vOILENT_oBJECT_2023.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOcTlXhDi6p3s1VycXEc+Z9",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sumanxg/Image-Classification/blob/master/vOILENT_oBJECT_2023.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CTyvRlIrTEmI",
        "outputId": "f3d2d1d3-7455-4864-929d-fc882678b5c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "Loading the images of dataset-gun\n",
            "\n",
            "Loading the images of dataset-knife\n",
            "\n",
            "Loading the images of dataset-violence\n",
            "\n",
            "Loading the images of dataset-testing\n",
            "\n",
            "(740, 128, 128)\n",
            "(array([-1,  0,  1,  2]), array([  3, 333, 300, 104]))\n",
            "(740, 128, 128, 1)\n",
            "Epoch 1/13\n",
            "37/37 [==============================] - 26s 677ms/step - loss: 1.0567 - accuracy: 0.5152 - val_loss: 1.0587 - val_accuracy: 0.4122\n",
            "Epoch 2/13\n",
            "37/37 [==============================] - 24s 637ms/step - loss: 0.7471 - accuracy: 0.7196 - val_loss: 0.5726 - val_accuracy: 0.8041\n",
            "Epoch 3/13\n",
            "37/37 [==============================] - 24s 649ms/step - loss: 0.5527 - accuracy: 0.7855 - val_loss: 0.4187 - val_accuracy: 0.8176\n",
            "Epoch 4/13\n",
            "37/37 [==============================] - 24s 663ms/step - loss: 0.4515 - accuracy: 0.8091 - val_loss: 0.4435 - val_accuracy: 0.7635\n",
            "Epoch 5/13\n",
            "37/37 [==============================] - 25s 666ms/step - loss: 0.4710 - accuracy: 0.8311 - val_loss: 0.3842 - val_accuracy: 0.8041\n",
            "Epoch 6/13\n",
            "37/37 [==============================] - 25s 667ms/step - loss: 0.3743 - accuracy: 0.8429 - val_loss: 0.6332 - val_accuracy: 0.7162\n",
            "Epoch 7/13\n",
            "37/37 [==============================] - 25s 669ms/step - loss: 0.3460 - accuracy: 0.8530 - val_loss: 0.3501 - val_accuracy: 0.8243\n",
            "Epoch 8/13\n",
            "37/37 [==============================] - 25s 666ms/step - loss: 0.2996 - accuracy: 0.8801 - val_loss: 0.3967 - val_accuracy: 0.7703\n",
            "Epoch 9/13\n",
            "37/37 [==============================] - 24s 658ms/step - loss: 0.2859 - accuracy: 0.8919 - val_loss: 0.4776 - val_accuracy: 0.8243\n",
            "Epoch 10/13\n",
            "37/37 [==============================] - 25s 672ms/step - loss: 0.2701 - accuracy: 0.8936 - val_loss: 0.8655 - val_accuracy: 0.6419\n",
            "Epoch 11/13\n",
            "37/37 [==============================] - 23s 628ms/step - loss: 0.3101 - accuracy: 0.8598 - val_loss: 0.4047 - val_accuracy: 0.8108\n",
            "Epoch 12/13\n",
            "37/37 [==============================] - 25s 660ms/step - loss: 0.2281 - accuracy: 0.9088 - val_loss: 0.4532 - val_accuracy: 0.8041\n",
            "Epoch 13/13\n",
            "37/37 [==============================] - 25s 667ms/step - loss: 0.2511 - accuracy: 0.9122 - val_loss: 0.4050 - val_accuracy: 0.8176\n",
            "Test Loss: 0.4049646556377411\n",
            "Test Accuracy: 0.8175675868988037\n",
            "Saved model to disk\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-b0a4a889fab1>\u001b[0m in \u001b[0;36m<cell line: 123>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    121\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Saved model to disk\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m \u001b[0mloaded_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'model.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    124\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Loaded model from disk\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'load_model' is not defined"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Import libraries\n",
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.utils import np_utils\n",
        "from keras.models import Sequential\n",
        "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
        "\n",
        "# Mount Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Define data path\n",
        "data_path = '/content/drive/MyDrive/data'\n",
        "data_dir_list = os.listdir(data_path)\n",
        "\n",
        "img_rows = 128\n",
        "img_cols = 128\n",
        "num_channel = 1\n",
        "num_epochs = 13\n",
        "\n",
