DE4_imaging.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b00a9de1-f097-4ccc-b6e5-c6cfcda7dc97",
   "metadata": {},
   "source": [
    "# Malware Image Classification with CNN\n",
    "In this notebook I continue working the files harvested from the system32 directory on my machine. My goal is to process the selected images to training, testing, and validation datasets. I have chosen to set my image size to 150x150. First I'll create the folder structure needed to support this experiment by using the DogsVsCats assignment from Week 5 of the Deep Learning course, shown below.\n"
   ]
  },
  {
   "attachments": {
    "53017a98-cd54-4441-b4dd-3d52335560fa.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "8e5ca1d6-3338-42b2-b4f2-cb86f5bcd082",
   "metadata": {},
   "source": [
    "![image.png](attachment:53017a98-cd54-4441-b4dd-3d52335560fa.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4a49bcb-199b-4ddd-a297-b430b1031bed",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Functions and Imports Needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d278048f-ad18-4639-8f4f-2b937edf3e4b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "# Importing Image class from PIL module \n",
    "from PIL import Image \n",
    "import pathlib\n",
    "# The resizeIM takes an image path and a save location along\n",
    "# with filename to process a single image input to a resized output.\n",
    "# This function does not destroy the input image but only copies \n",
    "# and resizes image.\n",
    "def resizeIM(im_path, save_path, file_name):\n",
    "    try:\n",
    "        # Opens a image in RGB mode\n",
    "        im = Image.open(im_path)\n",
    "        newsize = (150, 150)\n",
    "        im1 = im.resize(newsize)\n",
    "        file_name = str(file_name) + '.png'\n",
    "        save_path = save_path + \"\\\\\" + pathlib.PureWindowsPath(file_name).name\n",
    "        im1.save(save_path)\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "# The rangeSelect function takes an index range (start, end) and out directory \n",
    "# path to iterate over the dataframe and process images to the folder structure\n",
    "def rangeSelect(start, end, out):\n",
    "    for index, row in df.iterrows():\n",
    "        path = row['path']\n",
    "        if(index >= start and index <= end):\n",
    "            resizeIM(path, out, index)\n",
    "# dir_File_Cnt is used to verify file counts\n",
    "def dir_File_Cnt(path):\n",
    "    lst = os.listdir(path) # your directory path\n",
    "    number_files = len(lst)\n",
    "    print(number_files)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "775c8819-c168-4bd0-b1ec-0ccc0ee03dc3",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Process CFiles\n",
    "In this section I will process 2,154 files to the folder structure. I will divide the images as 1k to training, 500 to test, and 500 to validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5b9efa33-c561-4a56-8a65-d8f2996513bd",
   "metadata": {
    "tags": []
   },
   "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>Unnamed: 0</th>\n",
       "      <th>path</th>\n",
       "      <th>size</th>\n",
       "      <th>file_name</th>\n",
       "      <th>file_ext</th>\n",
       "      <th>dim</th>\n",
       "      <th>file_type</th>\n",
       "      <th>md5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>208</td>\n",
       "      <td>sys32imgs\\BatteryWidgetAddin.cat.png</td>\n",
       "      <td>9315</td>\n",
       "      <td>BatteryWidgetAddin.cat.png</td>\n",
       "      <td>.png</td>\n",
       "      <td>96</td>\n",
       "      <td>cat</td>\n",
       "      <td>3f08ffce1a59a3a1dd1b2cc1f376881b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>506</td>\n",
