PH2_TL_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PH2_TL_classification.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "RuedS1Aw1QE4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### author: zabiralnazi@yahoo.com\n",
        "> TL, DCNN as feature extractor, feature concatenation, comparison with different classifiers"
      ]
    },
    {
      "metadata": {
        "id": "xQ4atY_y05rt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "## data_seg.zip URL: https://drive.google.com/open?id=15Bav4EU2fEKMB76zerubFNIUkJjqy7V2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IuYpOQK21uC7",
        "colab_type": "code",
        "outputId": "1660b8d5-3d3f-493a-cbcb-2e1b96edad16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# Load the Drive helper and mount\n",
        "from google.colab import drive\n",
        "\n",
        "# This will prompt for authorization.\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Hac2HdpC2DXj",
        "colab_type": "code",
        "outputId": "d71bb290-fa54-4b1c-e182-35a71d470645",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "% cd /content/drive/My Drive"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "yOsCjems20pi",
        "colab_type": "code",
        "outputId": "4f697a7b-7eff-4114-d57d-b726494b6b8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "print(os.stat('/content/drive/My Drive/data_seg.zip').st_size/1000000000) # GigaBytes (approx)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.019925205\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "e40Ci1bS25Gr",
        "colab_type": "code",
        "outputId": "6f729392-b8e3-4902-eafb-8540d0291556",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "% cd /content/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zDbzj1Ko2_Jj",
        "colab_type": "code",
        "outputId": "40044760-d190-4122-dfcc-c9057934f311",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "! ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "drive  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bwA10n_H3Afa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# unzip to train_imgs folder\n",
        "data_seg = '/content/drive/My Drive/data_seg.zip'\n",
        "import zipfile\n",
        "zip_ref = zipfile.ZipFile(data_seg, 'r')\n",
        "zip_ref.extractall('img_seg')\n",
        "zip_ref.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PyRjPjA-3jMS",
        "colab_type": "code",
        "outputId": "1e18b81d-5b26-4c2e-a675-c138a859dbb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "! ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "drive  img_seg\tsample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Aw7CGg7p3lt8",
        "colab_type": "code",
        "outputId": "a4d9a71f-9ff9-440e-9a6f-273c83e1f3a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "% cd /content/img_seg/data_seg"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/img_seg/data_seg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "CbZvraAZ3oQr",
        "colab_type": "code",
        "outputId": "720a15c0-eb23-41d0-8ecc-ce9fd609b43f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "! ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "test  train\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fZ1hqolp3rWL",
        "colab_type": "code",
        "outputId": "a3fae85e-19dc-45d1-f3b8-91003af94a41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "print(os.stat('/content/drive/My Drive/label_dict.npy').st_size/1000000000) # GigaBytes (approx)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.724e-06\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1lVGH-H8Gzqh",
        "colab_type": "code",
        "outputId": "f27b7d04-5657-4f39-fcfd-b2978ba91622",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "label_dict = np.load('/content/drive/My Drive/label_dict.npy')\n",
        "print(label_dict)\n",
        "print(type(label_dict))\n",
        "print(label_dict.item().get('003'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'003': 0, '009': 0, '016': 0, '022': 0, '024': 0, '025': 0, '035': 0, '038': 0, '042': 0, '044': 0, '045': 0, '050': 0, '092': 0, '101': 0, '103': 0, '112': 0, '118': 0, '125': 0, '132': 0, '134': 0, '135': 0, '144': 0, '146': 0, '147': 0, '150': 0, '152': 0, '156': 0, '159': 0, '161': 0, '162': 0, '175': 0, '177': 0, '182': 0, '198': 0, '200': 0, '010': 0, '017': 0, '020': 0, '039': 0, '041': 0, '105': 0, '107': 0, '108': 0, '133': 0, '142': 0, '143': 0, '160': 0, '173': 0, '176': 0, '196': 0, '197': 0, '199': 0, '203': 0, '204': 0, '206': 0, '207': 0, '208': 0, '364': 0, '365': 0, '367': 0, '371': 0, '372': 0, '374': 0, '375': 0, '378': 0, '379': 0, '380': 0, '381': 0, '383': 0, '384': 0, '385': 0, '389': 0, '390': 0, '392': 0, '394': 0, '395': 0, '397': 0, '399': 0, '400': 0, '402': 0, '002': 1, '004': 1, '013': 1, '015': 1, '019': 1, '021': 1, '027': 1, '030': 1, '032': 1, '033': 1, '037': 1, '040': 1, '043': 1, '047': 1, '048': 1, '049': 1, '057': 1, '075': 1, '076': 1, '078': 