interactive_example.ipynb

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      "metadata": {
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        "id": "OpfVvxAK7LBB",
        "outputId": "ac552942-bf8f-4e04-ce5a-ee9709311e2f"
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      "source": [
        "!pip install pyclesperanto_prototype ipywidgets numpy_image_widget"
      ],
      "execution_count": 1,
      "outputs": [
        {
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          "text": [
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            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.8.4)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.7.1)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (1.4.3)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (3.3.0)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.5.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (2.0.1)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.5.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (20.9)\n",
            "Building wheels for collected packages: transforms3d, pytools\n",
            "  Building wheel for transforms3d (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transforms3d: filename=transforms3d-0.3.1-cp37-none-any.whl size=59374 sha256=3520acee18fd2252a98735cbe364a3190e386359a4eeca9a376b872e04f54f50\n",
            "  Stored in directory: /root/.cache/pip/wheels/3d/3c/84/28d36677f3c760c048bd02b5a547ea0c4027770cc9cdb9af1e\n",
            "  Building wheel for pytools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pytools: filename=pytools-2021.2.7-py2.py3-none-any.whl size=60644 sha256=c3e9ea744ca9d69e6a4d818886f0197a3e2b0f7888446abdcb97d12f854b101c\n",
            "  Stored in directory: /root/.cache/pip/wheels/a0/b5/e5/e65d25997fd77729b9aa214645add18688483e48bbcbab6ffc\n",
            "Successfully built transforms3d pytools\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Installing collected packages: transforms3d, scikit-image, pytools, pyopencl, pyclesperanto-prototype, ipyevents, image-attendant, numpy-image-widget\n",
            "  Found existing installation: scikit-image 0.16.2\n",
            "    Uninstalling scikit-image-0.16.2:\n",
            "      Successfully uninstalled scikit-image-0.16.2\n",
            "Successfully installed image-attendant-2020.7.29 ipyevents-0.8.2 numpy-image-widget-2020.7.21 pyclesperanto-prototype-0.9.1 pyopencl-2021.2.2 pytools-2021.2.7 scikit-image-0.18.1 transforms3d-0.3.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0NbhQZ6N7hXR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53bb1aec-5053-47e8-f468-4b7f1aa5eab5"
      },
      "source": [
        "import pyclesperanto_prototype as cle\n",
        "\n",
        "cle.get_device()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Tesla T4 on Platform: NVIDIA CUDA (1 refs)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUwuQ_Ha76f0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20819274-fa8b-4fb7-857b-fcd3113405fd"
      },
      "source": [
        "from skimage.io import imread\n",
        "\n",
        "image = cle.push(imread('https://samples.fiji.sc/blobs.png'))\n",
        "image.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(254, 256)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZiiPv8Ai6wk"
      },
      "source": [
        "The following function represents a image processing workflow using clesperanto."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDHWm6T8ev0Y"
      },
      "source": [
        "def process_image(sigma : float):\n",
        "    blurred = cle.gaussian_blur(image, sigma_x=sigma, sigma_y=sigma)\n",
        "    binary = cle.threshold_otsu(blurred)\n",
        "    labels = cle.connected_components_labeling_box(binary)\n",
        "    return cle.maximum_images(image, cle.detect_label_edges(labels) * 255)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T5ytO5X7jCmI"
      },
      "source": [
        "We will now add a simple graphical user interface for tuning parameters of the specified workflow above."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lnfXEpSkf__P"
      },
      "source": [
        "import ipywidgets as widgets\n",
        "import numpy_image_widget as niw"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279,
          "referenced_widgets": [
            "fdd56eea89ec483b919a0bb2c38876c4",
            "29b1f19b190d4d0782258155474e89c3"
          ]
        },
        "id": "tcjCsJlLgADC",
        "outputId": "781a1c96-faa6-491b-ebe7-34a2e26c4a61"
      },
      "source": [
        "# Add an image viewer\n",
        "blob_view = niw.NumpyImage(image)\n",
        "blob_view"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fdd56eea89ec483b919a0bb2c38876c4",
              "version_minor": 0,
              "version_major": 2
            },
            "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": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "f25741280e924c56b3c8a3a6388602ce",
            "d4102d2870364fbb86ffc6f7a3667d41",
            "98ba8d1ac0a5406da252d996990853f1"
          ]
        },
        "id": "7C_epyhFf3tt",
        "outputId": "b6170699-58d7-4ded-a98d-9085eafba943"
      },
      "source": [
        "# setup user interface for changing the sigma\n",
        "slider = widgets.FloatSlider(value=1, min=0, max=10)\n",
        "\n",
        "# event handler when the user changed something:\n",
        "def configuration_updated(event):\n",
        "    blob_view.data = process_image(slider.value)\n",
        "\n",
        "# connect user interface with event\n",
        "slider.observe(configuration_updated)\n",
        "\n",
        "# execute action once to update the viewer\n",
        "configuration_updated(None)\n",
        "\n",
        "slider"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f25741280e924c56b3c8a3a6388602ce",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "FloatSlider(value=1.0, max=10.0)"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ISjJU6Z_G1VS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}