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| a | b/get_data.ipynb | ||
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| 1 | { |
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| 2 | "cells": [ |
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| 3 | { |
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| 4 | "cell_type": "code", |
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| 5 | "execution_count": 12, |
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| 6 | "metadata": {}, |
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| 7 | "outputs": [], |
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| 8 | "source": [ |
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| 9 | "import os\n", |
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| 10 | "import numpy as np\n", |
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| 11 | "import pandas as pd\n", |
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| 12 | "import pickle" |
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| 13 | ] |
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| 14 | }, |
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| 15 | { |
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| 16 | "cell_type": "code", |
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| 17 | "execution_count": 2, |
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| 18 | "metadata": {}, |
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| 19 | "outputs": [ |
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| 20 | { |
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| 21 | "name": "stdout", |
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| 22 | "output_type": "stream", |
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| 23 | "text": [ |
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| 24 | "/home/ark576/Knee_Segmentation_Project\r\n" |
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| 25 | ] |
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| 26 | } |
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| 27 | ], |
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| 28 | "source": [ |
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| 29 | "!pwd" |
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| 30 | ] |
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| 31 | }, |
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| 32 | { |
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| 33 | "cell_type": "code", |
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| 34 | "execution_count": 3, |
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| 35 | "metadata": {}, |
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| 36 | "outputs": [], |
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| 37 | "source": [ |
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| 38 | "train_file_names = os.listdir('./Knee Cartilage Data/Train Data')" |
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| 39 | ] |
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| 40 | }, |
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| 41 | { |
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| 42 | "cell_type": "code", |
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| 43 | "execution_count": 4, |
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| 44 | "metadata": {}, |
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| 45 | "outputs": [], |
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| 46 | "source": [ |
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| 47 | "test_file_names = os.listdir('./Knee Cartilage Data/Test Data')" |
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| 48 | ] |
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| 49 | }, |
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| 50 | { |
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| 51 | "cell_type": "code", |
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| 52 | "execution_count": 5, |
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| 53 | "metadata": {}, |
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| 54 | "outputs": [], |
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| 55 | "source": [ |
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| 56 | "val_file_names = os.listdir('./Knee Cartilage Data/Validation Data')" |
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| 57 | ] |
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| 58 | }, |
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| 59 | { |
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| 60 | "cell_type": "code", |
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| 61 | "execution_count": 14, |
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| 62 | "metadata": {}, |
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| 63 | "outputs": [], |
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| 64 | "source": [ |
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| 65 | "pickle.dump(train_file_names, open('./Knee Cartilage Data/Train Data/train_file_names','wb'))" |
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| 66 | ] |
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| 67 | }, |
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| 68 | { |
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| 69 | "cell_type": "code", |
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| 70 | "execution_count": 15, |
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| 71 | "metadata": {}, |
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| 72 | "outputs": [], |
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| 73 | "source": [ |
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| 74 | "pickle.dump(test_file_names, open('./Knee Cartilage Data/Test Data/test_file_names','wb'))" |
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| 75 | ] |
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| 76 | }, |
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| 77 | { |
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| 78 | "cell_type": "code", |
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| 79 | "execution_count": 16, |
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| 80 | "metadata": {}, |
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| 81 | "outputs": [], |
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| 82 | "source": [ |
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| 83 | "pickle.dump(val_file_names, open('./Knee Cartilage Data/Validation Data/val_file_names','wb'))" |
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| 84 | ] |
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| 85 | }, |
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| 86 | { |
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| 87 | "cell_type": "code", |
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| 88 | "execution_count": 9, |
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| 89 | "metadata": {}, |
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| 90 | "outputs": [], |
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| 91 | "source": [ |
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| 92 | "train_file_names = file_names[:35]\n", |
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| 93 | "val_file_names = file_names[35:42]\n", |
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| 94 | "test_file_names = file_names[42:]" |
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| 95 | ] |
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| 96 | }, |
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| 97 | { |
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| 98 | "cell_type": "code", |
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| 99 | "execution_count": 10, |
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| 100 | "metadata": {}, |
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| 101 | "outputs": [ |
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| 102 | { |
