emotion_data_0.1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explanation of this code\n",
    "\n",
    "http://www.paulvangent.com/2016/04/01/emotion-recognition-with-python-opencv-and-a-face-dataset/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset: CK+, Jaffe, Rafd + Crawling Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Organising the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/ryan/git_ryan/emotion_recognitoin'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from shutil import copyfile\n",
    "import glob\n",
    "\n",
    "emotions = [\"neutral\", \"anger\", \"contempt\", \"disgust\", \"fear\", \"happy\", \"sadness\", \"surprise\"] #Define emotion order\n",
    "participants = glob.glob(\"source_emotion/*\") #Returns a list of all folders with participant numbers\n",
    "\n",
    "#Create folders\n",
    "\n",
    "for x in participants:\n",
    "    part = \"%s\" %x[-4:] #store current participant number\n",
    "    for sessions in glob.glob(\"%s/*\" %x): #Store list of sessions for current participant\n",
    "        for files in glob.glob(\"%s/*\" %sessions):\n",
    "            current_session = files[20:-30]\n",
    "            file = open(files, 'r')\n",
    "            \n",
    "            emotion = int(float(file.readline())) #emotions are encoded as a float, readline as float, then convert to integer.\n",
    "            \n",
    "            sourcefile_emotion = glob.glob(\"source_images/%s/%s/*\" %(part, current_session))[-1] #get path for last image in sequence, which contains the emotion\n",
    "            sourcefile_neutral = glob.glob(\"source_images/%s/%s/*\" %(part, current_session))[0] #do same for neutral image\n",
    "            \n",
    "            dest_neut = \"sorted_set/neutral/%s\" %sourcefile_neutral[25:] #Generate path to put neutral image\n",
    "            dest_emot = \"sorted_set/%s/%s\" %(emotions[emotion], sourcefile_emotion[25:]) #Do same for emotion containing image\n",
    "            \n",
    "            copyfile(sourcefile_neutral, dest_neut) #Copy file\n",
    "            copyfile(sourcefile_emotion, dest_emot) #Copy file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Extracting faces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import cv2\n",
    "\n",
    "faceDet = cv2.CascadeClassifier(\"haarcascade_frontalface_default.xml\")\n",
    "faceDet2 = cv2.CascadeClassifier(\"haarcascade_frontalface_alt2.xml\")\n",
    "faceDet3 = cv2.CascadeClassifier(\"haarcascade_frontalface_alt.xml\")\n",
    "faceDet4 = cv2.CascadeClassifier(\"haarcascade_frontalface_alt_tree.xml\")\n",
    "\n",
    "emotions = [\"neutral\", \"anger\", \"contempt\", \"disgust\", \"fear\", \"happy\", \"sadness\", \"surprise\"] #Define emotions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = glob.glob(\"sorted_set/%s/*\" %emotion)\n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "face found in file: sorted_set/surprise/22_001_00000012.png\n",
      "face found in file: sorted_set/surprise/22_001_00000030.png\n",
      "face found in file: sorted_set/surprise/24_001_00000014.png\n",
      "face found in file: sorted_set/surprise/25_007_00000009.png\n",
      "face found in file: sorted_set/surprise/26_001_00000015.png\n",
      "face found in file: sorted_set/surprise/26_004_00000012.png\n",
      "face found in file: sorted_set/surprise/27_001_00000017.png\n",
      "face found in file: sorted_set/surprise/29_002_00000011.png\n",
      "face found in file: sorted_set/surprise/30_001_00000018.png\n",
      "face found in file: sorted_set/surprise/31_001_00000016.png\n",
      "face found in file: sorted_set/surprise/32_001_00000022.png\n",
      "face found in file: sorted_set/surprise/32_008_00000010.png\n",
      "face found in file: sorted_set/surprise/33_009_00000006.png\n",
      "face found in file: sorted_set/surprise/34_001_00000029.png\n",
      "face found in file: sorted_set/surprise/35_001_00000015.png\n",
      "face found in file: sorted_set/surprise/35_001_00000039.png\n",
      "face found in file: sorted_set/surprise/36_001_00000019.png\n",
      "face found in file: sorted_set/surprise/37_001_00000014.png\n",
      "face found in file: sorted_set/surprise/37_001_00000020.png\n",
      "face found in file: sorted_set/surprise/38_004_00000013.png\n",
      "face found in file: sorted_set/surprise/42_001_00000019.png\n",
      "face found in file: sorted_set/surprise/44_001_00000024.png\n",
      "face found in file: sorted_set/surprise/46_002_00000006.png\n",
      "face found in file: sorted_set/surprise/50_002_00000018.png\n",
      "face found in file: sorted_set/surprise/51_002_00000019.png\n",
      "face found in file: sorted_set/surprise/52_001_00000015.png\n",
      "face found in file: sorted_set/surprise/53_001_00000023.png\n",
      "face found in file: sorted_set/surprise/54_003_00000007.png\n",
      "face found in file: sorted_set/surprise/55_001_00000012.png\n",
