notebooks/run_roi_segmentation.ipynb

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    "# Segmentation of cells/nuclei \n",
    "\n",
    "This notebook segments cells or nuclei in fluorescent images using [Cellpose](http://www.cellpose.org/) \n",
    "\n",
    "## Documentation\n",
    "\n",
    "[Details of the cell/nuclei segmentation procedure](../docs/roi_segmentation.md)\n",
    "\n",
    "[Table summarizing all parameters that are used in this notebook](../docs/parameters.md#list-of-parameters-for-ROI-segmentation)\n",
    "\n",
    "## Requirements\n",
    "\n",
    "- A folder with images to segment\n",
    "- A json parameter file specified using [setup_roi_segmentation.ipynb](setup_roi_segmentation.ipynb)\n",
    "\n",
    "\n",
    "<hr style=\"height:2px;\">\n",
    "\n",
    "## Config\n",
    "\n",
    "<hr style=\"height:2px;\">\n",
    "\n",
    "### The following code imports and declares functions used for the processing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-06-16 13:04:53,329 [INFO] WRITING LOG OUTPUT TO /home/amedyukh/.cellpose/run.log\n"
     ]
    }
   ],
   "source": [
    "#################################\n",
    "#  Don't modify the code below  #\n",
    "#################################\n",
    "\n",
    "import json\n",
    "import os\n",
    "from punctatools.lib.segment import segment_roi_batch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:0.5px;\">\n",
    "\n",
    "## Specify the parameters for segmentation\n",
    "\n",
    "<hr style=\"height:0.5px;\">\n",
    "\n",
    "`parameter_file`: json parameter file generated using [setup_roi_segmentation.ipynb](setup_roi_segmentation.ipynb). \n",
    "\n",
    "`input_dir`: folder with images to be converted; set to `None` to load this value from the parameter file\n",
    "\n",
    "`output_dir`: folder to save results; set to `None` to load this value from the parameter file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "parameter_file = '../example_data/parameters/parameters_roi.json'\n",
    "\n",
    "input_dir = None\n",
    "output_dir = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### The following code loads the parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_dir': '../example_data/stacks',\n",
       " 'output_dir': '../test_output/roi',\n",
       " 'channel': 0,\n",
       " 'diameter': 120,\n",
       " 'model_type': 'cyto',\n",
       " 'do_3D': False,\n",
       " 'remove_small_mode': '2D',\n",
       " 'remove_small_diam_fraction': 0.5,\n",
       " 'flow_threshold': 0.4,\n",
       " 'cellprob_threshold': 0,\n",
       " 'gpu': True}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#################################\n",
    "#  Don't modify the code below  #\n",
    "#################################\n",
    "\n",
    "with open(parameter_file) as f:\n",
    "    params = json.load(f)\n",
    "\n",
    "if input_dir is not None:\n",
    "    params['input_dir'] = input_dir.rstrip('/')\n",
    "if output_dir is not None:\n",
    "    params['output_dir'] = output_dir.rstrip('/')\n",
    "    \n",
    "os.makedirs(params['output_dir'], exist_ok=True)\n",
    "\n",
    "with open(os.path.join(params['output_dir'], '..', parameter_file.split('/')[-1]), 'w') as f:\n",
    "    json.dump(params, f, indent=4)\n",
    "    \n",
    "params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;\">\n",
    "\n",
    "## Processing\n",
    "\n",
    "<hr style=\"height:2px;\">\n",
    "\n",
    "### The following code segments all images and saves the results: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true,
    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../example_data/stacks/sample1_position1.tif\n",
      "Processing sample 1 of 2\n",
      "2022-06-16 13:04:57,856 [INFO] ** TORCH CUDA version installed and working. **\n",
      "2022-06-16 13:04:57,858 [INFO] >>>> using GPU\n",
      "2022-06-16 13:04:57,949 [WARNING] cellprob_threshold and dist_threshold are being deprecated in a future release, use mask_threshold instead\n",
      "2022-06-16 13:04:57,950 [INFO] ~~~ FINDING MASKS ~~~\n",
      "2022-06-16 13:04:57,952 [INFO] 0%|          | 0/5 [00:00<?, ?it/s]\n",
      "2022-06-16 13:04:58,714 [INFO] 20%|##        | 1/5 [00:00<00:03,  1.31it/s]\n",
      "2022-06-16 13:04:59,366 [INFO] 40%|####      | 2/5 [00:01<00:02,  1.44it/s]\n",
      "2022-06-16 13:05:00,015 [INFO] 60%|######    | 3/5 [00:02<00:01,  1.48it/s]\n",
      "2022-06-16 13:05:00,663 [INFO] 80%|########  | 4/5 [00:02<00:00,  1.50it/s]\n",
      "2022-06-16 13:05:01,311 [INFO] 100%|##########| 5/5 [00:03<00:00,  1.52it/s]\n",
      "2022-06-16 13:05:01,312 [INFO] 100%|##########| 5/5 [00:03<00:00,  1.49it/s]\n",
      "2022-06-16 13:05:01,312 [INFO] >>>> TOTAL TIME 3.36 sec\n",
      "../example_data/stacks/sample1_position2.tif\n",
      "Processing sample 2 of 2\n",
      "2022-06-16 13:05:01,403 [INFO] ** TORCH CUDA version installed and working. **\n",
      "2022-06-16 13:05:01,404 [INFO] >>>> using GPU\n",
      "2022-06-16 13:05:01,474 [WARNING] cellprob_threshold and dist_threshold are being deprecated in a future release, use mask_threshold instead\n",
      "2022-06-16 13:05:01,475 [INFO] ~~~ FINDING MASKS ~~~\n",
      "2022-06-16 13:05:01,475 [INFO] 0%|          | 0/5 [00:00<?, ?it/s]\n",
      "2022-06-16 13:05:02,126 [INFO] 20%|##        | 1/5 [00:00<00:02,  1.54it/s]\n",
      "2022-06-16 13:05:02,777 [INFO] 40%|####      | 2/5 [00:01<00:01,  1.54it/s]\n",
      "2022-06-16 13:05:03,428 [INFO] 60%|######    | 3/5 [00:01<00:01,  1.54it/s]\n",
      "2022-06-16 13:05:04,080 [INFO] 80%|########  | 4/5 [00:02<00:00,  1.54it/s]\n",
      "2022-06-16 13:05:04,749 [INFO] 100%|##########| 5/5 [00:03<00:00,  1.52it/s]\n",
      "2022-06-16 13:05:04,750 [INFO] 100%|##########| 5/5 [00:03<00:00,  1.53it/s]\n",
      "2022-06-16 13:05:04,750 [INFO] >>>> TOTAL TIME 3.28 sec\n",
      "CPU times: user 9.54 s, sys: 2.49 s, total: 12 s\n",
      "Wall time: 11.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#################################\n",
    "#  Don't modify the code below  #\n",
    "#################################\n",
    "\n",
    "segment_roi_batch(**params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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