       "      <td>sys32imgs\\Containers-ApplicationGuard-Package~...</td>\n",
       "      <td>7312</td>\n",
       "      <td>Containers-ApplicationGuard-Package~31bf3856ad...</td>\n",
       "      <td>.png</td>\n",
       "      <td>85</td>\n",
       "      <td>cat</td>\n",
       "      <td>2605f90b8b803b0d8093c84e9c9b7cb4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>507</td>\n",
       "      <td>sys32imgs\\Containers-ApplicationGuard-Package~...</td>\n",
       "      <td>7205</td>\n",
       "      <td>Containers-ApplicationGuard-Package~31bf3856ad...</td>\n",
       "      <td>.png</td>\n",
       "      <td>84</td>\n",
       "      <td>cat</td>\n",
       "      <td>6cfc42c37dcff85168682160d592c3d0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>508</td>\n",
       "      <td>sys32imgs\\Containers-ApplicationGuard-Package~...</td>\n",
       "      <td>7263</td>\n",
       "      <td>Containers-ApplicationGuard-Package~31bf3856ad...</td>\n",
       "      <td>.png</td>\n",
       "      <td>85</td>\n",
       "      <td>cat</td>\n",
       "      <td>f0bc379b2212a3885259fd8c34720d5e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>509</td>\n",
       "      <td>sys32imgs\\Containers-ApplicationGuard-Package~...</td>\n",
       "      <td>7425</td>\n",
       "      <td>Containers-ApplicationGuard-Package~31bf3856ad...</td>\n",
       "      <td>.png</td>\n",
       "      <td>86</td>\n",
       "      <td>cat</td>\n",
       "      <td>aa24d9bdaec878e2e86c11a2dd35a9f3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2149</th>\n",
       "      <td>6862</td>\n",
       "      <td>sys32imgs\\{97C9A618-D40C-40EE-8563-8799D0DB987...</td>\n",
       "      <td>8105</td>\n",
       "      <td>{97C9A618-D40C-40EE-8563-8799D0DB9872}~mfencbd...</td>\n",
       "      <td>.png</td>\n",
       "      <td>90</td>\n",
       "      <td>cat</td>\n",
       "      <td>25de8e0ecb1f47a7ad54f2301b81bc86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2150</th>\n",
       "      <td>6863</td>\n",
       "      <td>sys32imgs\\{A6CE337F-CBA5-412D-A06A-D872F2A8F08...</td>\n",
       "      <td>8105</td>\n",
       "      <td>{A6CE337F-CBA5-412D-A06A-D872F2A8F089}~mfencbd...</td>\n",
       "      <td>.png</td>\n",
       "      <td>90</td>\n",
       "      <td>cat</td>\n",
       "      <td>25de8e0ecb1f47a7ad54f2301b81bc86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2151</th>\n",
       "      <td>6864</td>\n",
       "      <td>sys32imgs\\{AC93B605-EEAA-4818-9136-9EDA5489749...</td>\n",
       "      <td>6835</td>\n",
       "      <td>{AC93B605-EEAA-4818-9136-9EDA54897497}~mfencbd...</td>\n",
       "      <td>.png</td>\n",
       "      <td>82</td>\n",
       "      <td>cat</td>\n",
       "      <td>e9ee343b7ba6d31c0f632c46ae219c97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2152</th>\n",
       "      <td>6865</td>\n",
       "      <td>sys32imgs\\{C03C9921-D1C8-49D2-83C4-162FBC62CCA...</td>\n",
       "      <td>6814</td>\n",
       "      <td>{C03C9921-D1C8-49D2-83C4-162FBC62CCA8}~mfencrk...</td>\n",
       "      <td>.png</td>\n",
       "      <td>82</td>\n",
       "      <td>cat</td>\n",
       "      <td>7a650afb2040f5e7e655ea9fe5170799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2153</th>\n",
       "      <td>6866</td>\n",
       "      <td>sys32imgs\\{C2B58781-FD1F-4930-8577-F0EA9F4C616...</td>\n",
       "      <td>8279</td>\n",
       "      <td>{C2B58781-FD1F-4930-8577-F0EA9F4C6169}~mfencrk...</td>\n",
       "      <td>.png</td>\n",
       "      <td>90</td>\n",
       "      <td>cat</td>\n",
       "      <td>c2afb5e2b7d868a552544cc85dcf7d23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2154 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                                               path  size  \\\n",
