{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scratch pad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "from os.path import join\n",
    "from pathlib import Path\n",
    "import random\n",
    "\n",
    "import pandas as pd\n",
    "import cv2 as cv\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sys\n",
    "sys.path.append('/work/megapixels_dev/megapixels')\n",
    "from app.models.bbox import BBox\n",
    "from app.utils import im_utils, file_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "a= [1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp_filepath = '/data_store_ssd/datasets/people/lfw/metadata/filepath.csv'\n",
    "df_filepath = pd.read_csv(fp_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_index = 12467"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12474\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "index                   12851\n",
       "ext                       jpg\n",
       "fn        Vladimir_Putin_0029\n",
       "subdir         Vladimir_Putin\n",
       "Name: 12474, dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_index += 1\n",
    "print(image_index)\n",
    "df_filepath.iloc[image_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import imutils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function build_montages in module imutils.convenience:\n",
      "\n",
      "build_montages(image_list, image_shape, montage_shape)\n",
      "    ---------------------------------------------------------------------------------------------\n",
      "    author: Kyle Hounslow\n",
      "    ---------------------------------------------------------------------------------------------\n",
      "    Converts a list of single images into a list of 'montage' images of specified rows and columns.\n",
      "    A new montage image is started once rows and columns of montage image is filled.\n",
      "    Empty space of incomplete montage images are filled with black pixels\n",
      "    ---------------------------------------------------------------------------------------------\n",
      "    :param image_list: python list of input images\n",
      "    :param image_shape: tuple, size each image will be resized to for display (width, height)\n",
      "    :param montage_shape: tuple, shape of image montage (width, height)\n",
      "    :return: list of montage images in numpy array format\n",
      "    ---------------------------------------------------------------------------------------------\n",
      "    \n",
      "    example usage:\n",
      "    \n",
      "    # load single image\n",
      "    img = cv2.imread('lena.jpg')\n",
      "    # duplicate image 25 times\n",
      "    num_imgs = 25\n",
      "    img_list = []\n",
      "    for i in xrange(num_imgs):\n",
      "        img_list.append(img)\n",
      "    # convert image list into a montage of 256x256 images tiled in a 5x5 montage\n",
      "    montages = make_montages_of_images(img_list, (256, 256), (5, 5))\n",
      "    # iterate through montages and display\n",
      "    for montage in montages:\n",
      "        cv2.imshow('montage image', montage)\n",
      "        cv2.waitKey(0)\n",
      "    \n",
      "    ----------------------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(imutils.build_montages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:megapixels]",
   "language": "python",
   "name": "conda-env-megapixels-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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