detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f9cd045e",
   "metadata": {},
   "source": [
    "## Face detection\n",
    "Detect, classify and localize face using Deep learning and CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "460dc2c1",
   "metadata": {},
   "source": [
    "### Import libraries and define vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0407f201",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all libraries\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras.layers import *\n",
    "from tensorflow.keras.applications import VGG16\n",
    "from tensorflow.keras.models import Model\n",
    "import json\n",
    "import numpy as np\n",
    "import cv2\n",
    "import os\n",
    "from keras.losses import BinaryCrossentropy\n",
    "from keras.optimizers import Adam\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4f996220",
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 10\n",
    "BATCH_SIZE = 16\n",
    "DATA_PATH = os.path.join(\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b7cdcc4",
   "metadata": {},
   "source": [
    "### Load data functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a408a247",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_images(image):\n",
    "    \"\"\"\n",
    "    This function will load and decode images\n",
    "    \"\"\"\n",
    "    # read and decode\n",
    "    load = tf.io.read_file(image)\n",
    "    decoded = tf.io.decode_jpeg(load)\n",
    "    \n",
    "    return decoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "df5edb22",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_labels(label_path):\n",
    "    \"\"\"\n",
    "    This function will load labels and return class and bboxes\n",
    "    \"\"\"\n",
    "    # read the label file (.json format)\n",
    "    with open(label_path.numpy(), 'r', encoding = \"utf-8\") as f:\n",
    "        label = json.load(f)\n",
    "        \n",
    "    # get the bounding box coordinates and class\n",
    "    label_class = [label['class']]\n",
    "    label_bbox = label['bbox']\n",
    "    \n",
    "    return label_class, label_bbox"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4276ffd0",
   "metadata": {},
   "source": [
    "### Create dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae43b3d0",
   "metadata": {},
   "source": [
    " We have to load our data (images and labels), then create our final dataset using `tensorflow`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bde918c",
   "metadata": {},
   "source": [
    "1. Load images "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb2e444d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load train images\n",
    "train_images = tf.data.Dataset.list_files('data/train/images/*.jpg', shuffle=False)\n",
    "train_images = train_images.map(load_images)\n",
    "train_images = train_images.map(lambda x: tf.image.resize(x, (120,120)))\n",
    "train_images = train_images.map(lambda x: x/255)\n",
    "\n",
    "# load validation images\n",
    "val_images = tf.data.Dataset.list_files('data/val/images/*.jpg', shuffle=False)\n",
    "val_images = val_images.map(load_images)\n",
    "val_images = val_images.map(lambda x: tf.image.resize(x, (120,120)))\n",
    "val_images = val_images.map(lambda x: x/255)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf0802bb",
   "metadata": {},
   "source": [
    "2. Load labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "44c74f78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load train labels\n",
    "train_labels = tf.data.Dataset.list_files('data/train/labels/*.json', shuffle=False)\n",
    "train_labels = train_labels.map(lambda x: tf.py_function(load_labels, [x], [tf.uint8, tf.float16]))\n",
    "\n",
    "# load validation labels\n",
    "val_labels = tf.data.Dataset.list_files('data/val/labels/*.json', shuffle=False)\n",
    "val_labels = val_labels.map(lambda x: tf.py_function(load_labels, [x], [tf.uint8, tf.float16]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8474f62c",
   "metadata": {},
   "source": [
    "3. Create final dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3fe3b670",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create train dataset\n",
    "train_dataset = tf.data.Dataset.zip((train_images, train_labels))\n",
    "train_dataset = train_dataset.shuffle(5000)\n",
    "train_dataset = train_dataset.batch(BATCH_SIZE)\n",
    "train_dataset = train_dataset.prefetch(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3c829303",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create validation dataset\n",
    "val_dataset = tf.data.Dataset.zip((val_images, val_labels))\n",
    "val_dataset = val_dataset.shuffle(5000)\n",
    "val_dataset = val_dataset.batch(BATCH_SIZE)\n",
    "val_dataset = val_dataset.prefetch(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecde822a",
   "metadata": {},
   "source": [
    "### Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0d78f01",
   "metadata": {},
   "source": [
    "To create the neural networks we are using VGG-16 as our base model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2a3f4fae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    # input layer\n",
    "    input_layer = Input(shape=(120, 120, 3))\n",
    "    \n",
    "    # vgg16 model\n",
    "    vgg = VGG16(include_top=False)(input_layer)\n",
    "    \n",
    "    # layers for classification\n",
    "    x = GlobalAveragePooling2D()(vgg)\n",
    "    x = Dense(128)(x)\n",
    "    x = Dense(128)(x)\n",
    "    x = Dropout(0.2)(x)\n",
    "    x = Dense(1, activation=\"sigmoid\")(x)\n",
    "    \n",
    "    # layers for detection\n",
    "    y = GlobalAveragePooling2D()(vgg)\n",
    "    y = Dense(2048, activation=\"relu\")(y)\n",
    "    y = Dense(2048, activation=\"relu\")(y)\n",
    "    y = Dropout(0.4)(y)\n",
    "    y = Dense(4, activation=\"sigmoid\")(y)\n",
    "\n",
    "    # create the final model\n",