1, '120': 1, '126': 1, '137': 1, '138': 1, '139': 1, '140': 1, '149': 1, '153': 1, '157': 1, '164': 1, '166': 1, '169': 1, '171': 1, '210': 1, '347': 1, '155': 1, '376': 1, '006': 1, '008': 1, '014': 1, '018': 1, '023': 1, '031': 1, '036': 1, '154': 1, '170': 1, '226': 1, '243': 1, '251': 1, '254': 1, '256': 1, '278': 1, '279': 1, '280': 1, '304': 1, '305': 1, '306': 1, '312': 1, '328': 1, '331': 1, '339': 1, '356': 1, '360': 1, '368': 1, '369': 1, '370': 1, '382': 1, '386': 1, '388': 1, '393': 1, '396': 1, '398': 1, '427': 1, '430': 1, '431': 1, '432': 1, '433': 1, '434': 1, '436': 1, '437': 1, '058': 2, '061': 2, '063': 2, '064': 2, '065': 2, '080': 2, '085': 2, '088': 2, '090': 2, '091': 2, '168': 2, '211': 2, '219': 2, '240': 2, '242': 2, '284': 2, '285': 2, '348': 2, '349': 2, '403': 2, '404': 2, '405': 2, '407': 2, '408': 2, '409': 2, '410': 2, '413': 2, '417': 2, '418': 2, '419': 2, '406': 2, '411': 2, '420': 2, '421': 2, '423': 2, '424': 2, '425': 2, '426': 2, '429': 2, '435': 2}\n",
            "<class 'numpy.ndarray'>\n",
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "poRmESebHruQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import glob\n",
        "img_files_tv = [] # train, validation\n",
        "for files in glob.glob('/content/img_seg/data_seg/train/image/*.png'):\n",
        "    img_files_tv.append(files)\n",
        "\n",
        "mask_files_tv = []\n",
        "for files in glob.glob('/content/img_seg/data_seg/train/label/*.png'):\n",
        "    mask_files_tv.append(files)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7An6FAj6b32H",
        "colab_type": "code",
        "outputId": "e1754ecd-ef3f-4b54-9a09-ebc5f9b93648",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "print(len(img_files_tv))\n",
        "print(len(mask_files_tv))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "170\n",
            "170\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "w0Z72KWdb8rY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "img_files_tst = [] # train, validation\n",
        "for files in glob.glob('/content/img_seg/data_seg/test/image/*.png'):\n",
        "    img_files_tst.append(files)\n",
        "\n",
        "mask_files_tst = []\n",
        "for files in glob.glob('/content/img_seg/data_seg/test/label/*.png'):\n",
        "    mask_files_tst.append(files)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UwYkR65ecShg",
        "colab_type": "code",
        "outputId": "9846845c-f5a4-45b2-95d7-ba5f349eb8a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "print(len(img_files_tst))\n",
        "print(len(mask_files_tst))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "30\n",
            "30\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Q3tLy2rhcU2O",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# To reproduce the results later\n",
        "from numpy.random import seed\n",
        "seed(1997)\n",
        "from tensorflow import set_random_seed\n",
        "set_random_seed(1997)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "a4WZJgQVcY4P",
        "colab_type": "code",
        "outputId": "57f3be2a-edbe-42f8-dfff-1b0539fb2b43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import random\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "img_files_tv.sort()\n",
        "mask_files_tv.sort()\n",
        "\n",
        "img_files_tst.sort()\n",
        "mask_files_tst.sort()\n",
        "\n",
        "img_arr = []\n",
        "mask_arr = []\n",
        "labels = []\n",
        "num_train = len(img_files_tv)\n",
        "print(num_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "170\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "D-kF8G0DrW8n",
        "colab_type": "code",
        "outputId": "ca5c5cdf-26c6-4dde-ff27-3de8bf7ae3eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "img_files_tv[0][-7:-4]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'002'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "Y0mS1xMqctML",
        "colab_type": "code",
        "outputId": "af016084-cc64-4f44-e79f-7bf473990577",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "cell_type": "code",
      "source": [
        "import random\n",
        "random.seed(1997) # same seed again\n",
        "for i in range(num_train):\n",
        "    a = cv2.imread(img_files_tv[i])\n",
        "    #a = cv2.resize(a, (224, 224)) \n",
        "    img_id = img_files_tv[i][-7:-4]\n",
        "    a = np.array(a,dtype=np.uint8)\n",
        "    img_arr.append(a)\n",
        "\n",
        "    b = cv2.imread(mask_files_tv[i],0)\n",
        "    #b = cv2.resize(b, (224, 224)) \n",
        "    b = np.array(b,dtype=np.uint8)\n",
        "    mask_arr.append(b)\n",
        "    \n",
        "    labels.append(label_dict.item().get(img_id))\n",
        "    \n",
        "    \n",
        "for i in range(len(img_files_tst)):\n",
        "    a = cv2.imread(img_files_tst[i])\n",
        "    #a = cv2.resize(a, (224, 224)) \n",
        "    img_id = img_files_tst[i][-7:-4]\n",
