{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tracking with manually annotated edges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One important feature of `LapTrack` is tracking particles with preserving the ground-truth (for example manually annotated) point-to-point connections. This notebooks demonstrates its example usage with `napari`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -q --upgrade -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import napari\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import json\n",
    "from IPython.display import display\n",
    "from skimage.io import imread\n",
    "from skimage.measure import regionprops_table\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from laptrack import LapTrack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load images and show them in the viewer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = imread(\"napari_interactive_fix_data/example_images.tif\")\n",
    "labels = imread(\"napari_interactive_fix_data/example_label.tif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "viewer = napari.Viewer()\n",
    "viewer.add_image(images, name=\"images\")\n",
    "viewer.add_labels(labels, name=\"labels\")\n",
    "original_camera_state = viewer.camera.dict()\n",
    "with open(\"napari_interactive_fix_data/camera_state.json\", \"r\") as f:\n",
    "    camera_state = json.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating centroids of the segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate centroids of each segmentation by `regionprops_table`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>centroid-0</th>\n",
       "      <th>centroid-1</th>\n",
       "      <th>frame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>103.091603</td>\n",
       "      <td>195.816794</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>151.812048</td>\n",
       "      <td>15.674699</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>31.552063</td>\n",
       "      <td>43.740668</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59</td>\n",
       "      <td>60.389432</td>\n",
       "      <td>76.156556</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64</td>\n",
       "      <td>125.100457</td>\n",
       "      <td>21.646119</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  centroid-0  centroid-1  frame\n",
       "0      3  103.091603  195.816794      0\n",
       "1     25  151.812048   15.674699      0\n",
       "2     35   31.552063   43.740668      0\n",
       "3     59   60.389432   76.156556      0\n",
       "4     64  125.100457   21.646119      0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calc_frame_regionprops(labels):  # noqa: E302\n",
    "    dfs = []\n",
    "    for frame in range(labels.shape[0]):\n",
    "        df = pd.DataFrame(\n",
    "            regionprops_table(labels[frame], properties=[\"label\", \"centroid\"])\n",
    "        )\n",
    "        df[\"frame\"] = frame\n",
    "        dfs.append(df)\n",
    "    return pd.concat(dfs)\n",
    "\n",
    "\n",
    "regionprops_df = calc_frame_regionprops(labels)\n",
    "display(regionprops_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tracking by LapTrack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tracking the centroids using the default `sqeuclidean` metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>centroid-0</th>\n",
       "      <th>centroid-1</th>\n",
       "      <th>frame_y</th>\n",
       "      <th>tree_id</th>\n",
       "      <th>track_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frame</th>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>103.091603</td>\n",
       "      <td>195.816794</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>151.812048</td>\n",
       "      <td>15.674699</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>31.552063</td>\n",
       "      <td>43.740668</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59</td>\n",
       "      <td>60.389432</td>\n",
       "      <td>76.156556</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64</td>\n",
       "      <td>125.100457</td>\n",
       "      <td>21.646119</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             label  centroid-0  centroid-1  frame_y  tree_id  track_id\n",
       "frame index                                                           \n",
       "0     0          3  103.091603  195.816794        0        0         0\n",
       "      1         25  151.812048   15.674699        0        1         1\n",
       "      2         35   31.552063   43.740668        0        2         2\n",
       "      3         59   60.389432   76.156556        0        3         3\n",
       "      4         64  125.100457   21.646119        0        4         4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lt = LapTrack(cutoff=100**2, splitting_cutoff=20**2)\n",
    "track_df, split_df, _ = lt.predict_dataframe(\n",
    "    regionprops_df,\n",
    "    coordinate_cols=[\"centroid-0\", \"centroid-1\"],\n",
    "    only_coordinate_cols=False,\n",
    ")\n",
    "track_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding the tracked data to the viewer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the tracking results. The objects with the same `track_id` is shown by the same label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "track_label_image = np.zeros_like(labels)\n",
    "for (frame, _), row in track_df.iterrows():\n",
    "    track_label_image[frame][labels[frame] == row[\"label\"]] = row[\"track_id\"] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Labels layer 'track_label_image' at 0x1800c6a70>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viewer.layers[\"labels\"].visible = False\n",
