kmeans.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import pickle\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import skimage.io\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from skimage.color import rgb2gray\n",
    "from skimage.feature import local_binary_pattern\n",
    "from skimage.transform import resize\n",
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "644\n"
     ]
    }
   ],
   "source": [
    "src1 = 'Data/train/flooded/'\n",
    "src2 = 'Data/train/non-flooded/'\n",
    "image_paths_1 = os.listdir(src1)\n",
    "image_paths_1 = [src1+ s for s in image_paths_1]\n",
    "image_paths_2 = os.listdir(src2)\n",
    "image_paths_2 = [src2+ s for s in image_paths_2]\n",
    "image_paths = image_paths_1 + image_paths_2\n",
    "print(len(image_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define parameters for LBP feature extraction\n",
    "radius = 1\n",
    "n_points = 8\n",
    "METHOD = 'uniform'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "644\n"
     ]
    }
   ],
   "source": [
    "print(len(image_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/644 [00:00<?, ?it/s]/home/iten/anaconda3/envs/bd/lib/python3.11/site-packages/skimage/feature/texture.py:353: UserWarning: Applying `local_binary_pattern` to floating-point images may give unexpected results when small numerical differences between adjacent pixels are present. It is recommended to use this function with images of integer dtype.\n",
      "  warnings.warn(\n",
      "100%|██████████| 644/644 [04:53<00:00,  2.19it/s]\n"
     ]
    }
   ],
   "source": [
    "grey_images = [ [] ] * len(image_paths)\n",
    "lbp_features = [ [] ] * len(image_paths)\n",
    "lbp_histograms = [ [] ] * len(image_paths)\n",
    "i = 0\n",
    "for image_path in tqdm(image_paths):\n",
    "    image = skimage.io.imread(image_path)\n",
    "    grey_image = rgb2gray(image)\n",
    "    grey_image = resize(grey_image, (grey_image.shape[0] // 2, image.shape[1] // 2), anti_aliasing=False)\n",
    "    grey_images[i] = grey_image\n",
    "    lbp = local_binary_pattern(grey_image, n_points, radius, METHOD)\n",
    "    hist, _ = np.histogram(lbp.ravel(), bins=np.arange(0, n_points + 3), range=(0, n_points + 2))\n",
    "    lbp_features[i] = lbp\n",
    "    lbp_histograms[i] = hist\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(644, (200, 200))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lbp_features), lbp_features[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/iten/anaconda3/envs/bd/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "KMeans(n_clusters=2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a K-means model with 2 clusters\n",
    "kmeans = KMeans(n_clusters=2)\n",
    "\n",
    "# Fit the K-means model to the LBP features\n",
    "kmeans.fit(lbp_histograms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the cluster labels for each image\n",
    "cluster_labels = kmeans.predict(lbp_histograms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "644"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_1 = [1] * 322\n",
    "labels_2 = [0] * 322\n",
    "labels = labels_1 + labels_2\n",
    "len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.59      0.71       322\n",
      "           1       0.69      0.91      0.79       322\n",
      "\n",
      "    accuracy                           0.75       644\n",
      "   macro avg       0.78      0.75      0.75       644\n",
      "weighted avg       0.78      0.75      0.75       644\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(labels, cluster_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the confusion matrix.\n",
    "cm = confusion_matrix(labels, cluster_labels)\n",
    "# Plot the confusion matrix.\n",
    "# Plot the confusion matrix.\n",
    "new_labels = [\"Not Flooded\", \"Flooded\"]\n",
    "# new_labels.sort()\n",
    "sns.heatmap(cm, annot=True, fmt='g', xticklabels=new_labels, yticklabels=new_labels)\n",
    "plt.xlabel('Prediction',fontsize=13)\n",
    "plt.ylabel('Actual',fontsize=13)\n",
    "plt.title('K-Means with LBP Histograms on Training Set',fontsize=17)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "140\n"
     ]
    }
   ],
   "source": [
    "src1 = 'Data/test/flooded/'\n",
    "src2 = 'Data/test/non-flooded/'\n",
    "image_paths_1 = os.listdir(src1)\n",
    "image_paths_1 = [src1+ s for s in image_paths_1]\n",
    "image_paths_2 = os.listdir(src2)\n",
    "image_paths_2 = [src2+ s for s in image_paths_2]\n",
    "image_paths = image_paths_1 + image_paths_2\n",
    "print(len(image_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/140 [00:00<?, ?it/s]/home/iten/anaconda3/envs/bd/lib/python3.11/site-packages/skimage/feature/texture.py:353: UserWarning: Applying `local_binary_pattern` to floating-point images may give unexpected results when small numerical differences between adjacent pixels are present. It is recommended to use this function with images of integer dtype.\n",
      "  warnings.warn(\n",
      "100%|██████████| 140/140 [01:53<00:00,  1.23it/s]\n"
     ]
    }
   ],
   "source": [
    "grey_images_test = [ [] ] * len(image_paths)\n",
