{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 引入人臉識別庫dlib\n",
    "import dlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 引入影象處理庫OpenCV\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cascade path\n",
    "cascPath = \"data/haarcascades/haarcascade_eye.xml\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the haar cascade\n",
    "eye_cascade = cv2.CascadeClassifier(cascPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用dlib庫提供的人臉提取器\n",
    "detector = dlib.get_frontal_face_detector()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 檔名流水號\n",
    "face_filename = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 讀取影片，並抓取幀畫面圖片中的人臉部位圖，將其轉為pgm格式，儲存於指定位置\n",
    "def videoToPng(videoPath, saveDir):\n",
    "    \n",
    "    # 讀取影片\n",
    "    cap = cv2.VideoCapture(videoPath)\n",
    "    \n",
    "    # 影片幀技術間隔頻率\n",
    "    timeF = 10 \n",
    "    \n",
    "    # 計數器\n",
    "    c=1\n",
    "    \n",
    "    # 循環讀取影片\n",
    "    while(True):\n",
    "        \n",
    "        # 從VideoCapture擷取一張影像\n",
    "        ret, frame = cap.read() \n",
    "        \n",
    "        # 每隔頻率進行操作\n",
    "        if(c%timeF == 0):\n",
    "            detectFaceAndSave(frame, saveDir)\n",
    "        \n",
    "        # 計數器遞增\n",
    "        c = c + 1\n",
    "        \n",
    "        # 刷新\n",
    "        cv2.waitKey(1)\n",
    "        \n",
    "        # 當最後讀取不到幀時，跳出迴圈\n",
    "        if ret == False:\n",
    "            print('videoToPng job finish')\n",
    "            break\n",
    "            \n",
    "    # 釋放攝影機\n",
    "    cap.release()\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 抓取圖片中的人臉部位圖，將其轉為pgm格式，儲存於指定位置\n",
    "def detectFaceAndSave(img, saveDir):\n",
    "    \n",
    "    # 將來源圖轉為灰度圖\n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
    "    \n",
    "    # 返回臉的資訊\n",
    "    faces = detector(gray)\n",
    "    \n",
    "    # 當人臉有一個以上時，則進入條件式\n",
    "    if len(faces) > 0:\n",
    "        \n",
    "        # 遍尋每張人臉\n",
    "        for face in faces:\n",
    "            \n",
    "            # 擷取圖片中，人臉的部位圖片\n",
    "            crop_img_gray = gray[face.top():face.bottom(),face.left():face.right()]\n",
    "            \n",
    "            # 偵測人臉的眼睛\n",
    "            eyes = eye_cascade.detectMultiScale(\n",
    "                crop_img_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",
    "            )\n",
    "            \n",
    "            # 使用全域變數\n",
    "            global face_filename\n",
    "            \n",
    "            # 當人臉有兩個眼睛時，則進入條件式\n",
    "            if(len(eyes) == 2):\n",
    "                \n",
    "                # 字串格式化處理儲存位置以及檔案名稱\n",
    "                name = '{}{}.pgm'.format(saveDir, face_filename)\n",
    "                print(name)\n",
    "                \n",
    "                # 統一圖片大小\n",
    "                crop_img_gray = cv2.resize(crop_img_gray, (400, 400))\n",
    "                \n",
    "                # 寫入圖片\n",
    "                cv2.imwrite(name, crop_img_gray)\n",
    "                \n",
    "                # 檔名流水號遞增\n",
    "                face_filename+= 1\n",
    "                \n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 程式進入點\n",
    "if __name__ == '__main__':\n",
    "    \n",
    "    # 影片檔來源路徑\n",
    "    # videoPath = 'data/video/Obama.mp4'\n",
    "    videoPath = 'data/video/TT_TZUYU.mp4'\n",
    "    \n",
    "    # 儲存圖片檔路徑\n",
    "    # saveDir = 'data/image/train/Obama/'\n",
    "    saveDir = 'data/image/train/TZUYU/'\n",
    "\n",
    "    videoToPng(videoPath, saveDir)"
   ]
  }
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