    "viewer.add_labels(track_label_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take screenshot of currente state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 3))\n",
    "viewer.camera.update(camera_state)\n",
    "viewer.dims.current_step = (1, 0, 0)\n",
    "screenshot_before1 = viewer.screenshot()\n",
    "viewer.dims.current_step = (2, 0, 0)\n",
    "screenshot_before2 = viewer.screenshot()\n",
    "viewer.camera.update(original_camera_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manual correction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Manually annotate a correct track by placing points using the `Points` layer.\n",
    "\n",
    "Here, this step is emurated by loading the data to the layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Points layer 'manually_validated_tracks' at 0x181b93520>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manual_corrected = np.load(\"napari_interactive_fix_data/manual_corrected.npy\")\n",
    "viewer.add_points(\n",
    "    manual_corrected, name=\"manually_validated_tracks\", size=10, face_color=\"red\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading manually annotated data\n",
    "The data from the `Points` layer and the `Labels` layer are loaded to get the updated segmentation and annotation results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "manual_corrected = viewer.layers[\"manually_validated_tracks\"].data.astype(np.int16)\n",
    "# you can also redraw the labels\n",
    "new_labels = viewer.layers[\"track_label_image\"].data\n",
    "# get label values at the placed points\n",
    "validated_track_labels = new_labels[tuple(manual_corrected.T)]\n",
    "validated_frames = manual_corrected[:, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "again calculate the centroids of the segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>centroid-0</th>\n",
       "      <th>centroid-1</th>\n",
       "      <th>frame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>103.091603</td>\n",
       "      <td>195.816794</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>151.812048</td>\n",
       "      <td>15.674699</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>31.552063</td>\n",
       "      <td>43.740668</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>60.389432</td>\n",
       "      <td>76.156556</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>125.100457</td>\n",
       "      <td>21.646119</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  centroid-0  centroid-1  frame\n",
       "0      1  103.091603  195.816794      0\n",
       "1      2  151.812048   15.674699      0\n",
       "2      3   31.552063   43.740668      0\n",
       "3      4   60.389432   76.156556      0\n",
       "4      5  125.100457   21.646119      0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_regionprops_df = calc_frame_regionprops(new_labels).reset_index(drop=True)\n",
    "new_regionprops_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0 34]\n",
      " [ 1 34]\n",
      " [ 2  6]\n",
      " [ 3  4]\n",
      " [ 4  9]\n",
      " [ 5  9]]\n"
     ]
    }
   ],
   "source": [
    "validated_points = np.array(list(zip(validated_frames, validated_track_labels)))\n",
    "validated_points = validated_points[np.argsort(validated_points[:, 0])]\n",
    "print(validated_points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eet the validated edges (track assigments) in terms of the `iloc` of the dataframe (making use of the index this time).\n",
    "\n",
    "`validated_point_iloc_pairs` is the pairs of the `iloc`s of the validated points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(33, 69), (69, 88), (88, 132), (132, 182), (182, 230)]\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>centroid-0</th>\n",
       "      <th>centroid-1</th>\n",
       "      <th>frame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>108.448276</td>\n",
       "      <td>108.586207</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>34</td>\n",
       "      <td>92.097561</td>\n",
       "      <td>112.112805</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>6</td>\n",
       "      <td>68.569405</td>\n",
       "      <td>116.014164</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>4</td>\n",
       "      <td>52.942993</td>\n",
       "      <td>114.076010</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>9</td>\n",
       "      <td>36.493888</td>\n",
       "      <td>117.039120</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>9</td>\n",
       "      <td>41.824363</td>\n",
       "      <td>113.631728</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     label  centroid-0  centroid-1  frame\n",
       "33      34  108.448276  108.586207      0\n",
       "69      34   92.097561  112.112805      1\n",
       "88       6   68.569405  116.014164      2\n",
       "132      4   52.942993  114.076010      3\n",
       "182      9   36.493888  117.039120      4\n",
       "230      9   41.824363  113.631728      5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_iloc(frame, label):\n",
    "    df = new_regionprops_df[\n",
    "        (new_regionprops_df[\"frame\"] == frame) & (new_regionprops_df[\"label\"] == label)\n",
    "    ]\n",
    "    assert len(df) == 1\n",
    "    return df.iloc[0].name\n",
    "\n",
    "\n",
    "validated_point_ilocs = [get_iloc(frame, label) for frame, label in validated_points]\n",
    "validated_point_iloc_pairs = list(\n",
    "    zip(validated_point_ilocs[:-1], validated_point_ilocs[1:])\n",
    ")\n",
    "print(validated_point_iloc_pairs)\n",