      "face found in file: sorted_set/surprise/56_003_00000010.png\n",
      "face found in file: sorted_set/surprise/57_001_00000019.png\n",
      "face found in file: sorted_set/surprise/58_001_00000020.png\n",
      "face found in file: sorted_set/surprise/59_001_00000018.png\n",
      "face found in file: sorted_set/surprise/60_003_00000018.png\n",
      "face found in file: sorted_set/surprise/61_001_00000012.png\n",
      "face found in file: sorted_set/surprise/62_002_00000016.png\n",
      "face found in file: sorted_set/surprise/63_001_00000013.png\n",
      "face found in file: sorted_set/surprise/64_001_00000012.png\n",
      "face found in file: sorted_set/surprise/65_003_00000022.png\n",
      "face found in file: sorted_set/surprise/66_002_00000022.png\n",
      "face found in file: sorted_set/surprise/67_002_00000014.png\n",
      "face found in file: sorted_set/surprise/68_003_00000014.png\n",
      "face found in file: sorted_set/surprise/69_002_00000014.png\n",
      "face found in file: sorted_set/surprise/70_002_00000016.png\n",
      "face found in file: sorted_set/surprise/71_001_00000013.png\n",
      "face found in file: sorted_set/surprise/73_001_00000013.png\n",
      "face found in file: sorted_set/surprise/74_002_00000016.png\n",
      "face found in file: sorted_set/surprise/75_002_00000014.png\n",
      "face found in file: sorted_set/surprise/76_001_00000017.png\n",
      "face found in file: sorted_set/surprise/77_001_00000028.png\n",
      "face found in file: sorted_set/surprise/78_001_00000033.png\n",
      "face found in file: sorted_set/surprise/79_001_00000012.png\n",
      "face found in file: sorted_set/surprise/80_001_00000018.png\n",
      "face found in file: sorted_set/surprise/81_001_00000019.png\n",
      "face found in file: sorted_set/surprise/82_001_00000015.png\n",
      "face found in file: sorted_set/surprise/84_001_00000010.png\n",
      "face found in file: sorted_set/surprise/85_003_00000013.png\n",
      "face found in file: sorted_set/surprise/86_001_00000019.png\n",
      "face found in file: sorted_set/surprise/87_001_00000011.png\n",
      "face found in file: sorted_set/surprise/88_001_00000017.png\n",
      "face found in file: sorted_set/surprise/89_001_00000016.png\n",
      "face found in file: sorted_set/surprise/90_002_00000011.png\n",
      "face found in file: sorted_set/surprise/92_001_00000017.png\n",
      "face found in file: sorted_set/surprise/94_001_00000010.png\n",
      "face found in file: sorted_set/surprise/95_001_00000016.png\n",
      "face found in file: sorted_set/surprise/96_001_00000007.png\n",
      "face found in file: sorted_set/surprise/97_001_00000021.png\n",
      "face found in file: sorted_set/surprise/99_001_00000014.png\n"
     ]
    }
   ],
   "source": [
    "def detect_faces(emotion):\n",
    "    files = glob.glob(\"sorted_set/%s/*\" %emotion) #Get list of all images with emotion\n",
    "    filenumber = 0\n",
    "    for f in files:\n",
    "        frame = cv2.imread(f) #Open image\n",
    "        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #Convert image to grayscale\n",
    "        \n",
    "        #Detect face using 4 different classifiers\n",
    "        face = faceDet.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)\n",
    "        face2 = faceDet2.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)\n",
    "        face3 = faceDet3.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)\n",
    "        face4 = faceDet4.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)\n",
    "\n",
    "        #Go over detected faces, stop at first detected face, return empty if no face.\n",
    "        if len(face) == 1:\n",
    "            facefeatures = face\n",
    "        elif len(face2) == 1:\n",
    "            facefeatures == face2\n",
    "        elif len(face3) == 1:\n",
    "            facefeatures = face3\n",
    "        elif len(face4) == 1:\n",
    "            facefeatures = face4\n",
    "        else:\n",
    "            facefeatures = \"\"\n",
    "        \n",
    "        #Cut and save face\n",
    "        for (x, y, w, h) in facefeatures: #get coordinates and size of rectangle containing face\n",
    "            print \"face found in file: %s\" %f\n",
    "            gray = gray[y:y+h, x:x+w] #Cut the frame to size\n",
    "            \n",
    "            try:\n",
    "                out = cv2.resize(gray, (350, 350)) #Resize face so all images have same size\n",
    "                cv2.imwrite(\"dataset/%s/%s.jpg\" %(emotion, filenumber), out) #Write image\n",
    "            except:\n",
    "               pass #If error, pass file\n",
    "        filenumber += 1 #Increment image number\n",
    "\n",
    "for emotion in emotions: \n",
    "    detect_faces(emotion) #Call functiona"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Creating the training and classification set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import glob\n",