        "    a = np.array(a,dtype=np.uint8)\n",
        "    img_arr.append(a)\n",
        "\n",
        "    b = cv2.imread(mask_files_tst[i],0)\n",
        "    #b = cv2.resize(b, (224, 224)) \n",
        "    b = np.array(b,dtype=np.uint8)\n",
        "    mask_arr.append(b)\n",
        "    labels.append(label_dict.item().get(img_id))\n",
        "    \n",
        "print(len(img_arr))\n",
        "print(len(mask_arr))\n",
        "print(len(labels))\n",
        "\n",
        "gen_batch = 0\n",
        "\n",
        "fin_arr = list(zip(img_arr,mask_arr,labels))\n",
        "random.shuffle(fin_arr)\n",
        "img_arr = [x[0] for x in fin_arr]\n",
        "mask_arr = [x[1] for x in fin_arr]\n",
        "labels = [x[2] for x in fin_arr]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "200\n",
            "200\n",
            "200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "A9UhJaeesf6k",
        "colab_type": "code",
        "outputId": "d4379bbe-3b04-444e-848d-fedaa3b36e85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "cell_type": "code",
      "source": [
        "print(labels)\n",
        "print(img_arr[0].shape)\n",
        "print(mask_arr[0].shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 1, 1, 0, 1, 0, 1, 1, 0, 2, 2, 0, 2, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 2, 1, 0, 1, 1, 2, 2, 0, 1, 1, 1, 0, 0, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 0, 2, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 2, 2, 1, 1, 0, 0, 0, 2, 1, 1, 1, 2, 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 0, 1, 2, 0, 2, 1, 2, 1, 1, 2, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 2, 2, 1, 1, 0, 2, 1, 0, 1, 0, 0, 0, 2, 2, 1, 2, 1, 2, 0, 0, 1, 2, 2, 0, 2, 0, 1, 2, 0, 0, 1, 1, 1, 2, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 2, 2, 0, 0, 0, 0, 0, 2, 1, 1, 1, 2, 1, 1, 1, 0, 0, 2, 1, 0, 2, 1, 1, 1, 1, 1, 1, 2]\n",
            "(224, 224, 3)\n",
            "(224, 224)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "rnCWpJwsdBvA",
        "colab_type": "code",
        "outputId": "9ebfc37f-a4b9-4ebb-db0d-d2bd43c78e4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "img_arr[0].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "Os_vjiWpdlqo",
        "colab_type": "code",
        "outputId": "e3a671ab-abe3-4ee1-9b3d-c63833ce78e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "mask_arr[0].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(224, 224)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "foFAWjUygOUP",
        "colab_type": "code",
        "outputId": "45eaac50-8a4f-49ba-e353-283a178e5f10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "type(img_arr[0][0,0,0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.uint8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "_YP9V3EHgVU3",
        "colab_type": "code",
        "outputId": "1d8f380c-4ad1-4dc2-c70e-0670d0f235df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "type(mask_arr[0][0,0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.uint8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "id": "AVYeLPtReVuw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "overlay_img = cv2.bitwise_and(img_arr[5], img_arr[5], mask = mask_arr[5])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5TQflEhueZRa",
        "colab_type": "code",
        "outputId": "2daed07a-b2b7-4c03-eb6a-87ce6f22039a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(overlay_img)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ee3t7zBdjYcl5gfBys9k0NC0mWLYvRKX7tsWYgagySEYgyzNEVKHqm5bWOgqS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\nrkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f230f364518>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "oTIVA9Esfpaa",
        "colab_type": "code",
        "outputId": "15742e08-b4c6-46b1-a081-fe9505a12b5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "cell_type": "code",
      "source": [
        "# print(overlay_img)\n",
        "(x, y, z_) = np.where(overlay_img > 0)\n",
        "print(np.min(x))\n",
        "print(np.max(x))\n",
        "print(np.min(y))\n",
        "print(np.max(y))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "48\n",
            "173\n",
            "83\n",
            "171\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OFwadoF7i70u",
        "colab_type": "code",
        "outputId": "8f16e67b-e4ac-45bc-cf2a-a0a807cda524",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "cell_type": "code",
      "source": [
        "crop_img = overlay_img[48:174,83:172,:]\n",
        "crop_img_r = cv2.resize(crop_img, (224,224))\n",
        "plt.imshow(crop_img_r)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PewmOnXxG/Nvt3/IKtOKFvUidcs+5s8O5q8jqS900GzVaakpDDub3s9Edy7Ov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\nsqb+PF2K0UZbFyzNqt+LaC7oXPfZ0cdwemBlMc4JucOplD3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wUaFV6Odq1ahEYCdpnKdY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JJn31OrgdY2SsToFKpCHmG5qpDgZJTaPqmHv7Y5PlrFlGoVDUXrqBoip0ZGadL3zBR5zcQdLJAU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\n/wtCvEDZlx1eoQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f230f2aca20>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hDNPyePVkZ0j",
