Egyptian_1_e_.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": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RPVqkNhgPlv0"
      },
      "source": [
        "from google.colab import files\n",
        "from IPython.display import Image"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oXd5i9OvQN5p",
        "outputId": "8dcf10ef-4a5f-483f-b5dd-60f797793743",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "uploaded = files.upload()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-663c0203-80b2-4c93-a4b6-6f5364dd6e9a\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-663c0203-80b2-4c93-a4b6-6f5364dd6e9a\">\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 Egyptian_cat_e.png to Egyptian_cat_e.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3RuLhrCQbzI",
        "outputId": "ffe3c9dd-18e2-4d18-c2a2-291aa62be073",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "img = Image('Egyptian_cat_e.png')\n",
        "print(type(img))"
      ],
      "execution_count": 36,
      "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": "e88eb6b5-b25f-499c-b0ee-11e8d9caf80e"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "im = plt.imread(\"Egyptian_cat_e.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": "0be16275-f52d-4d49-edc1-042506900838"
      },
      "execution_count": 39,
      "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": 40,
      "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": "cb8399f9-a350-4d4c-c29a-930d043ac7d4"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7359c8f6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "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": 42,
      "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": "d9aca811-308e-438a-ade6-059d0eda997b"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7359cc7dc0>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbAAAAGhCAYAAAAAxOEUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAi2ElEQVR4nO3dfXBU5d3/8c8mIStIdmMIySaa8FQFLJAq6jZjtVpSSKS0Km2V0ilaCkUDjqDWZlqhOh1DpbWdthT6hwU7VavMCCptmYkBklqX1AYzKDoZwkQTJRsqTHaTYEIert8f9+353WvCwya7Wa/wfs18Z7Lnus7Z77lm2Q+7e7JxGWOMAACwTFKiGwAAYCgIMACAlQgwAICVCDAAgJUIMACAlQgwAICVCDAAgJUIMACAlQgwAICVCDAAgJUSFmCbN2/W5MmTddFFF8nv9+vf//53oloBAFgoIQH2/PPPa926ddqwYYMOHjyogoICLViwQMePH09EOwAAC7kS8WW+fr9f1157rX7/+99Lkvr7+5WXl6c1a9boxz/+8Tn37+/v17Fjx5SWliaXyxXvdgEAMWaMUXt7u3Jzc5WUNLTXUikx7umcTp8+rdraWpWVlTnbkpKSVFRUpEAgMOg+3d3d6u