EPySeg_segment_v6.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/baigouy/notebooks/blob/master/EPySeg_segment_v6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "03dH2obICNBM"
      },
      "source": [
        "# Before you start\n",
        "\n",
        "__Note: You are using this at your own risk__, the author cannot be help responsible for damage to Google drive or misfunction of the e-mail account (due to drive overfilling). To avoid issues please use a second account dedicated to deep learning. __If you disagree, please quit this page__.\n",
        "\n",
        "[General colab notebook tips](https://github.com/baigouy/notebooks#getting-started)\n",
        "\n",
        "Please report bugs to baigouy@gmail.com"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2LwPS5x44SP7"
      },
      "source": [
        "# Step 1:\n",
        "- Mount Google drive ([How to](https://github.com/baigouy/notebooks#how-to-mount-google-drive-in-colab)).\n",
        "- Select a GPU runtime ([How to](https://github.com/baigouy/notebooks#select-a-gpu-runtime)).\n",
        "- Then run the code cell below ([How to](https://github.com/baigouy/notebooks#how-to-run-a-code-cell))."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZ_tom_AB8qr",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        },
        "outputId": "dc34588e-affd-42e2-bcda-0918eca58608"
      },
      "source": [
        "#@title <-- Press Run\n",
        "from __future__ import print_function # bug fix --> future import must always be first line\n",
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "import sys\n",
        "import ipywidgets as widgets\n",
        "from ipywidgets import interact, interactive, fixed, interact_manual\n",
        "from IPython.display import Markdown, display\n",
        "import os\n",
        "\n",
        "# defining an html/md formatting print function\n",
        "def printmd(string):\n",
        "    display(Markdown(string))\n",
        "\n",
        "# defining a filebrowser that can be used later on\n",
        "# FileBrowser modified from DrDub https://gist.github.com/DrDub/6efba6e522302e43d055 PD licence\n",
        "# new beahviour: single click selects a file or a folder, double click browses folders. Blocked browsing of files as if they were folders. Has an option to show files or not (files are shown by default).\n",
        "# Note that I have made a very quick n dirty hack of the initial nice code so that it handles double click, my changes would need some love but it seems functional, please report bugs to BA otherwise\n",
        "# TODO also maybe hack it to select files only or folders or maybe ignore and do the check later and put a warning accordingly (maybe simpler for now)\n",
        "\n",
        "\n",
        "import os\n",
        "import ipywidgets as widgets\n",
        "from ipywidgets import Layout, Button, VBox, Label,Box, HBox, ButtonStyle\n",
        "from timeit import default_timer as timer\n",
        "\n",
        "class FileBrowser(object):\n",
        "    def __init__(self, width='auto', height=\"200px\", item_width=\"auto\", item_height=\"auto\", folders_only=False):\n",
        "\n",
        "        self.folders_only = folders_only\n",
        "        self.path = os.getcwd()\n",
        "        self._update_files()\n",
        "\n",
        "        self.layout = Layout(overflow='scroll',\n",
        "                             border ='1px solid black',\n",
        "                             width  ='{}'.format(width),\n",
        "                             height ='{}'.format(height),\n",
        "                             flex_flow = \"column wrap\",\n",
        "                             align_items = \"flex-start\",\n",
        "                             display='flex')\n",
        "\n",
        "        self.button_dir_syle  = ButtonStyle(button_color='lightgray')\n",
        "        self.button_file_syle = ButtonStyle(button_color='#Fafaff')\n",
        "\n",
        "        self.button_layout    = Layout(left=\"0px\", width=\"{}\".format(item_width), height=\"{}\".format(item_height))\n",
        "        self.last_click = None\n",
        "        self.root_folder = None\n",
        "\n",
        "\n",
        "    def _update_files(self):\n",
        "        self.files = list()\n",
        "        self.dirs  = list()\n",
        "        # print('is dir', self.path, os.path.isdir(self.path))\n",
        "        if(os.path.isdir(self.path)):\n",
        "            for f in os.listdir(self.path):\n",
        "                ff = os.path.join(self.path,f)\n",
        "                if os.path.isdir(ff):\n",
        "                    self.dirs.append(f)\n",
        "                else:\n",
        "                    self.files.append(f)\n",
        "\n",
        "    def widget(self):\n",
        "        list_box = widgets.Box(layout=self.layout)\n",
        "        box = VBox([list_box, Label(self.path)])\n",
        "        self._update_box(box)\n",
        "\n",
        "        return box\n",
        "\n",
        "\n",
        "    def _update_box(self, main_box, double_click=True):\n",
        "        # print('double click in there', double_click)\n",
        "        path_label = main_box.children[1]\n",
        "        file_or_path = \"Selected file: {}\"\n",
        "        if os.path.isdir(self.path):\n",
        "          file_or_path = \"Selected path: {}\"\n",
        "        path_label.value = file_or_path.format(self.path)\n",
        "\n",
        "        if self.root_folder is None:\n",
        "          self.root_folder = self.path\n",
        "\n",
        "        box        = main_box.children[0]\n",
        "        if not double_click:\n",
        "          # print('skipping')\n",
        "          return box\n",
        "        else:\n",
        "          if (os.path.isfile(self.path)):\n",
        "            # prevent 'browsing' files\n",
        "            return box\n",
        "          # print('starting path', self.path)\n",
        "          self.root_folder = self.path\n",
        "\n",
        "        def on_click(b):\n",
        "            double_click = False\n",
        "\n",
        "            # detect single or double click and act accordingly (dirty hack by BA)\n",
        "            if self.last_click is not None:\n",
        "              # print('delay',timer()-self.last_click)\n",
        "              if timer()-self.last_click<=0.600:\n",
        "                # print('double click')\n",
        "                double_click = True\n",
        "              else:\n",
        "                # print('single click')\n",
        "                self.last_click = timer()\n",
        "            else:\n",
        "              self.last_click = timer()\n",
        "              # print('single click')\n",
        "\n",
        "            if b.description == '..':\n",
        "                if not double_click: #skip double click for previous folder\n",
        "                  self.path = os.path.split(self.root_folder)[0]\n",
        "\n",
        "                double_click=True\n",
        "                # print('path parent',self.path)\n",
        "            else:\n",
        "                if not double_click:\n",
        "                  self.path = os.path.join(self.root_folder, b.description)\n",
        "                else:\n",
        "                  self.path = os.path.join(self.root_folder, b.description)\n",
        "                  # keep path in fact\n",
        "                  # print('double click path', self.path)\n",
        "\n",
        "            # print('cur', self.path)\n",
        "            # if double_click:\n",
        "            self._update_files()\n",
        "            self._update_box(main_box, double_click)\n",
        "            # self.last_click = None\n",
        "\n",
        "        buttons = []\n",
        "        if os.path.dirname(self.path) != self.path:\n",
        "            button = widgets.Button(description='..', style=ButtonStyle(button_color='lightblue'), layout=self.button_layout)\n",
        "            button.on_click(on_click)\n",
        "            buttons.append(button)\n",
        "\n",
        "        for f in self.dirs:\n",
        "            button = widgets.Button(description=f, style=self.button_dir_syle, icon='fa-folder', tooltip=f, layout=self.button_layout)\n",
        "            button.on_click(on_click)\n",
        "            buttons.append(button)\n",
        "\n",
        "        if not self.folders_only:\n",
        "          for f in self.files:\n",
        "              button = widgets.Button(description=f, style=self.button_file_syle, tooltip=f, layout=self.button_layout)\n",
        "              button.on_click(on_click)\n",
        "              buttons.append(button)\n",
        "\n",
        "        box.children = buttons\n",
        "\n",
        "\n",
        "try:\n",
        "  # check that google drive is successfully mounted\n",
        "\n",
        "  if os.path.exists('/content/drive/My Drive'):#/content/dri #create an error # /content/drive/My Drive # full path\n",
        "    printmd('<font color=\"green\">Google drive successfully mounted!</font>')\n",
        "    # move to the drive folder\n",
        "    %cd /content/drive/My Drive\n",
        "  else:\n",
        "    from google.colab import drive\n",
        "    printmd('<font color=\"green\"><b>Open the external link and follow the instructions until you get the authorization code. Copy this code, paste it below then press \"Enter\"</b></font>')\n",
        "    drive.mount('/content/drive')\n",
        "\n",
        "    if not os.path.exists('/content/drive/My Drive'):\n",
        "      printmd('<font color=\"red\">Please connect to Google drive and repeat this step:</font>')\n",
        "      print('How to: https://github.com/baigouy/notebooks#how-to-mount-google-drive-in-colab') # could be useful indeed\n",
