Final_Face_Mask_Detection.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Final Face Mask Detection.ipynb",
      "provenance": [],
      "mount_file_id": "12dXerBchIk-BYH8KWqS_mrItImWqH9MA",
      "authorship_tag": "ABX9TyOHPiR9zrdF6RhCUu+r1RTk",
      "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/Dipin-Adhikari/Face-Mask-Detection/blob/main/Final_Face_Mask_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "puaKoNwZpY01"
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import cv2\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.applications.mobilenet_v2 import preprocess_input\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.applications import MobileNetV2\n",
        "from tensorflow.keras.layers import Input, AveragePooling2D, Dropout, Flatten, Dense\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from google.colab.patches import cv2_imshow"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mgcAwyhppknC"
      },
      "source": [
        "img_directory = \"/content/drive/MyDrive/New dataset for face mask\"\n",
        "categories = [\"with_mask\", \"without_mask\"]\n",
        "data = []\n",
        "labels = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SOCMzWMop_05"
      },
      "source": [
        "for category in categories:\n",
        "    path = os.path.join(img_directory, category)\n",
        "    for i, img_name in enumerate(os.listdir(path)):\n",
        "      img = cv2.imread(os.path.join(img_directory, category, img_name))\n",
        "      img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "      img = cv2.resize(img, (224, 224))\n",
        "      img = np.array(img)\n",
        "      img = preprocess_input(img)\n",
        "      data.append(img)\n",
        "      labels.append(category)\n",
        "\n",
        "lb = LabelBinarizer()\n",
        "labels = lb.fit_transform(labels)\n",
        "labels = to_categorical(labels)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tn7pjXFUqG1w"
      },
      "source": [
        "data = np.array(data, dtype=\"float32\")\n",
        "labels = np.array(labels)\n",
        "\n",
        "(trainX, testX, trainY, testY) = train_test_split(data, labels, test_size=0.20, stratify=labels, random_state=42)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3UcOd8r8vPiu"
      },
      "source": [
        "aug = ImageDataGenerator(\n",
        "\trotation_range=20,\n",
        "\tzoom_range=0.15,\n",
        "\twidth_shift_range=0.2,\n",
        "\theight_shift_range=0.2,\n",
        "\tshear_range=0.15,\n",
        "\thorizontal_flip=True,\n",
        "\tfill_mode=\"nearest\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PCmAEsB7vUSS",
        "outputId": "7e1aab14-a6af-447a-a4ed-a4ddad36bf3e"
      },
      "source": [
        "baseModel = MobileNetV2(weights=\"imagenet\", include_top=False,\n",
        "\tinput_tensor=Input(shape=(224, 224, 3)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
            "9412608/9406464 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S0t1_OmdvW9y"
      },
      "source": [
        "headModel = baseModel.output\n",
        "headModel = AveragePooling2D(pool_size=(7, 7))(headModel)\n",
        "headModel = Flatten(name=\"flatten\")(headModel)\n",
        "headModel = Dense(128, activation=\"relu\")(headModel)\n",
        "headModel = Dropout(0.5)(headModel)\n",
        "headModel = Dense(2, activation=\"softmax\")(headModel)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DiK6Pb4Rvbqy"
      },
      "source": [
        "model = Model(inputs=baseModel.input, outputs=headModel)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "btMT-KhHveJP"
      },
      "source": [
        "for layer in baseModel.layers:\n",
        "\tlayer.trainable = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9c6ks6Dovh2s"
      },
      "source": [
        "INIT_LR = 1e-4\n",
        "EPOCHS = 20\n",
        "BS = 32"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dj5O3cCnvm7Z",
        "outputId": "b4e6160a-e4ed-49d8-bcef-073afd984524"
      },
      "source": [
        "opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=opt, metrics=[\"accuracy\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "03yw1EtevsZN",
        "outputId": "dc0ac05f-2477-4988-dacc-b1971eb2b7e2"
      },
      "source": [
        "H = model.fit(\n",
        "\taug.flow(trainX, trainY, batch_size=BS),\n",
        "\tsteps_per_epoch=len(trainX) // BS,\n",
        "\tvalidation_data=(testX, testY),\n",
