REACTION.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Fgf06SjLx-Bc"
   },
   "source": [
    "# **REACTION: Breast Cancer Detection**\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qgDScaaN7G99"
   },
   "source": [
    "Read your mamography image file and provide an alternative result for breast cancer diagnose using image preprocesses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tb8pmQoEcbSL"
   },
   "outputs": [],
   "source": [
    "from ipywidgets import Layout, Button, Box, FileUpload, Label, Checkbox, IntSlider, IntText, Button\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "import threading\n",
    "import time\n",
    "\n",
    "# uploader to image cv2\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3skpTfYDceJE"
   },
   "outputs": [],
   "source": [
    "#define flag\n",
    "thread_status = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "26rKt6FMciHq"
   },
   "outputs": [],
   "source": [
    "# uploader\n",
    "uploader = widgets.FileUpload(\n",
    "    accept='image/*',  # Accepted file extension e.g. '.txt', '.pdf', 'image/*', 'image/*,.pdf'\n",
    "    multiple=False  # True to accept multiple files upload else False\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ObBk1-TEckY0"
   },
   "outputs": [],
   "source": [
    "# uploader container\n",
    "image_container_raw = widgets.VBox(\n",
    "    children = ()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4FQzfvXfcn4e"
   },
   "outputs": [],
   "source": [
    "# wrapper\n",
    "app = widgets.VBox(\n",
    "    children = (uploader, image_container_raw)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cTTpwJfpcqO-"
   },
   "outputs": [],
   "source": [
    "# uploader image content and extension\n",
    "def get_content_and_extension(uploader):    \n",
    "    for _, value in uploader.value.items():\n",
    "        return value['content'], value['metadata']['name'].split('.')[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "40F81sYzcuFR"
   },
   "outputs": [],
   "source": [
    "# image widget from uploader\n",
    "def image_widget_from_uploader(uploader):\n",
    "    if uploader.value:\n",
    "        file_content, file_extension = get_content_and_extension(uploader)\n",
    "        return widgets.Image(value=file_content, format=file_extension)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Os8m-pqjcwki"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread work:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\ACER\\anaconda3\\lib\\site-packages\\traitlets\\traitlets.py\", line 2252, in validate_elements\n",
      "    v = self._trait._validate(obj, v)\n",
      "  File \"C:\\Users\\ACER\\anaconda3\\lib\\site-packages\\traitlets\\traitlets.py\", line 591, in _validate\n",
      "    value = self.validate(obj, value)\n",
      "  File \"C:\\Users\\ACER\\anaconda3\\lib\\site-packages\\traitlets\\traitlets.py\", line 1677, in validate\n",
      "    self.error(obj, value)\n",
      "  File \"C:\\Users\\ACER\\anaconda3\\lib\\site-packages\\traitlets\\traitlets.py\", line 1524, in error\n",
      "    raise TraitError(e)\n",
      "traitlets.traitlets.TraitError: The 'None' trait of a VBox instance must be a Widget, but a value of class 'NoneType' (i.e. None) was specified.\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\ACER\\anaconda3\\lib\\threading.py\", line 926, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"C:\\Users\\ACER\\anaconda3\\lib\\threading.py\", line 870, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"<ipython-input-21-168fd36f4be1>\", line 57, in work\n",
      "    image_container_raw.children = (image_widget_raw,)\n",
      "  File \"C:\\Users\\ACER\\anaconda3\\lib\\site-packages\\traitlets\\traitlets.py\", line 585, in __set__\n",
      "    self.set(obj, value)\n",
      "  File \"C:\\Users\\ACER\\anaconda3\\lib\\site-packages\\traitlets\\traitlets.py\", line 559, in set\n",
      "    new_value = self._validate(obj, value)\n",
      "  File \"C:\\Users\\ACER\\anaconda3\\lib\\site-packages\\traitlets\\traitlets.py\", line 591, in _validate\n",
      "    value = self.validate(obj, value)\n",
      "  File \"C:\\Users\\ACER\\anaconda3\\lib\\site-packages\\traitlets\\traitlets.py\", line 2242, in validate\n",
      "    value = self.validate_elements(obj, value)\n",
      "  File \"C:\\Users\\ACER\\anaconda3\\lib\\site-packages\\traitlets\\traitlets.py\", line 2254, in validate_elements\n",
      "    self.element_error(obj, v, self._trait)\n",