        "      #assert False\n",
        "      raise Exception('error!')\n",
        "    else:\n",
        "      # move to the drive folder\n",
        "      %cd /content/drive/My Drive\n",
        "      printmd('<font color=\"green\">Google drive successfully mounted!</font>')\n",
        "    # sys.exit()\n",
        "    #quit(keep_kernel=False)\n",
        "\n",
        "  # check we are using GPU\n",
        "  device_name = tf.test.gpu_device_name()\n",
        "  if device_name != '/device:GPU:0':\n",
        "    printmd('<font color=\"red\">GPU not found, please repeat this step.</font>')\n",
        "    print('How to: https://github.com/baigouy/notebooks#select-a-gpu-runtime') # could be useful indeed\n",
        "    # should also do that first and print the link to help people use this stuff!!!\n",
        "    # quit\n",
        "    raise Exception('error!')\n",
        "  else:\n",
        "    printmd('<font color=\"green\">GPU found!</font>')\n",
        "    # should I merge all in one single step because if anyway anything fails it is not gonna work...\n",
        "    # maybe it's an idea\n",
        "\n",
        "  printmd('<br><font color=\"green\">Everything went fine, please move on to next step!</font>')\n",
        "except:\n",
        "  pass\n",
        "\n",
        "\n",
        "# printmd('this is a test')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<font color=\"green\"><b>Open the external link and follow the instructions until you get the authorization code. Copy this code, paste it below then press \"Enter\"</b></font>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lpxi5NktFAZU"
      },
      "source": [
        "# Step 2:\n",
        "- Install the required python libraries by running the code cell below (please be patient, this may take time...)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PSqOZJ75E_6k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9359e730-e3dc-45ea-e72a-0c0c33f04ffe",
        "cellView": "form"
      },
      "source": [
        "#@title <-- Press Run\n",
        "\n",
        "# slow --> keep it as a separate step\n",
        "# install python libraries necessary to run epyseg\n",
        "# TODO check that all those libs are reallyu required but ok for now\n",
        "!pip install czifile\n",
        "!pip install h5py\n",
        "!pip install Markdown\n",
        "!pip install matplotlib\n",
        "!pip install numpy\n",
        "!pip install numpydoc\n",
        "!pip install Pillow\n",
        "!pip install PyQt5\n",
        "!pip install PyQtWebEngine\n",
        "!pip install read-lif\n",
        "!pip install scikit-image\n",
        "!pip install scipy\n",
        "!pip install segmentation-models==1.0.1\n",
        "!pip install tifffile>=2021.11.2\n",
        "!pip install qtawesome\n",
        "!pip install natsort\n",
        "!pip install numexpr\n",
        "!pip install elasticdeform\n",
        "!pip install roifile\n",
        "!pip install prettytable\n",
        "!pip install pyperclip\n",
        "!pip install scikit-learn\n",
        "!pip install --no-deps epyseg # prevent reinstalling tf 2.x and rather use the google optimized tf\n",
        "# !pip install --no-deps --user https://test.pypi.org/project/epyseg/0.1.14/\n",
        "# !pip install --no-deps --index-url https://test.pypi.org/simple/ epyseg\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# libraries loaded checking epyseg to see if everything is functional\n",
        "try:\n",
        "  from epyseg.img import Img\n",
        "  # just try import any class from Epyseg --> will raise an error if loading fails if loads most likely everything should work\n",
        "  from epyseg.deeplearning.deepl import EZDeepLearning\n",
        "  from epyseg.deeplearning.augmentation.meta import MetaAugmenter\n",
        "  from epyseg.deeplearning.augmentation.generators.data import DataGenerator\n",
        "  deepTA = EZDeepLearning()\n",
        "  printmd('<br><font color=\"green\">EPySeg succesfully loaded, please move on to next step!</font>')\n",
        "except:\n",
        "  printmd('<br><font color=\"red\">EPySeg failed to load. Please repeat this step</font>.')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.23.5)\n",
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            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.2.0)\n",
            "Collecting epyseg\n",
            "  Downloading epyseg-0.1.44-py3-none-any.whl (12.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.3/12.3 MB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: epyseg\n",
            "Successfully installed epyseg-0.1.44\n",
            "Segmentation Models: using `tf.keras` framework.\n",
            "Using tensorflow version 2.13.0\n",
            "Using segmentation models version 1.0.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<br><font color=\"green\">EPySeg succesfully loaded, please move on to next step!</font>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ysOUju3zkOXg"
      },
      "source": [
        "# Step 3\n",
        "- Run the cell below and choose between loading or building a model, then move to next cell."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "brmF8PQOkOiY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "982ed415efa145b7835c82d99780ded8",
            "71de66b74d9d4a36b5c7be6f0c5e23cb",
            "5af1845047514e5fa4a1b319b1c69ca1",
            "ccf0299e1bd6401d8aaefcea8b1eac4e",
            "7d6d74d4259f4dd5ba5df9578ac89d50",
            "ef17de794127469e9ceac093dafe04cf",
            "ec053ce1e9ca445ca2315b10ab53ac02",
            "79346a22920c421594fd2bfaeba98582",
            "effb6cde3172457eba34606f43500e8a",
            "bacd71ee540743da9a0a09c95a7e4773",
            "752d98e23647478c850a3bd2bb12aebf",
            "bdfde6091b404ad481c0ccd01ae01bb9",
            "2277b757441b4f72b5177d0a70900ded"
          ]
        },
        "cellView": "form",
        "outputId": "e182d8f1-0001-435d-90a7-9459f14feb8f"
      },
      "source": [
        "#@title <-- Press Run\n",
        "\n",
        "label = widgets.Label('Please make a choice:')\n",
        "# cannot be horizontal https://github.com/jupyter-widgets/ipywidgets/issues/1247\n",
        "options = [['Load a model (pretrained to segment epithelia)',1], ['Load a custom model',2] ] #,['Use a pre-trained model',3]\n",
        "model = widgets.RadioButtons(options=options)\n",
        "\n",
        "\n",
        "model_version = widgets.Dropdown(\n",
        "    value='v2',\n",
        "    placeholder='',\n",
        "    options=['v2', 'v1'],\n",
        "    description='',\n",
        "    ensure_option=True,\n",
        "    disabled=False,\n",
        "    layout={'width': 'max-content'},\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "\n",
        "ui = widgets.VBox([label, widgets.HBox([model,model_version])])\n",
        "# ui = widgets.VBox([label, model,model_version])\n",
        "display(ui)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(Label(value='Please make a choice:'), HBox(children=(RadioButtons(options=(['Load a model (pret…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "982ed415efa145b7835c82d99780ded8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jjd5PUsSut7M"
      },
      "source": [
        "# Step 4\n",
        "- Please run the cell below and follow output instructions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Ixle9inksxs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        },
        "cellView": "form",
        "outputId": "30649d9a-f495-4643-9cd6-43f745a5b009"
      },
      "source": [
        "#@title <-- Press Run\n",
        "\n",
        "if model.value == 1:\n",
        "  # # load a pre trained model\n",
        "  # available_pretrained_models_label = Label('Please select the pretrained model (then move on to next step):')\n",
        "  # # get all available model architectures\n",
        "  available_pretrained_models = deepTA.get_available_pretrained_models()\n",
        "  # # print(available_pretrained_models)\n",
        "  # available_pretrained_models_drop = widgets.Dropdown(\n",
        "  #     # value='Linknet',\n",
        "  #     placeholder='Choose a model',\n",
        "  #     options=available_pretrained_models,\n",
        "  #     description='Pretrained models',\n",
        "  #     ensure_option=True,\n",
        "  #     disabled=False,\n",
        "  #     layout={'width': 'max-content'},\n",
        "  #     style={'description_width': 'initial'}\n",
        "  # )\n",
        "  # ui = widgets.VBox([available_pretrained_models_label, available_pretrained_models_drop])\n",
        "  # display(ui)\n",
        "  printmd('<br><font color=\"green\">Please move on to next step!</font>')\n",
        "elif model.value == 2:\n",
        "  # browse for input model\n",
        "  load_model_label = Label('Please select a model file and move on to next step:')\n",
        "  model_path = FileBrowser(folders_only=False) #item_width=\"140px\"\n",
        "  ui = widgets.VBox([load_model_label, model_path.widget()])\n",
        "  display(ui)\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<br><font color=\"green\">Please move on to next step!</font>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gu16_2PHnf7t"
      },
      "source": [
        "# Step 5\n",
        "- Please run the cell below to load the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p_dkyHcVoBmq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "cellView": "form",
        "outputId": "c3d0b770-e106-46a2-dd6a-db01ccf20f63"
      },
      "source": [
        "#@title <-- Press Run\n",
        "\n",
        "deepTA.model = None # does it really reset model or not\n",
        "try:\n",
        "  # First we try to build or load the model and if something goes wrong ask to repeat step 4\n",
        "  if model.value == 1:\n",
        "    # Load a pre-trained model\n",