    "lbp_features_test = [ [] ] * len(image_paths)\n",
    "lbp_histograms_test = [ [] ] * len(image_paths)\n",
    "i = 0\n",
    "for image_path in tqdm(image_paths):\n",
    "    image = skimage.io.imread(image_path)\n",
    "    grey_image = rgb2gray(image)\n",
    "    grey_image = resize(grey_image, (grey_image.shape[0] // 2, image.shape[1] // 2), anti_aliasing=False)\n",
    "    grey_images_test[i] = grey_image\n",
    "    lbp = local_binary_pattern(grey_image, n_points, radius, METHOD)\n",
    "    hist, _ = np.histogram(lbp.ravel(), bins=np.arange(0, n_points + 3), range=(0, n_points + 2))\n",
    "    lbp_features_test[i] = lbp\n",
    "    lbp_histograms_test[i] = hist\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the cluster labels for each image\n",
    "cluster_labels_test = kmeans.predict(lbp_histograms_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_1 = [1] * 70\n",
    "labels_2 = [0] * 70\n",
    "test_labels = labels_1 + labels_2\n",
    "len(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.61      0.71        70\n",
      "           1       0.70      0.89      0.78        70\n",
      "\n",
      "    accuracy                           0.75       140\n",
      "   macro avg       0.77      0.75      0.75       140\n",
      "weighted avg       0.77      0.75      0.75       140\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_labels, cluster_labels_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the confusion matrix.\n",
    "cm = confusion_matrix(test_labels, cluster_labels_test)\n",
    "# Plot the confusion matrix.\n",
    "# Plot the confusion matrix.\n",
    "new_labels = [\"Not Flooded\", \"Flooded\"]\n",
    "# new_labels.sort()\n",
    "sns.heatmap(cm, annot=True, fmt='g', xticklabels=new_labels, yticklabels=new_labels)\n",
    "plt.xlabel('Prediction',fontsize=13)\n",
    "plt.ylabel('Actual',fontsize=13)\n",
    "plt.title('K-Means with LBP Histograms on Test Set',fontsize=17)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "138\n"
     ]
    }
   ],
   "source": [
    "src1 = 'Data/val/flooded/'\n",
    "src2 = 'Data/val/non-flooded/'\n",
    "image_paths_1 = os.listdir(src1)\n",
    "image_paths_1 = [src1+ s for s in image_paths_1]\n",
    "image_paths_2 = os.listdir(src2)\n",
    "image_paths_2 = [src2+ s for s in image_paths_2]\n",
    "image_paths = image_paths_1 + image_paths_2\n",
    "print(len(image_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/138 [00:00<?, ?it/s]/home/iten/anaconda3/envs/bd/lib/python3.11/site-packages/skimage/feature/texture.py:353: UserWarning: Applying `local_binary_pattern` to floating-point images may give unexpected results when small numerical differences between adjacent pixels are present. It is recommended to use this function with images of integer dtype.\n",
      "  warnings.warn(\n",
      "100%|██████████| 138/138 [01:46<00:00,  1.30it/s]\n"
     ]
    }
   ],
   "source": [
    "grey_images_val = [ [] ] * len(image_paths)\n",
    "lbp_features_val = [ [] ] * len(image_paths)\n",
    "lbp_histograms_val = [ [] ] * len(image_paths)\n",
    "i = 0\n",
    "for image_path in tqdm(image_paths):\n",
    "    image = skimage.io.imread(image_path)\n",
    "    grey_image = rgb2gray(image)\n",
    "    grey_image = resize(grey_image, (grey_image.shape[0] // 2, image.shape[1] // 2), anti_aliasing=False)\n",
    "    grey_images_val[i] = grey_image\n",
    "    lbp = local_binary_pattern(grey_image, n_points, radius, METHOD)\n",
    "    hist, _ = np.histogram(lbp.ravel(), bins=np.arange(0, n_points + 3), range=(0, n_points + 2))\n",
    "    lbp_features_val[i] = lbp\n",
    "    lbp_histograms_val[i] = hist\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the cluster labels for each image\n",
    "cluster_labels_val = kmeans.predict(lbp_histograms_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_1 = [1] * 69\n",
    "labels_2 = [0] * 69\n",
    "val_labels = labels_1 + labels_2\n",
    "len(val_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.64      0.72        69\n",
      "           1       0.70      0.86      0.77        69\n",
      "\n",
      "    accuracy                           0.75       138\n",
      "   macro avg       0.76      0.75      0.74       138\n",
      "weighted avg       0.76      0.75      0.74       138\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(val_labels, cluster_labels_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the confusion matrix.\n",
    "cm = confusion_matrix(val_labels, cluster_labels_val)\n",
    "# Plot the confusion matrix.\n",
    "# Plot the confusion matrix.\n",
    "new_labels = [\"Not Flooded\", \"Flooded\"]\n",
    "# new_labels.sort()\n",
    "sns.heatmap(cm, annot=True, fmt='g', xticklabels=new_labels, yticklabels=new_labels)\n",
    "plt.xlabel('Prediction',fontsize=13)\n",
    "plt.ylabel('Actual',fontsize=13)\n",
    "plt.title('K-Means with LBP Histograms on Validation Set',fontsize=17)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "bd",
   "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": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}