Abyssinian_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": 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": "8d3d998d-98f7-4e4c-bca8-3b9f71ef533e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "uploaded = files.upload()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-e2a59952-96ec-4bf8-a389-f5d6f49ef578\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-e2a59952-96ec-4bf8-a389-f5d6f49ef578\">\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 Abyssinian_1_e.png to Abyssinian_1_e (1).png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3RuLhrCQbzI",
        "outputId": "ce618c09-c897-44d2-d132-4df0cb2de353",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "img = Image('Abyssinian_1_e.png')\n",
        "print(type(img))"
      ],
      "execution_count": 4,
      "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": "dcbd3426-aecd-473b-9376-3fae3491d6d8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "im = plt.imread(\"Abyssinian_1_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": "aa63dab1-35a1-40f0-b126-30bb7bfe5f6a"
      },
      "execution_count": 7,
      "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": 8,
      "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": "bf96f949-2657-4865-b206-f484b5083a9f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f735e1c4a30>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "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": 10,
      "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": "352b23a0-1ee4-4dc5-cf2f-6a6a8ead3ce6"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f735a21c1f0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "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": "ad7a6eb8-f1dc-4710-9fc1-fad3fb6464cc"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-edaa3927-043f-4bb1-8519-c3950a378769\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-edaa3927-043f-4bb1-8519-c3950a378769\">\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 Abyssinian_1.png to Abyssinian_1.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": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trimap = read_trimap(\"Abyssinian_1.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": 14,
      "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": 265
        },
        "id": "zZAg0eUS5Nfx",
        "outputId": "9412661c-4a02-4549-9620-2fd49c62026d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f735a0d2a60>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "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": "30dec228-c3ce-43b1-aab1-84cd3078b9b6"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04587155963302747"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "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": "2f0cad29-4e84-4e39-e8dd-0a35b1dd3dbf"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7359b0c760>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "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": "ebe3f77e-6850-4532-9a79-db3dfd09a9ca"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "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": "d259ea57-b8f2-46b2-ad7e-98065d304fa0"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7359fd6bb0>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "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": "c410cb98-4070-426a-c8f1-511137f172b3"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "EPG(binary_map, im)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0hTJYPFihY_",
        "outputId": "23676e59-6756-4590-9cd8-b6bc3da1295e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00928739597631199"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "EPG(ground_truth_box, im)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s2-Tl2SYihg0",
        "outputId": "ab43baf2-0158-4958-8df4-e1a076dace9e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4126045991472103"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pixel_wise_F_score(binary_map, im)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cpK5busrnXt9",
        "outputId": "485bc0e5-4e21-421d-e532-cf7567808bbd"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.016664353822523084"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pixel_wise_F_score(ground_truth_box, im)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xNm29qYdHAvY",
        "outputId": "fd5ad73f-8664-4d2e-91ec-d3eff9c75e83"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3901266044698129"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "AME(im, ground_truth_box)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cnGfaZ32HH9-",
        "outputId": "d3eba9a9-bce9-43cb-b011-3eb2927adf8a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2514642754702066"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "AME(im, binary_map)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vl7xmX-IFuC",
        "outputId": "fab253b1-7d5f-45ee-e247-541bed11b4f4"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.21365613953035814"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    }
  ]
}