{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "72b6ee91",
   "metadata": {
    "id": "72b6ee91"
   },
   "source": [
    "# Interactive image processing with ipywidgets and numpy_image_widget\n",
    "ipywidgets can be used in jupyter notebooks to interactively change parameters and view resuls of computation without adapting and rerunning code. The numpy image widget is a convenient addon allowing to use ipywidgets with image data as numpy arrays. Combining both allows to tune image processing workflow parameters right in your notebook.\n",
    "\n",
    "See also: \n",
    "* [Widget types](https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20List.html)\n",
    "* [Widget events](https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20Events.html)\n",
    "* [The Numpy Image Widget](https://github.com/Who8MyLunch/Numpy_Image_Widget)\n",
    "* [stackview](https://github.com/haesleinhuepf/stackview)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb5b3bfe",
   "metadata": {
    "id": "cb5b3bfe"
   },
   "source": [
    "Optionally execute this to install dependencies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ClB6qhHGq0H2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ClB6qhHGq0H2",
    "outputId": "4055344a-dd9f-4b55-8a03-89c236d67d84"
   },
   "outputs": [],
   "source": [
    "!pip install pyclesperanto_prototype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bkVOJSM_rQUK",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bkVOJSM_rQUK",
    "outputId": "c8c7b623-70be-4188-aa6c-0600e0994200"
   },
   "outputs": [],
   "source": [
    "!pip install ipywidgets numpy_image_widget stackview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e72770b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3e72770b",
    "outputId": "ba35993e-bd71-4b53-aae7-25930300b257"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gfx902 on Platform: AMD Accelerated Parallel Processing (2 refs)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "import pyclesperanto_prototype as cle\n",
    "import PIL\n",
    "import numpy as np\n",
    "import numpy_image_widget as niw\n",
    "from skimage.io import imread, imshow\n",
    "import stackview\n",
    "\n",
    "cle.get_device()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3a8e2ec2",
   "metadata": {
    "id": "3a8e2ec2"
   },
   "outputs": [],
   "source": [
    "blobs = imread('https://github.com/clEsperanto/pyclesperanto_prototype/blob/master/data/blobs.tif?raw=true')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4216d2a9",
   "metadata": {},
   "source": [
    "## stackview\n",
    "[stackview](https://github.com/haesleinhuepf/stackview) is a library for interactive visualization of image processing results in jupyert notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "de109dbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d3f35ed24834552acc7623318e263f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.0, continuous_update=False, description='sigma_x', max=20.0, min=-20…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa6352f654904c4893a4d3d2c6ef92b4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(NumpyImage(value=b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\xff\\xdb…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stackview.interact(cle.gaussian_blur, blobs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae807300",
   "metadata": {
    "id": "ae807300"
   },
   "source": [
    "## A simple viewer updated later on\n",
    "We start with a simple NumpyImage viewer that can be updated further down in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "52caa8dd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279,
     "referenced_widgets": [
      "a733b64da5e5473ab6a350898a479d9a",
      "fea8644820d04edcaeda7e3e393babd5"
     ]
    },
    "id": "52caa8dd",
    "outputId": "311f1c3f-35e7-475f-eec6-dac14f15e0b1"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c6e6f33be92462cb01cfe4f1cc24f5f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NumpyImage(value=b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\xff\\xdb\\x00C\\x00\\x05\\x…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blobs_view = niw.NumpyImage(blobs)\n",
    "blobs_view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b4df9798",
   "metadata": {
    "id": "b4df9798"
   },
   "outputs": [],
   "source": [
    "blobs_view.data = cle.gaussian_blur(blobs, sigma_x=15, sigma_y=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9762f9be",
   "metadata": {
    "id": "9762f9be"
   },
   "source": [
    "## Interactive viewers\n",
    "We load a 3D image first and define a function for rotating it around the Y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f7f52e62",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f7f52e62",
    "outputId": "d6357aa0-ac71-466f-9fe2-71bf2fa70517"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 160, 160)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "head = imread('https://github.com/clEsperanto/pyclesperanto_prototype/blob/master/data/Haase_MRT_tfl3d1.tif?raw=true', plugin='tifffile')\n",
    "head.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "yvN2Vuw-sb31",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yvN2Vuw-sb31",
    "outputId": "40b125d8-7f5f-4f46-c795-621367776349"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240, 320, 320)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoom_factor = 2\n",
    "\n",