        "    pretrained_model_parameters = deepTA.pretrained_models['Linknet-vgg16-sigmoid'] if model_version.value=='v1' else  deepTA.pretrained_models[\n",
        "                'Linknet-vgg16-sigmoid'+'-'+model_version.value]\n",
        "\n",
        "    # print(pretrained_model_parameters)\n",
        "    pretrained_model_name = 'Linknet-vgg16-sigmoid'  if model_version.value=='v1' else 'Linknet-vgg16-sigmoid'+'-'+model_version.value\n",
        "    # deepTA.load_or_build(model=pretrained_model_parameters['model'], model_weights=pretrained_model_parameters['model_weights'], architecture=pretrained_model_parameters['architecture'], backbone=pretrained_model_parameters['backbone'], activation=pretrained_model_parameters['activation'], classes=pretrained_model_parameters['classes'], input_width=pretrained_model_parameters['input_width'], input_height=pretrained_model_parameters['input_height'], input_channels=pretrained_model_parameters['input_channels'],pretraining=pretrained_model_name)\n",
        "    deepTA.load_or_build(architecture='Linknet', backbone='vgg16', activation='sigmoid', classes=7, pretraining=pretrained_model_name)\n",
        "  elif model.value == 2:\n",
        "    # try to load an existing model and report error if model could not be loaded\n",
        "    # check wether model can be loaded\n",
        "\n",
        "    path = model_path.path\n",
        "    if not os.path.exists(path):\n",
        "      printmd('<font color=\"red\">Please provide a valid model path. </font>')\n",
        "      raise Exception('error!')\n",
        "    else:\n",
        "      # try load the model and return error upon failure\n",
        "      # printmd('<font color=\"red\">test!</font>')\n",
        "      deepTA.load_or_build(model=path)\n",
        "\n",
        "\n",
        "  if deepTA.model is None:\n",
        "    printmd('<font color=\"red\">Empty model!</font>')\n",
        "    raise Exception('error!')\n",
        "  else:\n",
        "    deepTA.summary()\n",
        "    printmd('<font color=\"green\">Model succesfully loaded, please move on to next step!</font>')\n",
        "\n",
        "except:\n",
        "\n",
        "  printmd('<font color=\"red\">Something went wrong, please repeat steps 4 and 5</font>')\n",
        "  pass"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model file is not up to date\n",
            "Downloading file from https://gitlab.com/baigouy/models/raw/master/model_linknet-vgg16_shells_v2.h5\n",
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            "File saved as /root/.keras/epyseg/Linknet-vgg16-sigmoid-v2.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - 2023-09-19 12:49:29,955 - deepl.py - load_weights - line 860 - Loading weights ' /root/.keras/epyseg/Linknet-vgg16-sigmoid-v2.h5'\n",
            "\n",
            "INFO:master:Loading weights ' /root/.keras/epyseg/Linknet-vgg16-sigmoid-v2.h5'\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"Linknet-vgg16-sigmoid-pretrained\"\n",
            "______________________________________________________________________________________________________________________________________________________\n",
            " Layer (type)                                Output Shape                                 Param #        Connected to                                 \n",
            "======================================================================================================================================================\n",
            " input_1 (InputLayer)                        [(None, None, None, 1)]                      0              []                                           \n",
            "                                                                                                                                                      \n",
            " block1_conv1 (Conv2D)                       (None, None, None, 64)                       640            ['input_1[0][0]']                            \n",
            "                                                                                                                                                      \n",
            " block1_conv2 (Conv2D)                       (None, None, None, 64)                       36928          ['block1_conv1[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " block1_pool (MaxPooling2D)                  (None, None, None, 64)                       0              ['block1_conv2[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " block2_conv1 (Conv2D)                       (None, None, None, 128)                      73856          ['block1_pool[0][0]']                        \n",
            "                                                                                                                                                      \n",
            " block2_conv2 (Conv2D)                       (None, None, None, 128)                      147584         ['block2_conv1[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " block2_pool (MaxPooling2D)                  (None, None, None, 128)                      0              ['block2_conv2[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " block3_conv1 (Conv2D)                       (None, None, None, 256)                      295168         ['block2_pool[0][0]']                        \n",
            "                                                                                                                                                      \n",
            " block3_conv2 (Conv2D)                       (None, None, None, 256)                      590080         ['block3_conv1[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " block3_conv3 (Conv2D)                       (None, None, None, 256)                      590080         ['block3_conv2[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " block3_pool (MaxPooling2D)                  (None, None, None, 256)                      0              ['block3_conv3[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " block4_conv1 (Conv2D)                       (None, None, None, 512)                      1180160        ['block3_pool[0][0]']                        \n",
            "                                                                                                                                                      \n",
            " block4_conv2 (Conv2D)                       (None, None, None, 512)                      2359808        ['block4_conv1[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " block4_conv3 (Conv2D)                       (None, None, None, 512)                      2359808        ['block4_conv2[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " block4_pool (MaxPooling2D)                  (None, None, None, 512)                      0              ['block4_conv3[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " block5_conv1 (Conv2D)                       (None, None, None, 512)                      2359808        ['block4_pool[0][0]']                        \n",
            "                                                                                                                                                      \n",
            " block5_conv2 (Conv2D)                       (None, None, None, 512)                      2359808        ['block5_conv1[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " block5_conv3 (Conv2D)                       (None, None, None, 512)                      2359808        ['block5_conv2[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " block5_pool (MaxPooling2D)                  (None, None, None, 512)                      0              ['block5_conv3[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " center_block1_conv (Conv2D)                 (None, None, None, 512)                      2359296        ['block5_pool[0][0]']                        \n",
            "                                                                                                                                                      \n",
            " center_block1_bn (BatchNormalization)       (None, None, None, 512)                      2048           ['center_block1_conv[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " center_block1_relu (Activation)             (None, None, None, 512)                      0              ['center_block1_bn[0][0]']                   \n",
            "                                                                                                                                                      \n",
            " center_block2_conv (Conv2D)                 (None, None, None, 512)                      2359296        ['center_block1_relu[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " center_block2_bn (BatchNormalization)       (None, None, None, 512)                      2048           ['center_block2_conv[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " center_block2_relu (Activation)             (None, None, None, 512)                      0              ['center_block2_bn[0][0]']                   \n",
            "                                                                                                                                                      \n",
            " decoder_stage0a_conv (Conv2D)               (None, None, None, 128)                      65536          ['center_block2_relu[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage0a_bn (BatchNormalization)     (None, None, None, 128)                      512            ['decoder_stage0a_conv[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage0a_relu (Activation)           (None, None, None, 128)                      0              ['decoder_stage0a_bn[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage0_upsampling (UpSampling2D)    (None, None, None, 128)                      0              ['decoder_stage0a_relu[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage0b_conv (Conv2D)               (None, None, None, 128)                      147456         ['decoder_stage0_upsampling[0][0]']          \n",
