notebook/quick-start.ipynb

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   "source": [
    "### What's SC-Track?\n",
    "\n",
    "```\n",
    "SC-Track is an efficient algorithm for dynamic tracking of single cells on different time-lapse microscope images. It can use the segmentation results of various models to efficiently track single cells and reconstruct cell lines. It can track multi-generational cell division events without any additional information, only using the outline information of cells; and can reduce the noise of the segmentation, so as to use the noise segmentation results to generate accurate cell lineages.\n",
    "```"
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    "### Installation\n",
    "\n",
    "To install `SC-Track`, you can run with:\n",
    "\n",
    "`conda create -n sctrack python=3.10`\n",
    "\n",
    "`pip install SC-Track`\n",
    "\n",
    "For linux or macos, you may need to run with:\n",
    "\n",
    "`pip3 install SC-Track`\n",
    "\n",
    "Note: If you try to install the package on windows, you first need to install the `pylibtiff` package, this package cannot directly install by `pip`, you need to download the wheel file from :https://www.lfd.uci.edu/~gohlke/pythonlibs/#pylibtiff, and then install it offline."
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    "### Quick start\n",
    "\n",
    "To using `SC-Track`, an effective way is run the command line tool, you can run with this command: \n",
    "```sh\n",
    "sctrack -i ./examples/mask_annotation/01.tif -a ./examples/mask_annotation/SEG.tif\n",
    "sctrack -i ./examples/json_annotation/01.tif -a ./examples/json_annotation/SEG.json\n",
    "```\n",
    "or \n",
    "```sh\n",
    "sctrack -a ./examples/mask_annotation/SEG.tif\n",
    "sctrack -a ./examples/mask_annotation/SEG.json\n",
    "```\n",
    "\n",
    "**-i/--image:** `your raw image, it's just as the background for tracking visualization, in the future, it can be used for tracking. for current version its not  necessary. if you just care about the tracking result, don't want to save tracking visualization, you can ignore this param.`\n",
    "\n",
    "**-a/--annotation:** `your annotation file for image, these annotation files can be segmentation masks or json files. `\n",
    "\n",
    "\n",
    "You can download our demo dataset at [here](https://zenodo.org/record/8310572), or using the small [sample image](./examples/mask_annotation/01.tif) and [annotation](./examples/mask_annotation/SEG.tif) to run this guidance.\n",
    "\n",
    "For details on running our demo dataset, please refer [here](demo.ipynb)\n"
   ]
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   "source": [
    "**<font color=red>The annotation file  format is as follows：</color>**"
   ]
  },
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       "(115, 520, 696)"
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   "source": [
    "# 1. for the mask format\n",
    "\n",
    "import tifffile\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "SEG = './examples/mask_annotation/SEG.tif'\n",
    "\n",
    "mask = tifffile.imread(SEG)\n",
    "mask.shape"
   ]
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    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x29ef531e1d0>"
      ]
     },
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.axis('off')\n",
    "plt.imshow(mask[0], cmap='gray')  # show first frame of mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "72d0e29e-f99d-4b7e-bf9a-e1635608cc11",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6], dtype=uint16)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for the mask image, each segmented instance must has the unique id as the mask pixel values. \n",
    "# 0 is the background pixel value, so that the instance id cannot been zero.\n",
    "import numpy as np\n",
    "\n",
    "np.unique(mask[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e686c8b9-1f4f-42cc-88b1-eb0a653036d4",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "595747f8-9e2e-42b7-9f4c-694e31d1f1fd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 2. for json file format\n",
    "\n",
    "# this format can be read by vgg image annotator, the structure as the follow:\n",
    "\n",
    "\n",
    "# regions_tmp indicate each segmented instance in a single frame, \"all_points_x\" and \"all_points_y\" are the coordinates for the segmentation contours.\n",
