{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e0ed1ff-3ca2-4883-8b0a-3c0dee44a7f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7263abdc-c09b-4aa6-bb72-6006df1caceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "root_dir = \".\"\n",
    "\n",
    "classes = [\n",
    "    \"chefs_kiss\",\n",
    "    \"horns_sign\",\n",
    "    \"fist_bump\",\n",
    "    \"fist_up\",\n",
    "    \"heart_fingers\",\n",
    "    \"heart_hands\",\n",
    "    \"okay\",\n",
    "    \"palm\",\n",
    "    \"peace\",\n",
    "    \"thumbs_down\",\n",
    "    \"thumbs_up\",\n",
    "    \"vulcan_salute\"\n",
    "]\n",
    "\n",
    "class_list = []\n",
    "photo_count = []\n",
    "\n",
    "for dirpath, dirnames, filenames in os.walk(root_dir):\n",
    "    class_name = os.path.basename(dirpath.lower())  # Convert to lowercase for case-insensitive matching\n",
    "    \n",
    "    if class_name in classes:\n",
    "        class_list.append(class_name)\n",
    "        photo_count.append(len(filenames))\n",
    "        \n",
    "\n",
    "\n",
    "# Create the bar chart\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.bar(class_list, photo_count)\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Photos\")\n",
    "plt.title(\"Number of Photos per Class\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "83592ede",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files renamed successfully!\n"
     ]
    }
   ],
   "source": [
    "# List of folders to process\n",
    "folders_to_process = [\n",
    "    \"chefs_kiss\",\n",
    "    \"horns_sign\",\n",
    "    \"fist_bump\",\n",
    "    \"fist_up\",\n",
    "    \"heart_fingers\",\n",
    "    \"heart_hands\",\n",
    "    \"okay\",\n",
    "    \"palm\",\n",
    "    \"peace\",\n",
    "    \"thumbs_down\",\n",
    "    \"thumbs_up\",\n",
    "    \"vulcan_salute\"\n",
    "]\n",
    "\n",
    "# Iterate through each folder in folders_to_process\n",
    "for class_name in folders_to_process:\n",
    "    # Check if the folder exists in the current directory\n",
    "    if os.path.isdir(class_name):\n",
    "        # Get all the files in the current folder\n",
    "        files = glob.glob(os.path.join(class_name, \"*\"))\n",
    "        \n",
    "        # Sort the files for consistent numbering\n",
    "        files.sort()\n",
    "        \n",
    "        # Rename each file\n",
    "        for idx, file_path in enumerate(files):\n",
    "            # Get the current file extension\n",
    "            file_ext = os.path.splitext(file_path)[1]\n",
    "            \n",
    "            # Create new file name\n",
    "            new_name = f\"{class_name}{idx}{file_ext}\"\n",
    "            \n",
    "            # Create the new file path\n",
    "            new_path = os.path.join(class_name, new_name)\n",
    "            \n",
    "            # Rename the file\n",
    "            os.rename(file_path, new_path)\n",
    "\n",
    "print(\"Files renamed successfully!\")"
   ]
  }
 ],
 "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}