      "  File \"C:\\Users\\ACER\\anaconda3\\lib\\site-packages\\traitlets\\traitlets.py\", line 2233, in element_error\n",
      "    raise TraitError(e)\n",
      "traitlets.traitlets.TraitError: Element of the 'children' trait of a VBox instance must be a Widget, but a value of None <class 'NoneType'> was specified.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# image numpy from uploader\n",
    "def image_numpy_from_uploader(uploader):\n",
    "    file_content, extension = get_content_and_extension(uploader)\n",
    "    arr_from_buf = np.frombuffer(file_content, np.uint8)\n",
    "    image_numpy = cv2.imdecode(arr_from_buf, cv2.IMREAD_COLOR)\n",
    "    return image_numpy, extension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IWcv9jJKczYR"
   },
   "outputs": [],
   "source": [
    "# image widget from image numpy\n",
    "def image_widget_from_numpy(image_numpy, extension):\n",
    "    _, encoded_image = cv2.imencode('.'+extension, image_numpy,)\n",
    "    image_bytes = encoded_image.tobytes()\n",
    "    return widgets.Image(value=image_bytes, format=extension)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "icrY-n7fc2a6"
   },
   "outputs": [],
   "source": [
    "def prepro_clahe(image_numpy, clipLimit, tileGridSize):\n",
    "    img_numpy = cv2.cvtColor(image_numpy, cv2.COLOR_BGR2GRAY)\n",
    "    clahe = cv2.createCLAHE(clipLimit, (tileGridSize, tileGridSize))\n",
    "    cl1 = clahe.apply(img_numpy)\n",
    "    return cl1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DL0z6H5udBOS"
   },
   "outputs": [],
   "source": [
    "def prepro_normalize(image_numpy,resize_w=512,resize_l=512):\n",
    "    out_arr = np.zeros((resize_w,resize_l))\n",
    "    image_numpy = cv2.normalize(image_numpy, out_arr, 0, 255, cv2.NORM_MINMAX)\n",
    "    return image_numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2lS75qYYdGbf"
   },
   "outputs": [],
   "source": [
    "def prepro_gaussian(image_numpy, kernel_size):\n",
    "    img_numpy = cv2.GaussianBlur(image_numpy, (kernel_size, kernel_size), 0)\n",
    "    noise = img_numpy - image_numpy\n",
    "    filtered = image_numpy - noise\n",
    "    return  filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessing(kernel_size, clipLimit, tileGridSize, image_numpy,to_clahe = True,resize_w=512,resize_l=512,to_normalize=True, to_gaussian_filter=True):\n",
    "    if to_clahe:\n",
    "        image_numpy= prepro_clahe(image_numpy, clipLimit, tileGridSize)\n",
    "    if to_normalize:\n",
    "        image_numpy= prepro_normalize(image_numpy,resize_w=resize_w,resize_l=resize_l)\n",
    "    if to_gaussian_filter:\n",
    "        image_numpy = prepro_gaussian(image_numpy, kernel_size)\n",
    "    return image_numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "czPqENfU2ACS"
   },
   "outputs": [],
   "source": [
    "slider_ksize = widgets.IntSlider(\n",
    "    value=5,\n",
    "    min=1,\n",
    "    max=100,\n",
    "    step=2,\n",
    "    description='',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='d'\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "slider_clipLimit = widgets.IntSlider(\n",
    "    value=2.0,\n",
    "    min=1.0,\n",
    "    max=100.0,\n",
    "    step=1,\n",
    "    description='',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='d'\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "slider_tileGridSize = widgets.IntSlider(\n",
    "    value=8,\n",
    "    min=1,\n",
    "    max=100,\n",
    "    step=1,\n",
    "    description='',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='d'\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139,
     "referenced_widgets": [
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    "colab_type": "code",
    "id": "Ksv2E5ErdJRw",
    "outputId": "8a76c821-ccb3-43d6-a35e-3bd11ee1d2a1"
   },
   "outputs": [],
   "source": [
    "checkbox_clahe = widgets.Checkbox(\n",
    "    value=True,\n",
    "    description='clahe',\n",
    "    disabled=False,\n",
    "    indent=False\n",
    ")\n",
    "checkbox_normalize = widgets.Checkbox(\n",
    "    value=True,\n",
    "    description='normalize',\n",
    "    disabled=False,\n",
    "    indent=False\n",
    ")\n",
    "checkbox_gaussian_filter = widgets.Checkbox(\n",
    "    value=True,\n",
    "    description='gaussian',\n",
    "    disabled=False,\n",
    "    indent=False\n",
    ")\n",
    "checkbox_normalize_clahe = widgets.Checkbox(\n",
    "    value=True,\n",
    "    description='normalize + clahe',\n",
    "    disabled=False,\n",
    "    indent=False\n",
    ")\n",
