{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "im1 = cv2.imread('data/src/lena.jpg')\n",
    "im2 = cv2.imread('data/src/rocket.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im_v = cv2.vconcat([im1, im1])\n",
    "cv2.imwrite('data/dst/opencv_vconcat.jpg', im_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im_v_np = np.tile(im1, (2, 1, 1))\n",
    "cv2.imwrite('data/dst/opencv_vconcat_np.jpg', im_v_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def vconcat_resize_min(im_list, interpolation=cv2.INTER_CUBIC):\n",
    "    w_min = min(im.shape[1] for im in im_list)\n",
    "    im_list_resize = [cv2.resize(im, (w_min, int(im.shape[0] * w_min / im.shape[1])), interpolation=interpolation)\n",
    "                      for im in im_list]\n",
    "    return cv2.vconcat(im_list_resize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im_v_resize = vconcat_resize_min([im1, im2, im1])\n",
    "cv2.imwrite('data/dst/opencv_vconcat_resize.jpg', im_v_resize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im_h = cv2.hconcat([im1, im1])\n",
    "cv2.imwrite('data/dst/opencv_hconcat.jpg', im_h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im_h_np = np.tile(im1, (1, 2, 1))\n",
    "cv2.imwrite('data/dst/opencv_hconcat_np.jpg', im_h_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hconcat_resize_min(im_list, interpolation=cv2.INTER_CUBIC):\n",
    "    h_min = min(im.shape[0] for im in im_list)\n",
    "    im_list_resize = [cv2.resize(im, (int(im.shape[1] * h_min / im.shape[0]), h_min), interpolation=interpolation)\n",
    "                      for im in im_list]\n",
    "    return cv2.hconcat(im_list_resize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im_h_resize = hconcat_resize_min([im1, im2, im1])\n",
    "cv2.imwrite('data/dst/opencv_hconcat_resize.jpg', im_h_resize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def concat_tile(im_list_2d):\n",
    "    return cv2.vconcat([cv2.hconcat(im_list_h) for im_list_h in im_list_2d])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im1_s = cv2.resize(im1, dsize=(0, 0), fx=0.5, fy=0.5)\n",
    "im_tile = concat_tile([[im1_s, im1_s, im1_s, im1_s],\n",
    "                       [im1_s, im1_s, im1_s, im1_s],\n",
    "                       [im1_s, im1_s, im1_s, im1_s]])\n",
    "cv2.imwrite('data/dst/opencv_concat_tile.jpg', im_tile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im_tile_np = np.tile(im1_s, (3, 4, 1))\n",
    "cv2.imwrite('data/dst/opencv_concat_tile_np.jpg', im_tile_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def concat_tile_resize(im_list_2d, interpolation=cv2.INTER_CUBIC):\n",
    "    im_list_v = [hconcat_resize_min(im_list_h, interpolation=cv2.INTER_CUBIC) for im_list_h in im_list_2d]\n",
    "    return vconcat_resize_min(im_list_v, interpolation=cv2.INTER_CUBIC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im_tile_resize = concat_tile_resize([[im1],\n",
    "                                     [im1, im2, im1, im2, im1],\n",
    "                                     [im1, im2, im1]])\n",
    "cv2.imwrite('data/dst/opencv_concat_tile_resize.jpg', im_tile_resize)"
   ]
  }
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