{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "im = np.array(Image.open('data/src/lena_square.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "im_R = im.copy()\n",
    "im_R[:, :, (1, 2)] = 0\n",
    "im_G = im.copy()\n",
    "im_G[:, :, (0, 2)] = 0\n",
    "im_B = im.copy()\n",
    "im_B[:, :, (0, 1)] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 横に並べて結合（どれでもよい）\n",
    "im_RGB = np.concatenate((im_R, im_G, im_B), axis=1)\n",
    "# im_RGB = np.hstack((im_R, im_G, im_B))\n",
    "# im_RGB = np.c_['1', im_R, im_G, im_B]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pil_img_RGB = Image.fromarray(im_RGB)\n",
    "pil_img_RGB.save('data/dst/lena_numpy_split_color.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "im_gray = 0.299 * im[:, :, 0] + 0.587 * im[:, :, 1] + 0.114 * im[:, :, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint8\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "print(im.dtype)\n",
    "print(im_gray.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(512, 512, 3)\n",
      "(512, 512)\n"
     ]
    }
   ],
   "source": [
    "print(im.shape)\n",
    "print(im_gray.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pil_img_gray = Image.fromarray(np.uint8(im_gray))\n",
    "pil_img_gray.save('data/dst/lena_numpy_gray.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "im_swap = im.copy()\n",
    "im_swap[:, :, 0], im_swap[:, :, 2] = im[:, :, 2], im[:, :, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pil_img_swap = Image.fromarray(im_swap)\n",
    "pil_img_swap.save('data/dst/lena_numpy_swap_color.jpg')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}