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{
"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
}
|