    "# in the most case,  \"name\" value is \"polygon\", but if you using ellipse to draw the mask, the \"name\" value is \"ellipse\", and  \"shape_attributes\" no \n",
    "# longer include \"all_points_x\" and \"all_points_y\"，but instead is \"rx\", \"ry\", \"cx\", \"cy\", \"theta\". \n",
    "# \"region_attributes\" store the extend information, if you have the classification requirement, save the classification information to  \"cell_type\". \n",
    "# don't need to pay attention to the  \"cell_id\" and \"track_id\", it will generate in runtime. \n",
    "regions_tmp = {\n",
    "    \"shape_attributes\":\n",
    "        {\n",
    "            \"name\": \"polygon\",\n",
    "            \"all_points_x\": ['x1', 'x2', ..., 'xn'],\n",
    "            \"all_points_y\": ['y1', 'y2', ..., 'yn']\n",
    "        },\n",
    "    \"region_attributes\":\n",
    "        {\n",
    "            \"cell_type\": None,\n",
    "            \"cell_id\": None,\n",
    "            \"track_id\": None,\n",
    "        }\n",
    "}\n",
    "\n",
    "\n",
    "# frame_tmp saves all segmented instance information in one frame, \"size\" = image width * image height. note: for the filename it's not necessary\n",
    "# if you just want to run SC-Track, but if you want to the json  can be import by vgg image annotator, you mast make the finame endswith `.png`, \n",
    "# tiff is not support.\n",
    "width = 1024\n",
    "height = 1024\n",
    "frame_tmp = {\n",
    "        \"filename\": 'example.png',\n",
    "        \"size\": width * height,\n",
    "        \"regions\": [regions_tmp, ..., regions_tmp],\n",
    "        \"file_attributes\": {}\n",
    "    }\n",
    "\n",
    "json_file = {\n",
    "    'example_1.png': frame_tmp,\n",
    "    'example_2.png': frame_tmp,\n",
    "    # ...\n",
    "    'example_n.png': frame_tmp\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f38620a2-f89f-472a-9dc7-d370a8f14d45",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['mcy-0000.png', 'mcy-0001.png', 'mcy-0002.png', 'mcy-0003.png', 'mcy-0004.png', 'mcy-0005.png', 'mcy-0006.png', 'mcy-0007.png', 'mcy-0008.png', 'mcy-0009.png', 'mcy-0010.png', 'mcy-0011.png', 'mcy-0012.png', 'mcy-0013.png', 'mcy-0014.png', 'mcy-0015.png', 'mcy-0016.png', 'mcy-0017.png', 'mcy-0018.png', 'mcy-0019.png'])\n"
     ]
    }
   ],
   "source": [
    "# here is a sample of a real annotation file，you can see the detail about the format.\n",
    "\n",
    "import json\n",
    "from pprint import pprint\n",
    "\n",
    "file = \"./examples/json_annotation/SEG.json\"\n",
    "with open(file) as f:\n",
    "    annotation = json.load(f)\n",
    "pprint(annotation.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4821d4cf-5173-4b6b-9d8a-556397f4a642",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['filename', 'size', 'regions', 'file_attributes'])\n"
     ]
    }
   ],
   "source": [
    "pprint(annotation[list(annotation.keys())[0]].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4417d4f2-a635-46f5-a951-161d6b210a28",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "                                        1351.3887939453125,\n",
      "                                        1353.9344482421875,\n",
      "                                        1356.979736328125,\n",
      "                                        1359.819580078125,\n",
      "                                        1363.135498046875,\n",
      "                                        1366.961669921875,\n",
      "                                        1370.4237060546875,\n",
      "                                        1374.3040771484375],\n",
      "                       'name': 'polygon'}},\n",
      " {'region_attributes': {'cell_type': 'S'},\n",
      "  'shape_attributes': {'all_points_x': [739.28515625,\n",
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      "                                        723.2210083007812,\n",
      "                                        718,\n",
      "                                        713.1360473632812,\n",
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      " {'region_attributes': {'cell_type': 'G1/G2'},\n",
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      "                                        688,\n",
      "                                        690.8743286132812,\n",
      "                                        693.5912475585938,\n",
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      "                                        700.721923828125,\n",
      "                                        701.1867065429688],\n",
