eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis of Dataset\n",
    "# Dataset: https://www.kaggle.com/datasets/a2015003713/militaryaircraftdetectiondataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Duplicate key in file '/Users/arjunrajpal/.matplotlib/matplotlibrc', line 2 ('backend: TkAgg')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of files in each subdirectory in archive/crop are:\n",
      "F18: 1181 files\n",
      "F16: 1179 files\n",
      "P3: 195 files\n",
      "Tu95: 247 files\n",
      "AV8B: 363 files\n",
      "RQ4: 279 files\n",
      "B2: 424 files\n",
      "Tu160: 254 files\n",
      "Vulcan: 340 files\n",
      "V22: 751 files\n",
      "SR71: 248 files\n",
      "E2: 383 files\n",
      "F4: 535 files\n",
      "A10: 574 files\n",
      "C130: 842 files\n",
      "Be200: 255 files\n",
      "F35: 1000 files\n",
      "US2: 578 files\n",
      "XB70: 151 files\n",
      "Tornado: 389 files\n",
      "F15: 1147 files\n",
      "J20: 453 files\n",
      "Rafale: 466 files\n",
      "U2: 258 files\n",
      "F14: 458 files\n",
      "F22: 562 files\n",
      "YF23: 131 files\n",
      "B1: 546 files\n",
      "F117: 312 files\n",
      "AG600: 194 files\n",
      "EF2000: 510 files\n",
      "A400M: 383 files\n",
      "C17: 464 files\n",
      "C5: 338 files\n",
      "MQ9: 309 files\n",
      "C2: 743 files\n",
      "E7: 128 files\n",
      "Su57: 292 files\n",
      "B52: 453 files\n",
      "Mirage2000: 364 files\n",
      "Mig31: 314 files\n",
      "Su34: 310 files\n",
      "JAS39: 443 files\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of Images:  19746\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Specify the directory for which you want to list subdirectories\n",
    "directory_path = \"archive/crop\"\n",
    "\n",
    "image_count = 0\n",
    "\n",
    "def count_files_in_subdirectories(directory_path):\n",
    "    subdirectories = [d for d in os.listdir(directory_path) if os.path.isdir(os.path.join(directory_path, d))]\n",
    "    subdirectory_counts = {}\n",
    "    for subdirectory in subdirectories:\n",
    "        subdirectory_path = os.path.join(directory_path, subdirectory)\n",
    "        files = [f for f in os.listdir(subdirectory_path) if os.path.isfile(os.path.join(subdirectory_path, f))]\n",
    "        subdirectory_counts[subdirectory] = len(files)\n",
    "    return subdirectory_counts\n",
    "subdirectory_counts = count_files_in_subdirectories(directory_path)\n",
    "\n",
    "print(\"Number of files in each subdirectory in\", directory_path, \"are:\")\n",
    "for subdirectory, count in subdirectory_counts.items():\n",
    "    print(f\"{subdirectory}: {count} files\")\n",
    "    image_count += count\n",
    "\n",
    "\n",
    "subdirectory_counts = count_files_in_subdirectories(directory_path)\n",
    "\n",
    "\n",
    "subdirectories = list(subdirectory_counts.keys())\n",
    "file_counts = list(subdirectory_counts.values())\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(subdirectories, file_counts, color='skyblue')\n",
    "plt.xlabel('Class Names (Aircraft)')\n",
    "plt.ylabel('Number of Images')\n",
    "plt.title('Number of Images for Each Aircraft Class')\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "plt.tight_layout()\n",
    "plt.savefig('file_counts_bar_plot.png', dpi=300, bbox_inches='tight')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print(\"Total Number of Images: \", image_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import random\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# Define the directory paths\n",
    "original_directory = \"archive/crop\"\n",
    "train_directory = \"data/train\"\n",
    "test_directory = \"data/test\"\n",
    "\n",
    "# Function to create directories if they don't exist\n",
    "def create_directory(directory):\n",
    "    if not os.path.exists(directory):\n",
    "        os.makedirs(directory)\n",
    "\n",
    "# Create train and test directories\n",
    "create_directory(train_directory)\n",
    "create_directory(test_directory)\n",
    "\n",
    "# Split ratio for train and test data\n",
    "split_ratio = 0.8  # 80% train, 20% test\n",
    "\n",
    "# Function to perform data splitting and augmentation\n",
    "def split_and_augment_data(original_directory, train_directory, test_directory, split_ratio):\n",
    "    class_folders = os.listdir(original_directory)\n",
    "    for folder in class_folders:\n",
    "        class_path = os.path.join(original_directory, folder)\n",
    "        images = os.listdir(class_path)\n",
    "        random.shuffle(images)\n",
    "        train_size = int(len(images) * split_ratio)\n",
    "        \n",
    "        # Create train and test folders for each class\n",
    "        train_class_path = os.path.join(train_directory, folder)\n",
    "        test_class_path = os.path.join(test_directory, folder)\n",
    "        create_directory(train_class_path)\n",
    "        create_directory(test_class_path)\n",
    "        \n",
    "        # Split images into train and test sets\n",
    "        train_images = images[:train_size]\n",
    "        test_images = images[train_size:]\n",
    "        \n",
    "        # Copy images to train directory\n",
    "        for image in train_images:\n",
    "            img_path = os.path.join(class_path, image)\n",
    "            shutil.copy(img_path, os.path.join(train_class_path, image))\n",
    "        \n",
    "        # Copy images to test directory\n",
    "        for image in test_images:\n",
    "            img_path = os.path.join(class_path, image)\n",
    "            shutil.copy(img_path, os.path.join(test_class_path, image))\n",
    "\n",
    "# Split and augment data\n",
    "split_and_augment_data(original_directory, train_directory, test_directory, split_ratio)\n",
    "\n",
    "# Image augmentation using PyTorch transforms\n",
    "data_transforms = transforms.Compose([\n",
    "    transforms.RandomRotation(20),  # Rotate images randomly up to 20 degrees\n",
    "    transforms.RandomResizedCrop(224),  # Resize and crop images randomly\n",
    "    transforms.ToTensor(),\n",
    "])\n",
    "\n",
    "# Load the datasets\n",
    "train_dataset = datasets.ImageFolder(train_directory, transform=data_transforms)\n",
    "test_dataset = datasets.ImageFolder(test_directory, transform=data_transforms)\n",
    "\n",
    "# Create data loaders\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False)\n"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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