Experiments_Faces-HQ/Faces-HQ.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Faces-HQ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we show the results for Faces-HQ. You can create from scratch the features or use the pre-computed ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Create feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to create the features, first of all download the data from [link](https://cutt.ly/6enDLYG). Be sure to save the folder together with this notebook. \n",
    "\n",
    "Otherwise, just jump to section 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "thispersondoesntexists\n",
      "100KFake\n",
      "Flickr-Faces-HQ2_\n",
      "celebA-HQ_10K\n",
      "DATA Saved\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "import radialProfile\n",
    "import glob\n",
    "from matplotlib import pyplot as plt\n",
    "import pickle\n",
    "\n",
    "\n",
    "path = ['thispersondoesntexists', '100KFake','Flickr-Faces-HQ2_', 'celebA-HQ_10K']\n",
    "labels = [1,1,0,0]\n",
    "format_file = ['jpg','jpg','jpg', 'jpg']\n",
    "epsilon = 1e-8\n",
    "data = {}\n",
    "#number of samples from each dataset\n",
    "stop = 250\n",
    "number_iter = 4 * stop\n",
    "psd1D_total = np.zeros([number_iter, 722])\n",
    "label_total = np.zeros([number_iter])\n",
    "iter_ = 0\n",
    "\n",
    "for z in range(4):\n",
    "    cont = 0\n",
    "    psd1D_average_org = np.zeros(722)\n",
    "    print(path[z])\n",
    "    \n",
    "    for filename in glob.glob(path[z]+\"/*.\"+format_file[z]):  \n",
    "        img = cv2.imread(filename,0)\n",
    "        f = np.fft.fft2(img)\n",
    "        fshift = np.fft.fftshift(f)\n",
    "        fshift += epsilon\n",
    "\n",
    "        magnitude_spectrum = 20*np.log(np.abs(fshift))\n",
    "\n",
    "        # Calculate the azimuthally averaged 1D power spectrum\n",
    "        psd1D = radialProfile.azimuthalAverage(magnitude_spectrum)\n",
    "        psd1D_total[iter_,:] = psd1D\n",
    "        label_total[iter_] = labels[z]\n",
    "\n",
    "        cont+=1\n",
    "        iter_+=1\n",
    "        if cont >= stop:\n",
    "            break\n",
    "\n",
    "data[\"data\"] = psd1D_total\n",
    "data[\"label\"] = label_total\n",
    "\n",
    "output = open('dataset_freq_1000.pkl', 'wb')\n",
    "pickle.dump(data, output)\n",
    "output.close()\n",
    "print(\"DATA Saved\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Loading Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we load the features. Either the pre-computed ones or the features that you have created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "\n",
    "# load feature file\n",
    "pkl_file = open('dataset_freq_1000.pkl', 'rb')\n",
    "data = pickle.load(pkl_file)\n",
    "pkl_file.close()\n",
    "X = data[\"data\"]\n",
    "y = data[\"label\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We look at the label distribution, to be sure that we have a balanced dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f58511d1e10>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Check Spectrum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a look to the spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Power Spectrum')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num = int(X.shape[0]/2)\n",
    "num_feat = X.shape[1]\n",
    "\n",
    "psd1D_org_0 = np.zeros((num,num_feat))\n",
    "psd1D_org_1 = np.zeros((num,num_feat))\n",
    "psd1D_org_0_mean = np.zeros(num_feat)\n",
    "psd1D_org_0_std = np.zeros(num_feat)\n",
    "psd1D_org_1_mean = np.zeros(num_feat)\n",
    "psd1D_org_1_std = np.zeros(num_feat)\n",
    "\n",
    "cont_0=0\n",
    "cont_1=0\n",
    "\n",
    "# We separate real and fake using the label\n",
    "for x in range(X.shape[0]):\n",
    "    if y[x]==0:\n",
    "        psd1D_org_0[cont_0,:] = X[x,:]\n",
    "        cont_0+=1\n",
    "    elif y[x]==1:\n",
    "        psd1D_org_1[cont_1,:] = X[x,:]\n",
    "        cont_1+=1\n",
    "\n",
    "# We compute statistcis\n",
    "for x in range(num_feat):\n",
    "    psd1D_org_0_mean[x] = np.mean(psd1D_org_0[:,x])\n",
    "    psd1D_org_0_std[x]= np.std(psd1D_org_0[:,x])\n",
    "    psd1D_org_1_mean[x] = np.mean(psd1D_org_1[:,x])\n",
    "    psd1D_org_1_std[x]= np.std(psd1D_org_1[:,x])\n",
    "    \n",
    "# Plot\n",
    "x = np.arange(0, num_feat, 1)\n",
    "fig, ax = plt.subplots(figsize=(15, 9))\n",
    "ax.plot(x, psd1D_org_0_mean, alpha=0.5, color='red', label='Real', linewidth =2.0)\n",
    "ax.fill_between(x, psd1D_org_0_mean - psd1D_org_0_std, psd1D_org_0_mean + psd1D_org_0_std, color='red', alpha=0.2)\n",
    "ax.plot(x, psd1D_org_1_mean, alpha=0.5, color='blue', label='Fake', linewidth =2.0)\n",
    "ax.fill_between(x, psd1D_org_1_mean - psd1D_org_1_std, psd1D_org_1_mean + psd1D_org_1_std, color='blue', alpha=0.2)\n",
    "ax.legend()\n",
    "plt.tick_params(axis='x', labelsize=20)\n",
    "plt.tick_params(axis='y', labelsize=20)\n",
    "ax.legend(loc='best', prop={'size': 20})\n",
    "plt.xlabel(\"Spatial Frequency\", fontsize=20)\n",
    "plt.ylabel(\"Power Spectrum\", fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we classify using the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average SVM: 1.0\n",
      "Average LR: 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "\n",
    "num = 10\n",
    "LR = 0\n",
    "SVM = 0\n",
    "\n",
    "\n",
    "for z in range(num):\n",
    "    # read python dict back from the file\n",
    "    pkl_file = open('dataset_freq_1000.pkl', 'rb')\n",
    "    \n",
    "    data = pickle.load(pkl_file)\n",
    "\n",
    "    pkl_file.close()\n",
    "    X = data[\"data\"]\n",
    "    y = data[\"label\"]\n",
    "\n",
    "    try:\n",
    "\n",
    "        from sklearn.model_selection import train_test_split\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)\n",
    "\n",
    "        from sklearn.svm import SVC\n",
    "        svclassifier = SVC(kernel='linear')\n",
    "        svclassifier.fit(X_train, y_train)\n",
    "        #print('Accuracy on test set: {:.3f}'.format(svclassifier.score(X_test, y_test)))\n",
    "        \n",
    "        from sklearn.linear_model import LogisticRegression\n",
    "        logreg = LogisticRegression(solver='liblinear', max_iter=1000)\n",
    "        logreg.fit(X_train, y_train)\n",
    "        #print('Accuracy on test set: {:.3f}'.format(logreg.score(X_test, y_test)))\n",
    "\n",
    "        \n",
    "        SVM+=svclassifier.score(X_test, y_test)\n",
    "        LR+=logreg.score(X_test, y_test)\n",
    "\n",
    " \n",
    "    except:\n",
    "        num-=1\n",
    "        print(num)\n",
    "    \n",
    "print(\"Average SVM: \"+str(SVM/num))\n",
    "print(\"Average LR: \"+str(LR/num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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