      "                       'all_points_y': [1522,\n",
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      "                       'name': 'polygon'}},\n",
      " {'region_attributes': {'cell_type': 'G1/G2'},\n",
      "  'shape_attributes': {'all_points_x': [863.2644653320312,\n",
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      "                                        864.2969970703125,\n",
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      "                                        861.404296875,\n",
      "                                        858.4454345703125,\n",
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      "                                        851,\n",
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      "                                        844.713623046875,\n",
      "                                        842.3391723632812,\n",
      "                                        840.4340209960938,\n",
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      "                       'all_points_y': [1630,\n",
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      "                       'name': 'polygon'}},\n",
      " {'region_attributes': {'cell_type': 'G1/G2'},\n",
      "  'shape_attributes': {'all_points_x': [840.1339111328125,\n",
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      "                                        807.98681640625,\n",
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      "                                        820,\n",
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      "                       'all_points_y': [1640,\n",
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      "                       'name': 'polygon'}}]\n"
     ]
    }
   ],
   "source": [
    "pprint(annotation[list(annotation.keys())[0]][\"regions\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9e479b6a-4cc1-457b-b93a-7a13de259e70",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26\n",
      "{'region_attributes': {'cell_type': 'G1/G2'},\n",
      " 'shape_attributes': {'all_points_x': [1163.83837890625,\n",
      "                                       1162.650146484375,\n",
      "                                       1161.8702392578125,\n",
      "                                       1160.1978759765625,\n",
      "                                       1157.6004638671875,\n",
      "                                       1153.97998046875,\n",
      "                                       1149.2186279296875,\n",
      "                                       1143.99609375,\n",
      "                                       1139,\n",
      "                                       1134.3447265625,\n",
      "                                       1130.1923828125,\n",
      "                                       1127.18505859375,\n",
      "                                       1124.63427734375,\n",
      "                                       1122.6158447265625,\n",
      "                                       1120.847900390625,\n",
      "                                       1120.1517333984375,\n",
      "                                       1118.963134765625,\n",
      "                                       1119.1295166015625,\n",
      "                                       1118.8153076171875,\n",
      "                                       1119.80810546875,\n",
      "                                       1121.660400390625,\n",
      "                                       1124.5360107421875,\n",
      "                                       1128.439208984375,\n",
      "                                       1133.61328125,\n",
      "                                       1139,\n",
      "                                       1144.64208984375,\n",
      "                                       1150.371337890625,\n",
      "                                       1155.399658203125,\n",
      "                                       1159.4586181640625,\n",
      "                                       1161.9322509765625,\n",
      "                                       1163.501220703125,\n",
      "                                       1163.8558349609375],\n",
      "                      'all_points_y': [124,\n",
      "                                       128.70431518554688,\n",
      "                                       133.47317504882812,\n",
      "                                       138.1639404296875,\n",