            "                                                                                                                                                      \n",
            " decoder_stage0b_bn (BatchNormalization)     (None, None, None, 128)                      512            ['decoder_stage0b_conv[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage0b_relu (Activation)           (None, None, None, 128)                      0              ['decoder_stage0b_bn[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage0c_conv (Conv2D)               (None, None, None, 512)                      65536          ['decoder_stage0b_relu[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage0c_bn (BatchNormalization)     (None, None, None, 512)                      2048           ['decoder_stage0c_conv[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage0c_relu (Activation)           (None, None, None, 512)                      0              ['decoder_stage0c_bn[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage0_add (Add)                    (None, None, None, 512)                      0              ['decoder_stage0c_relu[0][0]',               \n",
            "                                                                                                          'block5_conv3[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " decoder_stage1a_conv (Conv2D)               (None, None, None, 128)                      65536          ['decoder_stage0_add[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage1a_bn (BatchNormalization)     (None, None, None, 128)                      512            ['decoder_stage1a_conv[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage1a_relu (Activation)           (None, None, None, 128)                      0              ['decoder_stage1a_bn[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage1_upsampling (UpSampling2D)    (None, None, None, 128)                      0              ['decoder_stage1a_relu[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage1b_conv (Conv2D)               (None, None, None, 128)                      147456         ['decoder_stage1_upsampling[0][0]']          \n",
            "                                                                                                                                                      \n",
            " decoder_stage1b_bn (BatchNormalization)     (None, None, None, 128)                      512            ['decoder_stage1b_conv[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage1b_relu (Activation)           (None, None, None, 128)                      0              ['decoder_stage1b_bn[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage1c_conv (Conv2D)               (None, None, None, 512)                      65536          ['decoder_stage1b_relu[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage1c_bn (BatchNormalization)     (None, None, None, 512)                      2048           ['decoder_stage1c_conv[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage1c_relu (Activation)           (None, None, None, 512)                      0              ['decoder_stage1c_bn[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage1_add (Add)                    (None, None, None, 512)                      0              ['decoder_stage1c_relu[0][0]',               \n",
            "                                                                                                          'block4_conv3[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " decoder_stage2a_conv (Conv2D)               (None, None, None, 128)                      65536          ['decoder_stage1_add[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage2a_bn (BatchNormalization)     (None, None, None, 128)                      512            ['decoder_stage2a_conv[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage2a_relu (Activation)           (None, None, None, 128)                      0              ['decoder_stage2a_bn[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage2_upsampling (UpSampling2D)    (None, None, None, 128)                      0              ['decoder_stage2a_relu[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage2b_conv (Conv2D)               (None, None, None, 128)                      147456         ['decoder_stage2_upsampling[0][0]']          \n",
            "                                                                                                                                                      \n",
            " decoder_stage2b_bn (BatchNormalization)     (None, None, None, 128)                      512            ['decoder_stage2b_conv[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage2b_relu (Activation)           (None, None, None, 128)                      0              ['decoder_stage2b_bn[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage2c_conv (Conv2D)               (None, None, None, 256)                      32768          ['decoder_stage2b_relu[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage2c_bn (BatchNormalization)     (None, None, None, 256)                      1024           ['decoder_stage2c_conv[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage2c_relu (Activation)           (None, None, None, 256)                      0              ['decoder_stage2c_bn[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage2_add (Add)                    (None, None, None, 256)                      0              ['decoder_stage2c_relu[0][0]',               \n",
            "                                                                                                          'block3_conv3[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " decoder_stage3a_conv (Conv2D)               (None, None, None, 64)                       16384          ['decoder_stage2_add[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage3a_bn (BatchNormalization)     (None, None, None, 64)                       256            ['decoder_stage3a_conv[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage3a_relu (Activation)           (None, None, None, 64)                       0              ['decoder_stage3a_bn[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage3_upsampling (UpSampling2D)    (None, None, None, 64)                       0              ['decoder_stage3a_relu[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage3b_conv (Conv2D)               (None, None, None, 64)                       36864          ['decoder_stage3_upsampling[0][0]']          \n",
            "                                                                                                                                                      \n",
            " decoder_stage3b_bn (BatchNormalization)     (None, None, None, 64)                       256            ['decoder_stage3b_conv[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage3b_relu (Activation)           (None, None, None, 64)                       0              ['decoder_stage3b_bn[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage3c_conv (Conv2D)               (None, None, None, 128)                      8192           ['decoder_stage3b_relu[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage3c_bn (BatchNormalization)     (None, None, None, 128)                      512            ['decoder_stage3c_conv[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage3c_relu (Activation)           (None, None, None, 128)                      0              ['decoder_stage3c_bn[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage3_add (Add)                    (None, None, None, 128)                      0              ['decoder_stage3c_relu[0][0]',               \n",
            "                                                                                                          'block2_conv2[0][0]']                       \n",
            "                                                                                                                                                      \n",
            " decoder_stage4a_conv (Conv2D)               (None, None, None, 32)                       4096           ['decoder_stage3_add[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage4a_bn (BatchNormalization)     (None, None, None, 32)                       128            ['decoder_stage4a_conv[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage4a_relu (Activation)           (None, None, None, 32)                       0              ['decoder_stage4a_bn[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage4_upsampling (UpSampling2D)    (None, None, None, 32)                       0              ['decoder_stage4a_relu[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage4b_conv (Conv2D)               (None, None, None, 32)                       9216           ['decoder_stage4_upsampling[0][0]']          \n",
            "                                                                                                                                                      \n",
            " decoder_stage4b_bn (BatchNormalization)     (None, None, None, 32)                       128            ['decoder_stage4b_conv[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage4b_relu (Activation)           (None, None, None, 32)                       0              ['decoder_stage4b_bn[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " decoder_stage4c_conv (Conv2D)               (None, None, None, 16)                       512            ['decoder_stage4b_relu[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage4c_bn (BatchNormalization)     (None, None, None, 16)                       64             ['decoder_stage4c_conv[0][0]']               \n",
            "                                                                                                                                                      \n",