       "0            208               sys32imgs\\BatteryWidgetAddin.cat.png  9315   \n",
       "1            506  sys32imgs\\Containers-ApplicationGuard-Package~...  7312   \n",
       "2            507  sys32imgs\\Containers-ApplicationGuard-Package~...  7205   \n",
       "3            508  sys32imgs\\Containers-ApplicationGuard-Package~...  7263   \n",
       "4            509  sys32imgs\\Containers-ApplicationGuard-Package~...  7425   \n",
       "...          ...                                                ...   ...   \n",
       "2149        6862  sys32imgs\\{97C9A618-D40C-40EE-8563-8799D0DB987...  8105   \n",
       "2150        6863  sys32imgs\\{A6CE337F-CBA5-412D-A06A-D872F2A8F08...  8105   \n",
       "2151        6864  sys32imgs\\{AC93B605-EEAA-4818-9136-9EDA5489749...  6835   \n",
       "2152        6865  sys32imgs\\{C03C9921-D1C8-49D2-83C4-162FBC62CCA...  6814   \n",
       "2153        6866  sys32imgs\\{C2B58781-FD1F-4930-8577-F0EA9F4C616...  8279   \n",
       "\n",
       "                                              file_name file_ext  dim  \\\n",
       "0                            BatteryWidgetAddin.cat.png     .png   96   \n",
       "1     Containers-ApplicationGuard-Package~31bf3856ad...     .png   85   \n",
       "2     Containers-ApplicationGuard-Package~31bf3856ad...     .png   84   \n",
       "3     Containers-ApplicationGuard-Package~31bf3856ad...     .png   85   \n",
       "4     Containers-ApplicationGuard-Package~31bf3856ad...     .png   86   \n",
       "...                                                 ...      ...  ...   \n",
       "2149  {97C9A618-D40C-40EE-8563-8799D0DB9872}~mfencbd...     .png   90   \n",
       "2150  {A6CE337F-CBA5-412D-A06A-D872F2A8F089}~mfencbd...     .png   90   \n",
       "2151  {AC93B605-EEAA-4818-9136-9EDA54897497}~mfencbd...     .png   82   \n",
       "2152  {C03C9921-D1C8-49D2-83C4-162FBC62CCA8}~mfencrk...     .png   82   \n",
       "2153  {C2B58781-FD1F-4930-8577-F0EA9F4C6169}~mfencrk...     .png   90   \n",
       "\n",
       "     file_type                               md5  \n",
       "0          cat  3f08ffce1a59a3a1dd1b2cc1f376881b  \n",
       "1          cat  2605f90b8b803b0d8093c84e9c9b7cb4  \n",
       "2          cat  6cfc42c37dcff85168682160d592c3d0  \n",
       "3          cat  f0bc379b2212a3885259fd8c34720d5e  \n",
       "4          cat  aa24d9bdaec878e2e86c11a2dd35a9f3  \n",
       "...        ...                               ...  \n",
       "2149       cat  25de8e0ecb1f47a7ad54f2301b81bc86  \n",
       "2150       cat  25de8e0ecb1f47a7ad54f2301b81bc86  \n",
       "2151       cat  e9ee343b7ba6d31c0f632c46ae219c97  \n",
       "2152       cat  7a650afb2040f5e7e655ea9fe5170799  \n",
       "2153       cat  c2afb5e2b7d868a552544cc85dcf7d23  \n",
       "\n",
       "[2154 rows x 8 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I use the generated index for this dataframe as I'll need to\n",
    "# keep track of my image count to ensure proper distribution to\n",
    "# the train and test folder structure\n",
    "df = pd.read_csv('cFileSelection.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4f36ae9b-8501-4fa1-87e5-fead75a19455",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Setup output paths for function calls\n",
    "train_path = 'bvsc/train/cFiles'\n",
    "test_path = 'bvsc/test/cFiles'\n",
    "val_path = 'bvsc/validation/cFiles'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "32d204e1-0eec-4df7-8c20-7407cb5be4b2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Use the range select function to process\n",
    "# all cFiles to the folder structure\n",
    "rangeSelect(1,1000,train_path) #1k\n",
    "rangeSelect(1001,1500,test_path) #500\n",
