diff --git a/Aplicacion/Codigo/Gestion_de_directorios.ipynb b/Aplicacion/Codigo/Gestion_de_directorios.ipynb
deleted file mode 100644
index 530722d98d76ac261ca3c6f78e6f353ca53aad80..0000000000000000000000000000000000000000
--- a/Aplicacion/Codigo/Gestion_de_directorios.ipynb
+++ /dev/null
@@ -1,455 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Importamos modulos"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os, shutil\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "from sklearn.model_selection import train_test_split"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Lectura de datos (metadatos)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>FILE NAME</th>\n",
- " <th>CLASE</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>COVID-19(1)</td>\n",
- " <td>COVID-19</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>COVID-19(2)</td>\n",
- " <td>COVID-19</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>COVID-19(3)</td>\n",
- " <td>COVID-19</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>COVID-19(4)</td>\n",
- " <td>COVID-19</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>COVID-19(5)</td>\n",
- " <td>COVID-19</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " FILE NAME CLASE\n",
- "0 COVID-19(1) COVID-19\n",
- "1 COVID-19(2) COVID-19\n",
- "2 COVID-19(3) COVID-19\n",
- "3 COVID-19(4) COVID-19\n",
- "4 COVID-19(5) COVID-19"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "metadata = pd.read_csv('../datos/Metadata/metadatos.csv')\n",
- "metadata.head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Separamos en entrenamiento y prueba"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "train, test = train_test_split(metadata, test_size=1/3, stratify=metadata.CLASE)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Observamos las mismas proporciones en todos los subconjuntos"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Creamos en el directorio datos los directorios train y test donde almacenaremos las imagenes correspondientes"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Intentamos obtener los nombres reales de las imagenes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "mypath = \"../datos/COVID-19\"\n",
- "covid_files = [f for f in os.listdir(mypath)]\n",
- "#covid_files"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "mypath = \"../datos/NORMAL\"\n",
- "normal_files = [f for f in os.listdir(mypath)]\n",
- "#normal_files"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "mypath = \"../datos/Viral Pneumonia\"\n",
- "pneumonia_files = [f for f in os.listdir(mypath)]\n",
- "#pneumonia_files"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [],
- "source": [
- "files= normal_files+pneumonia_files+covid_files\n",
- "#files.append(pneumonia_files)\n",
- "#files.append(covid_files)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "clase_n = ['NORMAL' for i in range(0,len(normal_files))]\n",
- "clase_p = ['Viral Pneumonia' for i in range(0,len(pneumonia_files))]\n",
- "clase_c = ['COVID-19' for i in range(0,len(covid_files))]\n",
- "\n",
- "clase = clase_n+clase_p+clase_c"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>FILENAME</th>\n",
- " <th>CLASS</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>NORMAL (1).png</td>\n",
- " <td>NORMAL</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>NORMAL (10).png</td>\n",
- " <td>NORMAL</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>NORMAL (100).png</td>\n",
- " <td>NORMAL</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>NORMAL (101).png</td>\n",
- " <td>NORMAL</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>NORMAL (102).png</td>\n",
- " <td>NORMAL</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2900</th>\n",
- " <td>COVID-19(215).png</td>\n",
- " <td>COVID-19</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2901</th>\n",
- " <td>COVID-19(216).png</td>\n",