7udm5/+OGHuvLKK+PeKwAgvpqbm3XZZZcNad8Rfwvxo48+Ul9fn7KzsyO2Z2dnKxgMDrpPeXm5vF6vU4QXAIwOaWlpQ97XiqsQy8rKFAqFnGpubk50SwCAGBjOx0Aj/hZiZmamkpOT1draGrG9tbVVPp9v0H3cbrfcbvdItAcAsMSIvwJLTU3V3LlzVVlZ6Wzr7+9XZWWlCgsLR7odAIClRvwVmCStW7dOy5Yt0zXXXKPrrrtOv/nNb9TZ2am77747Ee0AACyUkAC744479N///lfr169XMBjUF77wBe3Zs2fAhR0AAJxJQn4PbLjC4bC8Xm+i2wAADFMoFJLH4xnSvlZchQgAwKcRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACvFPMDKy8t17bXXKi0tTVlZWbr11ltVX18fMeemm26Sy+WKqFWrVsW6FQDAKBbzAKuqqlJpaakOHDigiooK9fT0aP78+ers7IyYt2LFCrW0tDj1xBNPxLoVAMAolhLrA+7Zsyfi9vbt25WVlaXa2lrdeOONzvZx48bJ5/PF+u4BABeIuH8GFgqFJEkZGRkR25955hllZmZq1qxZKisr06lTp854jO7uboXD4YgCAFzgTBz19fWZhQsXmuuvvz5i+x//+EezZ88ec+jQIfOXv/zFXHrppea2224743E2bNhgJFEURVGjrEKh0JAzJq4BtmrVKjNp0iTT3Nx81nmVlZVGkmloaBh0vKury4RCIaeam5sTvugURVHU8Gs4ARbzz8A+sXr1au3evVvV1dW67LLLzjrX7/dLkhoaGjRt2rQB4263W263Oy59AgDsFPMAM8ZozZo12rlzp/bv368pU6acc5+6ujpJUk5OTqzbAQCMUjEPsNLSUj377LN66aWXlJaWpmAwKEnyer0aO3asjh49qmeffVa33HKLJkyYoEOHDmnt2rW68cYbNWfOnFi3AwAYrYb85uMZ6Azvc27bts0YY0xTU5O58cYbTUZGhnG73eZzn/uceeihh6J6HzQUCiX8fVuKoihq+DWcz8Bc/xs6VgmHw/J6vYluAwAwTKFQSB6PZ0j78l2IAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACvFPMB+9rOfyeVyRdSMGTOc8a6uLpWWlmrChAkaP368Fi9erNbW1li3AQAY5eLyCuzzn/+8WlpanHrttdecsbVr1+qVV17Rjh07VFVVpWPHjun222+PRxsAgFEsJS4HTUmRz+cbsD0UCumpp57Ss88+q6985SuSpG3btmnmzJk6cOCAvvjFLw56vO7ubnV3dzu3w+FwPNoGAFgkLq/Ajhw5otzcXE2dOlVLly5VU1OTJKm2tlY9PT0qKipy5s6YMUP5+fkKBAJnPF55ebm8Xq9TeXl58WgbAGCRmAeY3+/X9u3btWfPHm3ZskWNjY264YYb1N7ermAwqNTUVKWnp0fsk52drWAweMZjlpWVKRQKOdXc3BzrtgEAlon5W4glJSXOz3PmzJHf79ekSZP0wgsvaOzYsUM6ptvtltvtjlWLAIBRIO6X0aenp+uKK65QQ0ODfD6fTp8+rba2tog5ra2tg35mBgDAmcQ9wDo6OnT06FHl5ORo7ty5GjNmjCorK53x+vp6NTU1qbCwMN6tAABGkZi/hfjggw9q0aJFmjRpko4dO6YNGzYoOTlZS5Yskdfr1fLly7Vu3TplZGTI4/FozZo1KiwsPOMViAAADCbmAfbBBx9oyZIlOnHihCZOnKgvfelLOnDggCZOnChJ+vWvf62kpCQtXrxY3d3dWrBggf7whz/Eug0AwCjnMsaYRDcRrXA4LK/Xm+g2HC6XS3PnznVCGkD8HD9+XAcPHpSFT10YRCgUksfjGdK+cflF5gtNUlKSfvKTn+hrX/taolsBRr2XX35Z3/72t9XX15foVpBgBFiMJCcnKyWF5QTiLTk5OdEt4DOCb6MHAFiJAAMAWIkAAwBYiQADAFiJAAMAWIkAAwBYiQADAFiJAAMAWIkAAwBYiQADAFiJAAMAWIkAAwBYiQADAFiJAAMAWIkAAwBYiQADAFiJAAMAWIkAAwBYiQADAFiJAAMAWIkAAwBYiQADAFiJAAMAWIkAAwBYiQADAFiJAAMAWIkAAwBYiQADAFiJAAMAWIkAAwBYiQADAFiJAAMAWCnmATZ58mS5XK4BVVpaKkm66aabBoytWrUq1m0AAEa5lFgf8I033lBfX59z++2339ZXv/pVfetb33K2rVixQo899phze9y4cbFuAwAwysU8wCZOnBhxe+PGjZo2bZq+/OUvO9vGjRsnn8933sfs7u5Wd3e3czscDg+/UQCA1eL6Gdjp06f1l7/8Rd///vflcrmc7c8884wyMzM1a9YslZWV6dSpU2c9Tnl5ubxer1N5eXnxbBsAYIGYvwL7v3bt2qW2tjbdddddzrbvfOc7mjRpknJzc3Xo0CE9/PDDqq+v14svvnjG45SVlWndunXO7XA4TIgBwAUurgH21FNPqaSkRLm5uc62lStXOj/Pnj1bOTk5mjdvno4ePapp06YNehy32y232x3PVgEAlonbW4jvv/++Xn31Vf3gBz846zy/3y9JamhoiFcrAIBRKG4Btm3bNmVlZWnhwoVnnVdXVydJysnJiVcrAIBRKC5vIfb392vbtm1atmyZUlL+/10cPXpUzz77rG655RZNmDBBhw4d0tq1a3XjjTdqzpw58WgFADBKxSXAXn31VTU1Nen73/9+xPbU1FS9+uqr+s1vfqPOzk7l5eVp8eLF+ulPfxqPNgAAo1hcAmz+/PkyxgzYnpeXp6qqqnjcJQDgAsN3IQIArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArBR1gFVXV2vRokXKzc2Vy+XSrl27IsaNMVq/fr1ycnI0duxYFRUV6ciRIxFzTp48qaVLl8rj8Sg9PV3Lly9XR0fHsE4EAHBhiTrAOjs7VVBQoM2bNw86/sQTT+i3v/2ttm7dqpqaGl188cVasGCBurq6nDlLly7V4cOHVVFRod27d6u6ulorV64c+lkAAC48ZhgkmZ07dzq3+/v7jc/nM5s2bXK2tbW1GbfbbZ577jljjDHvvPOOkWTeeOMNZ84//vEP43K5zIcffnhe9xsKhYykz0wlJyebl19+eThLCeA87dq1yyQnJyf83z0VmwqFQkN+LMT0M7DGxkYFg0EVFRU527xer/x+vwKBgCQpEAgoPT1d11xzjTOnqKhISUlJqqmpGfS43d3dCofDEQUAuLDFNMCCwaAkKTs7O2J7dna2MxYMBpWVlRUxnpKSooyMDGfOp5WXl8vr9TqVl5cXy7YBABay4irEsrIyhUIhp5qbmxPdEgAgwWIaYD6fT5LU2toasb21tdUZ8/l8On78eMR4b2+vTp486cz5NLfbLY/HE1EAgAtbTANsypQp8vl8qqysdLaFw2HV1NSosLBQklRYWKi2tjbV1tY6c/bu3av+/n75/f5YtgMAGMVSot2ho6NDDQ0Nzu3GxkbV1dUpIyND+fn5uv/++/Xzn/9cl19+uaZMmaJHHnlEubm5uvXWWyVJM2fOVHFxsVasWKGtW7eqp6dHq1ev1p133qnc3NyYnRgAYJSL9rLFffv2DXop5LJly4wx/3Mp/SOPPGKys7ON2+028+bNM/X19RHHOHHihFmyZIkZP3688Xg85u677zbt7e3n3QOX0QMXLi6jH101nMvoXcYYI8uEw2F5vd5Et+FITk7Wzp07tWjRokS3Aox6L730khYvXqy+vr5Et4IYCIVCQ76uwYqrEAEA+DQCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYKWoA6y6ulqLFi1Sbm6uXC6Xdu3a5Yz19PTo4Ycf1uzZs3XxxRcrNzdX3/ve93Ts2LGIY0yePFkulyuiNm7cOOyTAQBcOKIOsM7OThUUFGjz5s0Dxk6dOqWDBw/qkUce0cGDB/Xiiy+qvr5eX//61wfMfeyxx9TS0uLUmjVrhnYGAIALUkq0O5SUlKikpGTQMa/Xq4qKiohtv//973XdddepqalJ+fn5zva0tDT5fL5o7x4AAEkj8BlYKBSSy+VSenp6xPaNGzdqwoQJuuqqq7Rp0yb19vae8Rjd3d0Kh8MRBQC4sEX9CiwaXV1devjhh7VkyRJ5PB5n+3333aerr75aGRkZev3111VWVqaWlhY9+eSTgx6nvLxcjz76aDxbBQBYJm4B1tPTo29/+9syxmjLli0RY+vWrXN+njNnjlJTU/XDH/5Q5eXlcrvdA45VVlYWsU84HFZeXl68WgcAWCAuAfZJeL3//vvau3dvxKuvwfj9fvX29uq9997T9OnTB4y73e5Bgw0AcOGKeYB9El5HjhzRvn37NGHChHPuU1dXp6SkJGVlZcW6HQDAKBV1gHV0dKihocG53djYqLq6OmVkZCgnJ0ff/OY3dfDgQe3evVt9fX0KBoOSpIyMDKWmpioQCKimpkY333yz0tLSFAgEtHbtWn33u9/VJZdcErszAwCMbiZK+/btM5IG1LJly0xjY+OgY5LMvn37jDHG1NbWGr/fb7xer7nooovMzJkzzeOPP266urrOu4dQKHTG+0lEJScnm5dffjnapQQwBLt27TLJyckJ/3dPxaZCodCQHwtRvwK76aabZIw54/jZxiTp6quv1oEDB6K9WwAAIvBdiAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArEWAAACsRYAAAKxFgAAArRR1g1dXVWrRokXJzc+VyubRr166I8bvuuksulyuiiouLI+acPHlSS5culcfjUXp6upYvX66Ojo5hnQgA4MISdYB1dnaqoKBAmzdvPuOc4uJitbS0OPXcc89FjC9dulSHDx9WRUWFdu/ererqaq1cuTL67gEAF6yUaHcoKSlRSUnJWee43W75fL5Bx959913t2bNHb7zxhq655hpJ0u9+9zvdcsst+uUvf6nc3NwB+3R3d6u7u9u5HQ6Ho20bADDKxOUzsP379ysrK0vTp0/XPffcoxMnTjhjgUBA6enpTnhJUlFRkZKSklRTUzPo8crLy+X1ep3Ky8uLR9sAAIvEPMCKi4v15z//WZWVlfrFL36hqqoqlZSUqK+vT5IUDAaVlZUVsU9KSooyMjIUDAYHPWZZWZlCoZBTzc3NsW4bAGCZqN9CPJc777zT+Xn27NmaM2eOpk2bpv3792vevHlDOqbb7Zbb7Y5VizFnjFF1dTUXogAjoLa2VsaYRLeBz4CYB9inTZ06VZmZmWpoaNC8efPk8/l0/PjxiDm9vb06efLkGT83+6zr7+/XL3/5S7lcrkS3Aox6hBc+EfcA++CDD3TixAnl5ORIkgoLC9XW1qba2lrNnTtXkrR371719/fL7/fHu5244h8WAIycqAOso6NDDQ0Nzu3GxkbV1dUpIyNDGRkZevTRR7V48WL5fD4dPXpUP/rRj/S5z31OCxYskCTNnDlTxcXFWrFihbZu3aqenh6tXr1ad95556BXIAIAMCgTpX379hlJA2rZsmXm1KlTZv78+WbixIlmzJgxZtKkSWbFihUmGAxGHOPEiRNmyZIlZvz48cbj8Zi7777btLe3n3cPoVBo0B4oiqIouyoUCkUbQw6XMfa97xUOh+X1ehPdBgBgmEKhkDwez5D25bsQAQBWIsAAAFYiwAAAViLAAABWIsAAAFYiwAAAViLAAABWIsAAAFYiwAAAViLAAABWIsAAAFYiwAAAViLAAABWIsAAAFYiwAAAViLAAABWIsAAAFYiwAAAViLAAABWIsAAAFYiwAAAViLAAABWIsAAAFYiwAAAViLAAABWIsAAAFYiwAAAViLAAABWIsAAAFYiwAAAViLAAABWIsAAAFYiwAAAViLAAABWijrAqqurtWjRIuXm5srlcmnXrl0R4y6Xa9DatGmTM2fy5MkDxjdu3DjskwEAXDiiDrDOzk4VFBRo8+bNg463tLRE1J/+9Ce5XC4tXrw4Yt5jjz0WMW/NmjVDOwMAwAUpJdodSkpKVFJScsZxn88Xcfull17SzTffrKlTp0ZsT0tLGzAXAIDzFdfPwFpbW/W3v/1Ny5cvHzC2ceNGTZgwQVdddZU2bdqk3t7eMx6nu7tb4XA4ogAAF7aoX4FF4+mnn1ZaWppuv/32iO333Xefrr76amVkZOj1119XWVmZWlpa9OSTTw56nPLycj366KPxbBUAYBszDJLMzp07zzg+ffp0s3r16nMe56mnnjIpKSmmq6tr0PGuri4TCoWcam5uNpIoiqIoyysUCg01gkzcXoH985//VH19vZ5//vlzzvX7/ert7dV7772n6dOnDxh3u91yu93xaBMAYKm4fQb21FNPae7cuSooKDjn3Lq6OiUlJSkrKyte7QAARpmoX4F1dHSooaHBud3Y2Ki6ujplZGQoPz9fkhQOh7Vjxw796le/GrB/IBBQTU2Nbr75ZqWlpSkQCGjt2rX67ne/q0suuWQYpwIAuKBE+57jvn37Bn0fc9myZc6cP/7xj2bs2LGmra1twP61tbXG7/cbr9drLrroIjNz5kzz+OOPn/Hzr8GEQqGEv29LURRFDb+G8xmYyxhjZJlwOCyv15voNgAAwxQKheTxeIa0L9+FCACwEgEGALASAQYAsBIBBgCwEgEGALASAQYAsBIBBgCwEgEGALASAQYAsBIBBgCwEgEGALASAQYAsBIBBgCwEgEGALASAQYAsBIBBgCwEgEGALASAQYAsBIBBgCwEgEGALASAQYAsBIBBgCwEgEGALASAQYAsBIBBgCwEgEGALASAQYAsBIBBgCwEgEGALASAQYAsBIBBgCwEgEGALASAQYAsBIBBgCwUlQBVl5ermuvvVZpaWnKysrSrbfeqvr6+og5XV1dKi0t1YQJEzR+/HgtXrxYra2tEXOampq0cOFCjRs3TllZWXrooYfU29s7/LMBAFwwogqwqqoqlZaW6sCBA6qoqFBPT4/mz5+vzs5OZ87atWv1yiuvaMeOHaqqqtKxY8d0++23O+N9fX1auHChTp8+rddff11PP/20tm/frvXr18furAAAo58ZhuPHjxtJpqqqyhhjTFtbmxkzZozZsWOHM+fdd981kkwgEDDGGPP3v//dJCUlmWAw6MzZsmWL8Xg8pru7+7zuNxQKGUkURVGU5RUKhYacQcP6DCwUCkmSMjIyJEm1tbXq6elRUVGRM2fGjBnKz89XIBCQJAUCAc2ePVvZ2dnOnAULFigcDuvw4cOD3k93d7fC4XBEAQAubEMOsP7+ft1///26/vrrNWvWLElSMBhUamqq0tPTI+ZmZ2crGAw6c/5veH0y/snYYMrLy+X1ep3Ky8sbatsAgFFiyAFWWlqqt99+W3/9619j2c+gysrKFAqFnGpubo77fQIAPttShrLT6tWrtXv3blVXV+uyyy5ztvt8Pp0+fVptbW0Rr8JaW1vl8/mcOf/+978jjvfJVYqfzPk0t9stt9s9lFYBAKNUVK/AjDFavXq1du7cqb1792rKlCkR43PnztWYMWNUWVnpbKuvr1dTU5MKCwslSYWFhXrrrbd0/PhxZ05FRYU8Ho+uvPLK4ZwLAOBCEs0VH/fcc4/xer1m//79pqWlxalTp045c1atWmXy8/PN3r17zX/+8x9TWFhoCgsLnfHe3l4za9YsM3/+fFNXV2f27NljJk6caMrKys67D65CpCiKGh01nKsQowqwMzWwbds2Z87HH39s7r33XnPJJZeYcePGmdtuu820tLREHOe9994zJSUlZuzYsSYzM9M88MADpqen57z7aGtrS/iiUxRFUcOvtra2aGIogut/g8kqH3zwAVciAsAo0NzcHHEtRTSsDLD+/n7V19fryiuvVHNzszweT6JbGtXC4bDy8vJY6zhjnUcOaz0yzrbOxhi1t7crNzdXSUlDuyB+SFchJlpSUpIuvfRSSZLH4+EBOEJY65HBOo8c1npknGmdvV7vsI7Lt9EDAKxEgAEArGRtgLndbm3YsIFfcB4BrPXIYJ1HDms9MuK9zlZexAEAgLWvwAAAFzYCDABgJQIMAGAlAgwAYCUCDABgJWsDbPPmzZo8ebIuuugi+f3+AX9jDNH52c9+JpfLFVEzZsxwxru6ulRaWqoJEyZo/PjxWrx4sfN33HB21dXVWrRokXJzc+VyubRr166IcWOM1q9fr5ycHI0dO1ZFRUU6cuRIxJyTJ09q6dKl8ng8Sk9P1/Lly9XR0TGCZ/HZd651vuuuuwY8xouLiyPmsM7nVl5ermuvvVZpaWnKysrSrbfeqvr6+og55/N80dTUpIULF2rcuHHKysrSQw89pN7e3qh6sTLAnn/+ea1bt04bNmzQwYMHVVBQoAULFkT8jTFE7/Of/7xaWlqceu2115yxtWvX6pVXXtGOHTtUVVWlY8eO6fbbb09gt/bo7OxUQUGBNm/ePOj4E088od/+9rfaunWrampqdPHFF2vBggXq6upy5ixdulSHDx9WRUWF88dkV65cOVKnYIVzrbMkFRcXRzzGn3vuuYhx1vncqqqqVFpaqgMHDqiiokI9PT2aP3++Ojs7nTnner7o6+vTwoULdfr0ab3++ut6+umntX37dq1fvz66Zob8PfYJdN1115nS0lLndl9fn8nNzTXl5eUJ7MpuGzZsMAUFBYOOtbW1mTFjxpgdO3Y42959910jyQQCgRHqcHSQZHbu3Onc7u/vNz6fz2zatMnZ1tbWZtxut3nuueeMMca88847RpJ54403nDn/+Mc/jMvlMh9++OGI9W6TT6+zMcYsW7bMfOMb3zjjPqzz0Bw/ftxIMlVVVcaY83u++Pvf/26SkpJMMBh05mzZssV4PB7T3d193vdt3Suw06dPq7a2VkVFRc62pKQkFRUVKRAIJLAz+x05ckS5ubmaOnWqli5dqqamJklSbW2tenp6ItZ8xowZys/PZ82HqbGxUcFgMGJtvV6v/H6/s7aBQEDp6em65pprnDlFRUVKSkpSTU3NiPdss/379ysrK0vTp0/XPffcoxMnTjhjrPPQhEIhSVJGRoak83u+CAQCmj17trKzs505CxYsUDgc1uHDh8/7vq0LsI8++kh9fX0RJy5J2dnZCgaDCerKfn6/X9u3b9eePXu0ZcsWNTY26oYbblB7e7uCwaBSU1OVnp4esQ9rPnyfrN/ZHs/BYFBZWVkR4ykpKcrIyGD9o1BcXKw///nPqqys1C9+8QtVVVWppKREfX19kljnoejv79f999+v66+/XrNmzZKk83q+CAaDgz7mPxk7X1b+ORXEXklJifPznDlz5Pf7NWnSJL3wwgsaO3ZsAjsDYuPOO+90fp49e7bmzJmjadOmaf/+/Zo3b14CO7NXaWmp3n777YjPy0eSda/AMjMzlZycPOCKltbWVvl8vgR1Nfqkp6friiuuUENDg3w+n06fPq22traIOaz58H2yfmd7PPt8vgEXKPX29urkyZOs/zBMnTpVmZmZamhokMQ6R2v16tXavXu39u3bF/EXlc/n+cLn8w36mP9k7HxZF2CpqamaO3euKisrnW39/f2qrKxUYWFhAjsbXTo6OnT06FHl5ORo7ty5GjNmTMSa19fXq6mpiTUfpilTpsjn80WsbTgcVk1NjbO2hYWFamtrU21trTNn79696u/vl9/vH