scottish_terrier_1_p_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FoHsQnTVPyqM"
      },
      "source": [
        "#How to upload an image to Colab?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ryAQC-3gP0qC"
      },
      "source": [
        "# Use libraries\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "# Display\n",
        "from IPython.display import Image, display\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.cm as cm\n",
        "import io\n",
        "import os\n",
        "import cv2 as cv\n",
        "from PIL import Image\n",
        "import PIL\n",
        "\n",
        "# Import the necessary libraries\n",
        "from PIL import Image\n",
        "from numpy import asarray"
      ],
      "metadata": {
        "id": "lCZVSjk5iVAE"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RPVqkNhgPlv0"
      },
      "source": [
        "from google.colab import files\n",
        "from IPython.display import Image"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oXd5i9OvQN5p",
        "outputId": "1e7ae8fe-9891-4523-c4fb-76709309d084",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        }
      },
      "source": [
        "uploaded = files.upload()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-305aa82e-0a59-4c7a-9390-01acec042e5d\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-305aa82e-0a59-4c7a-9390-01acec042e5d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3RuLhrCQbzI",
        "outputId": "f325931c-aff2-436b-a25e-678bb82783f4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "img = Image('scottish_terrier_1_p.png')\n",
        "print(type(img))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'IPython.core.display.Image'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "JC60Tz_IPnP6",
        "outputId": "bddc5940-3b5e-4b8e-9df9-be98d4c3ed6d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "im = plt.imread(\"scottish_terrier_1_p.png\")\n",
        "# fig, ax = plt.subplots()\n",
        "# im = plt.imshow(im)\n",
        "plt.matshow(im)\n",
        "plt.show()\n",
        "# resized_heat_map = cv.resize(im, dsize= (350,500))\n",
        "# plt.imshow(resized_heat_map)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "eqosnbJp7hYR",
        "outputId": "79ea2c84-a6a7-449b-81da-a638c1002cc5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 480x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from skimage.measure import label\n",
        "import tensorflow as tf\n",
        "import io\n",
        "from PIL import Image\n",
        "\n",
        "#Binarize a saliency map using a threshold\n",
        "def binarize(saliency_map, threshold):\n",
        "    \n",
        "    #Get the dimensions of the saliency map\n",
        "    h, w,e = saliency_map.shape\n",
        "    \n",
        "    #The binarized version of the saliency map\n",
        "    result = np.zeros((h,w))\n",
        "    \n",
        "    for i in range(h):\n",
        "        \n",
        "        for j in range(w):\n",
        "            \n",
        "            if(saliency_map[i][j][0] >= threshold[0]):\n",
        "                \n",
        "                result[i][j] = 1\n",
        "                \n",
        "    return result\n",
        "\n",
        "#Donne la valeur moyenne des pixels dans une carte de chaleur\n",
        "def get_average(saliency_map):\n",
        "    \n",
        "    #Les dimensions de la carte de chaleur\n",
        "    n, m ,k= saliency_map.shape\n",
        "    \n",
        "    #La moyenne au départ elle est à 0\n",
        "    average = 0\n",
        "    \n",
        "    #Je somme pour chaque pixel\n",
        "    for i in range(n):\n",
        "        \n",
        "        for j in range(m):\n",
        "            \n",
        "            average += saliency_map[i][j]\n",
        "       \n",
        "    # ma moyenne c'est la somme divisé par le nombre de pixels\n",
        "    return average/(n*m)"
      ],
      "metadata": {
        "id": "G8yvTDwPXnkI"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "avearge_saliency = get_average(im)\n",
        "mask = binarize(im, avearge_saliency)\n",
        "plt.imshow(mask, \"gray\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "n85Jm4z8VrtR",
        "outputId": "3f9d4eb9-f986-4701-c0b5-cc1a41e651f6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f243f332970>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Retourne la plus grande boite qui entoure la composante connexe\n",
        "def get_bounding_box(image):\n",
        "    \n",
        "    #Dimension de l'image\n",
        "    n, m = image.shape\n",
        "    \n",
        "    #La coordonnée la plus haute\n",
        "    upper = 0\n",
        "    \n",
        "    #La plus petite coordonnée dans l'image\n",
        "    lower = 0\n",
        "    \n",
        "    #La coordonnée la plus à gauche\n",
        "    left_result = n\n",
        "    \n",
        "    #La coordonnée la plus à droite\n",
        "    right_result = 0\n",
        "    \n",
        "    #Pour savoir si la première bande supérieur a été trouvée\n",
        "    first_bound = True\n",
        "    \n",
        "    #Pour chaque ligne\n",
        "    for i in range(n):\n",
        "        \n",
        "        #Je cherhce le pixel le plus à gauche\n",
        "        left = n\n",
        "        \n",
        "        #et le pixel le plus à droite\n",
        "        right = 0\n",
        "        \n",
        "        #et je regarde si la colonne contient au moins un pixel\n",
        "        contain_pixel = False\n",
        "        \n",
        "        #Pour chaque colonne\n",
        "        for j in range(m):\n",
        "            \n",
        "            #Si l'image contient un pixel à 1\n",
        "            if(image[i][j]) :\n",
        "                \n",
        "                #Je sais que je suis à la première colonne\n",
        "                contain_pixel = True\n",
        "                \n",
        "                #Je compare quel pixel et le plus à gauche\n",
        "                if(j < left):\n",
        "                \n",
        "                    left = j\n",
        "                    \n",
        "                if(j > right):\n",
        "                    \n",
        "                    right = j\n",
        "                    \n",
        "        if(left < left_result):\n",
