Test_ON_IVC.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Test_ON_IVC.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "djLevBKDRMOG",
        "outputId": "f511ba63-dccd-4946-e85b-2812049de67e"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNm-Cdm-RyC7"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "from skimage import io\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras import backend as K \n",
        "from keras.preprocessing import image\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.layers import GlobalAveragePooling2D, Dense, Activation, BatchNormalization, Dropout, Input\n",
        "from keras.models import Model, Sequential\n",
        "from sklearn.model_selection import train_test_split\n",
        "import cv2\n",
        "from keras.applications.vgg16 import  VGG16, preprocess_input\n",
        "from skimage.util import view_as_windows \n",
        "from scipy.io import loadmat\n",
        "from scipy.stats import pearsonr, spearmanr"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z8SjCCzVSgT8"
      },
      "source": [
        "<h1>Loading test Set </h1>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9T4RSOZbUywn",
        "outputId": "b3cd1e23-c99b-461b-b187-a3ff2dde137b"
      },
      "source": [
        "!wget http://www.polytech.univ-nantes.fr/autrusseau-f/Databases/SelectiveEncryption/SelectiveEncryption.zip"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-09-22 23:39:38--  http://www.polytech.univ-nantes.fr/autrusseau-f/Databases/SelectiveEncryption/SelectiveEncryption.zip\n",
            "Resolving www.polytech.univ-nantes.fr (www.polytech.univ-nantes.fr)... 193.52.104.60, 2001:660:7220:386:193:52:104:60\n",
            "Connecting to www.polytech.univ-nantes.fr (www.polytech.univ-nantes.fr)|193.52.104.60|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 49926550 (48M) [application/zip]\n",
            "Saving to: ‘SelectiveEncryption.zip’\n",
            "\n",
            "SelectiveEncryption 100%[===================>]  47.61M  18.0MB/s    in 2.7s    \n",
            "\n",
            "2021-09-22 23:39:41 (18.0 MB/s) - ‘SelectiveEncryption.zip’ saved [49926550/49926550]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3hfNo5wd20HZ",
        "outputId": "eeb6ae64-af20-4391-a0eb-715f33ef7113"
      },
      "source": [
        "!unzip /content/SelectiveEncryption.zip"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/SelectiveEncryption.zip\n",
            "   creating: SelectiveEncryption/\n",
            "  inflating: SelectiveEncryption/.DS_Store  \n",
            "   creating: __MACOSX/\n",
            "   creating: __MACOSX/SelectiveEncryption/\n",
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            "  inflating: SelectiveEncryption/SDLPairComp/obj/linalg.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/obj/mat.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/obj/math.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/obj/myfuns.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/obj/parser.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/obj/poly.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/obj/quantizer.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/obj/random.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/obj/separable2D.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/obj/source.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/obj/source_fit.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/obj/source_func.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/obj/test_wavelet.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/obj/vec.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/obj/vlc.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/obj/wavelet.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/obj/wavelet2D.o  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/paircomp  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/paircomp.c  \n",
            "  inflating: __MACOSX/SelectiveEncryption/SDLPairComp/._paircomp.c  \n",
            "   creating: SelectiveEncryption/SDLPairComp/paircomp.dSYM/\n",
            "   creating: SelectiveEncryption/SDLPairComp/paircomp.dSYM/Contents/\n",
            "  inflating: SelectiveEncryption/SDLPairComp/paircomp.dSYM/Contents/Info.plist  \n",
            "   creating: SelectiveEncryption/SDLPairComp/paircomp.dSYM/Contents/Resources/\n",
            "   creating: SelectiveEncryption/SDLPairComp/paircomp.dSYM/Contents/Resources/DWARF/\n",
            "  inflating: SelectiveEncryption/SDLPairComp/paircomp.dSYM/Contents/Resources/DWARF/paircomp  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/playlist.txt  \n",
            "  inflating: __MACOSX/SelectiveEncryption/SDLPairComp/._playlist.txt  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/playlist2.txt  \n",
            "  inflating: __MACOSX/SelectiveEncryption/SDLPairComp/._playlist2.txt  \n",