    "head = np.asarray(cle.scale(head, cle.create(np.asarray(head.shape) * zoom_factor), factor_x=zoom_factor, factor_y=zoom_factor, factor_z=zoom_factor, linear_interpolation=True))\n",
    "head.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a113e725",
   "metadata": {
    "id": "a113e725"
   },
   "outputs": [],
   "source": [
    "def transform_image(image, angle, z=120):\n",
    "    rotated_stack = cle.rotate(image, angle_around_y_in_degrees=angle)\n",
    "    return rotated_stack[z]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c0678925",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 345,
     "referenced_widgets": [
      "0515bd324dda4bd8ba09516b1eb105ca",
      "cca85110e3b34db8b231d2e089dae729"
     ]
    },
    "id": "c0678925",
    "outputId": "0f95b12e-3e3f-418d-ea72-3b90dd382d9c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2868ede9cb0b48eebb1d527fa9b10190",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NumpyImage(value=b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\xff\\xdb\\x00C\\x00\\x05\\x…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head_view = niw.NumpyImage(head[120])\n",
    "head_view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "157b8a59",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "19d1f2c25bc34b64a6f2c0ce91cfb3fb",
      "64b6def74d9f42cead220c28fe8b34ad",
      "ca0bc55e0bd64cc1b212a945ee9f0c7c"
     ]
    },
    "id": "157b8a59",
    "outputId": "28dfbde1-e4da-415a-839a-12c08f2fb3a9"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "211fc701f1344ac9a26b88b5ed12260a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=0, max=180, min=-180)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setup user interface for changing the angle\n",
    "angle_slider = widgets.IntSlider(value=0, min=-180, max=180)\n",
    "\n",
    "# event handler when the user changed something:\n",
    "def configuration_updated(event):\n",
    "    head_view.data = transform_image(head, angle_slider.value)\n",
    "\n",
    "# connect user interface with event\n",
    "angle_slider.observe(configuration_updated)\n",
    "angle_slider"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07fad95a",
   "metadata": {
    "id": "07fad95a"
   },
   "source": [
    "# Complex graphical user interface"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c93518c",
   "metadata": {
    "id": "1c93518c"
   },
   "source": [
    "Loading example data (Courtesy [Célia Baroux, Ethel Mendocilla-Sato and Daphné Autran, University of Zurich, IRD Montpellier](https://datadryad.org/stash/dataset/doi:10.5061/dryad.02v6wwq2c) CC0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "66ea0db4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "id": "66ea0db4",
    "outputId": "ee65e92d-4f27-4cb0-ca97-e86dab065666"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x18ccad8aeb0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = imread('https://github.com/clEsperanto/pyclesperanto_prototype/blob/master/data/EM_C_6_c0.tif?raw=true', plugin='tifffile')\n",
    "\n",
    "depth, height, width = image.shape\n",
    "imshow(image[127])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fcf34f3",
   "metadata": {
    "id": "5fcf34f3"
   },
   "source": [
    "For a more complex graphical user interface (GUI) processing 3D data, we define a couple of sliders:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9bfbad2d",
   "metadata": {
    "id": "9bfbad2d"
   },
   "outputs": [],
   "source": [
    "z_slice_slider = widgets.IntSlider(\n",
    "    value=depth / 2,\n",
    "    min=0,\n",
    "    max=depth,\n",
    "    continuous_update=True,\n",
    "    orientation='vertical',\n",
    ")\n",
    "\n",
    "background_subtraction_radius_slider = widgets.FloatSlider(\n",
    "    value=4.5,\n",
    "    min=0,\n",
    "    max=20.0,\n",
    "    step=0.1,\n",
    "    description='Background subtraction:',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='.1f',\n",
    ")\n",
    "\n",
    "noise_removal_sigma_slider = widgets.FloatSlider(\n",
    "    value=2.2,\n",
    "    min=0,\n",
    "    max=20.0,\n",
    "    step=0.1,\n",
    "    description='Noise removal:',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='.1f',\n",
    ")\n",
    "\n",
    "edge_dilation_radius_slider = widgets.FloatSlider(\n",
    "    value=1.4,\n",
    "    min=0,\n",
    "    max=20,\n",
    "    step=0.1,\n",
    "    description='Edge dilation:',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='.1f',\n",
    ")\n",
    "\n",
    "threshold_slider = widgets.FloatSlider(\n",
    "    value=10,\n",
    "    min=0,\n",
    "    max=50.0,\n",
    "    step=0.1,\n",
    "    description='Threshold:',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='.1f',\n",
    ")\n",
    "\n",
    "output_selector = widgets.RadioButtons(\n",
    "    options=['background subtracted', 'denoised', 'edge eroded', 'binary', 'labels', 'result'],\n",
    "    value='result',\n",
    "    description='Show output:',\n",
    "    disabled=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17af6138",
   "metadata": {
    "id": "17af6138"
   },
   "source": [
    "Furthermore, we define two viewers for showing input and output images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e189cd60",
   "metadata": {
    "id": "e189cd60"
   },
   "outputs": [],
   "source": [
    "image_view = niw.NumpyImage(image[127])\n",
    "result_view = niw.NumpyImage(image[127])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ad77c3",
