Face Mask Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd1a27b5-0181-42eb-b11d-c548c08d46de",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c7bb76a-2aa5-4570-a133-68a27f710525",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing import image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bd6c0570-1aaa-49ba-b1ac-6ef91e465fb7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "decdff40-d030-4f04-9c62-2a22a0dbf266",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "categories=['with_mask','without_mask']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4c663d6a-014d-42f0-a7ba-0e30460039cd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data=[]\n",
    "for category in categories:\n",
    "    path=os.path.join('train',category)\n",
    "    label=categories.index(category)\n",
    "    \n",
    "    for file in os.listdir(path):\n",
    "        \n",
    "        img_path=os.path.join(path,file)\n",
    "        img=cv2.imread(img_path)\n",
    "        img=cv2.resize(img,(224,224))\n",
    "        \n",
    "        data.append([img,label])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "54b38456-ec09-485d-be7f-a1c5e191ab85",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1279"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "750bec1a-cec3-45a1-802c-14eebb2b46bc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b30b61d-293e-4697-b4f5-cee62d9548fe",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "random.shuffle(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a2ced94e-db4c-470e-ad74-182848518773",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x=[]\n",
    "y=[]\n",
    "\n",
    "for features,label in data:\n",
    "    x.append(features)\n",
    "    y.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f8733549-e0a8-4b69-89f6-78207aa25301",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "1279"
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "34137d9a-2636-44bb-bfff-13d76ed664d5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "1279"
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8dddfb8d-0b41-4c75-abb7-5ba1be78bbc2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fd191bb9-fde4-498a-976d-98c5caf76212",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x=np.array(x)\n",
    "y=np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ef31cbb9-2774-46f0-bfbd-e1cc6c245bd6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1279, 224, 224, 3)"
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a8ad043c-6e60-4600-9a95-c45ed53af6da",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1279,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1758582c-6c2e-459f-b3b3-971640a8d3f0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x=x/255"
   ]
  },
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   "cell_type": "code",
   "execution_count": 17,
   "id": "51eac5ef-6450-452a-9eb7-7fef7d469de5",
   "metadata": {
    "scrolled": true,
    "tags": []
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       "         [0.01568627, 0.01960784, 0.05882353],\n",
       "         [0.01176471, 0.01568627, 0.05490196],\n",
       "         ...,\n",
       "         [0.02745098, 0.03529412, 0.03921569],\n",
       "         [0.02745098, 0.03529412, 0.03921569],\n",
       "         [0.01960784, 0.02745098, 0.03137255]],\n",
       "\n",
       "        [[0.01568627, 0.01960784, 0.05882353],\n",
       "         [0.01568627, 0.01960784, 0.05882353],\n",
       "         [0.01176471, 0.01568627, 0.05490196],\n",
       "         ...,\n",
       "         [0.02745098, 0.03529412, 0.03921569],\n",
       "         [0.02745098, 0.03529412, 0.03921569],\n",
       "         [0.02352941, 0.03137255, 0.03529412]],\n",
       "\n",
       "        [[0.01568627, 0.01960784, 0.05882353],\n",
       "         [0.01568627, 0.01960784, 0.05882353],\n",
       "         [0.01176471, 0.01568627, 0.05490196],\n",
       "         ...,\n",
       "         [0.02745098, 0.03529412, 0.03921569],\n",
       "         [0.02745098, 0.03529412, 0.03921569],\n",
       "         [0.02745098, 0.03529412, 0.03921569]]]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2d3e0af4-48f5-47f7-840b-0c7e6284caf0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(x,y,test_size=0.15,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1e79a262-9067-4171-9796-e3c7f49dd3e8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1087, 224, 224, 3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4934febf-badd-4dcd-bc90-7642bde3ca7f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(192, 224, 224, 3)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cea72373-2b2e-4ee7-84d9-8b71d1a917cc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications.vgg16 import VGG16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ce81c688-3403-491c-8b5d-d8178bbcd584",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "vgg=VGG16()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b7b9526a-0c4e-433d-a0f2-b1ac73b46a68",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
      "                                                                 \n",
      " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 25088)             0         \n",
      "                                                                 \n",
      " fc1 (Dense)                 (None, 4096)              102764544 \n",
      "                                                                 \n",
      " fc2 (Dense)                 (None, 4096)              16781312  \n",
      "                                                                 \n",
      " predictions (Dense)         (None, 1000)              4097000   \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 138,357,544\n",
      "Trainable params: 138,357,544\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "vgg.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f84924fb-17bc-41f7-8acb-949940c5ecb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "341062d1-ae97-42f7-970b-10802be3e7ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e065ff79-d93f-4ad8-8ac8-da716792fbaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "models=vgg.layers[:-1]\n",
    "for layer in models:\n",
    "    model.add(layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d0745f9c-d1e6-4ec1-bb19-c9069cae10bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 25088)             0         \n",
      "                                                                 \n",