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| 103 | "data": { |
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| 104 | "text/plain": [ |
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| 105 | "(35,)" |
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| 106 | ] |
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| 107 | }, |
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| 108 | "execution_count": 10, |
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| 109 | "metadata": {}, |
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| 110 | "output_type": "execute_result" |
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| 111 | } |
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| 112 | ], |
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| 113 | "source": [ |
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| 114 | "train_file_names.shape" |
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| 115 | ] |
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| 116 | }, |
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| 117 | { |
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| 118 | "cell_type": "code", |
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| 119 | "execution_count": 11, |
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| 120 | "metadata": {}, |
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| 121 | "outputs": [ |
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| 122 | { |
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| 123 | "data": { |
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| 124 | "text/plain": [ |
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| 125 | "(7,)" |
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| 126 | ] |
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| 127 | }, |
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| 128 | "execution_count": 11, |
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| 129 | "metadata": {}, |
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| 130 | "output_type": "execute_result" |
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| 131 | } |
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| 132 | ], |
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| 133 | "source": [ |
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| 134 | "val_file_names.shape" |
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| 135 | ] |
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| 136 | }, |
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| 137 | { |
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| 138 | "cell_type": "code", |
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| 139 | "execution_count": 12, |
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| 140 | "metadata": {}, |
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| 141 | "outputs": [ |
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| 142 | { |
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| 143 | "data": { |
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| 144 | "text/plain": [ |
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| 145 | "(6,)" |
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| 146 | ] |
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| 147 | }, |
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| 148 | "execution_count": 12, |
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| 149 | "metadata": {}, |
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| 150 | "output_type": "execute_result" |
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| 151 | } |
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| 152 | ], |
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| 153 | "source": [ |
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| 154 | "test_file_names.shape" |
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| 155 | ] |
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| 156 | }, |
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| 157 | { |
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| 158 | "cell_type": "code", |
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| 159 | "execution_count": 13, |
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| 160 | "metadata": {}, |
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| 161 | "outputs": [], |
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| 162 | "source": [ |
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| 163 | "np.save('train_file_names',train_file_names)" |
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| 164 | ] |
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| 165 | }, |
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| 166 | { |
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| 167 | "cell_type": "code", |
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| 168 | "execution_count": 14, |
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| 169 | "metadata": {}, |
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| 170 | "outputs": [], |
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| 171 | "source": [ |
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| 172 | "np.save('val_file_names', val_file_names)\n", |
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| 173 | "np.save('test_file_names', test_file_names)" |
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| 174 | ] |
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| 175 | }, |
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| 176 | { |
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| 177 | "cell_type": "code", |
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| 178 | "execution_count": null, |
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| 179 | "metadata": {}, |
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| 180 | "outputs": [], |
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| 181 | "source": [ |
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| 182 | "pickle.load(open('file/path','rb'))" |
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| 183 | ] |
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| 184 | } |
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| 185 | ], |
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| 186 | "metadata": { |
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| 187 | "kernelspec": { |
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| 188 | "display_name": "Python 3", |
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| 189 | "language": "python", |
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| 190 | "name": "python3" |
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| 191 | }, |
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| 192 | "language_info": { |
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| 193 | "codemirror_mode": { |
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| 194 | "name": "ipython", |
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| 195 | "version": 3 |
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| 196 | }, |
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| 197 | "file_extension": ".py", |
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| 198 | "mimetype": "text/x-python", |
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| 199 | "name": "python", |
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| 200 | "nbconvert_exporter": "python", |
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| 201 | "pygments_lexer": "ipython3", |
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| 202 | "version": "3.6.3" |
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| 203 | } |
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| 204 | }, |
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| 205 | "nbformat": 4, |
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| 206 | "nbformat_minor": 2 |
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| 207 | } |