels_original.suggestions) == len(labels_loaded.suggestions)\n",
+    "assert labels_original.provenance == labels_loaded.provenance"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "io_dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pyproject.toml

@@ -24,7 +24,7 @@ dependencies = [
     "attrs",
     "h5py>=3.8.0",
     "pynwb",
-    "ndx-pose",
+    "ndx-pose @ git+https://github.com/rly/ndx-pose@a847ad4be75e60ef9e413b8cbfc99c616fc9fd05",
     "pandas",
     "simplejson",
     "imageio",

save_test.ipynb

@@ -0,0 +1,82 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "minimal_instance.pkg.nwb_images/img_0.png\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Can't write images with one color channel.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 16\u001b[0m\n\u001b[1;32m     14\u001b[0m     img_path \u001b[38;5;241m=\u001b[39m save_path \u001b[38;5;241m/\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimg_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.png\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     15\u001b[0m     \u001b[38;5;28mprint\u001b[39m(img_path)\n\u001b[0;32m---> 16\u001b[0m     \u001b[43miio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     17\u001b[0m     img_paths\u001b[38;5;241m.\u001b[39mappend(img_path)\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28mprint\u001b[39m(img_paths)\n",
+      "File \u001b[0;32m~/mambaforge3/envs/io_dev/lib/python3.12/site-packages/imageio/v3.py:147\u001b[0m, in \u001b[0;36mimwrite\u001b[0;34m(uri, image, plugin, extension, format_hint, **kwargs)\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Write an ndimage to the given URI.\u001b[39;00m\n\u001b[1;32m    106\u001b[0m \n\u001b[1;32m    107\u001b[0m \u001b[38;5;124;03mThe exact behavior depends on the file type and plugin used. To learn about\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    136\u001b[0m \n\u001b[1;32m    137\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    139\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m imopen(\n\u001b[1;32m    140\u001b[0m     uri,\n\u001b[1;32m    141\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    145\u001b[0m     extension\u001b[38;5;241m=\u001b[39mextension,\n\u001b[1;32m    146\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m img_file:\n\u001b[0;32m--> 147\u001b[0m     encoded \u001b[38;5;241m=\u001b[39m \u001b[43mimg_file\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m encoded\n",
+      "File \u001b[0;32m~/mambaforge3/envs/io_dev/lib/python3.12/site-packages/imageio/plugins/pillow.py:433\u001b[0m, in \u001b[0;36mPillowPlugin.write\u001b[0;34m(self, ndimage, mode, format, is_batch, **kwargs)\u001b[0m\n\u001b[1;32m    431\u001b[0m     is_batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    432\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m ndimage\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m ndimage\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 433\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt write images with one color channel.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    434\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m ndimage\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m ndimage\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4\u001b[39m]:\n\u001b[1;32m    435\u001b[0m     \u001b[38;5;66;03m# Note: this makes a channel-last assumption\u001b[39;00m\n\u001b[1;32m    436\u001b[0m     is_batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
+      "\u001b[0;31mValueError\u001b[0m: Can't write images with one color channel."
+     ]
+    }
+   ],
+   "source": [
+    "import imageio.v3 as iio\n",
+    "from pathlib import Path\n",
+    "import sleap_io as sio\n",
+    "\n",
+    "save_path = Path(\"minimal_instance.pkg.nwb_images\")\n",
+    "try:\n",
+    "    save_path.mkdir(parents=True, exist_ok=True)\n",
+    "except Exception as e:\n",
+    "    print(f\"An error {e} occurred. The directory could not be created.\")\n",
+    "img_paths = []\n",
+    "\n",
+    "labels_original = sio.load_slp(\"tests/data/slp/minimal_instance.pkg.slp\")\n",
+    "for i, lf in enumerate(labels_original):\n",
+    "    img_path = save_path / f\"img_{i}.png\"\n",
+    "    print(img_path)\n",
+    "    iio.imwrite(img_path, lf.image)\n",
+    "    img_paths.append(img_path)\n",
+    "print(img_paths)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "io_dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}