    "display(new_regionprops_df.iloc[validated_point_ilocs])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second tracking preserving manually corrected data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the core of this example: we can track the centroids again with preserving the annotation (`connected_edges`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "lt = LapTrack(cutoff=100**2, splitting_cutoff=20**2)\n",
    "new_track_df, _new_split_df, _ = lt.predict_dataframe(\n",
    "    new_regionprops_df,\n",
    "    coordinate_cols=[\"centroid-0\", \"centroid-1\"],\n",
    "    only_coordinate_cols=False,\n",
    "    connected_edges=validated_point_iloc_pairs,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>centroid-0</th>\n",
       "      <th>centroid-1</th>\n",
       "      <th>frame_y</th>\n",
       "      <th>tree_id</th>\n",
       "      <th>track_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frame</th>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>103.091603</td>\n",
       "      <td>195.816794</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>151.812048</td>\n",
       "      <td>15.674699</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>31.552063</td>\n",
       "      <td>43.740668</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>60.389432</td>\n",
       "      <td>76.156556</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>125.100457</td>\n",
       "      <td>21.646119</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
       "      <th>45</th>\n",
       "      <td>55</td>\n",
       "      <td>95.888179</td>\n",
       "      <td>48.552716</td>\n",
       "      <td>5</td>\n",
       "      <td>42</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>56</td>\n",
       "      <td>188.176301</td>\n",
       "      <td>71.471098</td>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>57</td>\n",
       "      <td>20.024096</td>\n",
       "      <td>196.915663</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>58</td>\n",
       "      <td>193.452991</td>\n",
       "      <td>89.854701</td>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>59</td>\n",
       "      <td>4.693182</td>\n",
       "      <td>191.176136</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>273 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             label  centroid-0  centroid-1  frame_y  tree_id  track_id\n",
       "frame index                                                           \n",
       "0     0          1  103.091603  195.816794        0        0         0\n",
       "      1          2  151.812048   15.674699        0        1         1\n",
       "      2          3   31.552063   43.740668        0        2         2\n",
       "      3          4   60.389432   76.156556        0        3         3\n",
       "      4          5  125.100457   21.646119        0        4         4\n",
       "...            ...         ...         ...      ...      ...       ...\n",
       "5     45        55   95.888179   48.552716        5       42        54\n",
       "      46        56  188.176301   71.471098        5       38        55\n",
       "      47        57   20.024096  196.915663        5       11        56\n",
       "      48        58  193.452991   89.854701        5       38        57\n",
       "      49        59    4.693182  191.176136        5       11        58\n",
       "\n",
       "[273 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_track_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding updated data to viewer\n",
    "Display the updated tracking results. The objects with the same `track_id` is shown by the same label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Labels layer 'new_track_label_image' at 0x160c9bc40>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_track_label_image = np.zeros_like(new_labels)\n",
    "for (frame, _), row in new_track_df.iterrows():\n",
    "    new_track_label_image[frame][new_labels[frame] == row[\"label\"]] = (\n",
    "        row[\"track_id\"] + 1\n",
    "    )\n",
    "viewer.layers[\"track_label_image\"].visible = False\n",
    "viewer.add_labels(new_track_label_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Take screenshots\n",
    "(the following codes exist to take screenshots of the viewer for the thumbnail.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 3))\n",
    "viewer.camera.update(camera_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x300 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 3))\n",
    "viewer.layers.move_multiple([3], 5)\n",
    "viewer.camera.update(camera_state)\n",
    "viewer.dims.current_step = (1, 0, 0)\n",
    "screenshot_after1 = viewer.screenshot()\n",
    "viewer.dims.current_step = (2, 0, 0)\n",
    "screenshot_after2 = viewer.screenshot()\n",
    "viewer.camera.update(original_camera_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "nbsphinx-thumbnail": {
     "tooltip": "Manual annotation helps tracking."
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 286,
       "width": 279
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(3, 3))\n",
    "screenshots = [\n",
    "    screenshot_before1,\n",
    "    screenshot_before2,\n",
    "    screenshot_after1,\n",
    "    screenshot_after2,\n",
    "]\n",
    "for ax, img in zip(np.ravel(axes.T), screenshots):\n",
    "    ax.imshow(img)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# camera_state = viewer.camera.dict()\n",
    "# with open(\"napari_interactive_fix_data/camera_state.json\",\"w\") as f:\n",
    "#     json.dump(camera_state,f)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.5 64-bit ('image_analysis')",
   "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": "3.10.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "112c4654da1b8403c1d0efe0680f4afdb1a0816fe1d810c3856d3bd0e24d8e17"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}