AlignedFaces_Extractor_Train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import numpy as np\n",
    "import cv2\n",
    "import os\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Train and Val Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2758\n",
      "3080\n",
      "3977\n"
     ]
    }
   ],
   "source": [
    "neg_train = sorted(os.listdir('./Dataset/CroppedFaces/train/Negative/'))\n",
    "neu_train = sorted(os.listdir('./Dataset/CroppedFaces/train/Neutral/'))\n",
    "pos_train = sorted(os.listdir('./Dataset/CroppedFaces/train/Positive/'))\n",
    "\n",
    "print(len(neg_train))\n",
    "print(len(neu_train))\n",
    "print(len(pos_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1231\n",
      "1368\n",
      "1747\n"
     ]
    }
   ],
   "source": [
    "neg_val = sorted(os.listdir('./Dataset/CroppedFaces/val/Negative/'))\n",
    "neu_val = sorted(os.listdir('./Dataset/CroppedFaces/val/Neutral/'))\n",
    "pos_val = sorted(os.listdir('./Dataset/CroppedFaces/val/Positive/'))\n",
    "\n",
    "print(len(neg_val))\n",
    "print(len(neu_val))\n",
    "print(len(pos_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Align Faces of Training Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-9f0a18df8b50>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m         \u001b[0mim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr\".\\Dataset\\AlignedCroppedImages2\\train\\Negative\\neg_\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\"_\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\".jpg\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m   1895\u001b[0m             \u001b[1;31m# do what we can to clean up\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1896\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mopen_fp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1897\u001b[1;33m                 \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1899\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mseek\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mframe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for j in range(len(neg_train)):\n",
    "    j = j+1\n",
    "    print(j)\n",
    "    data = np.load(\"./Dataset/CroppedFaces/train/Negative/neg_\"+str(j)+\".npz\")\n",
    "    \n",
    "    faces = data['a']\n",
    "    landmarks = data['b']\n",
    "    desiredFaceWidth = 112\n",
    "    desiredFaceHeight = 96\n",
    "    desiredLeftEye = [0.30, 0.30]\n",
    "    desiredRightEyeX = 1.0 - desiredLeftEye[0]\n",
    "    \n",
    "    if landmarks.all() == [0]:\n",
    "        continue\n",
    "            \n",
    "    for i in range(len(faces)):\n",
    "        leftEyeCenter = [landmarks[i][0], landmarks[i][5]]\n",
    "        rightEyeCenter = [landmarks[i][1], landmarks[i][6]]\n",
    "\n",
    "        dY = rightEyeCenter[1] - leftEyeCenter[1]\n",
    "        dX = rightEyeCenter[0] - leftEyeCenter[0]\n",
    "        angle = np.degrees(np.arctan2(dY, dX))\n",
    "\n",
    "        eyesCenter = ((leftEyeCenter[0]+rightEyeCenter[0])/2,(leftEyeCenter[1]+rightEyeCenter[1])/2)\n",
    "\n",
    "        dist = np.sqrt((dX ** 2) + (dY ** 2))\n",
    "        desiredDist = (desiredRightEyeX - desiredLeftEye[0])\n",
    "        desiredDist *= desiredFaceWidth\n",
    "        scale = desiredDist / dist\n",
    "\n",
    "        M = cv2.getRotationMatrix2D(eyesCenter, angle, scale)\n",
    "\n",
    "        tX = desiredFaceWidth * 0.5\n",
    "        tY = desiredFaceHeight * desiredLeftEye[1]\n",
    "        M[0, 2] += (tX - eyesCenter[0])\n",
    "        M[1, 2] += (tY - eyesCenter[1])\n",
    "\n",
    "        (w, h) = (desiredFaceWidth, desiredFaceHeight)\n",
    "        output = cv2.warpAffine(faces[i], M, (w, h),flags=cv2.INTER_CUBIC)\n",
    "        \n",
    "        im = Image.fromarray(output)\n",
    "        im.save(r\".\\Dataset\\AlignedCroppedImages\\train\\Negative\\neg_\"+str(j)+\"_\"+str(i)+\".jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-c81c0f57f6c6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m         \u001b[0mim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr\".\\Dataset\\AlignedCroppedImages2\\train\\Neutral\\neu_\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\"_\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\".jpg\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m   1895\u001b[0m             \u001b[1;31m# do what we can to clean up\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1896\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mopen_fp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1897\u001b[1;33m                 \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1899\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mseek\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mframe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for j in range(len(neu_train)):\n",