        "            \n",
        "            left_result = left\n",
        "            \n",
        "        if(right > right_result):\n",
        "            \n",
        "            right_result = right\n",
        "        \n",
        "        if(contain_pixel and first_bound):\n",
        "            \n",
        "            upper = i\n",
        "            first_bound = False\n",
        "            \n",
        "        elif(contain_pixel and not first_bound):\n",
        "            \n",
        "            lower = i\n",
        "            \n",
        "    return [upper, left_result], abs(left_result-right_result), abs(upper-lower)\n",
        "\n",
        "def make_matrix(mask, coordinates):\n",
        "    \n",
        "    result = np.zeros(mask.shape)\n",
        "    \n",
        "    for i in range(coordinates[0][0], coordinates[0][0]+coordinates[2]):\n",
        "        \n",
        "        for j in range(coordinates[0][1], coordinates[0][1]+coordinates[1]):\n",
        "            \n",
        "            result[i][j] = 1\n",
        "            \n",
        "    return result"
      ],
      "metadata": {
        "id": "H-PlsaLoMhAE"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_box = make_matrix(mask, get_bounding_box(mask))\n",
        "plt.imshow(predicted_box, \"gray\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "57xYbzgRMbnV",
        "outputId": "15b74d74-dabb-4672-abc4-6a03216c85c7"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f243f22d1f0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "sWf-Q7DPQVMH",
        "outputId": "b85b3625-23dd-4e4b-ea92-af4a55e51744"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-700c4405-40c7-4cba-ba0b-b8bc38832338\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-700c4405-40c7-4cba-ba0b-b8bc38832338\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving scottish_terrier_1_mask.png to scottish_terrier_1_mask.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def read_trimap(path):\n",
        "    \n",
        "    with tf.io.gfile.GFile(path, 'rb') as fid:\n",
        "        \n",
        "        encoded_mask_png = fid.read()\n",
        "    \n",
        "    encoded_png_io = io.BytesIO(encoded_mask_png)\n",
        "    \n",
        "    return np.array(Image.open(encoded_png_io))\n",
        "\n",
        "\n",
        "#Transform a trimap like the one above into a binary one \n",
        "def transform_trimap(trimap):\n",
        "    \n",
        "    h, w = trimap.shape\n",
        "    \n",
        "    result = []\n",
        "    \n",
        "    for i in range(h):\n",
        "        \n",
        "        line  = []\n",
        "        \n",
        "        for j in range(w):\n",
        "            \n",
        "            if(trimap[i][j] == 2):\n",
        "                \n",
        "                line.append(0)\n",
        "                \n",
        "            else:\n",
        "                \n",
        "                line.append(1)\n",
        "                \n",
        "        result.append(line)\n",
        "                \n",
        "    return np.array(result)"
      ],
      "metadata": {
        "id": "z7-ce3oSQmzo"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trimap = read_trimap(\"scottish_terrier_1_mask.png\")\n",
        "binary_map = transform_trimap(trimap)\n",
        "ground_truth_box = make_matrix(binary_map, get_bounding_box(binary_map))"
      ],
      "metadata": {
        "id": "qtpj93Ws45YK"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 3, figsize = (12, 12))\n",
        "\n",
        "ax[0].imshow(trimap)\n",
        "ax[1].imshow(binary_map, \"gray\")\n",
        "ax[2].imshow(ground_truth_box, \"gray\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "zZAg0eUS5Nfx",
        "outputId": "5c1b808a-d60d-4b02-bf90-638bcd664b8f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f243efd0cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 3 Axes>"
            ],
            "image/png": 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j1bF682h9n7d32Oya0OzjwC4ZZpNadPxFl8furHrs/7ey0KNW0yv+MWGs36QZWzKr3HZwdKHcCYVVjkPwoj7CCna7Xdddd11Alwyz2+264YYb1KdPn4Dsr1evXmratGlA9gVzUB8B1GXVbrjz8vK0YcMGbdiwQdLxiS42bNigXbt2yWazafz48Xr44Yf13//+Vxs3btS1116rhg0bls9Eefrpp+vCCy/UjTfeqNWrV+vLL7/ULbfcoquuuuqkM0wifBglxWp2d5bePKeLLvthoM/bDY4u1F/7LpIREZipvBtnZGthu/eUYI/yeZtrZ94izw8/mZgKVqM+Ihidc845mj59uiIDtKrHn/70J02bNk1xcXEB2R9CA/URQLiqdsO9du1anXnmmTrzzDMlSRMmTNCZZ56p++67T5L0f//3fxo3bpxuuukmnXXWWcrLy9OHH35Y4Rf9m2++qXbt2un888/XRRddpHPOOUf//ve//fSWUBd4DhxQyVU2DfxuiM/b3JDwk+wBmLU8tnmO3s94W06b72vnDvl+kFo/u73S5wr3xKjIKKnyNerHV+/+dgQe9RHB6uKLLzb1XuoyF154oZ577jlFRESYvq/f4gx38KM+AghXtVqH2yq5ublKSEhgHe4g52iQr+97/6fKcR1X/1ENLvuu8tdIS5Vrtk3vtf7Ip31Oz0nXIx9eVp2Y1WI4DN16/kcaX2+Hz9tc8WN/FVwZodK9ld9nbo+J0b82faIzXKc+W/59yTENendideLiFMxeh9sqZfURwa1Hjx5auXJlQPc5f/58XXLJJaa9vsvl0uuvv64rr7zStH2czM6dO9W8eXOF4J80Qa2u1UfJ2hrJOtwIRazDXTnL1uEG/M2zb7+KrzR0wXcX+zR+eNwOJbY6ZF4gu3R9QuUfDvzekgKHev/lJuVfYT9psw0AgXL++eeb2nA7nU4NGjTItNcHACAU0XDDNDabb+MM49QDPfv2yz4p0afZy2PtkYpylvq24xpo3m6vom0un8be+MENinpvtTz79p96oGHIIx8Plo/DAAQ3m68F0o+io6NNva/6qquuCth94pWx4pgCAFAVGm6Y5sVub1Q5ZntJnppMrHqtbtv6rerwwi3aW1r15SxvZfxH9jT/z+gd0zxH89vN9ene7Yu2XqR2D23z6XW9+fm65qkJVY5rGRGlzO5bfHpNAMHtueees2S///rXv9S1a1e/v+6AAQP07LPPyul0+v21fdGoUSPdfffdluwbAIBToeGGadIcVTfHXkneA79WOc4oKVbjv6/Q+3lVT/rTJCJW7/WaJkd6gS8xfRbpLFW0veqz21f+dL5s19rlOVj1+yrjPlz1fYcOm12JTv++JwDWSEpKsmS/6enpmj17trp16+bX142JiVFUlO+rNvibw+Goc/caAwDqBhpuhJSXfjzbp3FnuKK09Oypfmu6DYehN9q/6tPYNZtbqHT3z37ZLwD4W4sWLbRgwQK/Nd1ut1tPPfWUX14LAIC6hoYbISXpsRifxzaKiNW8zBf803Q7pCQfflqWF0rNZ1d9r3lNRdg9pr02gPCRlpamWbNm+aXpdjqdQXF2OSIigvu4AQBBh4YbIcV5KF/Tc9J9Hn+GK0qLek2V4ajdUjF/6LVKyY6qm/1Jd42W85N11X792F+K9WG+u8pxj6ZnyWbC/ekAwk+LFi307rvvyuXybSLIk3n00UeDYim6MWPGqFOnTlbHAACgAhpuhBTPpq2aeeOF+u+xaJ+3aRThVpv2tbjE2yYlRRyrcliet1Cu3JqdgXZ+sk5/23xZlePcNicTlQPwm7S0NP35z3+u8fZl904Hw5llt9sth6PqSS0BAAgkGm6EHPsXG/TctX9Q13VXar+n6kbYbXPqxkafyxtds2Y4qslRTUiqenbwzstHy/XxVzXahyQVfFVf+d7iKse1bbivxvsAgN9yuVy68sorlZaWVqPt+/btq6uuusrPqWquV69eVkcAAKACGm6EJFvW10q+9EfNz2vp0/hhsbm6v8/7NVrH2m73+rQUmLfELnlrfo9186c26aAPDfcjzebVeB8A8HvnnXeeXnnlFdnt1f+TwOFwKCIiwoRUNTNu3DirIwAAUAENN0KX16NZfx6oF46c5tPwftE/1Wg3PRrsqtF2ABAqunTpUu3Lwm02m/r06WNSIgAA6obg+VgaqAFb1td6dPlgjbp4WpVnodMcbnXt9oPWbGpx0jEd2u3W1emrKjx2eex+Sc5TvnaJ4ZHyTj0GAIJVUlKSJk6cqNdee+2kY4YPH64hQ4aU/9tms6l3796BiAcAQMii4UbIa3vb1+rR8Gp91W3WKce5bU7NbrFEJc0/PumYypv2qhvprCKH2k78WuYtCAYA5nG5XJoyZYr+8Y9/nHSMw+EIisnRAAAIJTTcCHlGUZFipyfo5855ahQRW+V4X+7HrlGO0lJTXhcAAsFmswXV/dgAANQF3MONOiHq/dUasHq01TEAAAAAoBwNN+qMgoPRx++ltsAXeW0t2S8AAACA4EXDjTqj3cRN+r6k6mW1zLDwob5cUg4AAACgAhpu1B0ejz7KOyPgu/2uOF/OPKZLAwAAAFARDTfqDG9hoWY9NjCg+9xbmqeR90+U68M1Ad0vAAAAgODHdKSoU9w5Hm0qLtAZrihT9zM7L0HfFZym+c/1Uf3XskzdFwAAAIDQRMONOiXq/dUamTxBHz7wLyU7Ymr0Gh7jf5eHl8qj0+eOU8SximvPtpiTK2P9JtUXzTYAAACAytFwo86p/8pKDXT8VWfftFbdYrcr3+vW5vyGPm3rNezaPKm9IvfmHX/AMNR6yxrJW3H2c8PfoQEAAADUOTTcqHsMQ8n/ztLWf0vfd7tQ9oISeTZt9Xlzp9bJmsXFAAAAANQlNNyo04y139I8AwAAALAEs5QDAAAAAGACGm4AAAAAAExAww0AAAAAgAlouGGaj45lVDkmzm5TQe92AUgDAMFj9erVVkcAAAABQMMN0zy/sXeVY1IdMTp0Y14A0gBA8HjyySetjgAAAAKAhhumKSlwal1RcZXjzjltuxytmgcgEQAEh4MHD2rLli1WxwAAACaj4YZp7Eecev1QryrHPX/aSh3qmRaARAAQHH788UctWrTI6hgAAMBkNNwAAAAAAJiAhhtBoSiRb0UAAAAAdQtdDky1Iru5crwFVY57947HlHdlzwAkAoDg8Pnnnysvj0kjAQCoy2i4Yapff6ivgx5PleOaO2P12JTndXR4GDfdaSn8QAJh5P3339eRI0esjgEAAEzE3/cwlyHNOJzp09CzI+16/bF/heWZbnun09Vj9mY1ioi1OgqAADEMQx988IHVMQAAgIlouGG62d910WFPvk9jWzpjlXd1juwxMSanCh6ONi3V9421uj9ls9VRAASQYRh6+eWXlZOTY3UUAABgEhpumM6THaX+G65TnrfQp/Ffd39b+eedYXKq4GDv2E495nynO+v/4PM2Lxzoa14gAAG1du1ajRw5knu5/WTevHlWRwAAoAIabgTEkR+TtLYo2ufxPf++WhEtmpkXKAg42rZS3zfXVfvM9uIf2pmUCIAVFixYoK1bt1odo06YPXu21REAAKiAhhuBYUjXL/uzPivw7Vvu0bQNajdnlxytW5gczBr29u101uzqndkGUDd5PB5dccUV+uqrr6yOAgAA/IyGGwFjz4nQ9UtHablvV5br8QZfqdlbe8wNZQW7Q93f3KgHUzZVe9MHD2TIc9BtQigAVtqxY4cuueQSbdiwweooIWv69On69ttvrY4BAEAFNNwIKHueQ+O/He7z+LPjf5BsNhMTBZ7NblPv2C012vZAcZxsnrp1PAAc98svv+jee++1OkbIOnTokIqKiqyOAQBABTTcCLj8Qpf2lvo2QdDlMXu14+G6tUzYD4910zmRPp7m/43tJXn6YF0nExIBCBaHDh3SgQMHrI4Rcn7++WdNmTLF6hgAAJyAhhsBV/JLjC5af4NPs5ZH210qSfAGIFXgGEklctuc1d7uob2DZPfxHngAoWnFihW6/vrrlZ/v21KKOG769Ok6dOiQ1TEAADgBf73DErk/Jar3VyP1sw9nuhObHJGjXr0ApDJf6fld9WW/p6u93Y8leVr+QysTEgEINh988IGGDx+u/fv3Wx0lJOzevZvZyQEAQYuGG5bJ+bGe3s87vcpxX3Wbpa3PNDM/UAB4XHY1iIit9nbPHuwrMVkaEDYWLFigL774wuoYIWHu3LnavLl6yysCABAoNNywVNaRlvIYVV8yPqHrJwFIE5x+Ls3Tf1d2tToGgADbsGGDvN66dUuNv+3bt0/333+/1TEAADgpGm5Y6ssNbVRklFY57sKY73Toz5kBSBR88g2bbCXMTA6Em6effppZt6tQWFjI/e4AgKBGww1L2Tw2fVKQWOW4ls5YHWpvmB8oCA3fMEoKz7cOhLWSkhKtW7fO6hhBbezYsfJ4PFbHAADgpGi4YSlbqU2Ttw1SiVH1H0wTLvxAjtYtApAquJR4HFZHAGCBgoICPfvssyotrfoqoHDF2W0AQLCj4Ybl9m9N0dtH06ocNzZxt7yJMQFIBADB4Z133tEnn4TvHBYAAIQ6Gm5Yz5C8YfKtWBzP2WoAvvN4PJzhBgAghIVHlwMEA5tNYx+aY3UKAAAAAAFCww0EUKStxOoIAAAAAAKkWg335MmTddZZZykuLk6pqam67LLLtHXr1gpjCgsLNXbsWNWvX1+xsbEaNmyY9u3bV2HMrl27NHjwYEVHRys1NVV33HEHl8wBCGnURwA4OWokgHBVrYZ72bJlGjt2rFauXKnFixerpKREAwYM0LFjx8rH3H777Zo/f77mzJmjZcuWac+ePRo6dGj58x6PR4MHD1ZxcbFWrFih1157Ta+++qruu+8+/70rIAhFpKcpxs6aunUV9REATo4aCSBc2QzDqPEKvwcOHFBqaqqWLVum3r17KycnRykpKXrrrbd0xRVXSJK2bNmi008/XVlZWerZs6cWLVqkIUOGaM+ePUpLOz4z9QsvvKA777xTBw4ckMvlqnK/ubm5SkhIUJMpD8seGVnT+Agi9w56V9fF769y3IWXXiNjzcYAJPK/76d11/ZL/13t7dqvHKGCnXEmJApv3sJC7brrHuXk5Cg+Pt7vr291fUTdMX/+fA0ZMsTqGEGpX79++vTTT62OUWeZVR+l8KyRZe8DCCV5eXlq0KCB8vLyrI4SVKpTH2t1D3dOTo4kKSkpSZK0bt06lZSUqH///uVj2rVrpyZNmigrK0vS8WLToUOH8kIpSQMHDlRubq42bdpU6X6KioqUm5tb4QsIOTarAyCQqI8AcHLUSADhosYNt9fr1fjx43X22Werffv2kqTs7Gy5XC4lJiZWGJuWlqbs7OzyMb8tlGXPlz1XmcmTJyshIaH8q3HjxjWNDQCmoz4CwMlRIwGEkxo33GPHjtW3336rmTNn+jNPpSZNmqScnJzyr927d5u+TwCoKeojAJwcNRJAOImoyUa33HKLFixYoOXLl6tRo0blj6enp6u4uFhHjhyp8Anlvn37lJ6eXj5m9erVFV6vbAbKsjG/53a75Xa7axIVAAKK+ggAJ0eNBBBuqnWG2zAM3XLLLZo3b56WLl2q5s2bV3i+a9eucjqdWrJkSfljW7du1a5du5SZmSlJyszM1MaNG7V///8myFq8eLHi4+OVkZFRm/cCAJahPgLAyVEjAYSrap3hHjt2rN566y29//77iouLK79fJiEhQVFRUUpISNCoUaM0YcIEJSUlKT4+XuPGjVNmZmb5rIwDBgxQRkaGrrnmGj322GPKzs7WPffco7Fjx/IJJKr0/chotV5jdQrgRNRHADg5aiSAcFWthnvatGmSpL59+1Z4fMaMGbruuuskSU8++aTsdruGDRumoqIiDRw4UM8//3z5WIfDoQULFmjMmDHKzMxUTEyMRo4cqYceeqh27wRh4Yben2mZoqyOAZyA+ggAJ0eNBBCuqtVw+7Jkd2RkpKZOnaqpU6eedEzTpk21cOHC6uwakCRdFPeNFgy/TXGzVlodBaiA+ggAJ0eNBBCuarUONxBond1uHTyTBa0BAAAABD8aboSUD/Ij1Wx+gdUxAspu91odAQCCksvlsjoCAACnRMONkPJ1flPZvtxgdYyAeqPzDImT+gBwgueee04Oh8PqGAAAnBQNN4LC01v7qcTwWB0jKCXaS62OAMBCL7zwgkpLqQOViYmJsToCAACnRMONoHBkb7y84tJpAPi9lStXyuulPgIAEIpouBEU+nb+ThGq+rLA2S+fH4A0wcUuyXBWPbsrgLrp1ltvVUREtRYVCRt2u11RUSwVCQAIXjTcsJw3sUQ3pC6Tw1b1t2N61tEAJAoujSJiNaTHV1bHAGCBFi1a6NJLL5Xdzq/ryqSlpenBBx+0OgYAACfFb3BYrm3TbJ0dWfW34s+leVJpeF5W6eY+biDs2Gw2DRw4UJ06dbI6SlBzOp1WRwAA4KRouGGpxFaH9G6beT6N7fPuX2Ws32RyIgAIDpdccomeeOIJq2MAAIBaoOGGZdyN87Sg0yuKtle9jmq+t1jOHL5dAYSHPn366OWXX1ZkZKTVUQAAQC3QwcAShl0a2uprNYiI9Wn8jNyWavpAlsmpAMB6ERERuvrqq5WcnGx1FAAAUEs03Ag4w2noknPW6sGUr33eZsYTQyQjtGfqPv3pw/qsoGY/cvUi8mU4Qvv9A6hadHS0/vnPf+qGG26wOkrIiI+Pl9vttjoGAACVouFGwCU2OaKnGqz1aVZySbp1z1lKW7LH5FTm82zZpl89vp3R/72/JW+VKz3fz4kABJt+/fpp/PjxzEpeDSNHjlTXrl2tjgEAQKX4jY6A8saV6vn2b/k8fuLeLvrxytNUun2niakAwHqNGzfWI488YnUMAADgRxFWB0B4ue6sFeoZ6fBp7K17ztK2qxrJ89N2k1MBgPUeeOABdejQweoYAADAjzjDjYBxnnZMtyat9WnsZwV2/fiHhvJsq0PNtmFo8mMjrE4BIAj16tVLw4YNszoGAADwMxpuBIzLVap6jmifxl6/+IY6eRl57C+lVkcAEIRiY2OVkJBgdQwAAOBnNNwIDJv0745v+DT0y0KvWr1ZYnIga0Qt26SMFX+yOgaAIOJwOPT4449bHQMAAJiAhhsBYdikRhEFVY7bXpKnO/9vjOyfrw9AqsDz5uer/lsx+rEkr9rbDm3t+zJqAEKHzWZTSkqK1TFC2jXXXGN1BAAAKkXDjcCoV6xIm63KYff+MkQx7/p2n3eoin5vrXaUVv/S0euSskxIA8BqrVq1ksvlsjpGSBs0aJBsPvyOAQAg0Gi4ERBXd1yjZEdMleN2PNFW8noCkMhChlc3fnCD1SkABAGbzabx48erXr16VkcBAAAmoOGG6Vp32q2/Ja+rctyH+W5FHSgOQCKLGYZO+9TqEACCwc0336yRI0daHQMAAJiEhhvmsklTW85StL3qyyVvXTtc9mV1897t34tfv1d/2tHX6hgALGS323X33XcrMjLS6igAAMAkNNwwlS2lSHH2qu+ry/EWKOLb2AAkCg6lO3Zp/fwM5XirnkgOQN3Uvn17RUVFWR0DAACYiIYbphrSbqNSfbh3e21RrJr+66sAJAoejf+1VltLIqyOAcAi1113nerXr291DAAAYCIabgSFOx+5Sd7CQqtjBJbh1aQfh1mdAgAAAIBJaLhhHpvktpf6NDSiyDA5TPAxSktV8HJDq2MAsIDD4ZDb7bY6BgAAMBkNN0zTtdsPeiQ1vC4Tr67YXQV6NTfV6hgAAmzixIm6+eabrY4BAABMRsMNvzPs0uln7tSrzRbJYav6W+yg55gcYXiGW5JsK77WY98O8GmsQ4a8bq/JiQCYKSIiQuPGjdMDDzwgh8NhdZw6w+FwKC4uzuoYAACcgIYbfmdPLtKCNot8WgpMki74apRi5642OVXwcn4Zr8Oe/CrHtXTGatBZ3wQgEQCznHHGGXr66aeZndzPGjVqpLvvvtvqGAAAnICGG3731zM/9nmsx/Aq53CMZITnGW5JavjiBi3Mb+zT2CTnMRn81AIh64EHHpDNVvVSiai+hIQEOZ1Oq2MAAFABf7rD786K2u7z2LO/vlJtR28yMU3w8+bn66kpV/o09uHUjXKkhNls7kAd0q5dO6sj1Fk333yzOnbsaHUMAAAqoOGGX7Xo+ItO9+EEg8fwqsva4ar/56PhtxxYJVIW/qih2y7waWxsDMcLCEU33nijmjVrZnWMOi01lUkoAQDBhYYbftUo5ohP926f880flHbVbpVm7wtAquDn2bdfP81prTxv1c30a51eNT8QAL877bTTFBkZaXWMOu3pp5+2OgIAABXQcMMShR+kyZtf9URh4aTh65t0yFv1uuWRNo+80Z4AJAKA0OJyuTjLDQAIKjTc8BtvlFeX1l9vdYw6r40zRv07b7Y6BoBqSElJ0XnnnWd1jDqvadOmGjdunNUxAAAoR8MNv/BGe/Tk+W/pspg8q6OEhUaRh2VEhO/M7kAoSUtL05w5c9S7d2+ro4SFBg0acOk+ACBo0HCj1rxRXj3Z722fm+2pRxordS2N+e95Cwp14erRPo29P2WzbPWKTU4EoLaSkpI0e/Zs9enTx+ooYWPUqFFq1aqV1TEAAJBEw41a8sZ4lNbkULXObP93bydp5TcmpgpNRlGRIj+J83l8/Xp8aAEEswYNGqhfv36c2bZA69atrY4AAIAkGm7UQocu2/X0eW9qZee5Pm+T4y3QgTmNTUwVPp7LeMvqCAAqYbPZNH78eM2ePVtz5syxOk5Yevjhh2Wz2ayOAQAADTeqz3AY6tdro