    "import random\n",
    "import numpy as np\n",
    "\n",
    "emotions = [\"neutral\", \"anger\", \"contempt\", \"disgust\", \"fear\", \"happy\", \"sadness\", \"surprise\"] #Emotion list\n",
    "fishface = cv2.createFisherFaceRecognizer() #Initialize fisher face classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training fisher face classifier\n",
      "size of training set is: 664 images\n",
      "predicting classification set\n",
      "got 26 percent correct!\n",
      "training fisher face classifier\n",
      "size of training set is: 664 images\n",
      "predicting classification set\n",
      "got 16 percent correct!\n",
      "training fisher face classifier\n",
      "size of training set is: 664 images\n",
      "predicting classification set\n",
      "got 20 percent correct!\n",
      "training fisher face classifier\n",
      "size of training set is: 664 images\n",
      "predicting classification set\n",
      "got 18 percent correct!\n",
      "training fisher face classifier\n",
      "size of training set is: 664 images\n",
      "predicting classification set\n",
      "got 26 percent correct!\n",
      "training fisher face classifier\n",
      "size of training set is: 664 images\n",
      "predicting classification set\n",
      "got 18 percent correct!\n",
      "training fisher face classifier\n",
      "size of training set is: 664 images\n",
      "predicting classification set\n",
      "got 22 percent correct!\n",
      "training fisher face classifier\n",
      "size of training set is: 664 images\n",
      "predicting classification set\n",
      "got 21 percent correct!\n",
      "training fisher face classifier\n",
      "size of training set is: 0 images\n"
     ]
    },
    {
     "ename": "error",
     "evalue": "-------src-dir-------/opencv-2.4.10/modules/contrib/src/facerec.cpp:554: error: (-5) Empty training data was given. You'll need more than one sample to learn a model. in function train\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-107-07aab80b81d5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[0mmetascore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m     \u001b[0mcorrect\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrun_recognizer\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     57\u001b[0m     \u001b[1;32mprint\u001b[0m \u001b[1;34m\"got\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcorrect\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"percent correct!\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[0mmetascore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcorrect\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-107-07aab80b81d5>\u001b[0m in \u001b[0;36mrun_recognizer\u001b[1;34m()\u001b[0m\n\u001b[0;32m     35\u001b[0m     \u001b[1;32mprint\u001b[0m \u001b[1;34m\"training fisher face classifier\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m     \u001b[1;32mprint\u001b[0m \u001b[1;34m\"size of training set is:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraining_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"images\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m     \u001b[0mfishface\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraining_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraining_labels\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     38\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     39\u001b[0m     \u001b[1;32mprint\u001b[0m \u001b[1;34m\"predicting classification set\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31merror\u001b[0m: -------src-dir-------/opencv-2.4.10/modules/contrib/src/facerec.cpp:554: error: (-5) Empty training data was given. You'll need more than one sample to learn a model. in function train\n"
     ]
    }
   ],
   "source": [
    "data = {}\n",
    "\n",
    "def get_files(emotion): #Define function to get file list, randomly shuffle it and split 80/20\n",
    "    files = glob.glob(\"dataset/%s/*\" %emotion)\n",
    "    random.shuffle(files)\n",
    "    training = files[:int(len(files)*0.8)] #get first 80% of file list\n",
    "    prediction = files[-int(len(files)*0.2):] #get last 20% of file list\n",
    "    return training, prediction\n",
    "\n",
    "def make_sets():\n",
    "    training_data = []\n",
    "    training_labels = []\n",
    "    prediction_data = []\n",
    "    prediction_labels = []\n",
    "    for emotion in emotions:\n",
    "        training, prediction = get_files(emotion)\n",
    "        #Append data to training and prediction list, and generate labels 0-7\n",
    "        for item in training:\n",
    "            image = cv2.imread(item) #open image\n",
    "            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #convert to grayscale\n",
    "            training_data.append(gray) #append image array to training data list\n",
