mask_detection.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KCWeEcpR5omI"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DoFngCc5I50E",
        "outputId": "6896d536-9231-4183-f3af-a7c24463e537"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yx4nOn5lLc5b",
        "outputId": "01342f38-8f69-456a-f919-2ab106741968"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'without_mask': 0, 'with_mask': 1}\n",
            "['without_mask', 'with_mask']\n",
            "[0, 1]\n"
          ]
        }
      ],
      "source": [
        "import cv2,os\n",
        "\n",
        "\n",
        "data_path='/content/drive/MyDrive/mask data'\n",
        "categories=os.listdir(data_path)\n",
        "labels=[i for i in range(len(categories))]\n",
        "\n",
        "label_dict=dict(zip(categories,labels)) #empty dictionary\n",
        "\n",
        "print(label_dict)\n",
        "print(categories)\n",
        "print(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QxXtcLl_iZXZ"
      },
      "outputs": [],
      "source": [
        "img_size=100\n",
        "data=[]\n",
        "target=[]\n",
        "\n",
        "\n",
        "for category in categories:\n",
        "    folder_path=os.path.join(data_path,category)\n",
        "    img_names=os.listdir(folder_path)\n",
        "\n",
        "    for img_name in img_names:\n",
        "        img_path=os.path.join(folder_path,img_name)\n",
        "        img=cv2.imread(img_path)\n",
        "\n",
        "        try:\n",
        "            gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
        "            #Coverting the image into gray scale\n",
        "            resized=cv2.resize(gray,(img_size,img_size))\n",
        "            #resizing the gray scale into 100x100, since we need a fixed common size for all the images in the dataset\n",
        "            data.append(resized)\n",
        "            target.append(label_dict[category])\n",
        "            #appending the image and the label(categorized) into the list (dataset)\n",
        "\n",
        "        except Exception as e:\n",
        "            print('Exception:',e)\n",
        "            #if any exception rasied, the exception will be printed here. And pass to the next image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "du-1TA00iltB"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "data=np.array(data)/255.0\n",
        "data=np.reshape(data,(data.shape[0],img_size,img_size,1))\n",
        "target=np.array(target)\n",
        "\n",
        "from keras.utils import np_utils\n",
        "\n",
        "new_target=np_utils.to_categorical(target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QBn-ExV5i19N"
      },
      "outputs": [],
      "source": [
        "np.save('data',data)\n",
        "np.save('target',new_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mbi7HDjfi-1E"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "data=np.load('data.npy')\n",
        "target=np.load('target.npy')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fDvYFsx9jEUe"
      },
      "outputs": [],
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Activation,Flatten,Dropout\n",
        "from keras.layers import Conv2D,MaxPooling2D\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "\n",
        "model=Sequential()\n",
        "\n",
        "model.add(Conv2D(200,(3,3),input_shape=data.shape[1:]))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "#The first CNN layer followed by Relu and MaxPooling layers\n",
        "\n",
        "model.add(Conv2D(100,(3,3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "#The second convolution layer followed by Relu and MaxPooling layers\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dropout(0.5))\n",
        "#Flatten layer to stack the output convolutions from second convolution layer\n",
        "model.add(Dense(50,activation='relu'))\n",
        "#Dense layer of 64 neurons\n",
        "model.add(Dense(2,activation='softmax'))\n",
        "#The Final layer with two outputs for two categories\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iq9nP4ntjUl_"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_data,test_data,train_target,test_target=train_test_split(data,target,test_size=0.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WMPhGAM4jhBF",
