{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "face_cascade_path = '/usr/local/opt/opencv/share/'\\\n",
    "                    'OpenCV/haarcascades/haarcascade_frontalface_default.xml'\n",
    "eye_cascade_path = '/usr/local/opt/opencv/share/'\\\n",
    "                   'OpenCV/haarcascades/haarcascade_eye.xml'\n",
    "\n",
    "face_cascade = cv2.CascadeClassifier(face_cascade_path)\n",
    "eye_cascade = cv2.CascadeClassifier(eye_cascade_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "src = cv2.imread('data/src/lena_square.png')\n",
    "src_gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "faces = face_cascade.detectMultiScale(src_gray)\n",
    "\n",
    "for x, y, w, h in faces:\n",
    "    cv2.rectangle(src, (x, y), (x + w, y + h), (255, 0, 0), 2)\n",
    "    face = src[y: y + h, x: x + w]\n",
    "    face_gray = src_gray[y: y + h, x: x + w]\n",
    "    eyes = eye_cascade.detectMultiScale(face_gray)\n",
    "    for (ex, ey, ew, eh) in eyes:\n",
    "        cv2.rectangle(face, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite('data/dst/opencv_face_detect_rectangle.jpg', src)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "src = cv2.imread('data/src/lena_square.png')\n",
    "src_gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "faces = face_cascade.detectMultiScale(src_gray)\n",
    "\n",
    "for x, y, w, h in faces:\n",
    "    src[y: y + h, x: x + w] = [0, 128, 255]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite('data/dst/opencv_face_detect_fill.jpg', src)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "src = cv2.imread('data/src/lena_square.png')\n",
    "src_gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "faces = face_cascade.detectMultiScale(src_gray)\n",
    "\n",
    "ratio = 0.05\n",
    "\n",
    "for x, y, w, h in faces:\n",
    "    small = cv2.resize(src[y: y + h, x: x + w], None, fx=ratio, fy=ratio, interpolation=cv2.INTER_NEAREST)\n",
    "    src[y: y + h, x: x + w] = cv2.resize(small, (w, h), interpolation=cv2.INTER_NEAREST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite('data/dst/opencv_face_detect_mosaic.jpg', src)"
   ]
  }
 ],
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