{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Image path\n",
    "imagePath = \"data/image/smile.jpg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cascade path\n",
    "cascPath = \"data/haarcascades/haarcascade_frontalface_default.xml\"\n",
    "smilePath = \"data/haarcascades/haarcascade_smile.xml\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the haar cascade\n",
    "faceCascade = cv2.CascadeClassifier(cascPath)\n",
    "smileCascade = cv2.CascadeClassifier(smilePath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the image\n",
    "image = cv2.imread(imagePath)\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detect faces in the image\n",
    "faces = faceCascade.detectMultiScale(\n",
    "    gray,\n",
    "    scaleFactor=1.1, #表示在前後兩次相繼的掃描中，搜索窗口的比例係數。默認為1.1即每次搜索窗口依次擴大10%\n",
    "    minNeighbors=5, #表示構成檢測目標的相鄰矩形的最小個數(默認為3個)\n",
    "    minSize=(30, 30),#限制得到的目標區域的最小範圍\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4 faces!\n"
     ]
    }
   ],
   "source": [
    "print(\"Found {0} faces!\".format(len(faces)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Draw a rectangle around the faces\n",
    "for (x, y, w, h) in faces:\n",
    "    cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)\n",
    "\n",
    "    roi_gray = gray[y:y+h, x:x+w]\n",
    "    roi_color = image[y:y+h, x:x+w]\n",
    "\n",
    "    smile = smileCascade.detectMultiScale(\n",
    "        roi_gray,\n",
    "        scaleFactor= 1.16,\n",
    "        minNeighbors=35,\n",
    "        minSize=(25, 25)\n",
    "    )\n",
    "\n",
    "    # Draw a rectangle around the smile faces and tag it\n",
    "    for (x2, y2, w2, h2) in smile:\n",
    "        cv2.rectangle(roi_color, (x2, y2), (x2+w2, y2+h2), (255, 0, 0), 2)\n",
    "        cv2.putText(image,'Smile',(x,y-7), 3, 1.2, (0, 255, 0), 2, cv2.LINE_AA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.imshow(\"Who is smiling ?\" ,image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.waitKey(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.destroyAllWindows()"
   ]
  }
 ],
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