        "outputId": "58f78587-01bc-42d6-e462-f8dd15c2977b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "32/32 [==============================] - ETA: 0s - loss: 0.7027 - accuracy: 0.5503INFO:tensorflow:Assets written to: model-001.model/assets\n",
            "32/32 [==============================] - 93s 3s/step - loss: 0.7027 - accuracy: 0.5503 - val_loss: 0.6483 - val_accuracy: 0.6055\n",
            "Epoch 2/20\n",
            "32/32 [==============================] - ETA: 0s - loss: 0.5519 - accuracy: 0.7165INFO:tensorflow:Assets written to: model-002.model/assets\n",
            "32/32 [==============================] - 91s 3s/step - loss: 0.5519 - accuracy: 0.7165 - val_loss: 0.5020 - val_accuracy: 0.7578\n",
            "Epoch 3/20\n",
            "32/32 [==============================] - ETA: 0s - loss: 0.3524 - accuracy: 0.8573INFO:tensorflow:Assets written to: model-003.model/assets\n",
            "32/32 [==============================] - 92s 3s/step - loss: 0.3524 - accuracy: 0.8573 - val_loss: 0.2420 - val_accuracy: 0.8984\n",
            "Epoch 4/20\n",
            "32/32 [==============================] - ETA: 0s - loss: 0.2304 - accuracy: 0.9130INFO:tensorflow:Assets written to: model-004.model/assets\n",
            "32/32 [==============================] - 90s 3s/step - loss: 0.2304 - accuracy: 0.9130 - val_loss: 0.2023 - val_accuracy: 0.9180\n",
            "Epoch 5/20\n",
            "32/32 [==============================] - ETA: 0s - loss: 0.1673 - accuracy: 0.9335INFO:tensorflow:Assets written to: model-005.model/assets\n",
            "32/32 [==============================] - 89s 3s/step - loss: 0.1673 - accuracy: 0.9335 - val_loss: 0.1730 - val_accuracy: 0.9180\n",
            "Epoch 6/20\n",
            "32/32 [==============================] - ETA: 0s - loss: 0.1060 - accuracy: 0.9677INFO:tensorflow:Assets written to: model-006.model/assets\n",
            "32/32 [==============================] - 89s 3s/step - loss: 0.1060 - accuracy: 0.9677 - val_loss: 0.1461 - val_accuracy: 0.9492\n",
            "Epoch 7/20\n",
            "32/32 [==============================] - ETA: 0s - loss: 0.0807 - accuracy: 0.9736INFO:tensorflow:Assets written to: model-007.model/assets\n",
            "32/32 [==============================] - 88s 3s/step - loss: 0.0807 - accuracy: 0.9736 - val_loss: 0.1128 - val_accuracy: 0.9648\n",
            "Epoch 8/20\n",
            "32/32 [==============================] - 86s 3s/step - loss: 0.0587 - accuracy: 0.9795 - val_loss: 0.1525 - val_accuracy: 0.9570\n",
            "Epoch 9/20\n",
            "32/32 [==============================] - 85s 3s/step - loss: 0.0566 - accuracy: 0.9804 - val_loss: 0.1704 - val_accuracy: 0.9492\n",
            "Epoch 10/20\n",
            "32/32 [==============================] - 85s 3s/step - loss: 0.0341 - accuracy: 0.9922 - val_loss: 0.1174 - val_accuracy: 0.9727\n",
            "Epoch 11/20\n",
            "32/32 [==============================] - 84s 3s/step - loss: 0.0443 - accuracy: 0.9863 - val_loss: 0.1157 - val_accuracy: 0.9648\n",
            "Epoch 12/20\n",
            "32/32 [==============================] - 85s 3s/step - loss: 0.0185 - accuracy: 0.9912 - val_loss: 0.1386 - val_accuracy: 0.9648\n",
            "Epoch 13/20\n",
            "32/32 [==============================] - 85s 3s/step - loss: 0.0396 - accuracy: 0.9902 - val_loss: 0.1447 - val_accuracy: 0.9648\n",
            "Epoch 14/20\n",
            "32/32 [==============================] - 85s 3s/step - loss: 0.0308 - accuracy: 0.9892 - val_loss: 0.1263 - val_accuracy: 0.9688\n",
            "Epoch 15/20\n",
            "32/32 [==============================] - 85s 3s/step - loss: 0.0139 - accuracy: 0.9951 - val_loss: 0.1447 - val_accuracy: 0.9531\n",
            "Epoch 16/20\n",
            "32/32 [==============================] - 85s 3s/step - loss: 0.0106 - accuracy: 0.9971 - val_loss: 0.1498 - val_accuracy: 0.9648\n",
            "Epoch 17/20\n",
            "32/32 [==============================] - 85s 3s/step - loss: 0.0133 - accuracy: 0.9941 - val_loss: 0.1492 - val_accuracy: 0.9688\n",
            "Epoch 18/20\n",