    "checkbox_normalize_gaussian = widgets.Checkbox(\n",
    "    value=True,\n",
    "    description='normalize + gaussian',\n",
    "    disabled=False,\n",
    "    indent=False\n",
    ")\n",
    "checkbox_clahe_gaussian = widgets.Checkbox(\n",
    "    value=True, \n",
    "    description='clahe + gaussian',\n",
    "    disabled=False,\n",
    "    indent=False\n",
    ")\n",
    "checkbox_clahe_normalize_gaussian = widgets.Checkbox(\n",
    "    value=True,\n",
    "    description='clahe + normalize + gaussian',\n",
    "    disabled=False,\n",
    "    indent=False\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
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    "colab_type": "code",
    "id": "FTTZonzidOyn",
    "outputId": "c8e1556b-28b2-4b69-d59f-93a7bcad80f0"
   },
   "outputs": [],
   "source": [
    "checkboxes = widgets.HBox(\n",
    "    children = (checkbox_clahe, checkbox_normalize, checkbox_gaussian_filter, checkbox_normalize_clahe, checkbox_normalize_gaussian, checkbox_clahe_gaussian, checkbox_clahe_normalize_gaussian)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "--ajYtcOdRQs"
   },
   "outputs": [],
   "source": [
    "image_container_prepro = widgets.VBox(\n",
    "    children = ()\n",
    ")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "colab_type": "code",
    "id": "mKIhYLrTdT9i",
    "outputId": "64497214-29ef-49e2-dd0c-337dfdc96878"
   },
   "outputs": [],
   "source": [
    "form_item_layout = Layout(\n",
    "    display='flex',\n",
    "    flex_flow='row',\n",
    "    justify_content='space-between'\n",
    ")\n",
    "\n",
    "form_items = [\n",
    "    Box([Label(value='Upload Mammography'), app], layout=form_item_layout),\n",
    "    Box(children = (checkboxes,), layout=form_item_layout),\n",
    "    Box([Label(value='Gaussian Filter Kernel Size'), slider_ksize], layout=form_item_layout),\n",
    "    Box([Label(value='CLAHE clipLimit'), slider_clipLimit], layout=form_item_layout),\n",
    "    Box([Label(value='CLAHE tileGridSize'), slider_tileGridSize], layout=form_item_layout),\n",
    "    Box([Label(value='Visualization'), image_container_prepro], layout=form_item_layout)\n",
    "]\n",
    "\n",
    "form = widgets.VBox(form_items, layout=Layout(\n",
    "    display='flex',\n",
    "    flex_flow='column',\n",
    "    border='solid 2px',\n",
    "    align_items='stretch',\n",
    "  width='100%'\n",
    "))\n",
    "# display(form)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zAa2w7E2dWfF"
   },
   "outputs": [],
   "source": [
    "def image_widget_to_labeled(image_numpy, message):\n",
    "    layout_img=widgets.Layout(\n",
    "        display='flex',\n",
    "        flex_flow='column',\n",
    "        border='solid 2px',\n",
    "        align_items='stretch',\n",
    "        width='50%'\n",
    "        )\n",
    "    return widgets.VBox([widgets.Label(value=message), image_numpy, layout_img])   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "taIYJS8Kzuun"
   },
   "outputs": [],
   "source": [
    "# label\n",
    "label_current_thread = widgets.Label()\n",
    "label_thread_list = widgets.Label()\n",
    "\n",
    "# button \n",
    "button_stop = widgets.Button(\n",
    "    description = 'stop',\n",
    "    icon = 'fa-stop',\n",
    "    button_style = 'warning',\n",
    "    layout = widgets.Layout(width='100px')\n",
    ")\n",
    "def button_stop_click(self):\n",
    "    global thread_status\n",
    "    thread_status = False\n",
    "button_stop.on_click(button_stop_click)\n",
    "\n",
    "button_check_thread = widgets.Button(\n",
    "    description = 'check thread',\n",
    "    button_style = '',\n",
    "    layout = widgets.Layout(width='100px')\n",
    ")\n",
    "def button_check_thread_click(self):\n",
    "    # print if work() still running\n",
    "    work_count = 0\n",
    "    name='work'\n",
    "    for thread_object in threading.enumerate():\n",
    "        if thread_object.name == name:\n",
    "            work_count+=1\n",
    "    if work_count > 0:\n",
    "        label_thread_list.value=f'{work_count} thread with name {name} still exist. Consider use exit() and restart kernel'\n",
    "    else:\n",
    "        label_thread_list.value=f'No thread with name {name} exist'\n",
    "button_check_thread.on_click(button_check_thread_click)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KpgzQ_e2db-v"
   },
   "outputs": [],
   "source": [
    "# main loop\n",
    "def work():\n",
    "\n",
    "    global thread_status\n",
    "    \n",
    "    if thread_status:\n",