2a1WqBLYnyfafyb4kKd9/AEpby40sR04eNMl10dumy3OgaA33C5XHr44Yf16KOP6pxzzrE6Tthq06aNbrvtNqtjAACgCKsDIPgZdkk2Q+077dSZibsVbS/WnfV/kOT0+TVyvAW6bsoEpbyQZVrOuiBtxWE9eCBD96dUPQu50+ZQPTdLqwFWioiIkN1u1+jRo9W+fXtFRUVpxIgRnF21WEREhBITE62OAQAADTdOzpZWqLiYQt3e9hNdGLNT9eyRctocNXqt7q9OUDPObFfJ+80WLclu61PDLUnPNlqsDoltZD/i+4cfAGqvc+fOatCggW699VadddZZio+Pl9PJz2EwueOOO/T666/rxx9/tDoKACCM0XDjBIZdikjL1/s9X9Dpruj//2hMjV/vn4daqvm8XBkGy1j5ouTVNP38aJ4aRcRWOTbWHim70xuAVAAkyel0qkuXLnr77bfVvHlzq+PgFKKjoxUVFWV1DABAmOMebkg6vrRX/Ta/qn6bX3X3gPf01Tn//k2zXXMlhkdLrukpY90mP6QMD/Fvr9Qf7vyrviz0rZF+pufbJicCwlv9+vV1+eWX6/LLL9e7776rpUuX0myHiGnTplkdAQAQ5jjDHaYMpyHDbujKzNVq5Dqshs7DGhab+5sRkbXeR4nh0RnL/6yW234S57arJ/7tlboteqy+fOgZuW2nvkw1zl4YoFRAeIiJiZHD4dDkyZOVmpqq5ORk9e3b1+pYqIGYmJpfnQUAgD/QcIeZ+BZHFOMu1ovt3lTTCJti7bVvrE+mzYc3q+3ob+QtKTZtH3VZ7PC9VTbbkpRiz5eSi6SD7gCkAuquQYMGKSkpSQ899JDS09MVFRXF5Gchrl69esrIyNDmzb7NiwEAgL/RcNdBhsOQ4ap4TnnoWWuVEb1HQ2N/Uj1HtCT/39f2fckxFRoOXT1tgmL2GDr9/U3y0GzXiD0yUqnRR30ae7orWl2b79K6g61NTgWEPrfbfcJZz/vvv18tWrRQ7969FR8fb1EymKFZs2YaMmQIDTcAwDI03KHOJjXOyJbT4Sl/6JzkH3VX8tcVhv3vTGnt78uuzCMH2+rzq8+Utu3QaUVZkmHIU/VmOInsG7poQfPnxDQLQM05HA6NGDFCLper/LHMzEz96U9/qjDO6XRyJhsAAJiChjtEeaO8mtRngeo78nRZzBE5bL9vzAKzPM3e0jz9J+dMfX5NF3m//S4g+wwHXocq+X96cvc0+kCXbhone37Nlm0D6pL69etr+vTpSkhI0LnnniuHg5+LcHbDDTfotdde0759+6yOAgAIQzTcIcQb69EZrX+WJI1p9KkGR5dNlhX4s6D/PNRS09b1UdLnbiW/ukZGKc22lTq6IiUnU9MhfDVs2FBDhw6VJA0fPlznnHOOxYkQLFq1asXyYAAAy9BwhwBXo2N6tPM7SnUcVc9I687UvJMXr6fu/KPsJYaidx9V66+/kiRmIAdgmXPOOUeTJ09WQkKCOnToYHUcAACACmi4g5g3yquuZ/ykZ5u+pwYRsZKsabbP33yJfvoxTW1mFCl65arj2SxJAgDHpaSkaOTIkbrzzjuVnJxsdRwAAIBK0XAHKW+UV5PPm6Or4g5Lig34/n8uzdMn+S30xJbz1ejmX9Vm35qAZwCAyiQlJenNN9/UBRdcYHUUAACAU6LhDkLeKK8e7TdLV8bmBGyfH+RHatzCkeX/jv/BobRnV6iBvmO2cQBBo379+po9e7b69etndRQAAIAqVWu2rWnTpqljx46Kj49XfHy8MjMztWjRovLnCwsLNXbsWNWvX1+xsbEaNmzYCbOC7tq1S4MHD1Z0dLRSU1N1xx13qLS01D/vpg4oO7NtdrN90HNMTxxqofM2XaoBV4zUc5ddpta3rir/Snt2han7h/9FxhVZHSGsUR/Nl5ycrLfffptmG9XWqFEjqyOEPWokgHBVrYa7UaNGmjJlitatW6e1a9eqX79+uvTSS7Vp0yZJ0u2336758+drzpw5WrZsmfbs2VM+a6wkeTweDR48WMXFxVqxYoVee+01vfrqq7rvvvv8+65CWI8O2/7/ZeTmWF1UolZvjtF5z9yhj9rHy3XBTtlWfC3vt1tM2ycC48kzZ1sdIaxRH813ww03cBk5qs1ms+mxxx6zOkbYo0YCCFc2wzBqNcl0UlKS/vnPf+qKK65QSkqK3nrrLV1xxRWSpC1btuj0009XVlaWevbsqUWLFmnIkCHas2eP0tLSJEkvvPCC7rzzTh04cEAul6vSfRQVFamo6H9n73Jzc9W4cWM1mfKw7JGRtYkfVAyHoWHnrNY/09fX6nU2FBVpbk63Co99dbixvBPqyV5UIs/m72v1+jBf9m299PWdz1drmw/z3Ro7/3qTEtU93sJC7brrHuXk5Cg+Pt6UfVhZH+sal8ulxx9/XLfccovVURCCsrKy1KtXL6tjhBwz66MUfjWy7L0AoSQvL08NGjRQXl6e1VGCSnXqY40XcPZ4PJo5c6aOHTumzMxMrVu3TiUlJerfv3/5mHbt2qlJkybKysqSdLzQdOjQobxQStLAgQOVm5tb/glnZcqWfCn7qot/TEqSEWHojOhfqr2dx/Cqw6qr1faVMWr7yhj9ZdJtWtPZUeHLc94eGes30WwDAUB99L+oqCi1aNHC6hgA/IAaCSCcVLvh3rhxo2JjY+V2uzV69GjNmzdPGRkZys7OlsvlUmJiYoXxaWlpys7OliRlZ2dXKJRlz5c9dzKTJk1STk5O+dfu3burGzsk2Ivs+vDX9tXa5qDnmAZdcb0aj/xZze7JUrN7shQ3c6VJCQGcCvXRPDk5Ofr888+tjgGgFqiRAMJRtWcpb9u2rTZs2KCcnBzNnTtXI0eO1LJly8zIVs7tdsvtdpu6j2DgjfbourQvq7XN2SvGqPlXW+QpYsIswGrUR/OkpqbqkksusToGgFqgRgIIR9VuuF0ul1q1aiVJ6tq1q9asWaOnn35aw4cPV3FxsY4cOVLhE8p9+/YpPT1dkpSenq7Vq1dXeL2yGSjLxoQrb3yppvV5XRdG+944rysqVsqcKBk020BQoD6ao0mTJpo1axb3PgIhjhoJIBzV+B7uMl6vV0VFRerataucTqeWLFlS/tzWrVu1a9cuZWZmSpIyMzO1ceNG7d+/v3zM4sWLFR8fr4yMjNpGCVneKG+1m+2DnmMa/ffbFDN3lYnJANQG9bH2UlJSaLaBOooaCSAcVOsM96RJkzRo0CA1adJER48e1VtvvaXPPvtMH330kRISEjRq1ChNmDBBSUlJio+P17hx45SZmVn+h9KAAQOUkZGha665Ro899piys7N1zz33aOzYsWF5uY/hMJTQNEePnvGOBkSX+LSNx/Bq+E8DtPOV1kp6lXu1gWBBffQvl8ulCy64QPfee6969OhhdRwAtUSNBBCuqtVw79+/X9dee6327t2rhIQEdezYUR999FH5uqhPPvmk7Ha7hg0bpqKiIg0cOFDPP/+/pY0cDocWLFigMWPGKDMzUzExMRo5cqQeeugh/76rUGCTzuu+SdObfOHzJiWGRxnLRqnVjT8o6ViWieEAVBf10X8cDofuvfde3X333bLZbFbHAeAH1EgA4arW63BbITc3VwkJCSG7Drdhly45Z60eT18th833q/qbL7xBbcdu5J7tMMA63OYLxDrcViirj6HK6XTqscce07hx4+RwOKyOgzqCdbhrpq7VR8naGsk63AhFrMNduYCsw42aM6I9ejQ9q1rNdsaKP6nd7VtptgHUaSkpKfrLX/5Csw0AAOoEGm4L/PXsD+W2OX0ev6GoSAnvxMp79KiJqQDAes8884xcLpfVMQAAAPyChjvAEloe1qWx3/k8/ufSPN340HjFv80EaQDqLpvNpiFDhujcc8+1OgoAAIDfVHsdbtRcQsvD+qLL64q2x/o0/pviQt147x1Kep0J0gDUbYMHD9bs2bMVFRVldRQAAAC/4Qx3gLgaHdMHnacr2u7bpZK7SvM06sHblUizDaCOO+ecc/TKK6/QbAMAgDqHhjsADLt0eauv1SDCtzPbm4oLdOWkvyppBs02gLotIiJCI0aMUEpKitVRAAAA/I6GOxDshu5I9v0e7GsnT1DCm9yzDaDuczqduvrqq62OAQAAYAoa7gCo3/ywou2+zUo+cW8XpS3JNjkRAASHgQMHyu12Wx0DAADAFDTcAXBF0/U+LQP2t30d9d3wpvJs2x6AVABgvaFDh9JwAwCAOouG22RGvRKdE7PVp7FZd3Wn2QYQNlq1aqWOHTtaHQMAAMA0NNwmS6h3TGdHVn2Yz8gaocgVvjXmqPsiDxnaXpJndQzAVG3btlWnTp2sjgEAAGAaGm4z2aRhzTf4NDT/cJS8R4+amwchI/H1LI3b/gerYwCmcTgcuuKKK6yOAQAAYCoabpN43V796bzPNan+5irHLi+UMh5kojTUTqI9X95oj9UxgCrFx8frhRde0LXXXmt1FISJ2NhYpaWlWR0DABCGaLhNEtsgTw+mbJLDVvUhPuqNVOkvewOQCqHk53eaq8go8Xl8z0iHOrTbbWIiwD+6d++uG264QXY7v4IQGB06dNDw4cOtjgEACEP8tWMGmzS982s+D3/gH9dLXs5MoqLT5u1UicH3BeoWh8Ohp59+2uoYAAAAAUHDbQLDbijdUeTT2Gt39lbqMs5u40Tew0fUa+31VscA/MrhcCgpKcnqGAAAAAFBw+1n3kivxvT7RKc5oqscu7qoRL9eHqXSn3aYHwwhx3vsmKLnJOib4kKrowB+kZiYqBkzZig1NdXqKAAAAAFBw+1nkckFuiPpR5/u3b5q/i0q3bc/AKkQqhLeXKn7dl7q8/gIG5egI3i1b99eV199NfduwxIOh8PqCACAMMRfPX7WOvWAT+P+eyxaLd8plgzD5EQIdcU3xuqRg219GvufFvMV3+KIuYGAGuratavVERDGHnroIQ0aNMjqGACAMEPD7U826clmc30aevuq4bIvW29yINQFnu9/1BtzzvdpbKw9UvecvtDkRED12e12TZw40eoYCGOxsbG64447rI4BAAgzNNx+ZEstVJzdVuW474rz1Xb8rgAkQjhq7PxV3kTflxMDAqFjx46Kjq56bgvATCkpKWrZsqXVMQAAYYSG248uarNJqY6YKscNmTdBnkNHzA+EsNTd7dSM3jPkdXutjgKUu+aaa1S/fn2rYyDMtW/fXrNmzVJ8fLzVUQAAYYKG2wLJ622suw1TnR1Zwk83AFSiY8eOcrvdVscAAIQJ/iT3EyOpWGOSl1U57tqdvZX86e4AJEI4c9ocGtz1a6tjAJKkNm3aaOjQoVbHACRJERER3MsNAAgYGm4/6d5qh053VX1/4sb9DVW6++cAJEK4uzb5CxlJxVbHADRs2DA1a9bM6hiAJMlms2nw4MFq29a31R8AAKgNGm4/ufe0D6ocs7c0T/WfZNIgBEZ3t1Mdm/1idQxAo0aNsjoCUEFGRoYGDRokm63qiU4BAKgNGm4/cJ52TEn2qu/JLjQk11fbApAIdU3KhlJ9VlD9H9dZrRYope1BExIBvunVq5cSEhKsjgGc4NFHH9UVV1xhdQwAQB1Hw+0H5zb9SQ0iYqscN/CtO+TJOxaARKhrIuev1txDZ1V7O7fNqfEtl8hwGiakAqo2ePBgJScnWx0DOIHL5dKNN96omJiqVxcBAKCmaLhryRvt0YX1NlY5bnpOuprNz2d2ctTYx590qdF2V8Ud1g29P5PBTzsCLDU1Vb169bI6BnBSF1xwgZ5//nk5nU6rowAA6ij+BK+lB/vM07DY3CrHzdjZS7YVzBqNmms1PVtfFtZsbe1bk76WIjjLjcB67bXX1LdvX6tjAKc0dOhQlgkDAJiGhrsW3I3zdEH0jirH5XuLlbcw3fxA1bD77l4qHNLd6hioBs+27frzW2NrtK3b5lTDlgf8nAg4uT59+qhbt25WxwCq5Ha7dfHFF1sdAwBQR9Fw15DhMHRZy298une7yChV4o8lMs7uLNkd5of7DZvbLe+5Z8p77pna9kRPXb75gC7ffEBZYx7XP5+dqss3H9BPj2bKxqf7IaHV1O166nCzam/ntDl0ReOv/B8IqITb7dZVV13FvdsICU6nU5deeqnVMQAAdVSE1QFCVmKJHkn7xqeh9RzR+uyll5TjLVDnhbdKpceXIYnIc6jl39bIKC31a7TS87vqp2HHG3t7XIm+7zddDtvvP1uJUne31N39i0ZfM01tS8ao2T1Zfs0B/yvdm61p7w7S+FHTrI4CnFTz5s01evRoq2MAAABYjoa7hv7e4/1qb5Ngj9L2IS+V/7vIKNH7lx0/A3TXymFq+mbNLzgojXZoxD8WKCkiTy2dq9XV7frNs1W/7pcj/6We7r+q1T1fySgqqnEOmK/lGwf0wR8jNTi60OooQKWefvppqyMAAAAEBRruGohqelT9onZKqvpy8lNx25y6MjZHknRl/1ek