            "32/32 [==============================] - 85s 3s/step - loss: 0.0153 - accuracy: 0.9951 - val_loss: 0.1725 - val_accuracy: 0.9570\n",
            "Epoch 19/20\n",
            "32/32 [==============================] - 85s 3s/step - loss: 0.0113 - accuracy: 0.9961 - val_loss: 0.1872 - val_accuracy: 0.9492\n",
            "Epoch 20/20\n",
            "32/32 [==============================] - 85s 3s/step - loss: 0.0538 - accuracy: 0.9756 - val_loss: 0.2550 - val_accuracy: 0.9414\n"
          ]
        }
      ],
      "source": [
        "checkpoint = ModelCheckpoint('model-{epoch:03d}.model',monitor='val_loss',verbose=0,save_best_only=True,mode='auto')\n",
        "history=model.fit(train_data,train_target,epochs=20,callbacks=[checkpoint],validation_split=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "VFUS3yB6sIOO",
        "outputId": "633eef3e-45be-4755-d144-d4ceb1f01c0a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "\n",
        "plt.plot(history.history['loss'],'r',label='training loss')\n",
        "plt.plot(history.history['val_loss'],label='validation loss')\n",
        "plt.xlabel('# epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "GdG9zt5W28AA",
        "outputId": "328cba21-edf5-4791-a358-5d8890f35467"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.plot(history.history['accuracy'],'r',label='training accuracy')\n",
        "plt.plot(history.history['val_accuracy'],label='validation accuracy')\n",
        "plt.xlabel('# epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FVky-BTdsUTW",
        "outputId": "0dae4226-a7da-4c15-eb57-b1bbfe10ba41"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "5/5 [==============================] - 3s 581ms/step - loss: 0.4552 - accuracy: 0.8951\n",
            "[0.45524483919143677, 0.8951048851013184]\n"
          ]
        }
      ],
      "source": [
        "print(model.evaluate(test_data,test_target))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CAy1bdz018FR"
      },
      "outputs": [],
      "source": [
        "from keras.models import load_model\n",
        "import cv2\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X4R_XQb2A0lC"
      },
      "outputs": [],
      "source": [
        "from IPython.display import display, Javascript\n",
        "from google.colab.output import eval_js\n",
        "from base64 import b64decode\n",
        "\n",
        "def take_photo(filename='photo.jpg', quality=0.8):\n",
        "  js = Javascript('''\n",
        "    async function takePhoto(quality) {\n",
        "      const div = document.createElement('div');\n",
        "      const capture = document.createElement('button');\n",
        "      capture.textContent = 'Capture';\n",
        "      div.appendChild(capture);\n",
        "\n",
        "      const video = document.createElement('video');\n",
        "      video.style.display = 'block';\n",
        "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
        "\n",
        "      document.body.appendChild(div);\n",
        "      div.appendChild(video);\n",
        "      video.srcObject = stream;\n",
        "      await video.play();\n",
        "\n",
        "      // Resize the output to fit the video element.\n",
        "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
        "\n",
        "      // Wait for Capture to be clicked.\n",
        "      await new Promise((resolve) => capture.onclick = resolve);\n",
        "\n",
        "      const canvas = document.createElement('canvas');\n",
        "      canvas.width = video.videoWidth;\n",
        "      canvas.height = video.videoHeight;\n",
        "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
        "      stream.getVideoTracks()[0].stop();\n",
        "      div.remove();\n",
        "      return canvas.toDataURL('image/jpeg', quality);\n",
        "    }\n",
        "    ''')\n",
        "  display(js)\n",
        "  data = eval_js('takePhoto({})'.format(quality))\n",
        "  binary = b64decode(data.split(',')[1])\n",
        "  with open(filename, 'wb') as f:\n",
        "    f.write(binary)\n",
        "  return filename\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cNHBGOmgCMNN"
      },
      "outputs": [],
      "source": [