    "rangeSelect(1502,2001,val_path) #500"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62443f5c-64f2-4ce0-ae54-2c78b4867670",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Process bFiles\n",
    "In this section I will process 1,292 files to the folder structure. I will divide the images as 1k to training, 500 to test, and 500 to validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4c1cd929-24f3-43b8-be96-8f448479c8d5",
   "metadata": {
    "tags": []
   },
   "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>Unnamed: 0</th>\n",
       "      <th>path</th>\n",
       "      <th>size</th>\n",
       "      <th>file_name</th>\n",
       "      <th>file_ext</th>\n",
       "      <th>dim</th>\n",
       "      <th>file_type</th>\n",
       "      <th>md5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26</td>\n",
       "      <td>sys32imgs\\aadjcsp.dll.png</td>\n",
       "      <td>47300</td>\n",
       "      <td>aadjcsp.dll.png</td>\n",
       "      <td>.png</td>\n",
       "      <td>217</td>\n",
       "      <td>dll</td>\n",
       "      <td>9f1d09153b02cc23b8a2a6fb9a00954a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>sys32imgs\\acppage.dll.png</td>\n",
       "      <td>85291</td>\n",
       "      <td>acppage.dll.png</td>\n",
       "      <td>.png</td>\n",
       "      <td>292</td>\n",
       "      <td>dll</td>\n",
       "      <td>1bebd7aa38f7d06aaa58ab8541540cee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43</td>\n",
       "      <td>sys32imgs\\AcSpecfc.dll.png</td>\n",
       "      <td>50664</td>\n",
       "      <td>AcSpecfc.dll.png</td>\n",
       "      <td>.png</td>\n",
       "      <td>225</td>\n",
       "      <td>dll</td>\n",
       "      <td>e05de3d9f75679086e61ea37435d4916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>sys32imgs\\ActionCenterCPL.dll.png</td>\n",
       "      <td>79046</td>\n",
       "      <td>ActionCenterCPL.dll.png</td>\n",
       "      <td>.png</td>\n",
       "      <td>281</td>\n",
       "      <td>dll</td>\n",
       "      <td>a7bd90244bf99f3b49ca776cc430027f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52</td>\n",
       "      <td>sys32imgs\\ActiveSyncCsp.dll.png</td>\n",
       "      <td>71006</td>\n",
       "      <td>ActiveSyncCsp.dll.png</td>\n",
       "      <td>.png</td>\n",
       "      <td>266</td>\n",
       "      <td>dll</td>\n",
       "      <td>a14d43d35eb7db27d344011bf2319553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>6851</td>\n",
       "      <td>sys32imgs\\XpsToPwgrConverter.dll.png</td>\n",
       "      <td>47862</td>\n",
       "      <td>XpsToPwgrConverter.dll.png</td>\n",
       "      <td>.png</td>\n",
       "      <td>218</td>\n",
       "      <td>dll</td>\n",
       "      <td>78f9751c86bf8098f3496cec97843f14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>6853</td>\n",
       "      <td>sys32imgs\\xwizard.exe.png</td>\n",
       "      <td>43502</td>\n",
       "      <td>xwizard.exe.png</td>\n",
       "      <td>.png</td>\n",
       "      <td>208</td>\n",
       "      <td>exe</td>\n",
       "      <td>deb694b9fedb0b84841cfd18af3d275a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>6855</td>\n",
       "      <td>sys32imgs\\xwreg.dll.png</td>\n",
       "      <td>72949</td>\n",
       "      <td>xwreg.dll.png</td>\n",
       "      <td>.png</td>\n",
       "      <td>270</td>\n",
       "      <td>dll</td>\n",
       "      <td>c345ef4c5f4182ffd98c13993fe48889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>6857</td>\n",
       "      <td>sys32imgs\\xwtpw32.dll.png</td>\n",
       "      <td>85088</td>\n",
       "      <td>xwtpw32.dll.png</td>\n",
       "      <td>.png</td>\n",
       "      <td>291</td>\n",
       "      <td>dll</td>\n",
       "      <td>e0a25842d74146225aea7a4677bf2ad0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>6858</td>\n",