/n4IJ0lyVT3kd5IdMfrmj0/rD9OuVOlPO/wVBCawFRZp0eFOGhy9yuoowAnOO+88nXnmmVbHAAAACArcw10DGanZPt27HWqi7S79+hyfwQQzR9tWav3uXj13Gs02glP37t2VkpJidQwAAICgQMNdTd4or25IX251DNM0SzgU8Ind4JuIBulq9eZOPdVgrdVRgErVr19fl19+udUxAAAAggYNd3W5vBoQXWJ1CtO83HShfnimG7OWB6EDA5rryQac2UbwiouLU/fuLDcIAABQhoa7mgZ1+NbqCKaKtUdq/WVPyZFUz+ooYc8eGSl7TIx+mpKpn985Q8898Ewls80DwWPMmDGy2WxWxwAAAAga3LBbTZfUW291BNM55dCRc5spdna21VHCUsmAbjp6mlOdR3+jO9I/ViPHp4q2uyQ5rY4GnFLv3r2tjgAAABBUaLhxgmi7S7E3/yzNtjpJ+Mkf2kP3PvbKb25biLE0DwAAAICa4/rUalp4pKPVEQKiV/JPcmS0sTpG2HC0bqHs23tp+pNP1Ok5AlC3rVixwuoIAAAAQYWGu5rmf9PJ6ggBcX/KZu3tl2x1jLDgSElR0muH9PUdz6uNkzPaCF3PPvusDMOwOgYAAEDQoOGurmK7lhSEx7JZuT0LmK3cZI7WLZQ2v1BvNPvM6ihAreXl5WntWpatAwAAKEPDXU32Arue3D1AJYbH6iim+6L3s7LHcsbVH2xOlyKaNVHR4LMU93ly+ddZc7dqRpPPrY4H+MXBgwf1n//8R6WlpVZHAQAACApMmlYD361vqqwmDvWOtDoJQkHJgG7acalD3176rBw2m9w2ZhtH3TV16lSNGjVKnTt3tjoKAACA5TjDXUO3b7rS6gimS3ZEacuDra2OEXIc9ZPkaNtKjrattP/9dho/9S39dPmLira7aLZR5xmGoQceeMDqGAAAAEGBM9w1dPhQrNURTOe0OZTW8qDVMYKSPTJSO/6vi4yIEyeIOq3XL/rw9FmSjh9DINxs2bLF6ggAAABBgYa7pnKduv/AGXowZZPVSUxVPypfnk6nWx3DclvGxOnKHqvL/x1pP6oFyc/JYTvZRSLB12j/UlTP6ggIEzt37tRLL72kG2+80eooQJUMw9CBAwesjgEAqKNouGvIVmrTOz921tik1Up11N2JxRa0WSQtsjpFsAqdOzLyvIV6J6u7bFYHQVgoLCzU22+/raFDh6p+/fpWxwFO6dixY5o0aZLVMQAAdVTodAxBqGBnnM5fe6P2luZZHQU4pZ9KJbE8MgLo008/1R//+EcdPMhtKQhue/fuldfrtToGAKCOouGupfwd8eq/5mbt9xyzOgpwUmO2XC1bCee3EViLFy/WlVdeqUOHDlkdBTip++67T/n5+VbHAADUUTTcflC46/iZ7uWFCov1uRFaDnvydbTQbXUMhKlPP/1Uw4cP1/r161VSUmJ1HKCCnJwcPhACAJiKe7j9JH9HvK7fMVqtO+1WortA05osUD1HtNWxAM3Ja6Vj2xOsjoEw9sknn6hLly4aPXq0EhMTNWnSJMXFxclm46oLWOuzzz7Txx9/bHUMAEAdVqsz3FOmTJHNZtP48ePLHyssLNTYsWNVv359xcbGatiwYdq3b1+F7Xbt2qXBgwcrOjpaqampuuOOO1RaWlqbKEHjh68ba83qNury0a1q8fEozTxaTx/nO+UxuD8M1vj22GlWRwhL1McTvfDCC5oyZYrat2+vNm3aaPHixVq1apU8Hq4MQuAZhqFt27ZZHSMsUR8BhJMan+Fes2aNXnzxRXXs2LHC47fffrs++OADzZkzRwkJCbrllls0dOhQffnll5Ikj8ejwYMHKz09XStWrNDevXt17bXXyul06pFHHqnduwki9qPHD+3dC4fLsEut2/8sp8OjXkk/6W/JWy1Oh3CR5y3U/DVncu9IgFEfT2337t2SpAEDBigiIkKjRo2Sy+VS165dde2113LmGwFRUFCghx9+2OoYYYf6CCDc1Ojv8Ly8PI0YMUIvvfSS6tX739q+OTk5mj59up544gn169dPXbt21YwZM7RixQqtXLlSkvTxxx9r8+bNeuONN9S5c2cNGjRIf//73zV16lQVFxf7510FGZtX2vZNI323vqleXtZXE/d2sToSwsB+zzFdsHGE7IW024FEfaye0tJSvfjii3r22Wc1evRoPfPMMzIMptSHuQ4dOqTrr79eOTk5VkcJK9RHAOGoRn+Jjx07VoMHD1b//v0rPL5u3TqVlJRUeLxdu3Zq0qSJsrKyJElZWVnq0KGD0tLSyscMHDhQubm52rRpU6X7KyoqUm5uboWvUGUrtWnel2fp/M2X6MtCr4qMmk0i5DGOb/v7LyZtCy+V/b8vMTzK9xbrko0jtX9LisUJww/1seYKCwt15513atSoUfrmm29UXFxco+bb4/GouLj4hC8mbQsfhmFU+H9fdtlxSUmJCgoKdPPNN2v27Nl8uBNgga6PUt2qkQBCU7UvKZ85c6a++uorrVmz5oTnsrOz5XK5lJiYWOHxtLQ0ZWdnl4/5bbEse77sucpMnjxZDz74YHWjBi2bx6YdGxvqT9+PluzSxd3Wq0vsTsXZCzUs9sRfBIc9+Xr/WLMKjz3/Yx8d2F3vhLFyejWp10JdHPO9GkTEmvQOYLVNxQWandNN/8k6u/wxV71C/V/Hj/WPVYNlFDpkL+DMdqBRH2uvqKhIM2bM0DvvvCOn06m//e1vateunaKjo9W3b98Txufk5JRfclrm5Zdf1vLly08YGxsbq6efflpnn322kpOTzXoLsNiPP/6opUuXatKkSeWPtW7dWnfffbduv/12HT58mJnJLWBFfZTqXo0EEHqq1XDv3r1bt912mxYvXqzIyEizMp1g0qRJmjBhQvm/c3Nz1bhx44Dt3yz2ouMN0Qefd9UH6irDZejpVvtPGJdTEKm8SmaZrrSdKrDr0Q8vkWvQu7ou/sTXQugqMkr0Wm5TvbGrh/b8miBjX2SF74HSgmg9sucy2SRxB2zgUR/9q+ws1MSJEyUdb5Yvu+yyE+7vPnjwoBYtWuTTa/7666+67LLLNH/+fA0ZMsS/gWGpoqIiffjhh3rnnXe0ceNGbdiwocLzv/76qy6++GJrwsGy+ijV3RoJIHRUq+Fet26d9u/fry5d/ncPssfj0fLly/Xcc8/po48+UnFxsY4cOVLhU8p9+/YpPT1dkpSenq7Vq1dXeN2yWSjLxvye2+2W21331xG2Fdv0y+a0qgf64MEVl2jEwBfltDn88nqwzn+PRWt3SX09teF8eX51y1ZKOx2MqI/mysvL0xtvvOGX1xo3bpwuvPBCRUSwMmao+/zzz3XgwAH94x//0ObNm1VYWGh1JFTCqvoohU+NBBC8qvXXxvnnn6+NGzdWeOz6669Xu3btdOedd6px48ZyOp1asmSJhg0bJknaunWrdu3apczMTElSZmam/vGPf2j//v1KTU2VJC1evFjx8fHKyMjwx3uCJJXQlIWafG+xSuTR/+3pp68P/m8pr32768mef/yDE/6vBi/qY+goKCiwOgKqqbCwUCUlJZo6darWrVtX/vjy5cu1fz9XcwU76iOAcFathjsuLk7t27ev8FhMTIzq169f/vioUaM0YcIEJSUlKT4+XuPGjVNmZqZ69uwp6fgyMBkZGbrmmmv02GOPKTs7W/fcc4/Gjh3LJ5D+5LHp04JIDYhmkqBg92G+WxsLG+nVrT1VsDf2+Bns38zjw53YoYH6GDoKCgq0bt069ejRw+ooqMLKlSu1bds2TZ8+XatXr1ZRURHrtocg6iOAcOb36+mefPJJ2e12DRs2TEVFRRo4cKCef/758ucdDocWLFigMWPGKDMzUzExMRo5cqQeeughf0cJa/Yiux7dMUjnnT6Py8qDTJ63UPmGR4/s76Plv7TU4f1x5eu2cwa7bqM+Bofc3Fy9+OKL6tq1K5eVB5n8/HwVFhbqtdde09KlS7Vhwwb9/PPPVsdCAFAfAdRVNiME18TIzc1VQkKCmkx5WPYAT74RUmxS357fanqTL6xOAklLChxantdO87Z3VN6ueNm8Fc9kI7C8hYXaddc9ysnJUXx8vNVx/KasPuLU7Ha7HnzwQd1zzz1WR4GkNWvW6Ouvv9bcuXP16aefqrS0VF6v1+pYYa+u1UfJ2hqZlZVVfsYeCBV5eXlq0KCB8vLyrI4SVKpTH/lovy4zpKUbT9fh0z5WPUe01WnC0t7SPD1x8Fwt2pGhY4ejZM/lTDYQDLxer6ZNm6Zx48bxAYVFDh48qJkzZ+q9997Ttm3btHPnTqsjAQDgdzTcdZw9z6Fz19yoD7u9qEasyx0wTx1upl+K6mnuV13LLxfnXmwguOzZs0fDhg3Tm2++ecL6vjDP7NmztX//fj366KP65ZdfFIIX2gEA4DMa7jBQsDNOF9lv1NKuLyvZEWN1nJC033NMR73H/yhcmHeGnt/c+5Tji/ZHy1Zso8kGgtySJUt07bXXatasWRWWI4LvDh06pGPHjkk6PsnZM888c8rxGzZs4NJEAEDYoOEOE8e2J6iv9yYt7vaiGnCm22czj9bTF7lt9PGPbeXZ6/tl+VwyDoSOjz/+WEOHDtWsWbOUkpJidZyQ8cknn2jDhg2aM2eO1qxZU/44Z6wBAPgfGu4wUrAzTgNsN+nNM1/R6U5n2M9e/mNJno4Zx38Epu4/T0u3tT1hTGmuS/YCzlMDdd2nn36qq6++Ws8//7yaNWsmp9NpdSRL7d69W4WFhZKkuXPn6p133jlhzK5du3TgwIFARwMAIKTQcIeZ/B3xunzHeJ3RZYduPu0zDY4utDpSrXxfckyP7h1Yo22Xbjxd9rxTf+hAqw2Ej08++URt2rTRrbfequHDh6tXr15WR6qVXbt26T//+U+Ntn3hhRf0yy+/+DkRAADhh4Y7TG36qplu2XKN7muYo2nt39SZLntQn/H+prhQ+V6nbvrmTzqW97+l4LzFDtlzavZtTDMNoDLPPPOMZs2apZ49e+rhhx9W27Ztg/qM97Zt21RYWKiJEydqz5495Y/n5eVpx44d1gUDAAA03OHMnu/QkW1J+uO2cTqjyw7FOwvVLjZb9yRvkSQ9e7ipVuU0P2G7eGehnm24Qg6b+S3r3/Z11K6CJH25sbXs+Sd+IEDTDMAM+/bt0/vvv6///ve/GjdunOLj49WyZUtdd911ko5fZv3111+fsF1sbKzuuOMO2e3mV6eys9D//ve/tX//ftP3BwAAqs9mhODsJrm5uUpISFCTKQ/LHhlZ9QbwmRFhSHGlx/9xLEK24kqm/7JJRmKJnFEler7bm3Ko4rfQX9ZfrcI8t38C5UbI5mEKMvift7BQu+66Rzk5OYqPj7c6jt+U1Uf4X1RUlE477TRJxxvyo0ePnjDG4XCoefPmSk5O1jPPPHNC4z1x4kS/Xaq9e/duFRUV+eW1gMrUtfooWVsjGzduLLfbT38fAQFiGIZ++uknJsT8nerUR85wowJbqU06XMWlk4ZkO+xU6WGnbvrvjZW/jgnZAMBKBQUF2rZt2ynHeDwebdu2Tdu2bVP37t0DlAxAKNi9e7fVEQBYgCtyAQAAAAAwAQ03AAAAAAAmoOEGAAAAAMAENNwAAAAAAJiAhhsAAAAAABPQcAMAAAAAYAIabgAAAAAATEDDDQAAAACACWi4AQAAAAAwAQ03AAAAAAAmoOEGAAAAAMAENNwAAAAAAJiAhhsAAAAAABPQcAMAAAAAYAIabgAAAAAATEDDDQAAAACACWi4AQAAAAAwAQ03AAAAAAAmoOEGAAAAAMAENNwAAAAAAJiAhhsAAAAAABPQcAMAAAAAYAIabgAAAAAATEDDDQAAAACACWi4AQAAAAAwAQ03AAAAAAAmoOEGAAAAAMAENNwAAAAAAJiAhhsAAAAAABPQcAMAAAAAYAIabgAAAAAATEDDDQAAAACACWi4AQAAAAAwAQ03AAAAAAAmoOEGAAAAAMAENNwAAAAAAJiAhhsAAAAAABPQcAMAAAAAYAIabgAAAAAATEDDDQAAAACACWi4AQAAAAAwAQ03AAAAAAAmoOEGAAAAAMAENNwAAAAAAJiAhhsAAAAAABPQcAMAAAAAYAIabgAAAAAATBBhdYCaMAxDkuQtLLQ4CYBQVVY/yupJXVHX3g8A69TFelIX3xOAwKtOLQnJhvvXX3+VJP38wMMWJwEQ6o4ePaqEhASrY/jN0aNHrY4AoI6oa/VR+t/fkABQG9WpjyHZcCclJUmSdu3aVed+EVRHbm6uGjdurN27dys+Pt7qOJbhOHAMylTnOBiGoaNHj6phw4YBShcYDRs21ObNm5WRkRHW3w/8TBzHceAYlKE+HsffkPxMlOE4HMdxML8+hmTDbbcfv/U8ISEhbL8xfis+Pp7jII6DxDEo4+txqIt/bNntdp122mmS+H6QOAZlOA4cgzLhXB8l/ob8LX4mjuM4HMdxMK8+MmkaAAAAAAAmoOEGAAAAAMAEIdlwu91u3X///XK73VZHsRTH4TiOA8egDMfhOI4Dx6AMx4FjUIbjcBzHgWNQhuNwHMfB/GNgM1gfAQAAAAAAvwvJM9wAAAAAAAQ7Gm4AAAAAAExAww0AAAAAgAlouAEAAAAAMAENNwAAAAAAJgjJhnvq1Klq1qyZIiMj1aNHD61evdrqSH6zfPlyXXzxxWrYsKFsNpvee++9Cs8bhqH77rtPDRo0UFRUlPr3768ffvihwphDhw5pxIgRio+PV2JiokaNGqW8vLwAvovamTx5ss466yzFxcUpNTVVl112mbZu3VphTGFhocaOHav69esrNjZWw4YN0759+yqM2bVrlwYPHqzo6GilpqbqjjvuUGlpaSDfSq1MmzZNHTt2VHx8vOLj45WZmalFixaVPx8Ox+D3pkyZIpvNpvHjx5c/Fo7H4VTqcn2UqJESNVKiPp4MNfLUqI/URyk8fiaokSeytD4aIWbmzJmGy+UyXnnlFWPTpk3GjTfeaCQmJhr79u2zOppfLFy40Lj77ruNd99915BkzJs3r8LzU6ZMMRISEoz33nvP+Prrr41LLrnEaN68uVFQUFA+5sILLzQ6depkrFy50vj888+NVq1aGX/84x8D/E5qbuDAgcaMGTOMb7/91tiwYYNx0UUXGU2aNDHy8vLKx4wePdpo3LixsWTJEmPt2rVGz549jV69epU/X1paarRv397o37+/sX79emPhwoVGcnKyMWnSJCveUo3897//NT744APj+++/N7Zu3Wr87W9/M5xOp/Htt98ahhEex+C3Vq9ebTRr1szo2LGjcdttt5U/Hm7H4VTqen00DGqkYVAjDYP6WBlq5KlRH6mPZcLhZ4IaWZHV9THkGu7u3bsbY8eOLf+3x+MxGjZsaEyePNnCVOb4fbH0er1Genq68c9//rP8sSNHjhhut9t4++23DcMwjM2bNxuSjDVr1pSPWbRokWGz2YxffvklYNn9af/+/YYkY9myZYZhHH/PTqfTmDNnTvmY7777zpBkZGVlGYZx/JeO3W43srOzy8dMmzbNiI+PN4qKigL7BvyoXr16xssvvxx2x+Do0aNG69atjcWLFxt9+vQpL5bhdhyqEk710TCokWWokceFa300DGqkL6iP1EfDCO+fiXCtkcFQH0PqkvLi4mKtW7dO/fv3L3/Mbrerf//+ysrKsjBZYGzfvl3Z2dkV3n9CQoJ69OhR/v6zsrKUmJiobt26lY/p37+/7Ha7Vq1aFfDM/pCTkyNJSkpKkiStW7dOJSUlFY5Du3bt1KRJkwrHoUOHDkpLSysfM3DgQOXm5mrTpk0BTO8fHo9HM2fO1LFjx5SZmRl2x2Ds2LEaPHhwhfcrhef3wsmEe32UqJHhWiPDvT5K1MiqUB+pj+FaHyVqZDDUx4havoeAOnjwoDweT4U3LUlpaWnasmWLRakCJzs7W5Iqff9lz2VnZys1NbXC8xEREUpKSiofE0q8Xq/Gjx+vs88+W+3bt5d0/D26XC4lJiZWGPv741DZcSp7LlRs3LhRmZmZKiwsVGxsrObNm6eMjAxt2LAhbI7BzJkz9dVXX2nNmjUnPBdO3wtVCff6KFEjw61GUh+Po0ZWjfpIfQy3+ihRI6XgqY8h1XAj/IwdO1bffvutvvjiC6ujWKJt27basGGDcnJyNHfuXI0cOVLLli2zOlbA7N69W7fddpsWL16syMhIq+MAQSeca2S410eJGgmcSjjXR4kaGUz1MaQuKU9OTpbD4Thh9rh9+/YpPT3dolSBU/YeT/X+09PTtX///grPl5aW6tChQyF3jG655RYtWLBAn376qRo1alT+eHp6uoqLi3XkyJEK439/HCo7TmXPhQqXy6VWrVqpa9eumjx5sjp16qSnn346bI7BunXrtH//fnXp0kURERGKiIjQsmXL9MwzzygiIkJpaWlhcRx8Ee71UaJGlgmX+hDu9VGiRvqK+kh9LBNO9SHca2Qw1ceQarhdLpe6du2qJUuWlD/m9Xq1ZMkSZWZmWpgsMJo3b6709PQK7z83N1erVq0qf/+ZmZk6cuSI1q1bVz5m6dKl8nq96tGjR8Az14RhGLrllls0b948LV26VM2bN6/wfNeuXeV0Oisch61bt2rXrl0VjsPGjRsr/OJYvHix4uPjlZGREZg3YgKv16uioqKwOQbnn3++Nm7cqA0bNpR/devWTSNGjCj/73A4Dr4I9/ooUSPLhEt9+L1wq48SNdJX1EfqY5lwqg+/F241Mqjqox8mfwuomTNnGm6323j11VeNzZs3GzfddJORmJhYYfa4UHb06FFj/fr1xvr16w1JxhNPPGGsX7/e2Llzp2EYx5d0SExMNN5//33jm2++MS699NJKl3Q488wzjVWrVhlffPGF0bp165Ba0mHMmDFGQkKC8dlnnxl79+4t/8rPzy8fM3r0aKNJkybG0qVLjbVr1xqZmZlGZmZm+fNl0/gPGDDA2LBhg/Hhhx8aKSkpIbWcwV133WUsW7bM2L59u/HNN98Yd911l2Gz2YyPP/7YMIzwOAaV+e0Mk4YRvsehMnW9PhoGNdIwqJGGQX08FWpk5aiP1Mcy4fAzQY2snFX1MeQabsMwjGeffdZo0qSJ4XK5jO7duxsrV660OpLffPrpp4akE75GjhxpGMbxZR3uvfdeIy0tzXC73cb5559vbN26tcJr/Prrr8Yf//hHIzY21oiPjzeuv/564+jRoxa8m5qp7P1LMmbMmFE+pqCgwPjLX/5i1KtXz4iOjjYuv/xyY+/evRVeZ8eOHcagQYOMqKgoIzk52Zg4caJRUlIS4HdTc3/+85+Npk2bGi6Xy0hJSTHOP//88kJpGOFxDCrz+2IZrsfhZOpyfTQMaqRhUCMNg/p4KtTIk6M+Uh8NIzx+JqiRlbOqPtoMwzB8Px8OAAAAAAB8EVL3cAMAAAAAECpouAEAAAAAMAENNwAAAAAAJqDhBgAAAADABDTcAAAAAACYgIYbAAAAAAAT0HADAAAAAGACGm4AAAAAAExAww0AAAAAgAlouAEAAAAAMAENNwAAAAAAJvh/yvyVyk4gHZ8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from math import log\n",
        "from skimage.measure import label\n",
        "import numpy as np\n",
        "\n",
        "##The ground_truth (mask/box)  and the predicted (mask/box) must be in a binary format\n",
        "##Please make sure that the ground_truth (mask/box) exists (the entry must be a matrix that contains at least a pixel with 1 value)\n",
        "def IoU(ground_truth, predicted_mask):\n",
        "    \n",
        "    #verify if the two images has the same dimensions\n",
        "    # assert ground_truth.shape == predicted_mask.shape\n",
        "    \n",
        "    #Get the dimension of the picture\n",
        "    m, n = ground_truth.shape\n",
        "    \n",
        "    #The variable that will contain the value of the intersection of the two masks area\n",
        "    intersection = 0\n",
        "    \n",
        "    #The same but for union\n",
        "    union = 0\n",
        "    \n",
        "    #We iterate on pixels\n",
        "    for i in range(175):\n",
        "        \n",
        "        for j in range(175):\n",
        "            \n",
        "            #If the two of pixels is equal to one that mean that the pixel is in both masks\n",
        "            if(int(ground_truth[i][j]) == 1 and int(predicted_mask[i][j]) == 1):\n",
        "                \n",
        "                intersection += 1\n",
        "                union += 1\n",
        "             \n",
        "            #If the pixel is in one of the twos without being in the other we add 1\n",
        "            if( int(ground_truth[i][j]) == 1 ^ int(predicted_mask[i][j]) == 1):\n",
        "                \n",
        "                union += 1\n",
        "    \n",
        "    return intersection/union\n",
        "\n",
        "#Dice coefficient F1 score\n",
        "#ground truth, preidcted --> (box/mask) must be numpy.array\n",
        "def DC(ground_truth, predicted):\n",
        "\n",
        "    #make sure that the dimensions are the same\n",
        "    # assert ground_truth.shape == predicted.shape\n",
        "\n",
        "    #Get the dimensions of the matrix\n",
        "    h, w = ground_truth.shape\n",
        "\n",
        "    #The number of pixel with 1 as value that are in both of ground_truth and predicted mask or box\n",
        "    TP = 0\n",
        "    #The number of pixels that are in the ground_truth but not in the predicted mask or box\n",
        "    TN = 0\n",
        "    #The same but for predicted instead of ground truth\n",
        "    FN = 0\n",
        "\n",
        "    #We iterate in each pixel in both ground_truth and predicted to see what does match\n",
        "    for i in range(175):\n",
        "\n",
        "        for j in range(175):\n",
        "            \n",
        "            #We verify if they had 1 pixels in commun (intersection)\n",
        "            if(int(ground_truth[i][j]) == 1 and int(predicted[i][j]) == 1):\n",
        "\n",
        "                TP += 1\n",
        "\n",
        "            #To count the number of pixels \"1\" that are in ground_truth but not in predicted\n",
        "            elif(int(ground_truth[i][j]) == 1 and int(predicted[i][j]) == 0):\n",
        "\n",
        "                FN += 1\n",
        "\n",
        "            #Same thing but for predicted\n",