      "                                       142.6004180908203,\n",
      "                                       146.41915893554688,\n",
      "                                       148.66998291015625,\n",
      "                                       149.11679077148438,\n",
      "                                       149.2032470703125,\n",
      "                                       147.40338134765625,\n",
      "                                       145.2635040283203,\n",
      "                                       141.68235778808594,\n",
      "                                       138.36575317382812,\n",
      "                                       134.94757080078125,\n",
      "                                       131.51882934570312,\n",
      "                                       127.74915313720703,\n",
      "                                       124,\n",
      "                                       120.04751586914062,\n",
      "                                       115.63920593261719,\n",
      "                                       111.1763916015625,\n",
      "                                       106.66041564941406,\n",
      "                                       102.35316467285156,\n",
      "                                       98.50411987304688,\n",
      "                                       96.91913604736328,\n",
      "                                       95.55976867675781,\n",
      "                                       95.63510131835938,\n",
      "                                       96.54724884033203,\n",
      "                                       99.4561538696289,\n",
      "                                       103.5414047241211,\n",
      "                                       108.67719268798828,\n",
      "                                       113.85127258300781,\n",
      "                                       119.05587005615234],\n",
      "                      'name': 'polygon'}}\n"
     ]
    }
   ],
   "source": [
    "regions = annotation[list(annotation.keys())[0]][\"regions\"]\n",
    "pprint(len(regions))\n",
    "pprint(regions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3c1117b9-1c29-4cca-b50a-93cc1adc872e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2183299\n"
     ]
    }
   ],
   "source": [
    "pprint(annotation[list(annotation.keys())[0]][\"size\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d1740f4d-5a7d-4da5-92f4-0d6dc2d39d69",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'mcy-0000.png'\n"
     ]
    }
   ],
   "source": [
    "pprint(annotation[list(annotation.keys())[0]][\"filename\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12f8a5ac-d35a-4271-b8f0-ce20a2cc8b15",
   "metadata": {},
   "source": [
    "-----------\n",
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f731745b-619c-4fb0-9c7c-ffa7c00e7338",
   "metadata": {},
   "source": [
    "For most cases, you may use deep learning for image segmentation, which outputs a series of masks as segmentation results. In this case, you need to combine a single frame mask into a single file. For example, your mask has 50 frames and an image size of 1024 * 1204. Finally, you need to generate a mask file with a shape of (50, 1024, 1024) as an annotation file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "69f97de6-4ac5-4806-b05e-68cbecc5e7b8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-26 19:02:06 WARNING: -o  not provided, using the default output file name: ./examples/mask_annotation\\output.json\n",
      "2023-07-26 19:02:06 INFO: Tracking result will saved to ./examples/mask_annotation\\tracking_output.\n",
      "2023-07-26 19:02:06 INFO: start tracking ...\n",
      "2023-07-26 19:02:06 INFO: convert mask to annotation file...\n",
      "\n",
      "convert process:   0%|          | 0/115 [00:00<?, ?it/s]\n",
      "convert process:   8%|▊         | 9/115 [00:00<00:01, 82.22it/s]\n",
      "convert process:  16%|█▌        | 18/115 [00:00<00:01, 78.72it/s]\n",
      "convert process:  23%|██▎       | 27/115 [00:00<00:01, 79.96it/s]\n",
      "convert process:  31%|███▏      | 36/115 [00:00<00:00, 81.52it/s]\n",
      "convert process:  39%|███▉      | 45/115 [00:00<00:00, 81.18it/s]\n",
      "convert process:  47%|████▋     | 54/115 [00:00<00:00, 83.04it/s]\n",
      "convert process:  55%|█████▍    | 63/115 [00:00<00:00, 81.59it/s]\n",
      "convert process:  63%|██████▎   | 72/115 [00:00<00:00, 78.91it/s]\n",
      "convert process:  70%|██████▉   | 80/115 [00:00<00:00, 79.08it/s]\n",