       "      <td>sys32imgs\\zipcontainer.dll.png</td>\n",
       "      <td>53931</td>\n",
       "      <td>zipcontainer.dll.png</td>\n",
       "      <td>.png</td>\n",
       "      <td>232</td>\n",
       "      <td>dll</td>\n",
       "      <td>cbd2de237cff0961777d32d07a997a17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1292 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                                  path   size  \\\n",
       "0             26             sys32imgs\\aadjcsp.dll.png  47300   \n",
       "1             41             sys32imgs\\acppage.dll.png  85291   \n",
       "2             43            sys32imgs\\AcSpecfc.dll.png  50664   \n",
       "3             45     sys32imgs\\ActionCenterCPL.dll.png  79046   \n",
       "4             52       sys32imgs\\ActiveSyncCsp.dll.png  71006   \n",
       "...          ...                                   ...    ...   \n",
       "1287        6851  sys32imgs\\XpsToPwgrConverter.dll.png  47862   \n",
       "1288        6853             sys32imgs\\xwizard.exe.png  43502   \n",
       "1289        6855               sys32imgs\\xwreg.dll.png  72949   \n",
       "1290        6857             sys32imgs\\xwtpw32.dll.png  85088   \n",
       "1291        6858        sys32imgs\\zipcontainer.dll.png  53931   \n",
       "\n",
       "                       file_name file_ext  dim file_type  \\\n",
       "0                aadjcsp.dll.png     .png  217       dll   \n",
       "1                acppage.dll.png     .png  292       dll   \n",
       "2               AcSpecfc.dll.png     .png  225       dll   \n",
       "3        ActionCenterCPL.dll.png     .png  281       dll   \n",
       "4          ActiveSyncCsp.dll.png     .png  266       dll   \n",
       "...                          ...      ...  ...       ...   \n",
       "1287  XpsToPwgrConverter.dll.png     .png  218       dll   \n",
       "1288             xwizard.exe.png     .png  208       exe   \n",
       "1289               xwreg.dll.png     .png  270       dll   \n",
       "1290             xwtpw32.dll.png     .png  291       dll   \n",
       "1291        zipcontainer.dll.png     .png  232       dll   \n",
       "\n",
       "                                   md5  \n",
       "0     9f1d09153b02cc23b8a2a6fb9a00954a  \n",
       "1     1bebd7aa38f7d06aaa58ab8541540cee  \n",
       "2     e05de3d9f75679086e61ea37435d4916  \n",
       "3     a7bd90244bf99f3b49ca776cc430027f  \n",
       "4     a14d43d35eb7db27d344011bf2319553  \n",
       "...                                ...  \n",
       "1287  78f9751c86bf8098f3496cec97843f14  \n",
       "1288  deb694b9fedb0b84841cfd18af3d275a  \n",
       "1289  c345ef4c5f4182ffd98c13993fe48889  \n",
       "1290  e0a25842d74146225aea7a4677bf2ad0  \n",
       "1291  cbd2de237cff0961777d32d07a997a17  \n",
       "\n",
       "[1292 rows x 8 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I use the generated index for this dataframe as I'll need to\n",
    "# keep track of my image count to ensure proper distribution to\n",
    "# the train and test folder structure\n",
    "df = pd.read_csv('bFileSelection.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d358edf7-bfe4-45d1-b551-7508a906eb23",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Setup output paths for function calls\n",
    "train_path = 'bvsc/train/bFiles'\n",
    "test_path = 'bvsc/test/bFiles'\n",
    "val_path = 'bvsc/validation/bFiles'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5b7b3fb6-a833-4673-8cc1-17440080a868",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image size (189695529 pixels) exceeds limit of 178956970 pixels, could be decompression bomb DOS attack.\n"
     ]
    }
   ],
   "source": [
    "# Use the range select function to process\n",