        "colab_type": "code",
        "outputId": "64c72483-21cd-4454-d327-8dd0da200d49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.applications.vgg16 import VGG16\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.vgg16 import preprocess_input\n",
        "import numpy as np\n",
        "\n",
        "model = VGG16(weights='imagenet', include_top=False)\n",
        "\n",
        "x = crop_img_r\n",
        "print(x.shape)\n",
        "x = np.expand_dims(x, axis=0)\n",
        "print(x.shape)\n",
        "x = np.array(x, dtype='float64')\n",
        "x = preprocess_input(x)\n",
        "\n",
        "features = model.predict(x)\n",
        "print(features.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 2s 0us/step\n",
            "(224, 224, 3)\n",
            "(1, 224, 224, 3)\n",
            "(1, 7, 7, 512)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "u-SNAlvdnYpk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "a_f = features.flatten()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BlNs1W-Wog8c",
        "colab_type": "code",
        "outputId": "6c24fe34-ab6b-4f61-f9f9-95ae0a93e712",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "print(a_f.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(25088,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4oT9VKCtoi-D",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def img_process(img, mask):\n",
        "  overlay_img = cv2.bitwise_and(img, img, mask = mask)\n",
        "  (x, y, z_) = np.where(overlay_img > 0)\n",
        "  mnx = (np.min(x))\n",
        "  mxx = (np.max(x))\n",
        "  mny = (np.min(y))\n",
        "  mxy = (np.max(y))\n",
        "  crop_img = overlay_img[mnx:mxx,mny:mxy,:]\n",
        "  crop_img_r = cv2.resize(crop_img, (224,224))\n",
        "  return crop_img_r"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0VPLCp7BuRbV",
        "colab_type": "code",
        "outputId": "494eb1e7-7b62-423a-f26a-5738227f61be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "cell_type": "code",
      "source": [
        "a = img_process(img_arr[123], mask_arr[123])\n",
        "plt.imshow(a)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Zjf9bf+tv4X3vex+uu+46PPDAA7j11lsv6o5+3ZsC1AFufiO7kvg8e+R1aKLD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NcVshzs1XorhEDHoh7pRzOmO542cEoh3Oq9vxb6pmpeFjcedQAcjo8apOksRojw8ECC6m9mV31K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JNz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f23061bcbe0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "OqU929WpuaRt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def extract_features(model, img):\n",
        "  x = img\n",
        "  # print(x.shape)\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "  # print(x.shape)\n",
        "  x = np.array(x, dtype='float64')\n",
        "  x = preprocess_input(x)\n",
        "\n",
        "  features = model.predict(x)\n",
        "  features = features.flatten()\n",
        "  return features"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Eo9AHfQSvHsA",
        "colab_type": "code",
        "outputId": "b1d2799a-9d9a-4fad-e192-da39ae728b54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "extract_features(model,a).shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25088,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "3RbRzH05vMf4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def generate_feature_vec(model):\n",
        "  X = []\n",
        "  y = labels\n",
        "\n",
        "  for i in range(len(img_arr)):\n",
        "    img = img_arr[i]\n",
        "    mask = mask_arr[i]\n",
        "    overlay_crop_r = img_process(img, mask)\n",
        "    f_set = extract_features(model, overlay_crop_r)\n",
        "    X.append(f_set)\n",
        "    \n",
        "  return (X,y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "p7X_pWaIwh83",
        "colab_type": "code",
        "outputId": "606267a1-54ce-4a95-db18-ddc0a391e1e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.applications.vgg16 import VGG16\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.vgg16 import preprocess_input\n",
        "\n",
        "model = VGG16(weights='imagenet', include_top=False)\n",
        "(X_vgg16, y) = generate_feature_vec(model)\n",
        "print(len(X_vgg16))\n",
        "print(X_vgg16[0].shape)\n",
        "print(len(y))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "200\n",
            "(25088,)\n",
            "200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SPXB8O3GynGx",
        "colab_type": "code",
        "outputId": "4c0a1033-e8ae-4e3c-fbde-165bac0d31df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_vgg16)\n",
        "\n",
        "print(scaler.mean_)\n",
        "\n",
        "\n",