    "        label_current_thread.value = 'Thread On'\n",
    "\n",
    "    current_state =[\n",
    "        checkbox_clahe.value,\n",
    "        checkbox_normalize.value,\n",
    "        checkbox_gaussian_filter.value,\n",
    "        checkbox_normalize_clahe.value,\n",
    "        checkbox_normalize_gaussian.value,\n",
    "        checkbox_clahe_gaussian.value, \n",
    "        checkbox_clahe_normalize_gaussian.value,\n",
    "        slider_ksize.value,\n",
    "        slider_clipLimit.value,\n",
    "        slider_tileGridSize.value,\n",
    "        uploader.value\n",
    "        ]\n",
    "    \n",
    "    while thread_status:\n",
    "        \n",
    "        # check if there is a change in uploader.value\n",
    "        if current_state != [\n",
    "            checkbox_clahe.value,\n",
    "            checkbox_normalize.value,\n",
    "            checkbox_gaussian_filter.value,\n",
    "            checkbox_normalize_clahe.value,\n",
    "            checkbox_normalize_gaussian.value,\n",
    "            checkbox_clahe_gaussian.value, \n",
    "            checkbox_clahe_normalize_gaussian.value,\n",
    "            slider_ksize.value,\n",
    "            slider_clipLimit.value,\n",
    "            slider_tileGridSize.value,\n",
    "            uploader.value\n",
    "            ]:\n",
    "            \n",
    "            # renew state reference\n",
    "            current_state = [\n",
    "                checkbox_clahe.value,\n",
    "                checkbox_normalize.value,\n",
    "                checkbox_gaussian_filter.value,\n",
    "                checkbox_normalize_clahe.value,\n",
    "                checkbox_normalize_gaussian.value,\n",
    "                checkbox_clahe_gaussian.value, \n",
    "                checkbox_clahe_normalize_gaussian.value,\n",
    "                slider_ksize.value,\n",
    "                slider_clipLimit.value,\n",
    "                slider_tileGridSize.value,\n",
    "                \n",
    "                uploader.value\n",
    "                ]\n",
    "            \n",
    "            \n",
    "            # image widget from uploader\n",
    "            image_widget_raw = image_widget_from_uploader(uploader)\n",
    "            \n",
    "            # renew container_raw\n",
    "            image_container_raw.children = (image_widget_raw,)\n",
    "\n",
    "            # read image numpy\n",
    "            image_numpy, extension = image_numpy_from_uploader(uploader)\n",
    "\n",
    "\n",
    "            # preprocess numpy\n",
    "           \n",
    "            if checkbox_clahe.value:\n",
    "                image_numpy_clahe    = prepro_clahe(image_numpy, slider_clipLimit.value, slider_tileGridSize.value)\n",
    "            if checkbox_normalize.value:\n",
    "                image_numpy_normalize = prepro_normalize(image_numpy,resize_w=512,resize_l=512)\n",
    "            if checkbox_gaussian_filter.value:\n",
    "                image_numpy_gaussian  = prepro_gaussian(image_numpy, slider_ksize.value)\n",
    "            if checkbox_normalize_clahe.value:\n",
    "                image_numpy_normalize_clahe = preprocessing(slider_ksize.value,  slider_clipLimit.value, slider_tileGridSize.value, image_numpy, to_clahe = True,resize_w=512,resize_l=512,to_normalize=True, to_gaussian_filter=False)\n",
    "            if checkbox_normalize_gaussian.value:\n",
    "                image_numpy_normalize_gaussian = preprocessing(slider_ksize.value, slider_clipLimit.value, slider_tileGridSize.value, image_numpy, to_clahe = False,resize_w=512,resize_l=512,to_normalize=True, to_gaussian_filter=True)\n",
    "            if checkbox_clahe_gaussian.value:\n",
    "                image_numpy_clahe_gaussian = preprocessing(slider_ksize.value, slider_clipLimit.value, slider_tileGridSize.value, image_numpy, to_clahe = True,resize_w=512,resize_l=512,to_normalize=False, to_gaussian_filter=True)\n",
    "            if checkbox_clahe_normalize_gaussian.value:\n",
    "                image_numpy_clahe_normalize_gaussian = preprocessing(slider_ksize.value, slider_clipLimit.value, slider_tileGridSize.value, image_numpy, to_clahe = True,resize_w=512,resize_l=512,to_normalize=True, to_gaussian_filter=True)\n",
    "\n",
    "\n",
    "            #numpy to widget\n",
    "            if checkbox_clahe.value:\n",
    "                image_widget_clahe    = image_widget_from_numpy(image_numpy_clahe, extension)\n",
    "            if checkbox_normalize.value:\n",