        "            elif(int(ground_truth[i][j]) == 0 and int(predicted[i][j]) == 1):\n",
        "\n",
        "                TN += 1\n",
        "\n",
        "    #The dice formula (2*Union/(cardinality(ground_truth)+cardinality(predicted))\n",
        "    return 2*TP/(2*TP+TN+FN)\n",
        "\n",
        "#Localization error\n",
        "def LE(ground_truth, prediction):\n",
        "    \n",
        "    return 1-IoU(ground_truth, prediction)\n",
        "\n",
        "\n",
        "#Energy pointing game it is equivalent to the \"Pixel-wise precision\"\n",
        "def EPG(ground_truth_mask, saliency_map):\n",
        "\n",
        "\n",
        "    # assert ground_truth_mask.shape ==  saliency_map.shape\n",
        "    \n",
        "    h, w = ground_truth_mask.shape\n",
        "    \n",
        "    #The saliency contained in the ground_truth (mask or box)\n",
        "    ground_truth_saliency = 0\n",
        "    \n",
        "    #The saliency on the hole image\n",
        "    global_saliency = 0\n",
        "    \n",
        "    \n",
        "    for i in range(175):\n",
        "        \n",
        "        for j in range(175):\n",
        "            \n",
        "            #We and the value of the saliency when we have a pixel of 1 in the ground_truth\n",
        "            ground_truth_saliency += float(saliency_map[i][j][0])*int(ground_truth_mask[i][j])\n",
        "            \n",
        "            #We sum the saliency of all pixels\n",
        "            global_saliency += float(saliency_map[i][j][0])\n",
        "\n",
        "    #The formula of energy pointing game\n",
        "    return ground_truth_saliency/global_saliency\n",
        "\n",
        "\n",
        "#The precision for the class of pixels with 1 value\n",
        "def precision(ground_truth, predicted):\n",
        "    \n",
        "    #verify if the dimensions are the same\n",
        "    # assert ground_truth.shape == predicted.shape\n",
        "    \n",
        "    #Get the dimensions of one of the matrices\n",
        "    h, w = ground_truth.shape\n",
        "    \n",
        "    #The well classified pixels\n",
        "    good_predictions = 0\n",
        "    \n",
        "    #The number of pixels with value of one\n",
        "    hypothesis = 0\n",
        "    \n",
        "    for i in range(175):\n",
        "        \n",
        "        for j in range(175):\n",
        "\n",
        "            #If the predicted label correspond to the ground label\n",
        "            if(ground_truth[i][j] == predicted[i][j] and ground_truth[i][j] == 1):\n",
        "                \n",
        "                good_predictions += 1\n",
        "                \n",
        "                hypothesis += 1\n",
        "                \n",
        "            #If it's not\n",
        "            elif(predicted[i][j] == 1 and ground_truth[i][j] == 0):\n",
        "                \n",
        "                hypothesis += 1\n",
        "                \n",
        "    return good_predictions/hypothesis\n",
        "\n",
        "\n",
        "#The recall of class 1 pixels\n",
        "def recall(ground_truth, predicted):\n",
        "\n",
        "    # assert ground_truth.shape == predicted.shape\n",
        "    \n",
        "    h, w = ground_truth.shape\n",
        "    \n",
        "    #Well classified pixels\n",
        "    good_predictions = 0\n",
        "    \n",
        "    #The number of pixels with 1 as value\n",
        "    refrences = 0\n",
        "    \n",
        "    #Same thing as the precision but we calculate the number of references pixels of ground truth\n",
        "    for i in range(175):\n",
        "        \n",
        "        for j in range(175):\n",
        "            \n",
        "            if(ground_truth[i][j] == predicted[i][j] and ground_truth[i][j] == 1):\n",
        "                \n",
        "                good_predictions += 1\n",
        "                refrences += 1\n",
        "                \n",
        "            elif(ground_truth[i][j] == 1 and predicted[i][j] == 0):\n",
        "                \n",
        "                refrences += 1\n",
        "                \n",
        "    return good_predictions/refrences\n",
        "\n",
        "\n",
        "#The beta F-score \n",
        "def F_score(ground_truth, predicted, beta=1):\n",
        "    \n",
        "    p = precision(ground_truth, predicted)\n",
        "    \n",
        "    r = recall(ground_truth, predicted)\n",
        "    print(p)\n",
        "    print(r)\n",
        "    \n",
        "    return (1+beta**2)*p*r/((beta**2)*p+r)\n",
        "\n",
        "\n",
        "#Calculate the fraction of the ground truth  that is covered by the saliency map\n",
        "def pixel_wise_recall(ground_truth, sallience_map):\n",
        "    \n",
        "    # assert ground_truth.shape == sallience_map.shape\n",
        "    \n",
        "    m, n = ground_truth.shape\n",
        "    \n",
        "    ground_truth_saliency = 0\n",
        "    \n",
        "    refrences = 0\n",
        "    \n",
        "    for i in range(175):\n",
        "        \n",
        "        for j in range(175):\n",
        "            \n",
        "            if(int(ground_truth[i][j]) == 1):\n",
        "                \n",
        "                refrences += 1\n",
        "                \n",
        "                ground_truth_saliency += float(sallience_map[i][j][0])\n",
        "                \n",
        "    \n",
        "    return ground_truth_saliency/refrences\n",
        "\n",
        "\n",
        "#The F_beta score\n",
        "def pixel_wise_F_score(ground_truth, saliency_map, beta=1):\n",
        "    \n",
        "    p = EPG(ground_truth, saliency_map)\n",
        "    \n",
        "    r = pixel_wise_recall(ground_truth, saliency_map)\n",
        "\n",
        "    return (1+beta**2)*(p*r)/((beta**2)*p+r)\n",
        "\n",
        "\n",
        "#Calculate the \"1\" class pixels in a binary mask or box\n",
        "def get_cardinality(predicted):\n",
        "    \n",
        "    h, w = predicted.shape\n",
        "    \n",
        "    result = 0\n",
        "    \n",
        "    for i in range(h):\n",
        "        \n",
        "        for j in range(w):\n",
        "            \n",
        "            if(int(predicted[i][j]) == 1):\n",
        "                \n",
