train_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Import Necessary Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from liblinear.liblinearutil import save_model, load_model, train, predict\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Import Functions from train.py**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from train import get_data, spatial_frequency_feature_fusion, prepare_data, train_linear_model, visualize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Run this for preprocessing**\n",
    "\n",
    "- must include /utils\n",
    "- example path: \"/Users/Danniel/Detection-of-GAN-Generated-Images-using-Spatial-Frequency-Domain-Fusion-Data/utils\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "# path of the folder \"Detection-of-GAN-Generated-Images-using-Spatial-Frequency-Domain-Fusion-Data\"\n",
    "sys.path.append(\"/Users/Danniel/Detection-of-GAN-Generated-Images-using-Spatial-Frequency-Domain-Fusion-Data/utils\")\n",
    "\n",
    "from preprocessing_save import load_image, save_image\n",
    "\n",
    "# directory for reading\n",
    "img_gan = \"/Users/Danniel/Downloads/Datasets/Datasets Scenes/Train/GAN\"\n",
    "img_real = \"/Users/Danniel/Downloads/Datasets/Datasets Scenes/Train/Real\"\n",
    "\n",
    "# directory for saving (must be an empty folder)\n",
    "save_dir_gan = \"/Users/Danniel/Downloads/Datasets/Datasets Scenes/Train/P_GAN\"\n",
    "save_dir_real = \"/Users/Danniel/Downloads/Datasets/Datasets Scenes/Train/P_Real\"\n",
    "\n",
    "# store the images\n",
    "image_real = load_image(img_real)\n",
    "image_gan = load_image(img_gan)\n",
    "\n",
    "save_image(image_real, save_dir_real)\n",
    "save_image(image_gan, save_dir_gan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Provide directory for Preprocessed Real and GAN-Generated Images**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gan_directory = \"/Users/Danniel/Downloads/Datasets/Datasets Scenes/Train/P_GAN\"\n",
    "real_directory = \"/Users/Danniel/Downloads/Datasets/Datasets Scenes/Train/P_Real\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *\"get_data\" function will load the preprocessed images from the directory and store it in a list*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run data preparation\n",
    "gan_data = get_data(gan_directory)\n",
    "real_data = get_data(real_directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Run to perform the proposed \"spatial frequency feature fusion\" method to extract meaningful features of an image to classify whether it is GAN-Generated or Real. The feature vector of each image will be stored in \"fused_features\" list*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run feature extraction & feature fusion\n",
    "extracted_gan = spatial_frequency_feature_fusion(gan_data)\n",
    "extracted_real = spatial_frequency_feature_fusion(real_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **For Data Visualization**\n",
    "- provide real and gan parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualize\n",
    "visualize(extracted_gan, extracted_real)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Data Preparation before feeding to the classifier**\n",
    "- assigning of labels\n",
    "- flattening of feature vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare the data\n",
    "labels, datasets = prepare_data(extracted_gan, extracted_real)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Store model data in hdf5 file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# provide directory for data folder\n",
    "os.chdir(\"/Users/Danniel/Downloads/File\")\n",
    "\n",
    "# rename the model data\n",
    "hf = h5py.File('data_faces_sample.h5', 'w')\n",
    "\n",
    "# store labels and datasets to the model data\n",
    "hf.create_dataset('labels', data=labels)\n",
    "hf.create_dataset('feature_vector',  data=datasets)\n",
    "\n",
    "# close the h5py\n",
    "hf.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Load hdf5 model data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# directory of data folder\n",
    "\n",
    "import os\n",
    "os.chdir(\"/Users/Danniel/Downloads/File\")\n",
    "\n",
    "# provide filename of the model data\n",
    "rf = h5py.File('data.h5', 'r')\n",
    "\n",
    "rf.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_labels = rf.get('labels')\n",
    "loaded_feature_vector = rf.get('feature_vector')\n",
    "\n",
    "# convert object to numpy array\n",
    "np_labels = np.array(loaded_labels)\n",
    "np_features = np.array(loaded_feature_vector)\n",
    "\n",
    "print(\"Loaded Labels: \",len(np_labels))\n",