      " fc1 (Dense)                 (None, 4096)              102764544 \n",
      "                                                                 \n",
      " fc2 (Dense)                 (None, 4096)              16781312  \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 134,260,544\n",
      "Trainable params: 134,260,544\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2031ecf2-040c-4f87-9028-e1d6329d4ae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in model.layers:\n",
    "    layer.trainable=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9fd5bd09-d792-46a4-85ef-6c74ae4187c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 25088)             0         \n",
      "                                                                 \n",
      " fc1 (Dense)                 (None, 4096)              102764544 \n",
      "                                                                 \n",
      " fc2 (Dense)                 (None, 4096)              16781312  \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 134,260,544\n",
      "Trainable params: 0\n",
      "Non-trainable params: 134,260,544\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "acd6f162-037b-48c5-9bb9-135aa9eff8b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dense,Conv2D,BatchNormalization,MaxPooling2D,Flatten,Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "93ccd582-5cad-471a-a72f-d464d8d238b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(1,activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7d6ad5a1-d96e-4298-9c42-43677b397411",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 25088)             0         \n",
      "                                                                 \n",
      " fc1 (Dense)                 (None, 4096)              102764544 \n",
      "                                                                 \n",
      " fc2 (Dense)                 (None, 4096)              16781312  \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 4097      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 134,264,641\n",
      "Trainable params: 4,097\n",
      "Non-trainable params: 134,260,544\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "955cb09d-9355-4c82-b0c0-523781b0d63c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "abbf85bc-6fdf-49ed-9794-d67583a1d956",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "68/68 [==============================] - 19s 144ms/step - loss: 0.4685 - accuracy: 0.8077 - val_loss: 0.3074 - val_accuracy: 0.9219\n",
      "Epoch 2/5\n",
      "68/68 [==============================] - 7s 98ms/step - loss: 0.2492 - accuracy: 0.9236 - val_loss: 0.2139 - val_accuracy: 0.9427\n",
      "Epoch 3/5\n",
      "68/68 [==============================] - 7s 100ms/step - loss: 0.1819 - accuracy: 0.9476 - val_loss: 0.1703 - val_accuracy: 0.9323\n",
      "Epoch 4/5\n",
      "68/68 [==============================] - 7s 101ms/step - loss: 0.1514 - accuracy: 0.9586 - val_loss: 0.1504 - val_accuracy: 0.9479\n",
      "Epoch 5/5\n",
      "68/68 [==============================] - 7s 100ms/step - loss: 0.1290 - accuracy: 0.9687 - val_loss: 0.1672 - val_accuracy: 0.9323\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a252489f40>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train,epochs=5,validation_data=(X_test,Y_test),batch_size=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "73d4f4b6-f6a8-42db-a8f2-cd02b9e5d3e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_face_mask(img):\n",
    "    y_pred=model.predict(img.reshape(1,224,224,3))\n",
    "    return y_pred[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4fed7cc0-ef06-4176-a22e-488b43080bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_label(img,text,pos,bg_color):\n",
    "    text_size=cv2.getTextSize(text,cv2.FONT_HERSHEY_SIMPLEX,1,cv2.FILLED)\n",
    "    end_x=pos[0]+text_size[0][0]+2\n",
    "    end_y=pos[0]+text_size[0][1]-2\n",
    "\n",
    "    cv2.rectangle(img,pos,(end_x,end_y),bg_color,cv2.FILLED)\n",
    "    cv2.putText(img,text,pos,cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,0),1,cv2.LINE_AA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ab549926-6394-43d6-994f-e96d4dca6065",
   "metadata": {},
   "outputs": [],
   "source": [
    "haar=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5a880ffc-a561-4e3d-919d-54cd67be0fd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_face(img):\n",
    "    coords=haar.detectMultiScale(img)\n",
    "    return coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dcf1a7a8-fe89-4068-8255-afc4cbb93a77",
   "metadata": {},
   "outputs": [],
   "source": [
    "cap=cv2.VideoCapture(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e9dd78b6-9fde-4f22-a7a3-fcac4603687d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 36ms/step\n",
      "1/1 [==============================] - 0s 36ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
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      "1/1 [==============================] - 0s 25ms/step\n"
     ]
    }
   ],
   "source": [
    "while True:\n",
    "    ret,frame=cap.read()\n",
    "    #Detection function\n",
    "    frame=cv2.flip(frame,1)\n",
    "    img=cv2.resize(frame,(224,224))\n",
    "    y_pred=detect_face_mask(img)\n",
    "    coords=detect_face(cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY))\n",
    "    for x,y,w,h in coords:\n",
    "        cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),3)\n",
    "    if(round(y_pred)>0.5):\n",
    "        draw_label(frame,\"No Mask\",(25,25),(0,0,255))\n",
    "    else:\n",
    "        draw_label(frame,\"Mask\",(25,25),(0,255,0))\n",
    "    \n",
    "    cv2.imshow(\"window\",frame)\n",
    "\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c09eb1f1-ed2c-49a0-a73a-1a353ca9ceca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#For Face Detection\n",
    "while True:\n",
    "    ret,frame=cap.read()\n",
    "    frame=cv2.flip(frame,1)\n",
    "    coords=detect_face(cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY))\n",
    "    for x,y,w,h in coords:\n",
    "        cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),3)\n",
    "    cv2.imshow(\"window\",frame)\n",
    "\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a42edd96-4a35-4e0e-bb88-09690c0a3aa9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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