        "X = scaler.transform(X_vgg16)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.08661335 0.         7.13118579 ... 0.         3.46471557 0.89670552]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "kkytAkpD4Qzj",
        "colab_type": "code",
        "outputId": "9b5f3b77-fb62-4f20-ef8b-8aa7e28468d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "print(np.mean(X))\n",
        "print(np.var(X))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-3.1947233973907567e-18\n",
            "0.8817362882653061\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YCdBWgyk5IlT",
        "colab_type": "code",
        "outputId": "fb383f85-6ef6-40be-a17f-ae06dc1cdeda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "for i in range(len(y)):\n",
        "  if y[i] == 1:\n",
        "    y[i] = 0\n",
        "for i in range(len(y)):\n",
        "  if y[i] == 2:\n",
        "    y[i] = 1\n",
        "print(y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "S_MBH8YU9nY3",
        "colab_type": "code",
        "outputId": "1161a2b1-7384-4fe2-c8eb-f25871cce908",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "c0 = 0\n",
        "c1 = 0\n",
        "for i in range(len(y)):\n",
        "  if y[i] == 0:\n",
        "    c0 += 1\n",
        "for i in range(len(y)):\n",
        "  if y[i] == 1:\n",
        "    c1 += 1\n",
        "print(c0, ' ', c1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "160   40\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Wct9U7EN-Lb-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class_weight = {0:0.25, 1:0.75}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bdrR0Wvv-xik",
        "colab_type": "code",
        "outputId": "08a4ae48-04c6-4628-a0ae-5e70cd9ae60f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn import svm\n",
        "\n",
        "clf = svm.SVC(kernel='linear', C=1, class_weight = class_weight)\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  0.8949999999999999 0.11357816691600543\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pLAZbFtH--oG",
        "colab_type": "code",
        "outputId": "49f5e991-16ce-4f2c-8e8f-20c36ee61146",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier(n_estimators=10, max_depth=None,\n",
        "    min_samples_split=2, random_state=0, class_weight = class_weight)\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  0.8550000000000001 0.10440306508910546\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "tgUuDWeQ_Xi3",
        "colab_type": "code",
        "outputId": "c8b0ffbf-8f31-4154-ba10-a20fcd5b712b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=70, weights = 'distance', p=1, leaf_size=1)\n",
        "\n",
        "scores = cross_val_score(knn, X, y, cv=10)\n",
        "print(scores)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8]\n",
            "Accuracy:  0.8 0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3olq4uYX_8gt",
        "colab_type": "code",
        "outputId": "613297c5-2f7b-42bf-acf4-17f2135e2311",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "clf = tree.DecisionTreeClassifier()\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  0.8150000000000001 0.11874342087037917\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SD4UOF3eFNdA",
        "colab_type": "code",
        "outputId": "078ec33d-8acf-4cf4-e669-05c5cec51150",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "clf = AdaBoostClassifier(n_estimators=100)\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  0.8850000000000001 0.13453624047073706\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "KGiIscy0FTvP",
        "colab_type": "code",
        "outputId": "efdb2a9e-876a-4406-a2a8-b74dde239bc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "clf = GradientBoostingClassifier()\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  0.915 0.1268857754044952\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "mFEdcldpI7gP",
        "colab_type": "code",
        "outputId": "c90593e9-99df-406f-856c-589ec0629814",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.applications.inception_resnet_v2 import InceptionResNetV2\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.inception_resnet_v2 import preprocess_input\n",
        "\n",
        "model = InceptionResNetV2(weights='imagenet', include_top=False)\n",
        "(X_incres2, y) = generate_feature_vec(model)\n",
        "print(len(X_incres2))\n",
        "print(X_incres2[0].shape)\n",
        "print(len(y))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.7/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "219062272/219055592 [==============================] - 7s 0us/step\n",
            "200\n",
            "(38400,)\n",
            "200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9HFxZ4ObKB7w",
        "colab_type": "code",
        "outputId": "b234f7ac-4870-46ea-f557-f2e14e4f2489",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_incres2)\n",
        "\n",
        "print(scaler.mean_)\n",
        "\n",
        "\n",