        "# Define the number of classes\n",
        "num_classes = 3\n",
        "\n",
        "unknown_label = -1  # Assign a unique integer value for 'Unknown'\n",
        "\n",
        "labels_name = {'gun': 0, 'knife': 1, 'violence': 2}\n",
        "\n",
        "img_data_list = []\n",
        "labels_list = []\n",
        "\n",
        "for dataset in data_dir_list:\n",
        "    img_list = os.listdir(os.path.join(data_path, dataset))\n",
        "    print('Loading the images of dataset-{}\\n'.format(dataset))\n",
        "    label = labels_name.get(dataset, unknown_label)\n",
        "    for img in img_list:\n",
        "        input_img = cv2.imread(os.path.join(data_path, dataset, img))\n",
        "        input_img = cv2.cvtColor(input_img, cv2.COLOR_BGR2GRAY)\n",
        "        input_img_resize = cv2.resize(input_img, (128, 128))\n",
        "        img_data_list.append(input_img_resize)\n",
        "        labels_list.append(label)\n",
        "\n",
        "\n",
        "img_data = np.array(img_data_list)\n",
        "img_data = img_data.astype('float32')\n",
        "img_data /= 255\n",
        "print(img_data.shape)\n",
        "\n",
        "labels = np.array(labels_list)\n",
        "print(np.unique(labels, return_counts=True))\n",
        "\n",
        "# convert class labels to one-hot encoding\n",
        "Y = np_utils.to_categorical(labels, num_classes)\n",
        "\n",
        "img_data = np.expand_dims(img_data, axis=3)\n",
        "print(img_data.shape)\n",
        "\n",
        "# Shuffle the dataset\n",
        "img_data, Y = shuffle(img_data, Y, random_state=2)\n",
        "\n",
        "# Split the dataset\n",
        "X_train, X_test, y_train, y_test = train_test_split(img_data, Y, test_size=0.2, random_state=2)\n",
        "\n",
        "# Defining the model\n",
        "input_shape = (img_rows, img_cols, num_channel)\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), padding='same', input_shape=input_shape))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Conv2D(64, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(num_classes))\n",
        "model.add(Activation('softmax'))\n",
        "\n",
        "model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=[\"accuracy\"])\n",
        "\n",
        "# Training\n",
        "hist = model.fit(X_train, y_train, batch_size=16, epochs=num_epochs, verbose=1, validation_data=(X_test, y_test))\n",
        "\n",
        "# Visualizing losses and accuracy\n",
        "train_loss = hist.history['loss']\n",
        "val_loss = hist.history['val_loss']\n",
        "train_acc = hist.history['accuracy']\n",
        "val_acc = hist.history['val_accuracy']\n",
        "xc = range(num_epochs)\n",
        "\n",
        "plt.figure(1, figsize=(7, 5))\n",
        "plt.plot(xc, train_loss)\n",
        "plt.plot(xc, val_loss)\n",
        "plt.xlabel('Number of Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Train Loss vs Validation Loss')\n",
        "plt.grid(True)\n",
        "plt.legend(['Train', 'Validation'])\n",
        "plt.style.use(['classic'])\n",
        "\n",
        "# Evaluating the model\n",
        "score = model.evaluate(X_test, y_test, verbose=0)\n",
        "print('Test Loss:', score[0])\n",
        "print('Test Accuracy:', score[1])\n",
        "\n",
        "# Saving and loading the model and weights\n",
        "model.save('model.h5')\n",
        "print(\"Saved model to disk\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import backend as K\n",
        "\n",
        "# Convert Keras model to TensorFlow SavedModel format\n",
        "tf.saved_model.save(model, '/content/drive/MyDrive/data/')\n",
        "\n",
        "# Convert to .pb file\n",
        "converter = tf.compat.v1.lite.TFLiteConverter.from_saved_model('/content/drive/MyDrive/data/')\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Save .pb file\n",
        "with open('/content/drive/MyDrive/data/trainedmodel.pb', 'wb') as f:\n",
        "    f.write(tflite_model)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PEQw4-nIU_Cw",
        "outputId": "76b78588-cc64-4d17-e792-a3de42f0e03a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla while saving (showing 4 of 4). These functions will not be directly callable after loading.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n",
            "Use '@tf.function' or '@defun' to decorate the function.\n"
          ]
        }
      ]
    }
  ]
}