    "                image_widget_normalize = image_widget_from_numpy(image_numpy_normalize, extension)\n",
    "            if checkbox_gaussian_filter.value:\n",
    "                image_widget_gaussian  = image_widget_from_numpy(image_numpy_gaussian, extension)\n",
    "            if checkbox_normalize_clahe.value:\n",
    "                image_widget_normalize_clahe = image_widget_from_numpy(image_numpy_normalize_clahe, extension)\n",
    "            if checkbox_normalize_gaussian.value:\n",
    "                image_widget_normalize_gaussian = image_widget_from_numpy(image_numpy_normalize_gaussian, extension)\n",
    "            if checkbox_clahe_gaussian.value:\n",
    "                image_widget_clahe_gaussian = image_widget_from_numpy(image_numpy_clahe_gaussian, extension)\n",
    "            if checkbox_clahe_normalize_gaussian.value:\n",
    "                image_widget_clahe_normalize_gaussian = image_widget_from_numpy(image_numpy_clahe_normalize_gaussian, extension)\n",
    "\n",
    "\n",
    "                \n",
    "            # image widget to labeled widget\n",
    "            if checkbox_clahe.value:\n",
    "                labeled_image_clahe = image_widget_to_labeled(image_widget_clahe, 'clahe')\n",
    "            if checkbox_normalize.value:\n",
    "                labeled_image_normalize = image_widget_to_labeled(image_widget_normalize, 'normalize')\n",
    "            if checkbox_gaussian_filter.value:\n",
    "                labeled_image_gaussian = image_widget_to_labeled(image_widget_gaussian, 'gaussian filter')\n",
    "            if checkbox_normalize_clahe.value:\n",
    "                labeled_image_normalize_clahe = image_widget_to_labeled(image_widget_normalize_clahe, 'normalize + clahe')\n",
    "            if checkbox_normalize_gaussian.value:\n",
    "                labeled_image_normalize_gaussian = image_widget_to_labeled(image_widget_normalize_gaussian, 'normalize + gaussian')\n",
    "            if checkbox_clahe_gaussian.value:\n",
    "                labeled_image_clahe_gaussian = image_widget_to_labeled(image_widget_clahe_gaussian, 'clahe + gaussian')\n",
    "            if checkbox_clahe_normalize_gaussian.value:\n",
    "                labeled_image_clahe_normalize_gaussian = image_widget_to_labeled(image_widget_clahe_normalize_gaussian, 'clahe + normalize + gaussian')\n",
    "            \n",
    "            temp_list = []\n",
    "             #checkbox_ .value:\n",
    "#                 temp_list.append(labeled_image_ )\n",
    "            if checkbox_clahe.value:\n",
    "                temp_list.append(labeled_image_clahe)\n",
    "            if checkbox_normalize.value:\n",
    "                temp_list.append(labeled_image_normalize)\n",
    "            if checkbox_gaussian_filter.value:\n",
    "                temp_list.append(labeled_image_gaussian)\n",
    "            if checkbox_normalize_clahe.value:\n",
    "                temp_list.append(labeled_image_normalize_clahe)\n",
    "            if checkbox_normalize_gaussian.value:\n",
    "                temp_list.append(labeled_image_normalize_gaussian)\n",
    "            if checkbox_clahe_gaussian.value:\n",
    "                temp_list.append(labeled_image_clahe_gaussian)\n",
    "            if checkbox_clahe_normalize_gaussian.value:\n",
    "                temp_list.append(labeled_image_clahe_normalize_gaussian)\n",
    "\n",
    "            image_container_prepro.children = (*temp_list,)\n",
    "\n",
    "       \n",
    "        # sleep for threading\n",
    "        time.sleep(0.05)\n",
    "        \n",
    "        if not thread_status:\n",
    "            label_current_thread.value = 'Thread Off'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "x00JWkLf0yuj"
   },
   "outputs": [],
   "source": [
    "# restart flag\n",
    "thread_status = False\n",
    "thread_status = True\n",
    "\n",
    "# create thread\n",
    "thread_work = threading.Thread(target=work)\n",
    "thread_work.name = 'work'\n",
    "\n",
    "# start thread\n",
    "thread_work.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85,
     "referenced_widgets": [
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   "source": [
    "#display(button_check_thread)\n",
    "#display(button_stop)\n",
    "#display(label_thread_list)"
   ]
  },
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       "VBox(children=(Box(children=(Label(value='Upload Mammography'), VBox(children=(FileUpload(value={}, accept='im…"
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