        "X = scaler.transform(X_incres2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.45715791 0.24072786 0.24614046 ... 0.21584713 0.25599666 0.25358778]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vLCdzCnyNiTB",
        "colab_type": "code",
        "outputId": "a4b9131f-436c-4571-c8fe-f913eed89a95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "print(np.mean(X))\n",
        "print(np.var(X))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-5.625129991434126e-19\n",
            "0.9999999999999991\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4H3vEhotNnYU",
        "colab_type": "code",
        "outputId": "a9973a0f-01dd-4461-8c9b-d3fd02807915",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn import svm\n",
        "\n",
        "clf = svm.SVC(kernel='linear', C=1, class_weight = class_weight)\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  0.9049999999999999 0.11357816691600543\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SPy96a2MNrty",
        "colab_type": "code",
        "outputId": "ba2f9b9d-0ea5-4770-a62c-ca9586fbdad1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier(n_estimators=10, max_depth=None,\n",
        "    min_samples_split=2, random_state=0, class_weight = class_weight)\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  0.8200000000000001 0.10198039027185571\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "slz4kEc4N0CK",
        "colab_type": "code",
        "outputId": "768c654e-3ef6-4599-af57-8cc777c6a984",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=70, weights = 'distance', p=1, leaf_size=1)\n",
        "\n",
        "scores = cross_val_score(knn, X, y, cv=10)\n",
        "print(scores)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8]\n",
            "Accuracy:  0.8 0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "PgsqQTYbN3l8",
        "colab_type": "code",
        "outputId": "cf8e392d-fc8d-4c70-f25e-275c237a4f13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "clf = tree.DecisionTreeClassifier()\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  0.82 0.17435595774162696\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "426aA7bhOFvS",
        "colab_type": "code",
        "outputId": "81808fab-0235-4a3a-e604-6dd466374a18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "clf = AdaBoostClassifier(n_estimators=100)\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  0.875 0.10246950765959595\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iAgiM5mdOO06",
        "colab_type": "code",
        "outputId": "4810cd0f-5de9-4457-b5e4-9f115201cf01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "clf = GradientBoostingClassifier()\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:  0.875 0.09219544457292884\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SzwmQljGQKuU",
        "colab_type": "code",
        "outputId": "1389cae9-524f-4311-d184-90ca773b202c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.applications.resnet50 import ResNet50\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.resnet50 import preprocess_input\n",
        "\n",
        "model = ResNet50(weights='imagenet', include_top=False)\n",
        "(X_res50, y) = generate_feature_vec(model)\n",
        "print(len(X_res50))\n",
        "print(X_res50[0].shape)\n",
        "print(len(y))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "200\n",
            "(2048,)\n",
            "200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "nw6kVwICTcjF",
        "colab_type": "code",
        "outputId": "ccb43481-f235-420d-a4b7-f376f4f21d78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_res50)\n",
        "\n",
        "print(scaler.mean_)\n",
        "\n",
        "\n",
        "X = scaler.transform(X_res50)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2.6004379  0.3997941  0.47288282 ... 0.10233943 0.15384562 2.25434253]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Rc3SWVcdUF2k",
        "colab_type": "code",
        "outputId": "0b765178-453d-4cae-f24c-fccba52823b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        }
      },
      "cell_type": "code",
      "source": [
        "print(np.mean(X))\n",
        "print(np.var(X))\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn import svm\n",
        "\n",
        "clf = svm.SVC(kernel='linear', C=1, class_weight = class_weight)\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier(n_estimators=10, max_depth=None,\n",
        "    min_samples_split=2, random_state=0, class_weight = class_weight)\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "\n",
        "\n",
        "from sklearn import tree\n",
        "clf = tree.DecisionTreeClassifier()\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "clf = AdaBoostClassifier(n_estimators=100)\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "clf = GradientBoostingClassifier()\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.498001805406602e-18\n",
            "1.0\n",
            "Accuracy:  0.9099999999999999 0.10770329614269007\n",
            "Accuracy:  0.85 0.1095445115010332\n",