            " decoder_stage4c_relu (Activation)           (None, None, None, 16)                       0              ['decoder_stage4c_bn[0][0]']                 \n",
            "                                                                                                                                                      \n",
            " conv2d (Conv2D)                             (None, None, None, 7)                        1015           ['decoder_stage4c_relu[0][0]']               \n",
            "                                                                                                                                                      \n",
            " sigmoid (Activation)                        (None, None, None, 7)                        0              ['conv2d[0][0]']                             \n",
            "                                                                                                                                                      \n",
            "======================================================================================================================================================\n",
            "Total params: 20324855 (77.53 MB)\n",
            "Trainable params: 20318039 (77.51 MB)\n",
            "Non-trainable params: 6816 (26.62 KB)\n",
            "______________________________________________________________________________________________________________________________________________________\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - 2023-09-19 12:49:30,353 - deepl.py - summary - line 881 - None\n",
            "\n",
            "INFO:master:None\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<font color=\"green\">Model succesfully loaded, please move on to next step!</font>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aq89G9s16Ib2"
      },
      "source": [
        "# Step 6\n",
        "- Run the cell below and follow the instructions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bMtzjyL6Iw9",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "outputId": "2cd35729-5863-42c3-ac72-778193598d8b"
      },
      "source": [
        "#@title <-- Press Run\n",
        "\n",
        "if model.value == 1:\n",
        "  printmd('<font color=\"green\">Please move on to next step.</font>')\n",
        "elif model.value == 2:\n",
        "  #@title <-- Press Run\n",
        "\n",
        "  f = FileBrowser(folders_only=False) #item_width=\"140px\"\n",
        "  options2 = [['No',2],['Yes',1] ] #,['Use a pre-trained model',3]\n",
        "  drop_load_weights = widgets.Dropdown(options=options2, description='Load model weights:', layout={'width': 'max-content'}, style={'description_width': 'initial'})\n",
        "\n",
        "  def browse_for_weights(x):\n",
        "      if x == 1:\n",
        "        weight_label = Label('Please select a weight file in your drive, then move on to next step:')\n",
        "        ui = widgets.VBox([weight_label, f.widget()])\n",
        "        display(ui)\n",
        "      else:\n",
        "        weight_label = Label('No weights to load, then please move on to next step')\n",
        "        display(weight_label)\n",
        "\n",
        "\n",
        "  interact(browse_for_weights, x=drop_load_weights);\n",
        "\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<font color=\"green\">Please move on to next step.</font>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "plnvUKFsMb2i"
      },
      "source": [
        "# Step 7\n",
        "- Run the cell below and follow the instructions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-lp-ChN9_eAS",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "outputId": "7be60867-5bc3-4ee6-bf4a-7dac3a5b3ed3"
      },
      "source": [
        "#@title <-- Press Run\n",
        "\n",
        "if model.value == 1:\n",
        "  printmd('<font color=\"green\">Please move on to next step.</font>')\n",
        "elif model.value == 2:\n",
        "  if drop_load_weights.value == 1:\n",
        "    try:\n",
        "      deepTA.load_weights(f.path)\n",
        "      printmd('<font color=\"green\">Weights succesfully loaded. Please move on to next step!</font>')\n",
        "    except:\n",
        "      printmd('<font color=\"red\">Something went wrong (file corrupt/wrong file), please repeat step 6.</font>')\n",
        "  else:\n",
        "    printmd('<font color=\"green\">Please move on to next step!</font>')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<font color=\"green\">Please move on to next step.</font>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jaLF1hPshFw1"
      },
      "source": [
        "# Step 8\n",
        "- Please choose a folder containing images to segment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQPRVMwrhSJ_",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281,
          "referenced_widgets": [
            "ebdeb5f25ca44b8197d5ede576148a38",
            "5b1e587d0a9146b9a019f6cde8490028",
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            "b08cfd40073f490d9afe56300e887134",
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            "1680f82c26e8424fb702fba9e9869da9",
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            "d06717c2d82444d5b21919536c53b524",
            "6017da9917364a2cabdafc233fca915d",
            "b9821104699a4254a62b733af9d758b2"
          ]
        },
        "outputId": "a3a5a333-0289-4685-e82c-8fea802ae0ac"
      },
      "source": [
        "#@title <-- Press Run\n",
        "\n",
        "folder_train_originals = FileBrowser(folders_only=True)\n",
        "folder_train_originals_label = Label('Please select the folder in your drive that contains the original images to use for training (then move on to next step):')\n",
        "ui = widgets.VBox([folder_train_originals_label, folder_train_originals.widget()])\n",
        "display(ui)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(Label(value='Please select the folder in your drive that contains the original images to use fo…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ebdeb5f25ca44b8197d5ede576148a38"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6zAAO4qWt-k"
      },
      "source": [
        "# Step 9\n",
        "-Run the cell below to check the input folder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OP-53HWcWuQP",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        },
        "outputId": "74d72eb1-a22c-4e56-985c-965084155ece"
      },
      "source": [
        "#@title <-- Press Run\n",
        "\n",
        "# print(folder_train_originals.path)\n",
        "\n",
        "originals_list = DataGenerator.get_list_of_images(folder_train_originals.path)\n",
        "# print(originals_list)\n",
        "print('originals n=',len(originals_list))\n",
        "\n",
        "first_orig = None\n",
        "\n",
        "if len(originals_list) > 0:\n",
        "  try:\n",
        "    print('first original image')\n",
        "    first_orig = Img(originals_list[0])\n",
        "    # first_orig.pop() # deprecated function\n",
        "    print(first_orig.shape)\n",
        "    printmd('<font color=\"green\">Everything seems fine, please move on to next step.</font>')\n",
        "  except:\n",
        "    printmd('<font color=\"red\">Problems detected. The first image could not be loaded. Please check your files and repeat step 8.</font>')\n",
        "else:\n",
        "    printmd('<font color=\"red\">Problems detected. The folder seems empty. Please repeat step 8.</font>')\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "originals n= 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "<font color=\"red\">Problems detected. The folder seems empty. Please repeat step 8.</font>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IqtQ2tJIhhyF"
      },
      "source": [
        "# Step 10\n",
        "- Please run the cell below to define normalization and clip model input images (original images)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ts-n2q4eFuNv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273,
          "referenced_widgets": [
            "523b9e454bc44fe68ba1ab4fec9aefaa",
            "e38fff2b44204c10849d03ceecad1f35",
            "defa98e58eda4f539195a798f030ab9d",
            "097d15e7875c49a58515c3508f935600",
            "b603d2a8b0544198a81820ed31367205",
            "301eb992c5df48babe463a6e0b2b9082",
            "376e9533f74c42bcb33938f9a93fb92b",
            "16dc23b6b8054447b7a47208db6552b6",
            "15c5714175334e27a3a2114adf47f794",
            "12df37ca5f554ffabc60c0be5fae3888",
            "ab46914d32da43f2b2d3c9b771943142",
            "e38a6c129f574b69833709b7faff6057",
            "71b3d35012ed4c229b09e48ba10f2bd1",
            "a493f2fd124c432f8d77b7d828c91c58",
            "d659e8a58dc845e7a2af111b8e24bed5",
            "0c011b5f9e674f2f9deae1a2abe17f40",
            "812bb9a191f54b75babd081d5a9ebfd3",
            "5221be5d49d64db8a2b834a816152f58",
            "d8f41d7080b6473fae11f548cf108780",
            "48584c28ac724aceb2c8ce7333c51a0d",
            "17f1d8ebeed747eea7aa35bc3b3c5191",
            "a74e263144e64909bb3960a575d8e687",
            "ce98d0445bc24846aa13d275261dad2f",
            "f525277bae0f49feb2d8f66a8c90e753",
            "7bb440dad108429590e3f1d2c955f490",
            "f7546a01efa142b5a9a4cdab0dc830d8",
            "72fee9c16ea144b88ef8d48320d1b9cd",
            "d65c3cb5b05448b4abd8522fbc212afa",
            "ccdf3133985a48488fe6ad4b75529095",
            "12515893745f44aab024365093b98f87",
            "ee307347ec414b3b82b892278cc905d6",
            "8dd596185de3466799d3344ba6209de3",
            "49c977f160cf408097cd4717b4658880",
            "005c031d70194ff68d07e46394991f64",
            "f82c8aed88b34624b98509ff2812b701",
            "4a2e2f9369eb49dd84f3013c748ffc34",
            "e0d99368f52b44249638e0bf481675d1",
            "e362fcb842124da390e12872ef781d5b",
            "627f61bf3a124e34aa371f8618f35ae4",
            "3aab47b477f94a4498aad5177f3bbef6",