   "metadata": {
    "id": "b0ad77c3"
   },
   "source": [
    "When executing longer workflows multiple times, it may make sense to allocate memory in advance. This spares re-allocation time in every execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a8554608",
   "metadata": {
    "id": "a8554608"
   },
   "outputs": [],
   "source": [
    "# allocate memory\n",
    "image_gpu = cle.push(image)\n",
    "slice_gpu = cle.create([height, width])\n",
    "\n",
    "# have global variables for reusing memory\n",
    "background_subtracted_gpu = cle.create_like(image_gpu)\n",
    "denoised_gpu = cle.create_like(image_gpu)\n",
    "eroded_gpu = cle.create_like(image_gpu)\n",
    "binary_gpu = cle.create_like(image_gpu)\n",
    "labels_gpu = cle.create_labels_like(image_gpu)\n",
    "result_gpu = cle.create_like(image_gpu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9203afcd",
   "metadata": {
    "id": "9203afcd"
   },
   "source": [
    "We now attach events to the GUI. Those events will be executed when the user changes parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0fccbc1c",
   "metadata": {
    "id": "0fccbc1c"
   },
   "outputs": [],
   "source": [
    "def view_changed(event):\n",
    "    \n",
    "    cle.copy_slice(image_gpu, slice_gpu, slice_index=z_slice_slider.value)\n",
    "    image_view.data = slice_gpu\n",
    "    if output_selector.value == 'background subtracted':\n",
    "        cle.copy_slice(background_subtracted_gpu, slice_gpu, slice_index=z_slice_slider.value)\n",
    "    elif output_selector.value == 'denoised':\n",
    "        cle.copy_slice(denoised_gpu, slice_gpu, slice_index=z_slice_slider.value)\n",
    "    elif output_selector.value == 'edge eroded':\n",
    "        cle.copy_slice(eroded_gpu, slice_gpu, slice_index=z_slice_slider.value)\n",
    "    elif output_selector.value == 'binary':\n",
    "        cle.copy_slice(binary_gpu, slice_gpu, slice_index=z_slice_slider.value)\n",
    "    elif output_selector.value == 'labels':\n",
    "        cle.copy_slice(labels_gpu, slice_gpu, slice_index=z_slice_slider.value)\n",
    "    elif output_selector.value == 'result':\n",
    "        cle.copy_slice(result_gpu, slice_gpu, slice_index=z_slice_slider.value)\n",
    "\n",
    "    # in case min(result) == max(result), the viewer crashes\n",
    "    if cle.maximum_of_all_pixels(slice_gpu) == cle.minimum_of_all_pixels(slice_gpu):\n",
    "        cle.set_row(slice_gpu, 0, 1)\n",
    "        cle.set_column(slice_gpu, 0, 0)\n",
    "        \n",
    "    result_view.data = slice_gpu\n",
    "\n",
    "def parameters_changed(event):\n",
    "    process_image(threshold_slider.value, \n",
    "                  background_subtraction_radius_slider.value, \n",
    "                  noise_removal_sigma_slider.value,\n",
    "                  edge_dilation_radius_slider.value\n",
    "                 )    \n",
    "    view_changed(event)\n",
    "\n",
    "# connect GUI elements with event handlers\n",
    "threshold_slider.observe(parameters_changed)\n",
    "background_subtraction_radius_slider.observe(parameters_changed)\n",
    "edge_dilation_radius_slider.observe(parameters_changed)\n",
    "noise_removal_sigma_slider.observe(parameters_changed)\n",
    "\n",
    "z_slice_slider.observe(view_changed)\n",
    "output_selector.observe(view_changed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69d22bc5",
   "metadata": {
    "id": "69d22bc5"
   },
   "source": [
    "Finally, we define the image processing workflow in a function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bb617393",
   "metadata": {
    "id": "bb617393"
   },
   "outputs": [],
   "source": [
    "def process_image(threshold, background_subtraction_radius, noise_removal_sigma, edge_dilation_radius):\n",
    "    # background removal\n",
    "    cle.top_hat_box(image_gpu, background_subtracted_gpu, radius_x=background_subtraction_radius, radius_y=background_subtraction_radius, radius_z=background_subtraction_radius)\n",
    "    \n",
    "    # denoising\n",
    "    cle.gaussian_blur(background_subtracted_gpu, denoised_gpu, sigma_x=noise_removal_sigma, sigma_y=noise_removal_sigma, sigma_z=noise_removal_sigma)\n",
    "    \n",
    "    # making membranes thicker in order to brige gaps\n",
    "    cle.maximum_box(denoised_gpu, eroded_gpu, radius_x=edge_dilation_radius, radius_y=edge_dilation_radius, radius_z=edge_dilation_radius)\n",
    "    \n",
    "    # threshold the image\n",
    "    cle.smaller_constant(eroded_gpu, binary_gpu, threshold)\n",
    "    \n",
    "    # label regions and fill black areas between regions\n",
    "    cle.voronoi_labeling(binary_gpu, labels_gpu)\n",
    "    \n",
    "    # output the borders between the labels\n",
    "    cle.detect_label_edges(labels_gpu, result_gpu)\n",
    "\n",
    "# call it once to initialize the view\n",
    "parameters_changed(None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cedf31e7",
   "metadata": {
    "id": "cedf31e7"
   },
   "source": [
    "We now use horizontal and vertical box arrangements to view the GUI elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8f7c55e5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 277,
     "referenced_widgets": [
      "0c53f07d742f4d31bacff91d38a033a2",
      "d31acf8e6d2649d9a163e9f095e460fe",
      "49225ff0a84a42cb90c743ea6b59b673",
      "121e20eb80374ddc886c035ef853d4d3",
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