Lecture 6.2 - Face & Eye Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Face & Eye Detection using HAAR Cascade Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "# We point OpenCV's CascadeClassifier function to where our \n",
    "# classifier (XML file format) is stored\n",
    "face_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')\n",
    "\n",
    "# Load our image then convert it to grayscale\n",
    "image = cv2.imread('images/Trump.jpg')\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# Our classifier returns the ROI of the detected face as a tuple\n",
    "# It stores the top left coordinate and the bottom right coordiantes\n",
    "faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
    "\n",
    "# When no faces detected, face_classifier returns and empty tuple\n",
    "if faces is ():\n",
    "    print(\"No faces found\")\n",
    "\n",
    "# We iterate through our faces array and draw a rectangle\n",
    "# over each face in faces\n",
    "for (x,y,w,h) in faces:\n",
    "    cv2.rectangle(image, (x,y), (x+w,y+h), (127,0,255), 2)\n",
    "    cv2.imshow('Face Detection', image)\n",
    "    cv2.waitKey(0)\n",
    "    \n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's combine face and eye detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    " \n",
    "face_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')\n",
    "eye_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_eye.xml')\n",
    " \n",
    "img = cv2.imread('images/Trump.jpg')\n",
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
    "\n",
    "# When no faces detected, face_classifier returns and empty tuple\n",
    "if faces is ():\n",
    "    print(\"No Face Found\")\n",
    "\n",
    "for (x,y,w,h) in faces:\n",
    "    cv2.rectangle(img,(x,y),(x+w,y+h),(127,0,255),2)\n",
    "    cv2.imshow('img',img)\n",
    "    cv2.waitKey(0)\n",
    "    roi_gray = gray[y:y+h, x:x+w]\n",
    "    roi_color = img[y:y+h, x:x+w]\n",
    "    eyes = eye_classifier.detectMultiScale(roi_gray)\n",
    "    for (ex,ey,ew,eh) in eyes:\n",
    "        cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(255,255,0),2)\n",
    "        cv2.imshow('img',img)\n",
    "        cv2.waitKey(0)\n",
    "    \n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's make a live face & eye detection, keeping the face inview at all times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "face_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')\n",
    "eye_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_eye.xml')\n",
    "\n",
    "def face_detector(img, size=0.5):\n",
    "    # Convert image to grayscale\n",
    "    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "    faces = face_classifier.detectMultiScale(gray, 1.3, 5)\n",
    "    if faces is ():\n",
    "        return img\n",
    "    \n",
    "    for (x,y,w,h) in faces:\n",
    "        x = x - 50\n",
    "        w = w + 50\n",
    "        y = y - 50\n",
    "        h = h + 50\n",
    "        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
    "        roi_gray = gray[y:y+h, x:x+w]\n",
    "        roi_color = img[y:y+h, x:x+w]\n",
    "        eyes = eye_classifier.detectMultiScale(roi_gray)\n",
    "        \n",
    "        for (ex,ey,ew,eh) in eyes:\n",
    "            cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,0,255),2) \n",
    "            \n",
    "    roi_color = cv2.flip(roi_color,1)\n",
    "    return roi_color\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "while True:\n",
    "\n",
    "    ret, frame = cap.read()\n",
    "    cv2.imshow('Our Face Extractor', face_detector(frame))\n",
    "    if cv2.waitKey(1) == 13: #13 is the Enter Key\n",
    "        break\n",
    "        \n",
    "cap.release()\n",
    "cv2.destroyAllWindows()      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Tuning Cascade Classifiers\n",
    "\n",
    "*ourClassifier*.**detectMultiScale**(input image, **Scale Factor** , **Min Neighbors**)\n",
    "\n",
    "- **Scale Factor**\n",
    "Specifies how much we reduce the image size each time we scale. E.g. in face detection we typically use 1.3. This means we reduce the image by 30% each time it’s scaled. Smaller values, like 1.05 will take longer to compute, but will increase the rate of detection.\n",
    "\n",
    "\n",
    "\n",
    "- **Min Neighbors**\n",
    "Specifies the number of neighbors each potential window should have in order to consider it a positive detection. Typically set between 3-6. \n",
    "It acts as sensitivity setting, low values will sometimes detect multiples faces over a single face. High values will ensure less false positives, but you may miss some faces.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:Python2.7]",
   "language": "python",
   "name": "conda-env-Python2.7-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}