            "  inflating: SelectiveEncryption/SDLPairComp/README.txt  \n",
            "  inflating: __MACOSX/SelectiveEncryption/SDLPairComp/._README.txt  \n",
            "  inflating: __MACOSX/SelectiveEncryption/._SDLPairComp  \n",
            "  inflating: SelectiveEncryption/SubjAndObjScoresTransparentEncryption.xls  \n",
            "  inflating: __MACOSX/SelectiveEncryption/._SubjAndObjScoresTransparentEncryption.xls  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "_TTafgpGSYEA",
        "outputId": "9f00f389-ab24-43fe-b8d5-6e61198bcf89"
      },
      "source": [
        "test = pd.read_csv('/content/drive/MyDrive/Projet-5-em/Project-kadid10k/IVC_test.csv')\n",
        "test.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>ground_truth</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CIMG7593-001-iwind_ec.pgm</td>\n",
              "      <td>0.228571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CIMG7593-001-iwind_nec.pgm</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CIMG7593-014-iwind_ec.pgm</td>\n",
              "      <td>0.847619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CIMG7593-res-0.pgm</td>\n",
              "      <td>0.209524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CIMG7593-res-2.pgm</td>\n",
              "      <td>0.276190</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         name  ground_truth\n",
              "0   CIMG7593-001-iwind_ec.pgm      0.228571\n",
              "1  CIMG7593-001-iwind_nec.pgm      0.200000\n",
              "2   CIMG7593-014-iwind_ec.pgm      0.847619\n",
              "3          CIMG7593-res-0.pgm      0.209524\n",
              "4          CIMG7593-res-2.pgm      0.276190"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OBwXkg-YTaya"
      },
      "source": [
        "# Loading our trained model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0TfMBRznULl0"
      },
      "source": [
        "model = keras.models.load_model('/content/drive/MyDrive/Projet-5-em/Project-kadid10k/MultiOut-VGG16-DOC/VGG16MAE-04-06-IVC.h5')"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tpcDZs-6UZG3"
      },
      "source": [
        "# Model Testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDZNlca-UYj8"
      },
      "source": [
        "y= []\n",
        "\n",
        "for pos in range(0, len(test), 1):\n",
        "  temp = test.iloc[pos:pos + 1] \n",
        "\n",
        "  for encimg in temp.name:\n",
        "    encrypted_image = image.load_img('/content/SelectiveEncryption/images/'+encimg)\n",
        "    encrypted_image = image.img_to_array(encrypted_image)\n",
        "\n",
        "    patches = view_as_windows(encrypted_image,(240, 240, 3),(240, 360, 3)).reshape((-1,240,240,3)) \n",
        "\n",
        "    for i in range(4):\n",
        "      resized = cv2.resize(patches[i], (224,224))\n",
        "      encrypted_batch = np.expand_dims(resized, axis=0)\n",
        "      encrypted_batch = preprocess_input(encrypted_batch) \n",
        "\n",
        "      y.append(model.predict(encrypted_batch))"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9UXJ1bIVOKj"
      },
      "source": [
        "image_s = []\n",
        "\n",
        "for val in y:\n",
        "  image_s.append(val[0][0])"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ha6zGgWy4VHL",
        "outputId": "afe5f558-f275-4539-f10d-4baedb302632"
      },
      "source": [
        "iq = []\n",
        "som, j = 0, 1\n",
        "for elt in image_s:\n",
        "  som += elt\n",
        "  if j >= 4:\n",
        "    iq.append(som/4)\n",
        "    j = 0\n",
        "    som = 0\n",
        "  j = j+1\n",
        "\n",
        "iq = np.array(iq)\n",
        "iq.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40,)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6y9maigCVS55"
      },
      "source": [
        "#Performance visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVbBZBWIVXy1"
      },
      "source": [
        "def chart_regression(pred, y, sort=True):\n",
        "    t = pd.DataFrame({'pred': pred, 'y': y})\n",
        "    if sort:\n",
        "        t.sort_values(by=['y'], inplace=True)\n",
        "    plt.plot(t['y'].tolist(), label='expected')\n",
        "    plt.plot(t['pred'].tolist(), label='prediction')\n",
        "    plt.ylabel('output')\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "Fxo0gzJHVcBm",
        "outputId": "22ad12a2-48c1-418c-cfa9-5056f3bcec55"
      },
      "source": [
        "chart_regression(iq, test.ground_truth.values)\n",
        "print(spearmanr(test.ground_truth.values, iq))\n",
        "print('PearsonrResult',pearsonr(test.ground_truth.values, iq))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SpearmanrResult(correlation=0.9827726924014933, pvalue=1.789252101744169e-29)\n",
            "PearsonrResult (0.9790727360293136, 6.98583749933687e-28)\n"
          ]
        }
      ]
    }
  ]
}