        "from IPython.display import display, Javascript\n",
        "from google.colab.output import eval_js\n",
        "from base64 import b64decode\n",
        "\n",
        "def take_photo(filename='photo.jpg', quality=0.8):\n",
        "  js = Javascript('''\n",
        "    async function takePhoto(quality) {\n",
        "      const div = document.createElement('div');\n",
        "      const capture = document.createElement('button');\n",
        "      capture.textContent = 'Capture';\n",
        "      div.appendChild(capture);\n",
        "\n",
        "      const video = document.createElement('video');\n",
        "      video.style.display = 'block';\n",
        "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
        "\n",
        "      document.body.appendChild(div);\n",
        "      div.appendChild(video);\n",
        "      video.srcObject = stream;\n",
        "      await video.play();\n",
        "\n",
        "      // Resize the output to fit the video element.\n",
        "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
        "\n",
        "      // Wait for Capture to be clicked.\n",
        "      await new Promise((resolve) => capture.onclick = resolve);\n",
        "\n",
        "      const canvas = document.createElement('canvas');\n",
        "      canvas.width = video.videoWidth;\n",
        "      canvas.height = video.videoHeight;\n",
        "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
        "      stream.getVideoTracks()[0].stop();\n",
        "      div.remove();\n",
        "      return canvas.toDataURL('image/jpeg', quality);\n",
        "    }\n",
        "    ''')\n",
        "  display(js)\n",
        "  data = eval_js('takePhoto({})'.format(quality))\n",
        "  binary = b64decode(data.split(',')[1])\n",
        "  with open(filename, 'wb') as f:\n",
        "    f.write(binary)\n",
        "  return filename"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5uRVtskq2Hqp"
      },
      "outputs": [],
      "source": [
        "model = load_model('model-007.model')\n",
        "\n",
        "face_clsfr=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
        "\n",
        "a1='/content/drive/MyDrive/mask data/without_mask/105.jpg'\n",
        "a3='/content/drive/MyDrive/mask data/without_mask/108.jpg'\n",
        "a2='/content/drive/MyDrive/mask data/with_mask/101-with-mask.jpg'\n",
        "a4='/content/drive/MyDrive/mask data/with_mask/50-with-mask.jpg'\n",
        "source=cv2.imread(a1)\n",
        "#cv2.VideoCapture(2)\n",
        "\n",
        "labels_dict={0:'NO MASK',1:'MASK'}\n",
        "color_dict={0:(0,255,0),1:(0,0,255)}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "yD-izm7v2bVA",
        "outputId": "70045e66-b24c-4427-a1f4-c40aecdc9fbc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=214x244 at 0x7F3644AC0AD0>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from google.colab.patches import cv2_imshow\n",
        "while(True):\n",
        "\n",
        "    # ret,img=source.read()\n",
        "    img = source\n",
        "    gray=cv2.cvtColor(source,cv2.COLOR_BGR2GRAY)\n",
        "    faces=face_clsfr.detectMultiScale(gray,1.3,5)\n",
        "\n",
        "    for (x,y,w,h) in faces:\n",
        "\n",
        "        face_img=gray[y:y+w,x:x+w]\n",
        "        resized=cv2.resize(face_img,(100,100))\n",
        "        normalized=resized/255.0\n",
        "        reshaped=np.reshape(normalized,(1,100,100,1))\n",
        "        result=model.predict(reshaped)\n",
        "\n",
        "        label=np.argmax(result,axis=1)[0]\n",
        "\n",
        "        cv2.rectangle(img,(x,y),(x+w,y+h),color_dict[label],2)\n",
        "        cv2.rectangle(img,(x,y-40),(x+w,y),color_dict[label],-1)\n",
        "        cv2.putText(img, labels_dict[label], (x, y-10),cv2.FONT_HERSHEY_SIMPLEX,0.8,(255,255,255),2)\n",
        "\n",
        "    # img1=cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "    cv2_imshow(img)\n",
        "    break\n",
        "    key=cv2.waitKey(1)\n",
        "\n",
        "    if(key==27):\n",
        "        break\n",
        "\n",
        "cv2.destroyAllWindows()\n",
        "#source.release()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}