    "print(\"Loaded Features: \", len(np_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Model Training**\n",
    "- Liblinear\n",
    "\n",
    "\n",
    "***\n",
    "    - change the parameter if the data are from loaded file\n",
    "    - train_model(np_labels, np_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train the data\n",
    "# model = train_linear_model(labels, datasets, C=1)\n",
    "\n",
    "\n",
    "model = train(np_labels, np_features, f'-s 1 -c 1 -B 1 -w1 10.0 -v 5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Save the model using \".model\" extension**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model\n",
    "model_file = \"/Users/Danniel/Downloads/Model/True Model/scenes.model\"\n",
    "save_model(model_file, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Load the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_file = load_model(\"/Users/Danniel/Downloads/Model/True Model/faces_new.model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Platt Scaling**\n",
    "- run this to provide the platt scaling of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, _, scores = predict(labels, datasets, model_file)\n",
    "\n",
    "validate = train(labels, scores, '-s 0 -c 1 -B 1 -v 5')\n",
    "platt_scale = train(labels, scores, '-s 0 -c 1 -B 1')\n",
    "\n",
    "save_model('/Users/Danniel/Downloads/Model/True Platt Scaling/platt_scale_scenes.model', platt_scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Model Testing**\n",
    "- provide directory (folder) for testing datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Testing Combined GAN and Real Images*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from preprocessing import preprocessing\n",
    "import os\n",
    "import numpy as np\n",
    "from train import spatial_frequency_feature_fusion\n",
    "\n",
    "# load platt scaler\n",
    "plat_file = load_model(\"platt scaler/platt_scale_faces.model\")\n",
    "\n",
    "# test the model\n",
    "def get_test_data(directory):\n",
    "    images = []\n",
    "\n",
    "    # load the images and store in images list\n",
    "    for filename in os.listdir(directory):\n",
    "        image = os.path.join(directory, filename)\n",
    "        if image is not None:\n",
    "            images.append(image)\n",
    "\n",
    "    # preprocessing\n",
    "    preprocessed_img = []\n",
    "    for i in images:\n",
    "        preprocessed_img.append(preprocessing(i))   \n",
    "\n",
    "    # flatten the feature vector\n",
    "    fused_features = spatial_frequency_feature_fusion(preprocessed_img)\n",
    "    labels = np.ones((len(fused_features), 1)) \n",
    "    true_label = labels.reshape(labels.shape[0])\n",
    "\n",
    "    feature_vector = []\n",
    "    for i in fused_features:\n",
    "        print(i)\n",
    "        feature_vector.append(i.flatten())\n",
    "\n",
    "\n",
    "    return feature_vector\n",
    "\n",
    "\n",
    "def prepare_test_data(gan, real):\n",
    "    print(\"\\n\\n-------------------PREPARING TEST DATA----------------------------\\n\")\n",
    "    #label real  and gan datasets\n",
    "    gan_label = np.ones((len(gan), 1))\n",
    "    real_label = np.zeros((len(real), 1))\n",
    "\n",
    "    # combine the labels and datasets\n",
    "    dataset_labels = np.vstack((gan_label, real_label))\n",
    "    datasets = np.vstack((gan, real))\n",
    "\n",
    "    feature_vector = [i.flatten() for i in datasets]\n",
    "    \n",
    "    true_label = dataset_labels.reshape(dataset_labels.shape[0])\n",
    "\n",
    "    return feature_vector, true_label\n",
    "\n",
    "\n",
    "def predict_labels(feature_vector, true_label, model):  \n",
    "    # predict the result\n",
    "    print(\"\\n\\n-------------------THE MODEL IS PREDICTING----------------------------\\n\")\n",
    "    _, _, svm_scores= predict(true_label, feature_vector, model)\n",
    "    predicted_labels, _, probability_estimates = predict(labels, svm_scores, plat_file, '-b 1')\n",
    "\n",
    "\n",
    "\n",
    "    print(\"------------------------------------------RESULT-----------------------------------\\n\")\n",
    "    result = []\n",
    "    for i in predicted_labels:\n",
    "        if i == 1.0:\n",
    "            result.append(\"GAN\")\n",
    "        elif i == 0.0:\n",
    "            result.append(\"Real\")\n",
    "\n",
    "    \n",
    "    print(result)\n",
    "    print(probability_estimates)\n",
    "\n",
    "\n",
    "    return svm_scores, predicted_labels\n",
    "\n",
    "\n",
    "\n",
    "while True:\n",
    "    option = input(\"Does your data came from text file? (y/n)\")\n",
    "\n",
    "    if option.lower() == \"y\":\n",