    "    j = j+1\n",
    "    print(j)\n",
    "    data = np.load(\"./Dataset/CroppedFaces/train/Neutral/neu_\"+str(j)+\".npz\")\n",
    "    \n",
    "    faces = data['a']\n",
    "    landmarks = data['b']\n",
    "    desiredFaceWidth = 112\n",
    "    desiredFaceHeight = 96\n",
    "    desiredLeftEye = [0.30, 0.30]\n",
    "    desiredRightEyeX = 1.0 - desiredLeftEye[0]\n",
    "    \n",
    "    if landmarks.all() == [0]:\n",
    "        continue\n",
    "            \n",
    "    for i in range(len(faces)):\n",
    "        leftEyeCenter = [landmarks[i][0], landmarks[i][5]]\n",
    "        rightEyeCenter = [landmarks[i][1], landmarks[i][6]]\n",
    "\n",
    "        dY = rightEyeCenter[1] - leftEyeCenter[1]\n",
    "        dX = rightEyeCenter[0] - leftEyeCenter[0]\n",
    "        angle = np.degrees(np.arctan2(dY, dX))\n",
    "\n",
    "        eyesCenter = ((leftEyeCenter[0]+rightEyeCenter[0])/2,(leftEyeCenter[1]+rightEyeCenter[1])/2)\n",
    "\n",
    "        dist = np.sqrt((dX ** 2) + (dY ** 2))\n",
    "        desiredDist = (desiredRightEyeX - desiredLeftEye[0])\n",
    "        desiredDist *= desiredFaceWidth\n",
    "        scale = desiredDist / dist\n",
    "\n",
    "        M = cv2.getRotationMatrix2D(eyesCenter, angle, scale)\n",
    "\n",
    "        tX = desiredFaceWidth * 0.5\n",
    "        tY = desiredFaceHeight * desiredLeftEye[1]\n",
    "        M[0, 2] += (tX - eyesCenter[0])\n",
    "        M[1, 2] += (tY - eyesCenter[1])\n",
    "\n",
    "        (w, h) = (desiredFaceWidth, desiredFaceHeight)\n",
    "        output = cv2.warpAffine(faces[i], M, (w, h),flags=cv2.INTER_CUBIC)\n",
    "        \n",
    "        im = Image.fromarray(output)\n",
    "        im.save(r\".\\Dataset\\AlignedCroppedImages\\train\\Neutral\\neu_\"+str(j)+\"_\"+str(i)+\".jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-26f5b024142c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m         \u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdesiredFaceWidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdesiredFaceHeight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m         \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarpAffine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfaces\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mM\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mINTER_CUBIC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m         \u001b[0mim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for j in range(len(pos_train)):\n",
    "    j = j+1\n",
    "    print(j)\n",
    "    data = np.load(\"./Dataset/CroppedFaces/train/Positive/pos_\"+str(j)+\".npz\")\n",
    "    \n",
    "    faces = data['a']\n",
    "    landmarks = data['b']\n",
    "    desiredFaceWidth = 112\n",
    "    desiredFaceHeight = 96\n",
    "    desiredLeftEye = [0.30, 0.30]\n",
    "    desiredRightEyeX = 1.0 - desiredLeftEye[0]\n",
    "    \n",
    "    if landmarks.all() == [0]:\n",
    "        continue\n",
    "            \n",
    "    for i in range(len(faces)):\n",
    "        leftEyeCenter = [landmarks[i][0], landmarks[i][5]]\n",
    "        rightEyeCenter = [landmarks[i][1], landmarks[i][6]]\n",
    "\n",
    "        dY = rightEyeCenter[1] - leftEyeCenter[1]\n",
    "        dX = rightEyeCenter[0] - leftEyeCenter[0]\n",
    "        angle = np.degrees(np.arctan2(dY, dX))\n",
    "\n",
    "        eyesCenter = ((leftEyeCenter[0]+rightEyeCenter[0])/2,(leftEyeCenter[1]+rightEyeCenter[1])/2)\n",
    "\n",
    "        dist = np.sqrt((dX ** 2) + (dY ** 2))\n",
    "        desiredDist = (desiredRightEyeX - desiredLeftEye[0])\n",
    "        desiredDist *= desiredFaceWidth\n",
    "        scale = desiredDist / dist\n",
    "\n",
    "        M = cv2.getRotationMatrix2D(eyesCenter, angle, scale)\n",
    "\n",
    "        tX = desiredFaceWidth * 0.5\n",
    "        tY = desiredFaceHeight * desiredLeftEye[1]\n",
    "        M[0, 2] += (tX - eyesCenter[0])\n",
    "        M[1, 2] += (tY - eyesCenter[1])\n",
    "\n",
    "        (w, h) = (desiredFaceWidth, desiredFaceHeight)\n",
    "        output = cv2.warpAffine(faces[i], M, (w, h),flags=cv2.INTER_CUBIC)\n",
    "        \n",
    "        im = Image.fromarray(output)\n",
