Demo of spp overlaid with GT

notebooks/demo_spp_and_gt.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Segmentation 'superpixelSegmentation' already exists. Skipping segmentation.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/kharrington/.album/micromamba/envs/nesoi/lib/python3.10/site-packages/morphometrics_engine/measure.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
+      "  from tqdm.autonotebook import tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import zarr\n",
+    "import napari\n",
+    "import numpy as np\n",
+    "import copick\n",
+    "import copick_utils\n",
+    "from magicgui import magicgui\n",
+    "from napari.types import ImageData, LabelsData\n",
+    "from pathlib import Path\n",
+    "from cellcanvas_spp.segmentation import superpixels\n",
+    "\n",
+    "try:\n",
+    "    # DATA_DIR = Path(\"/Users/kharrington/git/cellcanvas/superpixels/notebooks/my_synthetic_data_10439_dataportal.json\")\n",
+    "    DATA_DIR = Path(os.environ[\"COPICK_DATA\"])\n",
+    "except KeyError:\n",
+    "    raise ValueError(\n",
+    "        \"Please set the COPICK_DATA environment variable to point to the data directory\\n\\n\"\n",
+    "        \"$ export COPICK_DATA=</path/to/copick/data>\"\n",
+    "    )\n",
+    "\n",
+    "@magicgui(auto_call=True, sigma={\"widget_type\": \"FloatSlider\", \"min\": 0, \"max\": 10}, h_minima={\"widget_type\": \"FloatSlider\", \"min\": 0, \"max\": 50})\n",
+    "def _spp_widget(image: ImageData, sigma: float = 4, h_minima: float = 0.001) -> LabelsData:\n",
+    "    return superpixels(image, sigma=sigma, h_minima=h_minima)\n",
+    "\n",
+    "_spp_widget.h_minima._widget._readout_widget.setDecimals(4)\n",
+    "\n",
+    "def get_segmentation_array(copick_run, segmentation_name, voxel_spacing=10, is_multilabel=True):\n",
+    "    seg = copick_run.get_segmentations(is_multilabel=is_multilabel, name=segmentation_name, voxel_size=voxel_spacing)\n",
+    "    if len(seg) == 0:\n",
+    "        return None\n",
+    "    segmentation = zarr.open(seg[0].zarr().path, mode=\"r\")['0']\n",
+    "    return segmentation[:]\n",
+    "\n",
+    "def segment_superpixels(example_run, voxel_spacing=10, interactive: bool = False):\n",
+    "    \"\"\"\n",
+    "    This function handles the segmentation logic. If the segmentation exists in copick, it will be skipped.\n",
+    "    \"\"\"\n",
+    "    segmentation_name = \"superpixelSegmentation\"\n",
+    "\n",
+    "    # Check if the segmentation already exists\n",
+    "    seg = get_segmentation_array(example_run, segmentation_name, voxel_spacing=voxel_spacing, is_multilabel=True)\n",
+    "    if seg is not None:\n",
+    "        print(f\"Segmentation '{segmentation_name}' already exists. Skipping segmentation.\")\n",
+    "        return seg\n",
+    "\n",
+    "    # Proceed with superpixel segmentation if it does not exist\n",
+    "    tomo_type = \"wbp\"\n",
+    "    tomogram = example_run.voxel_spacings[0].tomograms[0]\n",
+    "\n",
+    "    # Open zarr\n",
+    "    z = zarr.open(tomogram.zarr())\n",
+    "    img = z[\"0\"]  # Get the highest resolution scale\n",
+    "\n",
+    "    if interactive:\n",
+    "        img = img[50:100, 180:360, 210:430]  # Cropping for interactive mode\n",
+    "\n",
+    "    print(\"Loading image into memory ...\")\n",
+    "    img = np.asarray(img)  # Loading into memory\n",
+    "\n",
+    "    print(\"Segmenting superpixels ...\")\n",
+    "    segm = superpixels(img, sigma=4, h_minima=0.0025)\n",
+    "    print(\"Done ...\")\n",
+    "\n",
+    "    # Save segmentation into copick\n",
+    "    print(\"Saving segmentation to copick...\")\n",
+    "    new_seg = example_run.new_segmentation(voxel_spacing, segmentation_name, session_id=\"0\", is_multilabel=True, user_id=\"cellcanvasSPP\")\n",
+    "    segmentation_group = zarr.open_group(new_seg.path, mode=\"a\")\n",
+    "    segmentation_group[\"0\"] = segm\n",
+    "    print(\"Segmentation saved.\")\n",
+    "\n",
+    "    return segm\n",
+    "\n",
+    "def prepare_and_run_segmentation(interactive: bool = False):\n",
+    "    \"\"\"\n",
+    "    Prepare and run the segmentation by fetching data from copick.\n",
+    "    \"\"\"\n",
+    "    config_file = DATA_DIR\n",
+    "    root = copick.from_file(config_file)\n",
+    "\n",
+    "    run_name = \"16193\"\n",
+    "    example_run = root.get_run(run_name)\n",
+    "    voxel_spacing = 10\n",
+    "\n",
+    "    # Check for existing segmentation or run segmentation if not found\n",
+    "    seg = segment_superpixels(example_run, voxel_spacing, interactive)\n",
+    "\n",
+    "    # Open viewer and add image\n",
+    "    viewer = napari.Viewer()\n",
+    "    img = np.asarray(zarr.open(example_run.voxel_spacings[0].tomograms[0].zarr())[\"0\"])  # Load image\n",
+    "    viewer.add_image(img)\n",
+    "    viewer.add_labels(seg, name=\"Superpixels\")\n",
+    "\n",
+    "    # Add ground truth\n",
+    "    base_seg = np.zeros_like(zarr.open(example_run.segmentations[0].zarr())[\"0\"])\n",
+    "    for idx, seg in enumerate(example_run.segmentations):\n",
+    "        z = zarr.open(seg.zarr())[\"0\"][:]\n",
+    "        base_seg = base_seg + (idx + 1) * z\n",
+    "    viewer.add_labels(base_seg)\n",
+    "\n",
+    "    # If interactive mode, show the widget\n",
+    "    if interactive:\n",
+    "        viewer.window.add_dock_widget(_spp_widget, area=\"right\")\n",
+    "\n",
+    "    # napari.run()\n",
+    "    return viewer\n",
+    "\n",
+    "# Run segmentation directly in a Jupyter notebook\n",
+    "viewer = prepare_and_run_segmentation(interactive=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
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