    "            training_labels.append(emotions.index(emotion))\n",
    "    \n",
    "        for item in prediction: #repeat above process for prediction set\n",
    "            image = cv2.imread(item)\n",
    "            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "            prediction_data.append(gray)\n",
    "            prediction_labels.append(emotions.index(emotion))\n",
    "\n",
    "    return training_data, training_labels, prediction_data, prediction_labels\n",
    "\n",
    "def run_recognizer():\n",
    "    training_data, training_labels, prediction_data, prediction_labels = make_sets()\n",
    "    \n",
    "    print \"training fisher face classifier\"\n",
    "    print \"size of training set is:\", len(training_labels), \"images\"\n",
    "    fishface.train(training_data, np.asarray(training_labels))\n",
    "\n",
    "    print \"predicting classification set\"\n",
    "    cnt = 0\n",
    "    correct = 0\n",
    "    incorrect = 0\n",
    "    for image in prediction_data:\n",
    "        pred, conf = fishface.predict(image)\n",
    "        if pred == prediction_labels[cnt]:\n",
    "            correct += 1\n",
    "            cnt += 1\n",
    "        else:\n",
    "            incorrect += 1\n",
    "            cnt += 1\n",
    "    return ((100*correct)/(correct + incorrect))\n",
    "\n",
    "#Now run it\n",
    "metascore = []\n",
    "for i in range(0,10):\n",
    "    correct = run_recognizer()\n",
    "    print \"got\", correct, \"percent correct!\"\n",
    "    metascore.append(correct)\n",
    "\n",
    "print \"\\n\\nend score:\", np.mean(metascore), \"percent correct!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Optimizing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For this I used google image search and the chrome plugin ZIG lite to batch-download the images from the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Looking at the difficulty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'training_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-84-7f0fcdd637e8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtraining_data\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'training_data' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def run_recognizer():\n",
    "    training_data, training_labels, prediction_data, prediction_labels = make_sets()\n",
    "    \n",
    "    print \"training fisher face classifier\"\n",
    "    print \"size of training set is:\", len(training_labels), \"images\"\n",
    "    fishface.train(training_data, np.asarray(training_labels))\n",
    "\n",
    "    print \"predicting classification set\"\n",
    "    cnt = 0\n",
    "    correct = 0\n",
    "    incorrect = 0\n",
    "    for image in prediction_data:\n",
    "        pred, conf = fishface.predict(image)\n",
    "        if pred == prediction_labels[cnt]:\n",
    "            correct += 1\n",
    "            cnt += 1\n",
    "        else:\n",
    "            cv2.imwrite(\"difficult/%s_%s_%s.jpg\" %(emotions[prediction_labels[cnt]], emotions[pred], cnt), image) #<-- this one is new\n",
    "            incorrect += 1\n",
    "            cnt += 1\n",
    "    return ((100*correct)/(correct + incorrect))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training fisher face classifier\n",
      "size of training set is: 86 images\n"
     ]
    },
    {
     "ename": "error",
     "evalue": "-------src-dir-------/opencv-2.4.10/modules/contrib/src/lda.cpp:1010: error: (-5) At least two classes are needed to perform a LDA. Reason: Only one class was given! in function lda\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-115-f6c60199d3b7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mrun_recognizer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-113-40954cd9da1f>\u001b[0m in \u001b[0;36mrun_recognizer\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mprint\u001b[0m \u001b[1;34m\"training fisher face classifier\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mprint\u001b[0m \u001b[1;34m\"size of training set is:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraining_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"images\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mfishface\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraining_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtraining_labels\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      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[1;32mprint\u001b[0m \u001b[1;34m\"predicting classification set\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31merror\u001b[0m: -------src-dir-------/opencv-2.4.10/modules/contrib/src/lda.cpp:1010: error: (-5) At least two classes are needed to perform a LDA. Reason: Only one class was given! in function lda\n"
     ]
    }
   ],
   "source": [
    "run_recognizer()"
   ]
  }
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