    "        im.save(r\".\\Dataset\\AlignedCroppedImages\\train\\Positive\\pos_\"+str(j)+\"_\"+str(i)+\".jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Align Faces of Val Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-921d88c84665>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m         \u001b[0mim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr\".\\Dataset\\AlignedCroppedImages2\\val\\Negative\\neg_\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\"_\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\".jpg\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m   1895\u001b[0m             \u001b[1;31m# do what we can to clean up\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1896\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mopen_fp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1897\u001b[1;33m                 \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1898\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1899\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mseek\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mframe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for j in range(len(neg_val)):\n",
    "    j = j+1\n",
    "    print(j)\n",
    "    data = np.load(\"./Dataset/CroppedFaces/val/Negative/neg_\"+str(j)+\".npz\")\n",
    "    \n",
    "    faces = data['a']\n",
    "    landmarks = data['b']\n",
    "    desiredFaceWidth = 112\n",
    "    desiredFaceHeight = 96\n",
    "    desiredLeftEye = [0.30, 0.30]\n",
    "    desiredRightEyeX = 1.0 - desiredLeftEye[0]\n",
    "    \n",
    "    if landmarks.all() == [0]:\n",
    "        continue\n",
    "            \n",
    "    for i in range(len(faces)):\n",
    "        leftEyeCenter = [landmarks[i][0], landmarks[i][5]]\n",
    "        rightEyeCenter = [landmarks[i][1], landmarks[i][6]]\n",
    "\n",
    "        dY = rightEyeCenter[1] - leftEyeCenter[1]\n",
    "        dX = rightEyeCenter[0] - leftEyeCenter[0]\n",
    "        angle = np.degrees(np.arctan2(dY, dX))\n",
    "\n",
    "        eyesCenter = ((leftEyeCenter[0]+rightEyeCenter[0])/2,(leftEyeCenter[1]+rightEyeCenter[1])/2)\n",
    "\n",
    "        dist = np.sqrt((dX ** 2) + (dY ** 2))\n",
    "        desiredDist = (desiredRightEyeX - desiredLeftEye[0])\n",
    "        desiredDist *= desiredFaceWidth\n",
    "        scale = desiredDist / dist\n",
    "\n",
    "        M = cv2.getRotationMatrix2D(eyesCenter, angle, scale)\n",
    "\n",
    "        tX = desiredFaceWidth * 0.5\n",
    "        tY = desiredFaceHeight * desiredLeftEye[1]\n",
    "        M[0, 2] += (tX - eyesCenter[0])\n",
    "        M[1, 2] += (tY - eyesCenter[1])\n",
    "\n",
    "        (w, h) = (desiredFaceWidth, desiredFaceHeight)\n",
    "        output = cv2.warpAffine(faces[i], M, (w, h),flags=cv2.INTER_CUBIC)\n",
    "        \n",
    "        im = Image.fromarray(output)\n",
    "        im.save(r\".\\Dataset\\AlignedCroppedImages\\val\\Negative\\neg_\"+str(j)+\"_\"+str(i)+\".jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-f56c116a7516>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m         \u001b[0mim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr\".\\Dataset\\AlignedCroppedImages2\\val\\Neutral\\neu_\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\"_\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\".jpg\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m   1891\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1892\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1893\u001b[1;33m             \u001b[0msave_handler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1894\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1895\u001b[0m             \u001b[1;31m# do what we can to clean up\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\PIL\\JpegImagePlugin.py\u001b[0m in \u001b[0;36m_save\u001b[1;34m(im, fp, filename)\u001b[0m\n\u001b[0;32m    737\u001b[0m                   len(extra) + 1)\n\u001b[0;32m    738\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 739\u001b[1;33m     \u001b[0mImageFile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_save\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"jpeg\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrawmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbufsize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    740\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    741\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\PIL\\ImageFile.py\u001b[0m in \u001b[0;36m_save\u001b[1;34m(im, fp, tile, bufsize)\u001b[0m\n\u001b[0;32m    494\u001b[0m                 \u001b[0ml\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode_to_pyfd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    495\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 496\u001b[1;33m                 \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode_to_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbufsize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    497\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    498\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"encoder error %d when writing image file\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for j in range(len(neu_val)):\n",