/GeR4sPPvhAJ06cUE5OjiTW+XwZY7R69Wrt3LlTe/fu1ZQpUyLGz+f5orCwUG+99VbEfxgqKirk8Xh05ZVXRtWMdf76178at9tttm/fbt555x2zcuVKk56eHnFFC6LzwAMPmP3795vGxkbzr3/9yxQVFZnMzExz/PhxY4wxq1atMvn5+Wbv3r3mP//5jyksLDSFhYUJ7toO7e3t5s033zRvvvmmkWSefPJJ8+abb5r333/fGGPMxo0bTXp6unnppZfMoUOHzDe+8Q0zZcoU8/HHHzvHKC4uNldddZWpqakxr732mrn88svNkiVLEnVKn0lnW+f29nbz4IMPmkAgYBobG82rr75qrr76anP55Zebrq4u5xis87ndc889xuv1mv3795uWlhanTp065cw51/NFb2+vmTVrlpk/f76pq6sze/bsMRMnTjRlZWVR9WJlgBljzO9+9zuTn59vUlNTzXXXXWcOHDiQ6Jasdscdd5icnByTmppqLr30UnPHHXeYhoYGZ/zjjz829957r7nkkkvMuHHjzG233WZaWloS2LE99u3bZyQNqGXLlhlj/udS+kceecRkZ2cbt9tt5s2bZ+rr6yOOceLECbNkyRIzfvx44/F4zN13323a29sTcDafXWdb51OnTpn58+ebiRMnmjFjxphJkyaZFStWDPhPL+t8boOtsSSzbds2Z875PF+89957pqSkxIwdO9ZkZmaaBx54wPT09ETVC38PDABgJes+AwMAQCLAAACWIsAAAFYiwAAAViLAAABWIsAAAFYiwAAAViLAAABWIsAAAFYiwAAAViLAAABW+n+g3RsbYfGwNgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "sWf-Q7DPQVMH",
        "outputId": "a2cd8ad1-05cb-4d63-e741-28b6cc7312ac"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-3758f8fb-03f0-4226-bd5e-ed5486a42ba7\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-3758f8fb-03f0-4226-bd5e-ed5486a42ba7\">\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 Egyptian_Mau_1_mask.png to Egyptian_Mau_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": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trimap = read_trimap(\"Egyptian_Mau_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": 47,
      "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": 274
        },
        "id": "zZAg0eUS5Nfx",
        "outputId": "b39d85c5-e82e-45ca-f8cb-31eeacea85c9"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7359a51df0>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 3 Axes>"
            ],
            "image/png": "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\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": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "LE(ground_truth_box, predicted_box)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1UK_RwPGRKJb",
        "outputId": "37c120f0-1c3f-4bba-eed2-0eecb19168e3"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5544253957837749"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "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": 504
        },
        "id": "7eKjLKsrhB2x",