        "                result += 1\n",
        "                \n",
        "    return result\n",
        "\n",
        "#The saliency metric\n",
        "def SM(predicted, probability_class):\n",
        "    \n",
        "    h, w = predicted.shape\n",
        "    \n",
        "    return log( max( 0.05, get_cardinality(predicted) / (h*w) ) ) - log( probability_class )\n",
        "\n",
        "\n",
        "#The absolute mean error\n",
        "def AME(saliency_map, ground_truth):\n",
        "    \n",
        "    # assert saliency_map.shape == ground_truth.shape\n",
        "    \n",
        "    h, w ,v = saliency_map.shape\n",
        "    \n",
        "    result = 0\n",
        "    \n",
        "    for i in range(175):\n",
        "        \n",
        "        for j in range(175):\n",
        "            \n",
        "            result += abs(float(saliency_map[i][j][0]) - int(ground_truth[i][j]))\n",
        "            \n",
        "    return result/(h*w)\n",
        "\n",
        "#The mask and box accuaracy it works for both of them\n",
        "#Max value refer to sigma the second threshold\n",
        "def accuaracy(ground_truths, saliency_maps, thresholds_list, max_value=0.5):\n",
        "    \n",
        "    assert len(ground_truths) == len(saliency_maps)\n",
        "    \n",
        "    results = np.zeros((len(thresholds_list),0))\n",
        "    \n",
        "    for i in range(len(ground_truths)):\n",
        "        \n",
        "        for j in len(thresholds_list):\n",
        "            \n",
        "            if(IoU(ground_truths[i], saliency_maps[i]) >= max_value):\n",
        "                \n",
        "                results[j] += 1\n",
        "                \n",
        "    results = results/len(ground_truths)  \n",
        "    \n",
        "    return np.max(results), results\n",
        "\n",
        "\n",
        "\n",
        "def concept_influence(binary_segmentation_mask, top_k_binary_mask, k = 1_000):\n",
        "    \n",
        "    assert binary_segmentation_mask.shape ==  top_k_binary_mask.shape\n",
        "    \n",
        "    h, w = binary_segmentation_mask.shape\n",
        "    \n",
        "    top_k_binary_mask = 0\n",
        "    \n",
        "    relative_size_concept = 0\n",
        "    \n",
        "    for i in range(h):\n",
        "        \n",
        "        for j in range(w):\n",
        "            \n",
        "            top_k_binary_mask += binary_segmentation_mask[i][j]*top_k_binary_mask[i][j]\n",
        "            \n",
        "            relative_size_concept += binary_segmentation_mask[i][j]\n",
        "    \n",
        "    top_k_binary_mask /= k\n",
        "    \n",
        "    return top_k_binary_mask/relative_size_concept\n",
        "\n",
        "def relevance_rank_accuracy(ground_truth, top_k_pixels):\n",
        "    \n",
        "    h, w = ground_truth.shape\n",
        "    \n",
        "    result = 0\n",
        "    \n",
        "    for pixel in top_k_pixels:\n",
        "        \n",
        "        result += ground_truth[pixel[0]][pixel[1]]\n",
        "        \n",
        "    return result/(h*w)"
      ],
      "metadata": {
        "id": "VNOG0hXsQpHQ"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "LE(ground_truth_box, predicted_box)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1UK_RwPGRKJb",
        "outputId": "704cadb3-6827-4cf4-d0aa-91abbddc5f14"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 2, figsize = (12, 12))\n",
        "\n",
        "ax[0].imshow(predicted_box, \"gray\")\n",
        "ax[1].imshow(ground_truth_box, \"gray\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "7eKjLKsrhB2x",
        "outputId": "479e1b8e-c6fc-460c-d1eb-38c304e93aa8"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f243ef69c70>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "LE(binary_map, mask)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4fDpgarxhI82",
        "outputId": "4e9e1c64-b072-4693-9b41-b975d58609d8"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5742049469964665"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 2, figsize = (12, 12))\n",
        "\n",
        "ax[0].imshow(binary_map, \"gray\")\n",
        "ax[1].imshow(mask, \"gray\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "1inCczJhhOTN",
        "outputId": "95fc595d-4461-412f-f6c5-600c8edd4576"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f243ee108e0>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "1- DC(binary_map, mask)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cml_32RDhQpG",
        "outputId": "f6ec45c2-ea86-41b3-e2c8-ce9a0101ad68"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9391874842291194"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "EPG(binary_map, im)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0hTJYPFihY_",
        "outputId": "ba5921dd-f5f8-4947-b446-030bf448d40e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.03138557853999823"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "EPG(ground_truth_box, im)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s2-Tl2SYihg0",
        "outputId": "d2fc23aa-274b-4596-cbd5-bb23e3aab3b3"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.44524075944253977"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pixel_wise_F_score(binary_map, im)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cpK5busrnXt9",
        "outputId": "a1628416-1f15-41bd-a5e6-217e9811ddb9"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.057477198252508306"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pixel_wise_F_score(ground_truth_box, im)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xNm29qYdHAvY",
        "outputId": "70c64712-34ae-4bcf-8544-f1397890f174"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.44124257514265375"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "AME(im, ground_truth_box)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cnGfaZ32HH9-",
        "outputId": "464f5588-12c1-40a1-f641-0174b210e97b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3330212372076948"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "AME(im, binary_map)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vl7xmX-IFuC",
        "outputId": "b0c00987-499c-47b3-9086-c3ecb59ef18f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.303988764601051"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    }
  ]
}