{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "im1 = Image.open('data/src/lena.jpg')\n",
    "im2 = Image.open('data/src/rocket.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_h(im1, im2):\n",
    "    dst = Image.new('RGB', (im1.width + im2.width, im1.height))\n",
    "    dst.paste(im1, (0, 0))\n",
    "    dst.paste(im2, (im1.width, 0))\n",
    "    return dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_v(im1, im2):\n",
    "    dst = Image.new('RGB', (im1.width, im1.height + im2.height))\n",
    "    dst.paste(im1, (0, 0))\n",
    "    dst.paste(im2, (0, im1.height))\n",
    "    return dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "get_concat_h(im1, im1).save('data/dst/pillow_concat_h.jpg')\n",
    "get_concat_v(im1, im1).save('data/dst/pillow_concat_v.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_h_cut(im1, im2):\n",
    "    dst = Image.new('RGB', (im1.width + im2.width, min(im1.height, im2.height)))\n",
    "    dst.paste(im1, (0, 0))\n",
    "    dst.paste(im2, (im1.width, 0))\n",
    "    return dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_v_cut(im1, im2):\n",
    "    dst = Image.new('RGB', (min(im1.width, im2.width), im1.height + im2.height))\n",
    "    dst.paste(im1, (0, 0))\n",
    "    dst.paste(im2, (0, im1.height))\n",
    "    return dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "get_concat_h_cut(im1, im2).save('data/dst/pillow_concat_h_cut.jpg')\n",
    "get_concat_v_cut(im1, im2).save('data/dst/pillow_concat_v_cut.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_h_cut_center(im1, im2):\n",
    "    dst = Image.new('RGB', (im1.width + im2.width, min(im1.height, im2.height)))\n",
    "    dst.paste(im1, (0, 0))\n",
    "    dst.paste(im2, (im1.width, (im1.height - im2.height) // 2))\n",
    "    return dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_v_cut_center(im1, im2):\n",
    "    dst = Image.new('RGB', (min(im1.width, im2.width), im1.height + im2.height))\n",
    "    dst.paste(im1, (0, 0))\n",
    "    dst.paste(im2, ((im1.width - im2.width) // 2, im1.height))\n",
    "    return dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "get_concat_h_cut_center(im1, im2).save('data/dst/pillow_concat_h_cut_center.jpg')\n",
    "get_concat_v_cut_center(im1, im2).save('data/dst/pillow_concat_v_cut_center.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_h_blank(im1, im2, color=(0, 0, 0)):\n",
    "    dst = Image.new('RGB', (im1.width + im2.width, max(im1.height, im2.height)), color)\n",
    "    dst.paste(im1, (0, 0))\n",
    "    dst.paste(im2, (im1.width, 0))\n",
    "    return dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_v_blank(im1, im2, color=(0, 0, 0)):\n",
    "    dst = Image.new('RGB', (max(im1.width, im2.width), im1.height + im2.height), color)\n",
    "    dst.paste(im1, (0, 0))\n",
    "    dst.paste(im2, (0, im1.height))\n",
    "    return dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "get_concat_h_blank(im1, im2).save('data/dst/pillow_concat_h_blank.jpg')\n",
    "get_concat_v_blank(im1, im2, (0, 64, 128)).save('data/dst/pillow_concat_v_blank.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_h_resize(im1, im2, resample=Image.BICUBIC, resize_big_image=True):\n",
    "    if im1.height == im2.height:\n",
    "        _im1 = im1\n",
    "        _im2 = im2\n",
    "    elif (((im1.height > im2.height) and resize_big_image) or\n",
    "          ((im1.height < im2.height) and not resize_big_image)):\n",
    "        _im1 = im1.resize((int(im1.width * im2.height / im1.height), im2.height), resample=resample)\n",
    "        _im2 = im2\n",
    "    else:\n",
    "        _im1 = im1\n",
    "        _im2 = im2.resize((int(im2.width * im1.height / im2.height), im1.height), resample=resample)\n",
    "    dst = Image.new('RGB', (_im1.width + _im2.width, _im1.height))\n",
    "    dst.paste(_im1, (0, 0))\n",
    "    dst.paste(_im2, (_im1.width, 0))\n",
    "    return dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_v_resize(im1, im2, resample=Image.BICUBIC, resize_big_image=True):\n",
    "    if im1.width == im2.width:\n",
    "        _im1 = im1\n",