        "outputId": "c6c469c9-261f-48b2-8878-b685b000f59f"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f73599cd340>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "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": "87909f95-1e38-4505-ddac-16ff107e81e7"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5712895692786715"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "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": 504
        },
        "id": "1inCczJhhOTN",
        "outputId": "0dedcba5-d3b6-4de6-fbb0-fae88248a25e"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f73598d3670>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "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": "c107a0c6-2b5e-4067-df9f-004fc146cfa7"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.43965407605239726"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "EPG(binary_map, im)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0hTJYPFihY_",
        "outputId": "5ccf3779-bd9e-4459-cf43-0fcebde293c5"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5272777448562103"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "EPG(ground_truth_box, im)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s2-Tl2SYihg0",
        "outputId": "2169806b-fd42-42a8-bf82-622b6632c651"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8109035414850372"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pixel_wise_F_score(binary_map, im)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cpK5busrnXt9",
        "outputId": "52146539-8c50-4df0-eff8-716488509300"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5468794427719459"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pixel_wise_F_score(ground_truth_box, im)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xNm29qYdHAvY",
        "outputId": "15484d60-d411-4e15-d47c-c7defae294d1"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6570741702139008"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "AME(im, ground_truth_box)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cnGfaZ32HH9-",
        "outputId": "f4fe2751-909b-4008-c0d8-1c1e8a6156d2"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.33775461195928347"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "AME(im, binary_map)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vl7xmX-IFuC",
        "outputId": "58b935bf-75fc-49ea-c135-4a2a76dde3e5"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.348664632670779"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    }
  ]
}