    "        # mode prediction\n",
    "        scores = predict_labels(np_features, np_labels, model_file)\n",
    "        break\n",
    "\n",
    "    elif option.lower() == \"n\":           \n",
    "        #provide directory for testing dataset\n",
    "        dir_gan = \"/Users/Danniel/Downloads/Datasets/Datasets Faces/Test/GAN\"\n",
    "        dir_real = \"/Users/Danniel/Downloads/Datasets/Datasets Faces/Test/Real\"\n",
    "\n",
    "        # undergo spatial-frequency-feature fusion\n",
    "        gan = get_test_data(dir_gan)\n",
    "        real = get_test_data(dir_real)\n",
    "\n",
    "        # combine the real and gan data\n",
    "        features, labels = prepare_test_data(gan, real)\n",
    "\n",
    "        # model prediction\n",
    "        scores, predicted_labels = predict_labels(features, labels, model_file)\n",
    "        break\n",
    "    \n",
    "    else:\n",
    "        print(\"Invalid Input\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Live Datacats (3).png', 'Live Datacats (4).png', 'Live Datacats (5).png', 'Live Datacats (6).png', 'Live Datacats (7).png', 'Live Datacats (8).png', 'Live Datacats (9).png', 'Live Datacats (10).png', 'Live Datacats (11).png', 'Live Datacats (12).png', 'Live Datacats (13).png', 'Live Datacats (14).png', 'Live Datacats (15).png', 'Live Datacats (16).png', 'Live Datacats (17).png', 'Live Datacats (18).png', 'Live Datacats (19).png', 'Live Datacats (20).png', 'Live Datacats (21).png', 'Live Datacats (22).png', 'Live Datacats (1).png', 'Live Datacats (2).png', 'Live Datacats (24).png', 'Live Datacats (25).png', 'Live Datacats (26).png', 'Live Datacats (27).png', 'Live Datacats (28).png', 'Live Datacats (29).png', 'Live Datacats (30).png', 'Live Datacats (23).png', 'Live DataOIG (5).jpg', 'Live DataOIG (6).jpg', 'Live Datadogs (29).png', 'Live Datadogs (30).png', 'Live Datadogs (28).png', 'Live Datadogs (3).png', 'Live Datadogs (4).png', 'Live Datadogs (5).png', 'Live Datadogs 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'Live Data30.png', 'Live Data31.png', 'Live Data32.png', 'Live Data33.png', 'Live Data34.png', 'Live Data35.png', 'Live Data36.png', 'Live Data37.png', 'Live Data38.png', 'Live Data39.png', 'Live Data46.png', 'Live Data47.png', 'Live Data48.png', 'Live Data49.png', 'Live Data50.png', 'Live Data51.png', 'Live Data59.png', 'Live Data60.png', 'Live Data61.png', 'Live Data62.png', 'Live Data63.png', 'Live Data64.png', 'Live Data65.png', 'Live Data66.png', 'Live Data67.png', 'Live Data68.png', 'Live Data69.png', 'Live Data70.png', 'Live Data71.png', 'Live Data72.png', 'Live Data73.png', 'Live Data74.png', 'Live Data75.png', 'Live Data76.png', 'Live Data77.png', 'Live Data78.png', 'Live Data79.png', 'Live Data80.png', 'Live Data81.png', 'Live Data82.png', 'Live Data113.png', 'Live Data114.png', 'Live Data115.png', 'Live Data116.png', 'Live Data117.png', 'Live Data118.png', 'Live Datachurch (12).png', 'Live Datachurch (6).png', 'Live Datachurch (13).png', 'Live Datachurch (2).png', 'Live Datachurch (15).png', 'Live Datachurch (5).png', 'Live Datachurch (4).png', 'Live Datachurch (7).png', 'Live Datachurch (16).png', 'Live Datachurch (9).png', 'Live Datachurch (14).png', 'Live Datachurch (3).png', 'Live Datachurch (10).png', 'Live Datachurch (11).png', 'Live Datachurch (8).png', 'Live Datachurch (1).png', 'Live Databridge (14).png', 'Live Databridge (13).png', 'Live Databridge (15).png', 'Live Databridge (5).png', 'Live Databridge (12).png', 'Live Databridge (2).png', 'Live Databridge (9).png', 'Live Databridge (11).png', 'Live Databridge (4).png', 'Live Databridge (7).png', 'Live Databridge (16).png', 'Live Databridge (8).png', 'Live Databridge (6).png', 'Live Databridge (3).png', 'Live Databridge (10).png', 'Live Databridge (1).png', 'Live Datarestaurant (5).png', 'Live Datarestaurant (4).png', 'Live Datarestaurant (3).png', 'Live Datarestaurant (10).png', 'Live Datarestaurant (15).png', 'Live Datarestaurant (13).png', 'Live Datarestaurant (7).png', 'Live Datarestaurant (11).png', 'Live Datarestaurant (12).png', 'Live Datarestaurant (14).png', 'Live Datarestaurant (9).png', 'Live Datarestaurant (1).png', 'Live Datarestaurant (2).png', 'Live Datarestaurant (6).png', 'Live Datarestaurant (8).png', 'Live Datatower (16).png', 'Live Datatower (1).png', 'Live Datatower (2).png', 'Live Datatower (4).png', 'Live Datatower (15).png', 'Live Datatower (3).png', 'Live Datatower (8).png', 'Live Datatower (9).png', 'Live Datatower (5).png', 'Live Datatower (11).png', 'Live Datatower (7).png', 'Live Datatower (6).png', 'Live Datatower (13).png', 'Live Datatower (14).png', 'Live Datatower (12).png', 'Live Datatower (10).png', 'Live Dataimage_07748.jpg', 'Live Dataimage_07754.jpg', 'Live Dataimage_07749.jpg', 'Live Dataimage_07753.jpg', 'Live Dataimage_07750.jpg', 'Live Dataimage_07751.jpg', 'Live Dataimage_07752.jpg', 'Live Data022120.png', 'Live Data022126.png', 'Live Data022098.png', 'Live Data022116.png', 'Live Data022099.png', 'Live 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Data512-12.jpg', 'Live Dataseed0646.jpg', 'Live Data512-92.jpg', 'Live Dataseed10609.jpg', 'Live Dataseed0695.jpg', 'Live Dataseed0828.jpg', 'Live Dataseed0657.jpg', 'Live Data512-98.jpg', 'Live Dataseed0845.jpg']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "df = pd.read_csv('/Users/Danniel/Downloads/metadata.csv')\n",