    "    j = j+1\n",
    "    print(j)\n",
    "    data = np.load(\"./Dataset/CroppedFaces/val/Neutral/neu_\"+str(j)+\".npz\")\n",
    "    \n",
    "    faces = data['a']\n",
    "    landmarks = data['b']\n",
    "    desiredFaceWidth = 112\n",
    "    desiredFaceHeight = 96\n",
    "    desiredLeftEye = [0.30, 0.30]\n",
    "    desiredRightEyeX = 1.0 - desiredLeftEye[0]\n",
    "    \n",
    "    if landmarks.all() == [0]:\n",
    "        continue\n",
    "            \n",
    "    for i in range(len(faces)):\n",
    "        leftEyeCenter = [landmarks[i][0], landmarks[i][5]]\n",
    "        rightEyeCenter = [landmarks[i][1], landmarks[i][6]]\n",
    "\n",
    "        dY = rightEyeCenter[1] - leftEyeCenter[1]\n",
    "        dX = rightEyeCenter[0] - leftEyeCenter[0]\n",
    "        angle = np.degrees(np.arctan2(dY, dX))\n",
    "\n",
    "        eyesCenter = ((leftEyeCenter[0]+rightEyeCenter[0])/2,(leftEyeCenter[1]+rightEyeCenter[1])/2)\n",
    "\n",
    "        dist = np.sqrt((dX ** 2) + (dY ** 2))\n",
    "        desiredDist = (desiredRightEyeX - desiredLeftEye[0])\n",
    "        desiredDist *= desiredFaceWidth\n",
    "        scale = desiredDist / dist\n",
    "\n",
    "        M = cv2.getRotationMatrix2D(eyesCenter, angle, scale)\n",
    "\n",
    "        tX = desiredFaceWidth * 0.5\n",
    "        tY = desiredFaceHeight * desiredLeftEye[1]\n",
    "        M[0, 2] += (tX - eyesCenter[0])\n",
    "        M[1, 2] += (tY - eyesCenter[1])\n",
    "\n",
    "        (w, h) = (desiredFaceWidth, desiredFaceHeight)\n",
    "        output = cv2.warpAffine(faces[i], M, (w, h),flags=cv2.INTER_CUBIC)\n",
    "        \n",
    "        im = Image.fromarray(output)\n",
    "        im.save(r\".\\Dataset\\AlignedCroppedImages\\val\\Neutral\\neu_\"+str(j)+\"_\"+str(i)+\".jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-18085dbad646>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m         \u001b[0mim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr\".\\Dataset\\AlignedCroppedImages2\\val\\Positive\\pos_\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\"_\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\".jpg\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, fp, format, **params)\u001b[0m\n\u001b[0;32m   1888\u001b[0m             \u001b[1;31m# Open also for reading (\"+\"), because TIFF save_all\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1889\u001b[0m             \u001b[1;31m# writer needs to go back and edit the written data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1890\u001b[1;33m             \u001b[0mfp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"w+b\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1891\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1892\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for j in range(len(pos_val)):\n",
    "    j = j+1\n",
    "    print(j)\n",
    "    data = np.load(\"./Dataset/CroppedFaces/val/Positive/pos_\"+str(j)+\".npz\")\n",
    "    \n",
    "    faces = data['a']\n",
    "    landmarks = data['b']\n",
    "    desiredFaceWidth = 112\n",
    "    desiredFaceHeight = 96\n",
    "    desiredLeftEye = [0.30, 0.30]\n",
    "    desiredRightEyeX = 1.0 - desiredLeftEye[0]\n",
    "    \n",
    "    if landmarks.all() == [0]:\n",
    "        continue\n",
    "            \n",
    "    for i in range(len(faces)):\n",
    "        leftEyeCenter = [landmarks[i][0], landmarks[i][5]]\n",
    "        rightEyeCenter = [landmarks[i][1], landmarks[i][6]]\n",
    "\n",
    "        dY = rightEyeCenter[1] - leftEyeCenter[1]\n",
    "        dX = rightEyeCenter[0] - leftEyeCenter[0]\n",
    "        angle = np.degrees(np.arctan2(dY, dX))\n",
    "\n",
    "        eyesCenter = ((leftEyeCenter[0]+rightEyeCenter[0])/2,(leftEyeCenter[1]+rightEyeCenter[1])/2)\n",
    "\n",
    "        dist = np.sqrt((dX ** 2) + (dY ** 2))\n",
    "        desiredDist = (desiredRightEyeX - desiredLeftEye[0])\n",
    "        desiredDist *= desiredFaceWidth\n",
    "        scale = desiredDist / dist\n",
    "\n",
    "        M = cv2.getRotationMatrix2D(eyesCenter, angle, scale)\n",
    "\n",
    "        tX = desiredFaceWidth * 0.5\n",
    "        tY = desiredFaceHeight * desiredLeftEye[1]\n",
    "        M[0, 2] += (tX - eyesCenter[0])\n",
    "        M[1, 2] += (tY - eyesCenter[1])\n",
    "\n",
    "        (w, h) = (desiredFaceWidth, desiredFaceHeight)\n",
    "        output = cv2.warpAffine(faces[i], M, (w, h),flags=cv2.INTER_CUBIC)\n",
    "        \n",
    "        im = Image.fromarray(output)\n",
    "        im.save(r\".\\Dataset\\AlignedCroppedImages\\val\\Positive\\pos_\"+str(j)+\"_\"+str(i)+\".jpg\")"
   ]
  }
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