hybrid_sn_prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyP9cE9dM0A6ktcNKQe8vScN",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BohdanLiuisk/neural-networks/blob/main/hybrid_sn_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jvigG7YNbEkA",
        "outputId": "b6516f99-2a6f-47e4-ae3b-cd787718223d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting spectral\n",
            "  Downloading spectral-0.23.1-py3-none-any.whl (212 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.9/212.9 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from spectral) (1.22.4)\n",
            "Installing collected packages: spectral\n",
            "Successfully installed spectral-0.23.1\n"
          ]
        }
      ],
      "source": [
        "!pip install spectral"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "hsi_datasets_path = '/content/drive/My Drive/hsi_datasets'\n",
        "hsi_datasets_local_path = '/content/dataset'\n",
        "shutil.copytree(hsi_datasets_path, hsi_datasets_local_path)\n",
        "\n",
        "print(\"Current working directory:\", os.getcwd())\n",
        "print(\"Files in the working directory:\")\n",
        "os.listdir('.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jh5hqkKDbNxW",
        "outputId": "c927f45a-3a0b-49a3-833b-0404f0e32856"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "Current working directory: /content\n",
            "Files in the working directory:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['.config', 'models', 'drive', 'dataset', 'sample_data']"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import scipy.io as sio\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras.layers import Input, Conv3D, Conv2D, Dense, Flatten, Reshape, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score\n",
        "from keras.utils import np_utils\n",
        "import spectral\n",
        "from matplotlib import colors\n",
        "\n",
        "spectral.settings.WX_GL_DEPTH_SIZE = 16\n",
        "\n",
        "DATASET = 'IP'\n",
        "DATA_PATH = '/content/dataset/'\n",
        "MODEL_PATH = '/content/drive/MyDrive/trained_models/hybrid_sn.h5'\n",
        "WINDOW_SIZE = 25\n",
        "TEST_RATIO = 0.7\n",
        "\n",
        "def load_data(name):\n",
        "    if name == 'IP':\n",
        "        data = sio.loadmat(os.path.join(DATA_PATH, 'Indian_pines_corrected.mat'))['indian_pines_corrected']\n",
        "        labels = sio.loadmat(os.path.join(DATA_PATH, 'Indian_pines_gt.mat'))['indian_pines_gt']\n",
        "    elif name == 'SA':\n",
        "        data = sio.loadmat(os.path.join(DATA_PATH, 'Salinas_corrected.mat'))['salinas_corrected']\n",
        "        labels = sio.loadmat(os.path.join(DATA_PATH, 'Salinas_gt.mat'))['salinas_gt']\n",
        "    elif name == 'PU':\n",
        "        data = sio.loadmat(os.path.join(DATA_PATH, 'PaviaU.mat'))['paviaU']\n",
        "        labels = sio.loadmat(os.path.join(DATA_PATH, 'PaviaU_gt.mat'))['paviaU_gt']\n",
        "    return data, labels\n",
        "\n",
        "def splitTrainTestSet(X, y, testRatio, randomState=345):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testRatio, random_state=randomState, stratify=y)\n",
        "    return X_train, X_test, y_train, y_test\n",
        "  \n",
        "def applyPCA(X, numComponents=75):\n",
        "    newX = np.reshape(X, (-1, X.shape[2]))\n",
        "    pca = PCA(n_components=numComponents, whiten=True)\n",
        "    newX = pca.fit_transform(newX)\n",
        "    newX = np.reshape(newX, (X.shape[0],X.shape[1], numComponents))\n",
        "    return newX, pca\n",
        "  \n",
        "def padWithZeros(X, margin=2):\n",
        "    newX = np.zeros((X.shape[0] + 2 * margin, X.shape[1] + 2* margin, X.shape[2]))\n",
        "    x_offset = margin\n",
        "    y_offset = margin\n",
        "    newX[x_offset:X.shape[0] + x_offset, y_offset:X.shape[1] + y_offset, :] = X\n",
        "    return newX\n",
        "\n",
        "def createImageCubes(X, y, windowSize=5, removeZeroLabels = True):\n",
        "    margin = int((windowSize - 1) / 2)\n",
        "    zeroPaddedX = padWithZeros(X, margin=margin)\n",