            "2190c7bf4eaf455b9b77dcc335379824",
            "ea08f919932d4c5a90b9becde6e25c43",
            "5b52854df13047d384fabc9983cc97e2",
            "806f4ed431ae49a29c06b00011427dc2",
            "f5a174b9a4774d0ab71a4f0650016683",
            "15ba321047004d48914ab96b359c095d",
            "a0e40a120792434cb9c1466ddf386969"
          ]
        },
        "outputId": "3f5cc64b-4cc6-4ada-cd71-baed49a53a98",
        "cellView": "form"
      },
      "source": [
        "#@title <-- Press Run\n",
        "\n",
        "# Normalization for orig data\n",
        "\n",
        "info_label_orig=widgets.Label('If you don\\'t know what to do, just leave pamaters unchanged and move on to next step.')\n",
        "\n",
        "# if image has no or 1 channel then ignore COI\n",
        "channels_orig = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
        "if first_orig is not None:\n",
        "  if first_orig.has_c():\n",
        "    channels_orig = []\n",
        "    for c in range(first_orig.get_dimension('c')):\n",
        "      channels_orig.append(c)\n",
        "  else:\n",
        "    channels_orig = [0]\n",
        "\n",
        "channel_COI_orig = widgets.Dropdown(\n",
        "    placeholder='Choose a channel of interest',\n",
        "    options=channels_orig,\n",
        "    description='Channel of interest (COI)',\n",
        "    ensure_option=True,\n",
        "    disabled=False,\n",
        "    layout={'width': 'max-content'},\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "rule_reduc_orig = widgets.Dropdown(\n",
        "    placeholder='Rule to reduce nb of channels (if needed)',\n",
        "    options=['copy the COI to all available channels','force copy the COI to all available channels even if nb of channels is ok','remove extra channels'],\n",
        "    description='Rule to reduce nb of channels (if needed)',\n",
        "    ensure_option=True,\n",
        "    disabled=False,\n",
        "    layout={'width': 'max-content'},\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "rule_aug_orig = widgets.Dropdown(\n",
        "    placeholder='Rule to increase nb of channels (if needed)',\n",
        "    options=['copy the COI to all channels', 'force copy the COI to all available channels even if nb of channels is ok', 'copy the COI to missing channels only', 'add empty channels (0 filled)'],\n",
        "    description='Rule to increase nb of channels (if needed)',\n",
        "    ensure_option=True,\n",
        "    disabled=False,\n",
        "    layout={'width': 'max-content'},\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "\n",
        "# TODO REMOVE LINE BELOW AND UPDATE LIBRARY!!!!\n",
        "# ̣̣̣!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n",
        "Img.clipping_methods = ['ignore outliers', '+', '+/-', '-']\n",
        "\n",
        "# list of available normalization methods\n",
        "# print(Img.normalization_methods)\n",
        "# Img.normalization_ranges =[[0, 1], [-1, 1]]\n",
        "# print('a',Img.normalization_ranges)\n",
        "from epyseg.img import normalization_ranges\n",
        "from epyseg.img import normalization_methods\n",
        "ranges = normalization_ranges.copy()\n",
        "# print(ranges)\n",
        "\n",
        "for n,r in enumerate(ranges):\n",
        "  # print(r,n, str(r))\n",
        "  ranges[n]=str(r)\n",
        "\n",
        "# print(ranges)\n",
        "\n",
        "normalization_methods_orig = widgets.Dropdown(\n",
        "    # value='Rescaling (min-max normalization)',\n",
        "    placeholder='Normalization',\n",
        "    options=normalization_methods,\n",
        "    description='Normalization method',\n",
        "    ensure_option=True,\n",
        "    disabled=False,\n",
        "    layout={'width': 'max-content'},\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "normalization_range_orig = widgets.Dropdown(\n",
        "    # value='[0, 1]',\n",
        "    placeholder='Range',\n",
        "    options=ranges,\n",
        "    description='Normalization range',\n",
        "    ensure_option=True,\n",
        "    disabled=False,\n",
        "    # layout={'width': 'max-content'},\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "clipping_methods_orig = widgets.Dropdown(\n",
        "    placeholder='Clipping outliers',\n",
        "    options=Img.clipping_methods,\n",
        "    description='clip intensity',\n",
        "    ensure_option=True,\n",
        "    disabled=False,\n",
        "    layout={'width': 'max-content'},\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "per_channel_orig = widgets.Checkbox(True,\n",
        "                       description='Per channel normalization',\n",
        "                       style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "cliprange_orig = widgets.FloatSlider(description='range (clipping)', value=0.005, min=0, max=0.30, step=0.001,style={'description_width': 'initial'})\n",
        "\n",
        "\n",
        "\n",
        "percentile_min_orig = widgets.FloatSlider(description='min percentile', value=2., min=0., max=30., step=0.1,style={'description_width': 'initial'})\n",
        "percentile_max_orig = widgets.FloatSlider(description='max percentile', value=99.8, min=70., max=100., step=0.1,style={'description_width': 'initial'})\n",
        "clip_percentile_orig_checkbox = widgets.Checkbox(\n",
        "    value=False,\n",
        "    description='Clip',\n",
        "    disabled=False,\n",
        "    indent=False,\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "percentile_min_orig.layout.visibility = 'hidden'\n",
        "percentile_max_orig.layout.visibility = 'hidden'\n",
        "clip_percentile_orig_checkbox.layout.visibility = 'hidden'\n",
        "\n",
        "\n",
        "def display_percentile_range(x):\n",
        "  if not 'ercent' in normalization_methods_orig.value:\n",
        "    percentile_min_orig.layout.visibility = 'hidden'\n",
        "    percentile_max_orig.layout.visibility = 'hidden'\n",
        "    clip_percentile_orig_checkbox.layout.visibility = 'hidden'\n",
        "    normalization_range_orig.layout.visibility = 'visible'\n",
        "  else:\n",
        "    percentile_min_orig.layout.visibility = 'visible'\n",
        "    percentile_max_orig.layout.visibility = 'visible'\n",
        "    clip_percentile_orig_checkbox.layout.visibility = 'visible'\n",
        "    normalization_range_orig.layout.visibility = 'hidden'\n",
        "\n",
        "interactive(display_percentile_range, x=normalization_methods_orig);\n",
        "\n",
        "# display(per_channel)\n",
        "# def changed(b):\n",
        "#     print(b)\n",
        "\n",
        "# per_channel.observe(changed)\n",
        "\n",
        "# wdgts = []\n",
        "# ui = widgets.VBox(wdgts)\n",
        "# display(ui)\n",
        "\n",
        "invert_orig = widgets.Checkbox(False,\n",
        "                       description='Negative/invert intensity',\n",
        "                       style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "\n",
        "# print(range_value)\n",
        "# print(per_channel.value) #recover checkbox value\n",
        "\n",
        "# ui = widgets.VBox([info_label_orig, channel_COI_orig, rule_reduc_orig, rule_aug_orig, normalization_methods_orig, normalization_range_orig, per_channel_orig, clipping_methods_orig,  cliprange_orig, invert_orig])\n",
        "ui = widgets.VBox([info_label_orig, widgets.HBox([clipping_methods_orig,  cliprange_orig]), channel_COI_orig, rule_reduc_orig, rule_aug_orig,  widgets.HBox([normalization_methods_orig, normalization_range_orig, per_channel_orig]),widgets.HBox([percentile_min_orig, percentile_max_orig, clip_percentile_orig_checkbox]),  invert_orig])\n",
        "display(ui)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(Label(value=\"If you don't know what to do, just leave pamaters unchanged and move on to next st…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "523b9e454bc44fe68ba1ab4fec9aefaa"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IMB9Uxh7x_mR"
      },
      "source": [
        "# Step 11\n",
        "- Please run the cell to set the tiling parameters (to reduce memory usage)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edC0r13ryAWM",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "ae4f1ae2b72049f693cb55e5385d931a",
            "b6994df6f83f4ffe9973b0e7b20ce72b",
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            "9a12687d09284d289fc4b0e029b53152",
            "8b607e49ee8740e19665dc8a7a7d85a8"
          ]
        },
        "outputId": "342859dc-e8ab-4bd1-b027-7dab291b03da"
      },
      "source": [
        "#@title <-- Press Run\n",
        "\n",
        "test_boolean = True # True # I can have a conditional GUI in fact\n",
        "wdgts = []\n",
        "\n",
        "input_shape = deepTA.get_inputs_shape()\n",
        "output_shape = deepTA.get_outputs_shape()\n",
        "\n",
        "# so far assume model has one entry and one output maybe allow several inputs and outputs later\n",
        "# print('model input shape', input_shape[0])\n",
        "# print('model output shape', output_shape[0])\n",
        "\n",
        "input_val_width = 128\n",
        "input_val_height = 128\n",
        "if input_shape[0][-2] is not None:\n",
        "  # print(input_shape[0][-2]) # default tile width\n",
        "  input_val_width=input_shape[0][-2]\n",
        "  test_boolean = False\n",
        "if input_shape[0][-3] is not None:\n",
        "  # print(input_shape[0][-3]) # default tile height\n",
        "  input_val_height=input_shape[0][-3]\n",
        "  test_boolean = False\n",
        "\n",
        "tile_width_slider = widgets.IntSlider(description='tile width', value=input_val_width, min=16, max=1024, step=2,style={'description_width': 'initial'})\n",
        "wdgts.append(tile_width_slider)\n",
        "tile_height_slider = widgets.IntSlider(description='tile height', value=input_val_height, min=16, max=1024, step=2,style={'description_width': 'initial'})\n",