    "filename = df['Image Path']\n",
    "\n",
    "filenames = [\"Live Data\" + os.path.basename(f) for f in filename]\n",
    "print(filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (1).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (10).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (11).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (12).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (13).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (14).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (15).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (16).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (17).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (18).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (19).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (2).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (20).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (21).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (22).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (23).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (24).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (25).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (26).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (27).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\bridge (28).png\n",
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      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\tower (74).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\tower (75).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\tower (8).png\n",
      "..\\Downloads\\Datasets\\Datasets Scenes\\GAN\\tower (9).png\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import csv\n",
    "\n",
    "dir = \"/Users/Danniel/Downloads/Datasets/Datasets Scenes/GAN\"\n",
    "directory = \"..\\Downloads\\Datasets\\Datasets Scenes\\GAN\"\n",
    "filename = os.listdir(dir)\n",
    "\n",
    "final_files = []\n",
    "for file in filename:\n",
    "    full_path = os.path.join(directory, file)\n",
    "    final_files.append(full_path)\n",
    "    print(full_path)\n",
    "\n",
    "with open('datasets.csv', 'a', newline='') as csv_file:\n",
    "    csv_writer = csv.writer(csv_file)\n",
    "\n",
    "    # csv_writer.writerow(['Filename', 'True Labels'])\n",
    "\n",
    "    for file in final_files:\n",
    "        csv_writer.writerow([file, 1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real bridge 1.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real bridge 10.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real bridge 11.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real bridge 12.jpeg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real bridge 13.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real bridge 14.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real bridge 15.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real bridge 2.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real bridge 3.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real bridge 4.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real bridge 5.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real bridge 6.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real bridge 7.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real bridge 8.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real bridge 9.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real bridge.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (1).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (10).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (11).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (12).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (13).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (14).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (15).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (16).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (17).