        "    # split patches\n",
        "    patchesData = np.zeros((X.shape[0] * X.shape[1], windowSize, windowSize, X.shape[2]))\n",
        "    patchesLabels = np.zeros((X.shape[0] * X.shape[1]))\n",
        "    patchIndex = 0\n",
        "    for r in range(margin, zeroPaddedX.shape[0] - margin):\n",
        "        for c in range(margin, zeroPaddedX.shape[1] - margin):\n",
        "            patch = zeroPaddedX[r - margin:r + margin + 1, c - margin:c + margin + 1]   \n",
        "            patchesData[patchIndex, :, :, :] = patch\n",
        "            patchesLabels[patchIndex] = y[r-margin, c-margin]\n",
        "            patchIndex = patchIndex + 1\n",
        "    if removeZeroLabels:\n",
        "        patchesData = patchesData[patchesLabels>0,:,:,:]\n",
        "        patchesLabels = patchesLabels[patchesLabels>0]\n",
        "        patchesLabels -= 1\n",
        "    return patchesData, patchesLabels\n",
        "  \n",
        "def Patch(data,height_index,width_index):\n",
        "    height_slice = slice(height_index, height_index+PATCH_SIZE)\n",
        "    width_slice = slice(width_index, width_index+PATCH_SIZE)\n",
        "    patch = data[height_slice, width_slice, :]\n",
        "    return patch\n",
        "\n",
        "X, y = load_data(DATASET)\n",
        "X.shape, y.shape\n",
        "K = 30 if DATASET == 'IP' else 15\n",
        "X,pca = applyPCA(X,numComponents=K)\n",
        "K = X.shape[2]\n",
        "X, y = createImageCubes(X, y, windowSize=WINDOW_SIZE)\n",
        "Xtrain, Xtest, ytrain, ytest = splitTrainTestSet(X, y, TEST_RATIO)\n",
        "Xtrain.shape, Xtest.shape, ytrain.shape, ytest.shape\n",
        "Xtrain = Xtrain.reshape(-1, WINDOW_SIZE, WINDOW_SIZE, K, 1)\n",
        "ytrain = np_utils.to_categorical(ytrain)\n",
        "S = WINDOW_SIZE\n",
        "L = K\n",
        "output_units = 9 if (DATASET == 'PU' or DATASET == 'PC') else 16\n",
        "model = load_model(MODEL_PATH)\n",
        "adam = tf.keras.optimizers.legacy.Adam(lr=0.001, decay=1e-06)\n",
        "model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
        "Xtest = Xtest.reshape(-1, WINDOW_SIZE, WINDOW_SIZE, K, 1)\n",
        "ytest = np_utils.to_categorical(ytest)\n",
        "Y_pred_test = model.predict(Xtest)\n",
        "y_pred_test = np.argmax(Y_pred_test, axis=1)\n",
        "\n",
        "# load the original image\n",
        "X, y = load_data(DATASET)\n",
        "height = y.shape[0]\n",
        "width = y.shape[1]\n",
        "PATCH_SIZE = WINDOW_SIZE\n",
        "numComponents = K\n",
        "X,pca = applyPCA(X, numComponents=numComponents)\n",
        "X = padWithZeros(X, PATCH_SIZE//2)\n",
        "outputs = np.zeros((height,width))\n",
        "for i in range(height):\n",
        "    for j in range(width):\n",
        "        target = int(y[i,j])\n",
        "        if target == 0 :\n",
        "            continue\n",
        "        else :\n",
        "            image_patch=Patch(X,i,j)\n",
        "            X_test_image = image_patch.reshape(1,image_patch.shape[0],image_patch.shape[1], image_patch.shape[2], 1).astype('float32')                                   \n",
        "            prediction = (model.predict(X_test_image))\n",
        "            prediction = np.argmax(prediction, axis=1)\n",
        "            outputs[i][j] = prediction+1\n",
        "\n",
        "ground_truth = spectral.imshow(classes = y,figsize =(7,7))\n",
        "\n",
        "predict_image = spectral.imshow(classes = outputs.astype(int),figsize =(7,7))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wT0sn9bgcEjA",
        "outputId": "2f4ddb13-5144-405d-ea22-94e8aaefb86b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
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            "1/1 [==============================] - 0s 49ms/step\n",
            "1/1 [==============================] - 0s 53ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/spectral/graphics/spypylab.py:796: UserWarning: Failed to create RectangleSelector object. Interactive pixel class labeling will be unavailable.\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}