        "\tvalidation_steps=len(testX) // BS,\n",
        "\tepochs=EPOCHS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "95/95 [==============================] - 140s 1s/step - loss: 0.3622 - accuracy: 0.8761 - val_loss: 0.1274 - val_accuracy: 0.9739\n",
            "Epoch 2/20\n",
            "95/95 [==============================] - 136s 1s/step - loss: 0.1397 - accuracy: 0.9654 - val_loss: 0.0681 - val_accuracy: 0.9817\n",
            "Epoch 3/20\n",
            "95/95 [==============================] - 135s 1s/step - loss: 0.0894 - accuracy: 0.9763 - val_loss: 0.0467 - val_accuracy: 0.9896\n",
            "Epoch 4/20\n",
            "95/95 [==============================] - 134s 1s/step - loss: 0.0767 - accuracy: 0.9796 - val_loss: 0.0388 - val_accuracy: 0.9922\n",
            "Epoch 5/20\n",
            "95/95 [==============================] - 135s 1s/step - loss: 0.0672 - accuracy: 0.9825 - val_loss: 0.0326 - val_accuracy: 0.9922\n",
            "Epoch 6/20\n",
            "95/95 [==============================] - 135s 1s/step - loss: 0.0521 - accuracy: 0.9845 - val_loss: 0.0309 - val_accuracy: 0.9935\n",
            "Epoch 7/20\n",
            "95/95 [==============================] - 135s 1s/step - loss: 0.0554 - accuracy: 0.9848 - val_loss: 0.0273 - val_accuracy: 0.9948\n",
            "Epoch 8/20\n",
            "95/95 [==============================] - 133s 1s/step - loss: 0.0486 - accuracy: 0.9842 - val_loss: 0.0235 - val_accuracy: 0.9948\n",
            "Epoch 9/20\n",
            "95/95 [==============================] - 136s 1s/step - loss: 0.0461 - accuracy: 0.9858 - val_loss: 0.0232 - val_accuracy: 0.9948\n",
            "Epoch 10/20\n",
            "95/95 [==============================] - 136s 1s/step - loss: 0.0423 - accuracy: 0.9845 - val_loss: 0.0206 - val_accuracy: 0.9948\n",
            "Epoch 11/20\n",
            "95/95 [==============================] - 134s 1s/step - loss: 0.0351 - accuracy: 0.9881 - val_loss: 0.0199 - val_accuracy: 0.9948\n",
            "Epoch 12/20\n",
            "95/95 [==============================] - 134s 1s/step - loss: 0.0388 - accuracy: 0.9891 - val_loss: 0.0196 - val_accuracy: 0.9948\n",
            "Epoch 13/20\n",
            "95/95 [==============================] - 129s 1s/step - loss: 0.0380 - accuracy: 0.9911 - val_loss: 0.0182 - val_accuracy: 0.9948\n",
            "Epoch 14/20\n",
            "95/95 [==============================] - 129s 1s/step - loss: 0.0362 - accuracy: 0.9891 - val_loss: 0.0186 - val_accuracy: 0.9948\n",
            "Epoch 15/20\n",
            "95/95 [==============================] - 129s 1s/step - loss: 0.0309 - accuracy: 0.9908 - val_loss: 0.0194 - val_accuracy: 0.9961\n",
            "Epoch 16/20\n",
            "95/95 [==============================] - 128s 1s/step - loss: 0.0306 - accuracy: 0.9911 - val_loss: 0.0161 - val_accuracy: 0.9948\n",
            "Epoch 17/20\n",
            "95/95 [==============================] - 134s 1s/step - loss: 0.0344 - accuracy: 0.9895 - val_loss: 0.0161 - val_accuracy: 0.9948\n",
            "Epoch 18/20\n",
            "95/95 [==============================] - 128s 1s/step - loss: 0.0317 - accuracy: 0.9904 - val_loss: 0.0176 - val_accuracy: 0.9961\n",
            "Epoch 19/20\n",
            "95/95 [==============================] - 129s 1s/step - loss: 0.0248 - accuracy: 0.9924 - val_loss: 0.0163 - val_accuracy: 0.9961\n",
            "Epoch 20/20\n",
            "95/95 [==============================] - 129s 1s/step - loss: 0.0240 - accuracy: 0.9924 - val_loss: 0.0156 - val_accuracy: 0.9961\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "XMRO4VV68TSW",
        "outputId": "a2c3982f-fca7-41f8-b695-064dcad7fc88"
      },
      "source": [
        "path = \"/content/drive/MyDrive/Face Mask Dataset/Test/WithMask/1174.png\"\n",
        "\n",
        "img = cv2.imread(path)\n",
        "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "img_resize = cv2.resize(img, (224, 224))\n",
        "\n",
        "img_array = np.array(img_resize)\n",
        "\n",
        "final = np.expand_dims(img_array, axis=0)\n",
        "\n",
        "prediction = model.predict(final)\n",
        "\n",
        "cv2_imshow(img)\n",
        "\n",
        "print(prediction)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=168x168 at 0x7F86214D5090>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[[9.9987054e-01 1.2941776e-04]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y4iJQ0OR8qpP"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}