      "convert process:  77%|███████▋  | 88/115 [00:01<00:00, 78.86it/s]\n",
      "convert process:  83%|████████▎ | 96/115 [00:01<00:00, 77.18it/s]\n",
      "convert process:  90%|█████████ | 104/115 [00:01<00:00, 77.48it/s]\n",
      "convert process:  97%|█████████▋| 112/115 [00:01<00:00, 77.65it/s]\n",
      "convert process: 100%|██████████| 115/115 [00:01<00:00, 79.30it/s]\n",
      "\n",
      "tracking process:   0%|          | 0/113 [00:00<?, ?it/s]\n",
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      "\n",
      "save tracking visualization:   0%|          | 0/115 [00:00<?, ?it/s]\n",
      "save tracking visualization:  49%|████▊     | 56/115 [00:00<00:00, 548.37it/s]\n",
      "save tracking visualization:  99%|█████████▉| 114/115 [00:00<00:00, 562.04it/s]\n",
      "save tracking visualization: 100%|██████████| 115/115 [00:00<00:00, 560.08it/s]\n",
      "2023-07-26 19:02:14 INFO: tracking cost time: 8.7080\n"
     ]
    }
   ],
   "source": [
    "# here is a running example using mask annotation:\n",
    "\n",
    "!sctrack -i ./examples/mask_annotation/01.tif -a ./examples/mask_annotation/SEG.tif"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "442be7c7-b71b-4349-a11a-0940d3b9274e",
   "metadata": {},
   "source": [
    "Now you can find the tracking result in `./examples/mask_annotation/tracking_output`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9b1753b6-e571-4e46-998d-54f98ed45157",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['01.tif', 'SEG.tif', 'tracking_output']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "os.listdir('./examples/mask_annotation/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a4d08e6d-daf1-4417-9df4-f724db51a63e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mask',\n",
       " 'result_with_track.json',\n",
       " 'TRA.txt',\n",
       " 'track.csv',\n",
       " 'TrackTree',\n",
       " 'track_visualization.tif']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('./examples/mask_annotation/tracking_output')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97d8b8c1-d1fc-4d7f-8d2f-aa5ecf3ef9d7",
   "metadata": {},
   "source": [
    "In this output, `track.csv` is the main tracking result for downstream analysis, `track_visualization.tif` is the tracking visualization result.\n",
    "`result_with_track.json` is is the json file introduced earlier, which contains tracking information. `TrackTree` save each tracking structure for SC-Track."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bd14190-75b5-4167-841d-7b766de9eec6",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c4b81345-3608-4a92-b740-b6a1afd8792e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-26 19:11:08 WARNING: -o  not provided, using the default output file name: ./examples/json_annotation\\output.json\n",
      "2023-07-26 19:11:08 INFO: Tracking result will saved to ./examples/json_annotation\\tracking_output.\n",
      "2023-07-26 19:11:08 INFO: start tracking ...\n",
      "\n",
      "tracking process:   0%|          | 0/18 [00:00<?, ?it/s]\n",
      "tracking process:  17%|█▋        | 3/18 [00:00<00:00, 24.44it/s]\n",
      "tracking process:  33%|███▎      | 6/18 [00:00<00:00, 23.82it/s]\n",
      "tracking process:  50%|█████     | 9/18 [00:00<00:00, 23.56it/s]\n",
      "tracking process:  67%|██████▋   | 12/18 [00:00<00:00, 23.14it/s]\n",
      "tracking process:  83%|████████▎ | 15/18 [00:00<00:00, 22.82it/s]\n",
      "tracking process: 100%|██████████| 18/18 [00:00<00:00, 22.31it/s]\n",
      "tracking process: 100%|██████████| 18/18 [00:00<00:00, 21.63it/s]\n",
      "\n",
      "save tracking visualization:   0%|          | 0/20 [00:00<?, ?it/s]\n",
      "save tracking visualization:  55%|█████▌    | 11/20 [00:00<00:00, 105.91it/s]\n",
      "save tracking visualization: 100%|██████████| 20/20 [00:00<00:00, 92.62it/s] \n",
      "2023-07-26 19:11:13 INFO: tracking cost time: 5.5147\n"
     ]
    }
   ],
   "source": [
    "# here is a running example using json annotation:\n",
    "\n",
    "!sctrack -i ./examples/json_annotation/01.tif -a ./examples/json_annotation/SEG.json"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44e4e331-7f74-4fa6-b3be-204a8f8e92b2",
   "metadata": {},
   "source": [
    "**Note: the visualization is too big, the most frame are deleted, if you want to have a looking, pelase re-run these commands.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2d685015-f1ff-4a5b-bece-042e7a20596b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now, you can see the visualization, you can re-run this cell to see the GIF.\n",