        "wdgts.append(tile_height_slider)\n",
        "tile_width_slider_overlap = widgets.IntSlider(description='tile width overlap', value=32, min=0, max=256, step=2,style={'description_width': 'initial'})\n",
        "wdgts.append(tile_width_slider_overlap)\n",
        "tile_height_slider_overlap = widgets.IntSlider(description='tile height overlap', value=32, min=0, max=256, step=2,style={'description_width': 'initial'})\n",
        "wdgts.append(tile_height_slider_overlap)\n",
        "\n",
        "# now the batch size and alike parameters\n",
        "if not test_boolean:\n",
        "  tile_width_slider.disabled = True\n",
        "  tile_height_slider.disabled = True\n",
        "  printmd('<font color=\"green\">input width and height is already defined with the model, please move on to next step</font>')\n",
        "else:\n",
        "  ui = widgets.VBox(wdgts)\n",
        "  display(ui)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(IntSlider(value=128, description='tile width', max=1024, min=16, step=2, style=SliderStyle(desc…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ae4f1ae2b72049f693cb55e5385d931a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gis92suiwM7V"
      },
      "source": [
        "# Step 12\n",
        "- Set the post processing parameters (to refine masks, mostly useful for pretrained models)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GXHZc5lXhZA-",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
            "43bba20aee7d476f81663ea32f6a7178",
            "d2882f4d751b4c2a92d57eff63e6a02a",
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            "b67db4c0388f4ccb936091b76eb8914a",
            "dfc57d5d50ba48af8ad8284de2791d99",
            "f39bc6063b204c73979b8b608a3d9f13",
            "3c4e921c5ea74436a3dcedbce19bc26d",
            "d3282b31c8ac47a2b274afb59a4064ce",
            "06c0634f4a614d588fb0dec003c47746",
            "a9941192e920483192cf4f453978fc5c",
            "b85d4c0107e743f4aeeb6670d4f0fc71",
            "5d89d2ce032c4d24a37f2d6674ca363c",
            "7c49004a1aa3452480004df4e86a5e70",
            "63a84333250b4ddaa25aafe3801af63c",
            "5a89e33b6724499f8994656fb47461e5",
            "06b07c8d1e204af9aa0e8e43f3af6323",
            "f39132bf9e3e4754a1f3094db8f476bc",
            "21456ccf147f44b2b28bba7d4579e7cd",
            "927af79b2a754b06a561c9ee1b458e59",
            "ca5fdaa2cc0c47bba8531d020b2f1059",
            "2ef06c590af0458ab484924057159f07",
            "1d5fb84734dc415ebdf5ab0f07442ff5",
            "577a73b3e574437aae5559fec008a21e",
            "93947baa6b8641a593e6e65cb09c530c",
            "dec7293e4aac41319d57b414e05809a2",
            "cf05d5119e1a48dc9e4798302a8e2c58",
            "b077cf83b2294117891fc45646fb9a32",
            "53105bebf190417e8938c1e6da56c47f",
            "94296c28aefe4f3ea65fd08f176addc4",
            "511ca8ae1afd4780a7145259dc796f1d",
            "dd47c70a50b84cf88497cbe303ef94c3",
            "b7cf90011155401c8f9aebbd0b9872af",
            "99b97e3880ed49eb892a216863f13bb0",
            "f1845b4fd48b4d0599e39ace5e4b6633",
            "313d9bb30d6941829ae84689730ea160",
            "3d15767f5a604745919694340f4bffe2",
            "b44a23f248a94a72bdadebb87fe9c600"
          ]
        },
        "outputId": "2dc3e6de-ed9a-4303-9400-89f388030e63"
      },
      "source": [
        "#@title <-- Press Run\n",
        "\n",
        "hq_pred = widgets.Checkbox(True,\n",
        "                       description='HQ predictions (up to 12 times slower/better predictions)',layout={'width': 'max-content'},\n",
        "                       style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "enable_or_not = False\n",
        "if model.value == 1:\n",
        "  enable_or_not = True\n",
        "\n",
        "# I can connect the checkbox but hide it --> good idea\n",
        "enable_post_process = widgets.Checkbox(enable_or_not,\n",
        "                       description='Refine masks (Recommended only for EPySeg pre-trained model)',layout={'width': 'max-content'},\n",
        "                       style={'description_width': 'initial'}\n",
        ")\n",
        "enable_post_process.layout.visibility = 'hidden'\n",
        "\n",
        "if model.value == 1:\n",
        "  default_post_process = 'Default (Slow/robust) (EPySeg pre-trained model only!)'\n",
        "else:\n",
        "  default_post_process = 'Simply binarize output using threshold'\n",
        "\n",
        "post_process_method_selection_combo = widgets.Dropdown(\n",
        "    # value='Rescaling (min-max normalization)',\n",
        "    placeholder='Post process method',\n",
        "    options=['Default (Slow/robust) (EPySeg pre-trained model only!)', 'Fast (May contain more errors) (EPySeg pre-trained model only!)','Old method (Sometimes better than default) (EPySeg pre-trained model only!)','Simply binarize output using threshold','None (Raw model output)'],\n",
        "    description='Post process method',\n",
        "    ensure_option=True,\n",
        "    disabled=False,\n",
        "    layout={'width': 'max-content'},\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "\n",
        "filter_by_cell_size_combo = widgets.Dropdown(\n",
        "    # value='Rescaling (min-max normalization)',\n",
        "    placeholder='Filter segmentation/cells by size:',\n",
        "    options=['None (quite often the best choice)','Local median (slow/very good) divided by','Cells below size (in px)','Cells below Average area (global) divided by','Global median divided by'],\n",
        "    description='Filter segmentation/cells by size:',\n",
        "    ensure_option=True,\n",
        "    disabled=False,\n",
        "    layout={'width': 'max-content'},\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "avg_area_division_or_size_spinbox = widgets.IntSlider(description='', value=2, min=1, max=300, step=1,style={'description_width': 'initial'})\n",
        "# add a listener to model Architecture\n",
        "\n",
        "restore_secure_cells = widgets.Checkbox(True,\n",
        "                       description='Restore most likely cells',\n",
        "                       style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "prevent_exclusion_of_too_many_cells_together = widgets.Checkbox(True,\n",
        "                       description='Do not exclude groups bigger than',\n",
        "                       style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "max_nb_of_cells_to_be_excluded_together_spinbox = widgets.IntSlider(description='', value=3, min=1, max=100, step=1,style={'description_width': 'initial'})\n",
        "\n",
        "threshold_bonds = widgets.FloatSlider(description='', value=0.42, min=0, max=1, step=0.01,style={'description_width': 'initial'})\n",
        "auto_threshold = widgets.Checkbox(True,\n",
        "                       description='Auto',layout={'width': 'max-content'},\n",
        "                       style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "# nb can I define different groups based on stuff\n",
        "# worst case scenario split into two cells to allow proper selection of options\n",
        "\n",
        "label_threshold = Label('Threshold: (in case of over/under segmentation, please increase/decrease, respectively)')\n",
        "\n",
        "def show_hide_options(x):\n",
        "  if enable_post_process.value == True:\n",
        "    ui = widgets.VBox([widgets.HBox([restore_secure_cells, prevent_exclusion_of_too_many_cells_together, max_nb_of_cells_to_be_excluded_together_spinbox]),hq_pred])\n",
        "  else:\n",
        "    ui = widgets.VBox([widgets.HBox([label_threshold,threshold_bonds, auto_threshold]), hq_pred])\n",
        "  display(ui)\n",
        "\n",
        "# ça a l'air de marcher --> faire ça correctement même si ça va être compliqué\n",
        "def show_hide_options2(x):\n",
        "  if 'Old' in post_process_method_selection_combo.value:\n",
        "      threshold_bonds.layout.visibility = 'hidden'\n",
        "      label_threshold.layout.visibility = 'hidden'\n",
        "      auto_threshold.layout.visibility = 'hidden'\n",
        "      filter_by_cell_size_combo.layout.visibility = 'visible'\n",
        "      avg_area_division_or_size_spinbox.layout.visibility = 'visible'\n",
        "      enable_post_process.value = True # ça marche aussi --> je peux peut être y arriver comme ça alors\n",
        "  elif 'None' in post_process_method_selection_combo.value:\n",
        "      threshold_bonds.layout.visibility = 'hidden'\n",
        "      label_threshold.layout.visibility = 'hidden'\n",
        "      auto_threshold.layout.visibility = 'hidden'\n",
        "      filter_by_cell_size_combo.layout.visibility = 'hidden'\n",
        "      avg_area_division_or_size_spinbox.layout.visibility = 'hidden'\n",
        "      # maybe hide all --> in fact it's very simple but need define all the variables before\n",
        "      enable_post_process.value = False\n",
        "  elif 'imply' in post_process_method_selection_combo.value:\n",
        "      threshold_bonds.layout.visibility = 'visible'\n",
        "      label_threshold.layout.visibility = 'visible'\n",
        "      auto_threshold.layout.visibility = 'visible'\n",
        "      filter_by_cell_size_combo.layout.visibility = 'hidden'\n",
        "      avg_area_division_or_size_spinbox.layout.visibility = 'hidden'\n",
        "      enable_post_process.value = False\n",
        "  else:\n",
        "      threshold_bonds.layout.visibility = 'visible'\n",
        "      label_threshold.layout.visibility = 'visible'\n",
        "      auto_threshold.layout.visibility = 'visible'\n",
        "      filter_by_cell_size_combo.layout.visibility = 'visible'\n",
        "      avg_area_division_or_size_spinbox.layout.visibility = 'visible'\n",
        "      enable_post_process.value = False\n",
        "\n",
        "interact(show_hide_options2, x=post_process_method_selection_combo);\n",