    "# all cFiles to the folder structure\n",
    "rangeSelect(1,1001,train_path) #1k\n",
    "rangeSelect(792,1292,test_path) #500\n",
    "rangeSelect(700,1199,val_path) #500"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40773f62-5f60-4b92-90fa-c0d2f5d540b3",
   "metadata": {},
   "source": [
    "# Verify and Zip\n",
    "In this section I will verify my folder structure quantities and compress the dataset for future use."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2eaab5f-040b-41fa-bd59-ee491d17bac1",
   "metadata": {},
   "source": [
    "### Verify bFiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d055ed01-52cd-44bc-9ad0-2dde94a5fbcd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Setup output paths for function calls\n",
    "train_path = 'bvsc/train/bFiles'\n",
    "test_path = 'bvsc/test/bFiles'\n",
    "val_path = 'bvsc/validation/bFiles'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a252c547-0bf0-4d6b-b04d-6b2e2d3ff1c7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n"
     ]
    }
   ],
   "source": [
    "dir_File_Cnt(train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "736f2073-0f2e-4c97-9a42-a029b797149d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n"
     ]
    }
   ],
   "source": [
    "dir_File_Cnt(test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4d84fdc2-49ea-4f84-98e3-26dc3b2ac795",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n"
     ]
    }
   ],
   "source": [
    "dir_File_Cnt(val_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaade59e-c6b1-432f-b8d1-a8cc47caff22",
   "metadata": {},
   "source": [
    "### Verify cFiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "68ce03c5-7d04-4719-bb8c-53dfe7de4cec",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Setup output paths for function calls\n",
    "train_path = 'bvsc/train/cFiles'\n",
    "test_path = 'bvsc/test/cFiles'\n",
    "val_path = 'bvsc/validation/cFiles'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2272f0ff-6c5e-4b84-9838-5838aee60940",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n"
     ]
    }
   ],
   "source": [
    "dir_File_Cnt(train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bb1d4902-ed48-43b2-af84-f1de18d143c8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n"
     ]
    }
   ],
   "source": [
    "dir_File_Cnt(test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4b4c6aff-1521-45f9-9af7-9b31a66b6aa5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n"
     ]
    }
   ],
   "source": [
    "dir_File_Cnt(val_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b75efdfe-51fe-456e-9ed7-218044b0646e",
   "metadata": {},
   "source": [
    "### Zip Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "60be21e1-4a99-4d96-ae6e-6f6be08d43ed",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import zipfile\n",
    "\n",
    "zf = zipfile.ZipFile(\"bvsc.zip\", \"w\")\n",
    "for dirname, subdirs, files in os.walk(\"bvsc\"):\n",
    "    zf.write(dirname)\n",
    "    for filename in files:\n",
    "        zf.write(os.path.join(dirname, filename))\n",
    "zf.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fc33a8d-fc3e-448a-aa50-23c5d87b3651",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "In this notebook I've successfuly processed binary and non binary files to a folder sturture suitable for CNN experimentation. Due to the limited size of the binary files in the source system I had to reuse images accross all train and test sets. This will no doubt cause overfitting issues. However, my work up to this point provides me with reusable code and notebooks I can use in the comming weeks should it be necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "384af682-b931-45c6-a351-0820bc8c135a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}