    "from IPython import display\n",
    "import matplotlib.pyplot as plt\n",
    "filepath = './examples/mask_annotation/tracking_output/track_visualization.tif'\n",
    "images = map(tifffile.imread, [os.path.join(filepath, i) for i in os.listdir(filepath)])\n",
    "fig, ax = plt.subplots()\n",
    "for im in images:\n",
    "    plt.imshow(im)\n",
    "    display.clear_output(wait=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac7034d8-c139-4ca0-92bd-2e1dda578581",
   "metadata": {},
   "source": [
    "For the track table, it include `frame_index`, `track_id`, `cell_id`, `parent_id`, `center_x`, `center_y`, `cell_type`, `mask_of_x_points`, `mask_of_y_points`. If your tracking annotation don't have the classification information, `cell_type` column is None."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "19e94d38-51c4-4b9e-97ee-10368b96eed9",
   "metadata": {
    "tags": []
   },
   "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>frame_index</th>\n",
       "      <th>track_id</th>\n",
       "      <th>cell_id</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>center_x</th>\n",
       "      <th>center_y</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>mask_of_x_points</th>\n",
       "      <th>mask_of_y_points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1_0</td>\n",
       "      <td>1_0</td>\n",
       "      <td>76.429268</td>\n",
       "      <td>108.413872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60, 5...</td>\n",
       "      <td>[77, 78, 78, 78, 78, 79, 79, 80, 81, 81, 82, 8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1</td>\n",
       "      <td>1_0</td>\n",
       "      <td>1_0</td>\n",
       "      <td>73.822846</td>\n",
       "      <td>104.553082</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70, 6...</td>\n",
       "      <td>[75, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1_0</td>\n",
       "      <td>1_0</td>\n",
       "      <td>72.629953</td>\n",
       "      <td>104.476204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[65, 64, 64, 63, 62, 61, 60, 59, 58, 57, 56, 5...</td>\n",
       "      <td>[71, 72, 73, 74, 75, 75, 76, 76, 76, 76, 76, 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1_0</td>\n",
       "      <td>1_0</td>\n",
       "      <td>70.773744</td>\n",
       "      <td>103.675535</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 5...</td>\n",
       "      <td>[69, 70, 70, 71, 72, 73, 74, 74, 75, 75, 75, 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1_0</td>\n",
       "      <td>1_0</td>\n",
       "      <td>69.009823</td>\n",
       "      <td>104.166068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 5...</td>\n",
       "      <td>[68, 69, 69, 70, 71, 71, 72, 72, 72, 72, 73, 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1_0</td>\n",
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   "source": [
    "# here show the detail of track.csv\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "table = \"./examples/mask_annotation/tracking_output/track.csv\"\n",
    "\n",
    "data = pd.read_csv(table)\n",
    "\n",
    "data.head(10)"
   ]
  },
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       "   frame_index  ...                                   mask_of_y_points\n",
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   "source": [
    "table2 = \"./examples/json_annotation/tracking_output/track.csv\"\n",
    "\n",
    "data2 = pd.read_csv(table2)\n",
    "\n",
    "data2.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17da7d13-4049-45bb-b8a6-d945e62b73bf",
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    "In these fields, The same **track_id** indicates that the cell is the same cell or a daughter cell derived from the cell division. in contrast，the **cell_id** is different, the same cell_id indicates that the cell is the same cell, when the cell has been division, this cell_id will be terminate. **parent_id** is the cell_id of the mother cell of the cell, if cell doesn't undergo the cell division, the parent_id is same as it cell_id. "
   ]
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   "metadata": {},
   "source": [
    "If you want to use the SC-Track's API, please refer [here](./API.ipynb)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "sctrack",
   "language": "python",
   "name": "sctrack"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}