cling-cv.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# cling-jupyterでopencv顔検出"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.インクルードパスとライブラリパスを通す\n",
    "` .I path` でインクルードパスを、 `.L path`でライブラリのパスを通します.\n",
    "本当は１セルにまとめて実行したいところですが、1セルにまとめるとそのあとのインクルードで失敗する現象を確認しました. なので、仕方なく1セルごとに実行・・・.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ".I /usr/local/Cellar/opencv/3.3.0_3/include"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ".L /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_highgui.3.3.0.dylib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ".L /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_core.3.3.0.dylib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ".L /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_imgproc.3.3.0.dylib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ".L /usr/local/Cellar/opencv/3.3.0_3/lib/libopencv_objdetect.3.3.0.dylib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 2. あとは普通に書いて実行する\n",
    "パスを通して環境が出来上がったので、ヘッダファイルインクルードして普通にC++を書く."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#include <opencv2/core.hpp>\n",
    "#include <opencv2/imgproc.hpp>\n",
    "#include <opencv2/highgui.hpp>\n",
    "#include <opencv2/objdetect.hpp>\n",
    "#include <iostream>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(cv::Mat &) @0x1026d4118\n"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv::Mat src_img;\n",
    "cv::Mat dst_img;\n",
    "src_img = cv::imread(\"/Users/xxxxxx/lenna.png\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### メモ\n",
    "python-jupyter的な感覚で試行錯誤のために同じセルをガンガン実行するためのコツとして、変数の宣言は別のセルにするのが良いです.  \n",
    "あくまでもC++なので、変数二重で宣言すると当然エラーになります.  \n",
    "エラー出すと、たまにnotebookごと落ちます・・・"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv::CascadeClassifier cascade;\n",
    "std::vector<cv::Rect> faces;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(void) @0x700008997358\n"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cascade.load(\"/usr/local/Cellar/opencv/3.3.0_3/share/OpenCV/haarcascades/haarcascade_frontalface_default.xml\");\n",
    "cascade.detectMultiScale(src_img, faces, 1.1, 3, 0, cv::Size(100,100));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for(auto r : faces){\n",
    "cv::rectangle(src_img, cv::Point(r.x, r.y), cv::Point(r.x + r.width, r.y+r.height), cv::Scalar(0, 200), 3, CV_AA);\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(void) @0x700008997358\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv::namedWindow(\"lenna\", CV_WINDOW_AUTOSIZE);\n",
    "cv::imshow(\"lenna\", src_img);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(void) @0x70000ad3b358\n"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv::destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "C++11",
   "language": "",
   "name": "cling-cpp11"
  },
  "language_info": {
   "codemirror_mode": "c++",
   "file_extension": ".c++",
   "mimetype": "text/x-c++src",
   "name": "c++"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}