- " <td>COVID-19</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2902</th>\n",
- " <td>COVID-19(217).png</td>\n",
- " <td>COVID-19</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2903</th>\n",
- " <td>COVID-19(218).png</td>\n",
- " <td>COVID-19</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2904</th>\n",
- " <td>COVID-19(219).png</td>\n",
- " <td>COVID-19</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>2905 rows × 2 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " FILENAME CLASS\n",
- "0 NORMAL (1).png NORMAL\n",
- "1 NORMAL (10).png NORMAL\n",
- "2 NORMAL (100).png NORMAL\n",
- "3 NORMAL (101).png NORMAL\n",
- "4 NORMAL (102).png NORMAL\n",
- "... ... ...\n",
- "2900 COVID-19(215).png COVID-19\n",
- "2901 COVID-19(216).png COVID-19\n",
- "2902 COVID-19(217).png COVID-19\n",
- "2903 COVID-19(218).png COVID-19\n",
- "2904 COVID-19(219).png COVID-19\n",
- "\n",
- "[2905 rows x 2 columns]"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df = pd.DataFrame(list(zip(files, clase)), \n",
- " columns =['FILENAME', 'CLASS']) \n",
- "df "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Separacion en entrenamiento y prueba"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [],
- "source": [
- "train, test = train_test_split(df, test_size=1/3, stratify=df.CLASS)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "METADATA:\n",
- "Total frecuencies: \n",
- " Viral Pneumonia 1345\n",
- "NORMAL 1341\n",
- "COVID-19 219\n",
- "Name: CLASS, dtype: int64\n",
- "Proportion: \n",
- " Viral Pneumonia 0.462995\n",
- "NORMAL 0.461618\n",
- "COVID-19 0.075387\n",
- "Name: CLASS, dtype: float64\n",
- "----------------------------------------------\n",
- "TRAIN:\n",
- "Total frecuencies: \n",
- " Viral Pneumonia 896\n",
- "NORMAL 894\n",
- "COVID-19 146\n",
- "Name: CLASS, dtype: int64\n",
- "Proportion: \n",
- " Viral Pneumonia 0.462810\n",
- "NORMAL 0.461777\n",
- "COVID-19 0.075413\n",
- "Name: CLASS, dtype: float64\n",
- "----------------------------------------------\n",
- "TEST:\n",
- "Total frecuencies: \n",
- " Viral Pneumonia 449\n",
- "NORMAL 447\n",
- "COVID-19 73\n",
- "Name: CLASS, dtype: int64\n",
- "Proportion: \n",
- " Viral Pneumonia 0.463364\n",
- "NORMAL 0.461300\n",
- "COVID-19 0.075335\n",
- "Name: CLASS, dtype: float64\n"
- ]
- }
- ],
- "source": [
- "print(\"METADATA:\")\n",
- "print(\"Total frecuencies: \\n\",df.CLASS.value_counts())\n",
- "print(\"Proportion: \\n\",df.CLASS.value_counts()/df.shape[0])\n",
- "print(\"----------------------------------------------\")\n",
- "print(\"TRAIN:\")\n",
- "print(\"Total frecuencies: \\n\",train.CLASS.value_counts())\n",
- "print(\"Proportion: \\n\",train.CLASS.value_counts()/train.shape[0])\n",
- "print(\"----------------------------------------------\")\n",
- "print(\"TEST:\")\n",
- "print(\"Total frecuencies: \\n\",test.CLASS.value_counts())\n",
- "print(\"Proportion: \\n\",test.CLASS.value_counts()/test.shape[0])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Comprobamos que las proporciones se mantienen, hemos realizado un muestreo balanceado."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [],
- "source": [
- "for i in range(train.shape[0]):\n",
- " #train.iloc[i,1] = CLASE\n",
- " #train.iloc[i,0] = FILE NAME\n",
- " src = os.path.join(\"../datos\",str(train.iloc[i,1]),str(train.iloc[i,0]))\n",
- " dst = os.path.join(\"../datos/train\",str(train.iloc[i,1]),str(train.iloc[i,0]))\n",
- " shutil.copyfile(src,dst)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [],
- "source": [
- "for i in range(test.shape[0]):\n",
- " #train.iloc[i,1] = CLASE\n",
- " #train.iloc[i,0] = FILE NAME\n",
- " src = os.path.join(\"../datos\",str(test.iloc[i,1]),str(test.iloc[i,0]))\n",
- " dst = os.path.join(\"../datos/test\",str(test.iloc[i,1]),str(test.iloc[i,0]))\n",
- " shutil.copyfile(src,dst)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}