        "\n",
        "\n",
        "def change_spinbox_value(x):\n",
        "  if 'Cells below size (in px)' in  filter_by_cell_size_combo.value:\n",
        "    avg_area_division_or_size_spinbox.max = 100000\n",
        "    avg_area_division_or_size_spinbox.step = 50\n",
        "    avg_area_division_or_size_spinbox.value = 300\n",
        "  else:\n",
        "    avg_area_division_or_size_spinbox.max = 100\n",
        "    avg_area_division_or_size_spinbox.step = 1\n",
        "    avg_area_division_or_size_spinbox.value = 2\n",
        "\n",
        "\n",
        "\n",
        "interactive(change_spinbox_value, x=filter_by_cell_size_combo)\n",
        "\n",
        "\n",
        "ui = widgets.VBox([hq_pred]) #, widgets.HBox([filter_by_cell_size_combo, avg_area_division_or_size_spinbox]), widgets.HBox([restore_secure_cells, prevent_exclusion_of_too_many_cells_together, max_nb_of_cells_to_be_excluded_together_spinbox])])\n",
        "interact(show_hide_options, x=enable_post_process);\n",
        "display(widgets.HBox([filter_by_cell_size_combo, avg_area_division_or_size_spinbox]));\n",
        "# or put None by default and do stuff accordingly\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(Dropdown(description='Post process method', layout=Layout(width='max-content'), options=…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "43bba20aee7d476f81663ea32f6a7178"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(Checkbox(value=False, description='Refine masks (Recommended only for EPySeg pre-trained…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3c4e921c5ea74436a3dcedbce19bc26d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "HBox(children=(Dropdown(description='Filter segmentation/cells by size:', layout=Layout(visibility='visible', …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5a89e33b6724499f8994656fb47461e5"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DNjjScbK-axN"
      },
      "source": [
        "# Segment\n",
        "- Use the current neural network model to segment an epithelium (__segmentation will be saved in a 'predict' folder within the specified input folder__)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cgd4ulIk-bEI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "outputId": "7db46a4e-8f12-47f9-f94e-b079fb58449b"
      },
      "source": [
        "#@title <-- Press Run\n",
        "\n",
        "# just need get parameters and run --> TODO\n",
        "\n",
        "\n",
        "\n",
        "import os\n",
        "# print(deepTA._get_inputs())\n",
        "# print(deepTA._get_outputs())\n",
        "\n",
        "# print('input shapes', deepTA.get_inputs_shape())\n",
        "# print('output shapes', deepTA.get_outputs_shape())\n",
        "\n",
        "input_shape = deepTA.get_inputs_shape()\n",
        "output_shape = deepTA.get_outputs_shape()\n",
        "\n",
        "default_input_width = tile_width_slider.value # 256  # 576  # 128 # 64\n",
        "default_input_height = tile_height_slider.value # 256  # 576 # 128 # 64\n",
        "\n",
        "def get_clip_by_freq():\n",
        "      if 'ignore' in clipping_methods_orig.value:\n",
        "          return {'lower_cutoff': None, 'upper_cutoff': None,\n",
        "                  'channel_mode': per_channel_orig.value}\n",
        "      elif clipping_methods_orig.value == '+':\n",
        "          return {'lower_cutoff': None, 'upper_cutoff': cliprange_orig.value,\n",
        "                  'channel_mode': per_channel_orig.value}\n",
        "      elif clipping_methods_orig.value == '-':\n",
        "          return {'lower_cutoff': cliprange_orig.value, 'upper_cutoff': None,\n",
        "                  'channel_mode': per_channel_orig.value}\n",
        "      else:\n",
        "        #  '/' in clipping_methods_orig.value:\n",
        "          return {'lower_cutoff': cliprange_orig.value, 'upper_cutoff': cliprange_orig.value,\n",
        "                  'channel_mode': per_channel_orig.value}\n",
        "\n",
        "input_folder = folder_train_originals.path\n",
        "\n",
        "# input_normalization = {'method': normalization_methods_orig.value,\n",
        "#                        'individual_channels': per_channel_orig.value,\n",
        "#                        'range': normalization_range_orig.value}\n",
        "\n",
        "range_input = normalization_range_orig.value\n",
        "if percentile_min_orig.layout.visibility == 'visible':\n",
        "    # TODO need a check that values are ok --> but must be ok because I bounded them and bounds are non overlapping\n",
        "    range_input=[percentile_min_orig.value, percentile_max_orig.value]\n",
        "input_normalization = {'method': normalization_methods_orig.value,\n",
        "                                          'individual_channels': per_channel_orig.value,\n",
        "                                          'range': range_input,\n",
        "                                          'clip': True if clip_percentile_orig_checkbox.value and clip_percentile_orig_checkbox.layout.visibility == 'visible' else False} # can have different values for the range\n",
        "\n",
        "predict_generator = deepTA.get_predict_generator(\n",
        "    inputs=[input_folder], input_shape=input_shape,\n",
        "    output_shape=output_shape,\n",
        "    default_input_tile_width=default_input_width,\n",
        "    default_input_tile_height=default_input_height,\n",
        "    tile_width_overlap=tile_width_slider_overlap.value,\n",
        "    tile_height_overlap=tile_height_slider_overlap.value,\n",
        "    input_normalization=input_normalization,\n",
        "    clip_by_frequency=get_clip_by_freq())\n",
        "\n",
        "def _get_post_process_filter():\n",
        "    current_filter = filter_by_cell_size_combo.value.lower()\n",
        "    if 'one' in current_filter:\n",
        "        return None\n",
        "    if 'size' in current_filter:\n",
        "        return avg_area_division_or_size_spinbox.value\n",
        "    if 'verage' in current_filter:\n",
        "        return 'avg'\n",
        "    if 'local' in current_filter:\n",
        "        return 'local'\n",
        "    if 'global' in current_filter:\n",
        "        return 'global median'\n",
        "\n",
        "post_process_parameters={}\n",
        "if not 'None' in post_process_method_selection_combo.value:\n",
        "  post_process_parameters['filter'] = _get_post_process_filter()\n",
        "  post_process_parameters['correction_factor'] = avg_area_division_or_size_spinbox.value\n",
        "  post_process_parameters['restore_safe_cells'] = restore_secure_cells.value\n",
        "  post_process_parameters['cutoff_cell_fusion'] = max_nb_of_cells_to_be_excluded_together_spinbox.value if prevent_exclusion_of_too_many_cells_together.value else None\n",
        "  post_proc_method = post_process_method_selection_combo.value\n",
        "  if 'none' in post_proc_method:\n",
        "      post_process_parameters['post_process_algorithm'] = None\n",
        "  else:\n",
        "      post_process_parameters['post_process_algorithm'] = post_proc_method\n",
        "  post_process_parameters['threshold'] = threshold_bonds.value\n",
        "  if auto_threshold.layout.visibility == 'visible' and auto_threshold.value:\n",
        "      post_process_parameters[\n",
        "          'threshold'] = None  # None means autothrehsold # maybe add more options some day\n",
        "  # if avg_area_division_or_size_spinbox.isEnabled():\n",
        "  #     post_process_parameters['correction_factor'] = avg_area_division_or_size_spinbox.value()\n",
        "  # if restore_secure_cells.isEnabled():\n",
        "  #     post_process_parameters['restore_safe_cells'] = restore_secure_cells.isChecked()\n",
        "  # if max_nb_of_cells_to_be_excluded_together_spinbox.isEnabled():\n",
        "  #     post_process_parameters[\n",
        "  #         'cutoff_cell_fusion'] = max_nb_of_cells_to_be_excluded_together_spinbox.value() if prevent_exclusion_of_too_many_cells_together.isChecked() else None\n",
        "  if 'Old' in post_process_method_selection_combo.value:\n",
        "      # just for max use that --> maybe do this as an option some day\n",
        "      post_process_parameters['hq_predictions'] = 'max'\n",
        "\n",
        "\n",
        "if False: #True #False\n",
        "  print(post_process_parameters, input_normalization) # for a test\n",
        "else:\n",
        "  predict_output_folder = os.path.join(input_folder, 'predict')\n",
        "  deepTA.predict(predict_generator,\n",
        "                output_shape,\n",
        "                predict_output_folder=predict_output_folder,\n",
        "                batch_size=1, **post_process_parameters)"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO - 2023-05-04 10:09:48,610 - deepl.py - predict - line 1504 - post processing/refining mask, please wait...\n",
            "\n",
            "INFO:master:post processing/refining mask, please wait...\n",
            "INFO - 2023-05-04 10:09:53,916 - deepl.py - predict - line 1575 - saving file as /content/drive/MyDrive/predict_test/predict/12.tif\n",
            "\n",
            "INFO:master:saving file as /content/drive/MyDrive/predict_test/predict/12.tif\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
            "91.66666666666666%\n",
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          "text": [
            "INFO - 2023-05-04 10:09:59,104 - deepl.py - get_HQ_predictions - line 2003 - HQ predictions\n",
            "\n",
            "INFO:master:HQ predictions\n"
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          "text": [
            "INFO - 2023-05-04 10:10:57,628 - deepl.py - predict - line 1504 - post processing/refining mask, please wait...\n",
            "\n",
            "INFO:master:post processing/refining mask, please wait...\n"
          ]
        }
      ]
    }
  ]
}