1 files changed, 0 insertions, 202 deletions
diff --git a/megapixels/notebooks/_local_scratch.ipynb b/megapixels/notebooks/_local_scratch.ipynb
deleted file mode 100644
index cee17cba..00000000
--- a/megapixels/notebooks/_local_scratch.ipynb
+++ /dev/null
@@ -1,202 +0,0 @@
-{
- "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",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}