    "        _im2 = im2\n",
    "    elif (((im1.width > im2.width) and resize_big_image) or\n",
    "          ((im1.width < im2.width) and not resize_big_image)):\n",
    "        _im1 = im1.resize((im2.width, int(im1.height * im2.width / im1.width)), resample=resample)\n",
    "        _im2 = im2\n",
    "    else:\n",
    "        _im1 = im1\n",
    "        _im2 = im2.resize((im1.width, int(im2.height * im1.width / im2.width)), resample=resample)\n",
    "    dst = Image.new('RGB', (_im1.width, _im1.height + _im2.height))\n",
    "    dst.paste(_im1, (0, 0))\n",
    "    dst.paste(_im2, (0, _im1.height))\n",
    "    return dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "get_concat_h_resize(im1, im2).save('data/dst/pillow_concat_h_resize.jpg')\n",
    "get_concat_v_resize(im1, im2, resize_big_image=False).save('data/dst/pillow_concat_v_resize.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_h_multi_blank(im_list):\n",
    "    _im = im_list.pop(0)\n",
    "    for im in im_list:\n",
    "        _im = get_concat_h_blank(_im, im)\n",
    "    return _im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "get_concat_h_multi_blank([im1, im2, im1]).save('data/dst/pillow_concat_h_multi_blank.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_h_multi_resize(im_list, resample=Image.BICUBIC):\n",
    "    min_height = min(im.height for im in im_list)\n",
    "    im_list_resize = [im.resize((int(im.width * min_height / im.height), min_height),resample=resample)\n",
    "                      for im in im_list]\n",
    "    total_width = sum(im.width for im in im_list_resize)\n",
    "    dst = Image.new('RGB', (total_width, min_height))\n",
    "    pos_x = 0\n",
    "    for im in im_list_resize:\n",
    "        dst.paste(im, (pos_x, 0))\n",
    "        pos_x += im.width\n",
    "    return dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_v_multi_resize(im_list, resample=Image.BICUBIC):\n",
    "    min_width = min(im.width for im in im_list)\n",
    "    im_list_resize = [im.resize((min_width, int(im.height * min_width / im.width)),resample=resample)\n",
    "                      for im in im_list]\n",
    "    total_height = sum(im.height for im in im_list_resize)\n",
    "    dst = Image.new('RGB', (min_width, total_height))\n",
    "    pos_y = 0\n",
    "    for im in im_list_resize:\n",
    "        dst.paste(im, (0, pos_y))\n",
    "        pos_y += im.height\n",
    "    return dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "get_concat_h_multi_resize([im1, im2, im1]).save('data/dst/pillow_concat_h_multi_resize.jpg')\n",
    "get_concat_v_multi_resize([im1, im2, im1]).save('data/dst/pillow_concat_v_multi_resize.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_tile_resize(im_list_2d, resample=Image.BICUBIC):\n",
    "    im_list_v = [get_concat_h_multi_resize(im_list_h, resample=resample) for im_list_h in im_list_2d]\n",
    "    return get_concat_v_multi_resize(im_list_v, resample=resample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "get_concat_tile_resize([[im1],\n",
    "                        [im1, im2],\n",
    "                        [im1, im2, im1]]).save('data/dst/pillow_concat_tile_resize.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_h_repeat(im, column):\n",
    "    dst = Image.new('RGB', (im.width * column, im.height))\n",
    "    for x in range(column):\n",
    "        dst.paste(im, (x * im.width, 0))\n",
    "    return dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_v_repeat(im, row):\n",
    "    dst = Image.new('RGB', (im.width, im.height * row))\n",
    "    for y in range(row):\n",
    "        dst.paste(im, (0, y * im.height))\n",
    "    return dst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_concat_tile_repeat(im, row, column):\n",
    "    dst_h = get_concat_h_repeat(im, column)\n",
    "    return get_concat_v_repeat(dst_h, row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "im_s = im1.resize((im1.width // 2, im1.height // 2))\n",
    "get_concat_tile_repeat(im_s, 3, 4).save('data/dst/pillow_concat_tile_repeat.jpg')"
   ]
  }
 ],
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