            "Accuracy:  0.8450000000000001 0.15132745950421556\n",
            "Accuracy:  0.9 0.14832396974191325\n",
            "Accuracy:  0.8799999999999999 0.17435595774162696\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2LnteKHDUu4y",
        "colab_type": "code",
        "outputId": "86d31176-aead-4168-ad14-67beaa754b05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.applications.vgg19 import VGG19\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.vgg19 import preprocess_input\n",
        "\n",
        "model = VGG19(weights='imagenet', include_top=False)\n",
        "(X_vgg19, y) = generate_feature_vec(model)\n",
        "print(len(X_vgg19))\n",
        "print(X_vgg19[0].shape)\n",
        "print(len(y))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "80142336/80134624 [==============================] - 3s 0us/step\n",
            "200\n",
            "(25088,)\n",
            "200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "XD7YzL7ewmLC",
        "colab_type": "code",
        "outputId": "27d59333-80d3-4708-d48f-f45007e62183",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "print('nabil')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "nabil\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "UEeuVF7GXgR0",
        "colab_type": "code",
        "outputId": "6060c5b8-217a-4351-e3a6-af2b01465819",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_vgg19)\n",
        "\n",
        "print(scaler.mean_)\n",
        "\n",
        "X = scaler.transform(X_vgg19)\n",
        "\n",
        "print(np.mean(X))\n",
        "print(np.var(X))\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn import svm\n",
        "\n",
        "clf = svm.SVC(kernel='linear', C=1, class_weight = class_weight)\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier(n_estimators=10, max_depth=None,\n",
        "    min_samples_split=2, random_state=0, class_weight = class_weight)\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "\n",
        "\n",
        "from sklearn import tree\n",
        "clf = tree.DecisionTreeClassifier()\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "clf = AdaBoostClassifier(n_estimators=100)\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "clf = GradientBoostingClassifier()\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.04617819 0.         3.80703191 ... 0.         0.14583112 0.25908767]\n",
            "-7.250436079184696e-19\n",
            "0.8730468749999998\n",
            "Accuracy:  0.9000000000000001 0.11832159566199228\n",
            "Accuracy:  0.8550000000000001 0.094339811320566\n",
            "Accuracy:  0.775 0.21563858652847825\n",
            "Accuracy:  0.905 0.12999999999999998\n",
            "Accuracy:  0.86 0.10770329614269007\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "05Xos1bJX02u",
        "colab_type": "code",
        "outputId": "b255f0d3-0489-4ec7-ec68-186b445c4d20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.applications.xception import Xception\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.xception import preprocess_input\n",
        "\n",
        "model = Xception(weights='imagenet', include_top=False)\n",
        "(X_x, y) = generate_feature_vec(model)\n",
        "print(len(X_x))\n",
        "print(X_x[0].shape)\n",
        "print(len(y))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "83689472/83683744 [==============================] - 3s 0us/step\n",
            "200\n",
            "(100352,)\n",
            "200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "6VHV6JBEgvqD",
        "colab_type": "code",
        "outputId": "8b4e4f00-12c7-4e53-d6d2-0a37a19e146e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_x)\n",
        "\n",
        "print(scaler.mean_)\n",
        "\n",
        "X = scaler.transform(X_x)\n",
        "\n",
        "print(np.mean(X))\n",
        "print(np.var(X))\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn import svm\n",
        "\n",
        "clf = svm.SVC(kernel='linear', C=1, class_weight = class_weight)\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier(n_estimators=10, max_depth=None,\n",
        "    min_samples_split=2, random_state=0, class_weight = class_weight)\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "\n",
        "\n",
        "from sklearn import tree\n",
        "clf = tree.DecisionTreeClassifier()\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "clf = AdaBoostClassifier(n_estimators=100)\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "clf = GradientBoostingClassifier()\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.00223656 0.00273003 0.00081848 ... 0.32296943 0.37922437 0.33682621]\n",
            "2.9001744316738784e-18\n",
            "0.9921376753826545\n",
            "Accuracy:  0.885 0.10049875621120886\n",
            "Accuracy:  0.8550000000000001 0.122065556157337\n",
            "Accuracy:  0.7899999999999999 0.12489995996796796\n",
            "Accuracy:  0.86 0.12489995996796795\n",