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (18).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (2).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (3).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (4).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (5).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (6).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (7).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (8).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real church (9).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real tower 1.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real tower 10.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real tower 11.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real tower 12.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real tower 13.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real tower 14.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real tower 15.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real tower 2.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real tower 3.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real tower 4.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real tower 5.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real tower 6.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real tower 7.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real tower 8.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/real tower 9.jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/restaurant (real) (1).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/restaurant (real) (10).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/restaurant (real) (11).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/restaurant (real) (12).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/restaurant (real) (13).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/restaurant (real) (14).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/restaurant (real) (2).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/restaurant (real) (3).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/restaurant (real) (4).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/restaurant (real) (5).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/restaurant (real) (6).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/restaurant (real) (7).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/restaurant (real) (8).jpg\n",
      "/content/drive/MyDrive/Live Data/Live Scenes Real/restaurant (real) (9).jpg\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import csv\n",
    "\n",
    "dir = \"/Users/Danniel/Downloads/Live Data/Live Scenes Real\"\n",
    "directory = \"/content/drive/MyDrive/Live Data/Live Scenes Real/\"\n",
    "filename = os.listdir(dir)\n",
    "\n",
    "final_files = []\n",
    "for file in filename:\n",
    "    full_path = directory + file\n",
    "    final_files.append(full_path)\n",
    "    print(full_path)\n",
    "\n",
    "with open('metadata.csv', 'a', newline='') as csv_file:\n",
    "    csv_writer = csv.writer(csv_file)\n",
    "\n",
    "    # csv_writer.writerow(['Filename', 'True Labels'])\n",
    "\n",
    "    for file in final_files:\n",
    "        csv_writer.writerow([file, 0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮▮ |  100.00%\r"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import os\n",
    "\n",
    "dir = \"/Users/Danniel/Downloads/Pixelated/GAN\"\n",
    "\n",
    "if os.path.isdir(dir):\n",
    "    pass\n",
    "else:\n",
    "    os.makedirs(dir)\n",
    "\n",
    "def pixelate(image, i):\n",
    "    # Load the image in grayscale\n",
    "    image = cv2.imread(image)\n",
    "\n",
    "    pixelated_image = cv2.resize(image, dsize=(64, 64))\n",
    "    cv2.imwrite(f\"/Users/Danniel/Downloads/Pixelated/GAN/pixelated_gan({i}).png\", pixelated_image)\n",
    "\n",
    "def progress_bar(progress, total):\n",
    "    percent = 100 * (progress / float(total))\n",
    "    bar = '▮' * int(percent) + '-' * (100 - int(percent))\n",
    "    print(f\"\\r|{bar} | {percent: .2f}%\", end=\"\\r\")    \n",
    "\n",
    "\n",
    "# load preprocessed images\n",
    "directory = \"/Users/Danniel/Downloads/combined class test gan\"\n",
    "img = []\n",
    "for filename in os.listdir(directory):\n",
    "    image = os.path.join(directory, filename)\n",
    "    if image is not None:\n",
    "        img.append(image)   \n",
    "\n",
    "for index, image in enumerate(img):\n",
    "    pixelate(image, index)\n",
    "    progress_bar(index + 1, len(img))"
   ]
  }
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