            "Accuracy:  0.8150000000000001 0.16763054614240208\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "l8G3PoaJiLiE",
        "colab_type": "code",
        "outputId": "c0e4f713-da24-48c9-f826-b7b9d7b6c476",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.applications.inception_v3 import InceptionV3\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.inception_v3 import preprocess_input\n",
        "\n",
        "model = InceptionV3(weights='imagenet', include_top=False)\n",
        "(X_inc3, y) = generate_feature_vec(model)\n",
        "print(len(X_inc3))\n",
        "print(X_inc3[0].shape)\n",
        "print(len(y))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87916544/87910968 [==============================] - 4s 0us/step\n",
            "200\n",
            "(51200,)\n",
            "200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dmNf4n_nAifh",
        "colab_type": "code",
        "outputId": "94b94f60-0d97-4176-e10e-8fed070b87fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_inc3)\n",
        "\n",
        "print(scaler.mean_)\n",
        "\n",
        "X = scaler.transform(X_inc3)\n",
        "\n",
        "print(np.mean(X))\n",
        "print(np.var(X))\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn import svm\n",
        "\n",
        "clf = svm.SVC(kernel='linear', C=1, class_weight = class_weight)\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier(n_estimators=10, max_depth=None,\n",
        "    min_samples_split=2, random_state=0, class_weight = class_weight)\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "\n",
        "\n",
        "from sklearn import tree\n",
        "clf = tree.DecisionTreeClassifier()\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "clf = AdaBoostClassifier(n_estimators=100)\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "clf = GradientBoostingClassifier()\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.16593921 0.02876234 0.03965574 ... 0.87909791 1.38054225 0.30340776]\n",
            "2.9753977059954196e-18\n",
            "1.0000000000000009\n",
            "Accuracy:  0.8949999999999999 0.12206555615733701\n",
            "Accuracy:  0.8099999999999999 0.09797958971132713\n",
            "Accuracy:  0.78 0.18\n",
            "Accuracy:  0.86 0.15362291495737213\n",
            "Accuracy:  0.8250000000000002 0.12041594578792297\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RVR6lwRlBwEQ",
        "colab_type": "code",
        "outputId": "996582b1-c034-41f9-9fd2-3c334c999a95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.applications.densenet import DenseNet201\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.densenet import preprocess_input\n",
        "\n",
        "model = DenseNet201(weights='imagenet', include_top=False)\n",
        "(X_d, y) = generate_feature_vec(model)\n",
        "print(len(X_d))\n",
        "print(X_d[0].shape)\n",
        "print(len(y))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.8/densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "76398592/76391848 [==============================] - 3s 0us/step\n",
            "200\n",
            "(94080,)\n",
            "200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5cf8Znm9CF-7",
        "colab_type": "code",
        "outputId": "96570315-47d5-4cd3-8900-ad23f6c72929",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_d)\n",
        "\n",
        "print(scaler.mean_)\n",
        "\n",
        "X = scaler.transform(X_d)\n",
        "\n",
        "print(np.mean(X))\n",
        "print(np.var(X))\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn import svm\n",
        "\n",
        "clf = svm.SVC(kernel='linear', C=1, class_weight = class_weight)\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier(n_estimators=10, max_depth=None,\n",
        "    min_samples_split=2, random_state=0, class_weight = class_weight)\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "\n",
        "\n",
        "from sklearn import tree\n",
        "clf = tree.DecisionTreeClassifier()\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "clf = AdaBoostClassifier(n_estimators=100)\n",
        "\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)\n",
        "\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "clf = GradientBoostingClassifier()\n",
        "scores = cross_val_score(clf, X, y, cv=10)\n",
        "print('Accuracy: ', scores.mean(), scores.std() * 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-9.51864998e-05  3.55345527e-03  8.66860743e-03 ...  4.27639772e-01\n",
            "  4.37626430e-01  2.73508079e-01]\n",
            "1.522591576628786e-18\n",
            "1.0000000000000002\n",
            "Accuracy:  0.9200000000000002 0.142828568570857\n",
            "Accuracy:  0.8350000000000002 0.10049875621120889\n",
            "Accuracy:  0.825 0.12845232578665128\n",
            "Accuracy:  0.9099999999999999 0.13266499161421597\n",
            "Accuracy:  0.835 0.16155494421403516\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}