    "    model = Model(inputs=input_layer, outputs=[x, y])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "63f76170",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 120, 120, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " vgg16 (Functional)             (None, None, None,   14714688    ['input_1[0][0]']                \n",
      "                                512)                                                              \n",
      "                                                                                                  \n",
      " global_average_pooling2d (Glob  (None, 512)         0           ['vgg16[0][0]']                  \n",
      " alAveragePooling2D)                                                                              \n",
      "                                                                                                  \n",
      " global_average_pooling2d_1 (Gl  (None, 512)         0           ['vgg16[0][0]']                  \n",
      " obalAveragePooling2D)                                                                            \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 128)          65664       ['global_average_pooling2d[0][0]'\n",
      "                                                                 ]                                \n",
      "                                                                                                  \n",
      " dense_3 (Dense)                (None, 2048)         1050624     ['global_average_pooling2d_1[0][0\n",
      "                                                                 ]']                              \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 128)          16512       ['dense[0][0]']                  \n",
      "                                                                                                  \n",
      " dense_4 (Dense)                (None, 2048)         4196352     ['dense_3[0][0]']                \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, 128)          0           ['dense_1[0][0]']                \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)            (None, 2048)         0           ['dense_4[0][0]']                \n",
      "                                                                                                  \n",
      " dense_2 (Dense)                (None, 1)            129         ['dropout[0][0]']                \n",
      "                                                                                                  \n",
      " dense_5 (Dense)                (None, 4)            8196        ['dropout_1[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 20,052,165\n",
      "Trainable params: 20,052,165\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# define the detection model\n",
    "detection_model = build_model()\n",
    "detection_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cf4dde0",
   "metadata": {},
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bf6e6164",
   "metadata": {},
   "outputs": [],
   "source": [
    "def localization_loss(y_true, yhat):\n",
    "    \"\"\"\n",
    "    This function is the loss function of object localization\n",
    "    \"\"\"\n",
    "    delta_coord = tf.reduce_sum(tf.square(y_true[:,:2] - yhat[:,:2]))\n",
    "                  \n",
    "    h_true = y_true[:,3] - y_true[:,1] \n",
    "    w_true = y_true[:,2] - y_true[:,0] \n",
    "\n",
    "    h_pred = yhat[:,3] - yhat[:,1] \n",
    "    w_pred = yhat[:,2] - yhat[:,0] \n",
    "    \n",
    "    delta_size = tf.reduce_sum(tf.square(w_true - w_pred) + tf.square(h_true-h_pred))\n",
    "    \n",
    "    return delta_coord + delta_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ba83eae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define losses\n",
    "classification_loss = BinaryCrossentropy()\n",
    "detection_loss = localization_loss\n",
    "\n",
    "# define optimizer\n",
    "batches_per_epoch = len(train_dataset)\n",
    "learning_rate_decay = (1./0.75 - 1) / batches_per_epoch\n",
    "optimizer = Adam(learning_rate=0.0001, decay=learning_rate_decay)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef0e480b",
   "metadata": {},
   "source": [
    "Custom training loop: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c9477edd",
   "metadata": {},
   "outputs": [],
   "source": [
    "class FaceDetectionModel(Model):\n",
    "    def __init__(self, trackermodel, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.model = trackermodel\n",
    "\n",
    "    def compile(self, optimizer, classification_loss, detection_loss, **kwargs):\n",
    "        \"\"\"\n",
    "        This function will compile the model\n",
    "        \"\"\"\n",
    "        super().compile(**kwargs)\n",
    "        # define losses and optimizer\n",
    "        self.classification_loss = classification_loss\n",
    "        self.detection_loss = detection_loss\n",
    "        self.optimizer = optimizer\n",
    "    \n",
    "    def train_step(self, batch, **kwargs):\n",
    "        \"\"\"\n",
    "        This function containes a train step of our model\n",
    "        \"\"\"\n",
    "        # define input and output datas as X and y\n",
    "        X, y = batch\n",
    "\n",
    "        # backpropegation\n",
    "        with tf.GradientTape() as tape:\n",
    "            # make model to train on classes and coordinates\n",
    "            classes, coords = self.model(X, training=True)\n",
    "            # calculate losses\n",
    "            class_loss = self.classification_loss(y[0], classes)\n",
    "            loc_loss = self.detection_loss(tf.cast(y[1], tf.float32), coords)\n",
    "\n",
    "            # calculate the total loss\n",
    "            total_loss = loc_loss  +0.5 * class_loss\n",
    "\n",
    "            # define the gradient\n",
    "            grad = tape.gradient(total_loss, self.model.trainable_variables)\n",
    "\n",
    "        # apply gradient\n",
    "        optimizer.apply_gradients(zip(grad, self.model.trainable_variables))\n",
    "\n",
    "        return {\"total_loss\":total_loss, \"class_loss\":class_loss, \"loc_loss\":loc_loss}\n",
    "\n",
    "    def call(self, X, **kwargs): \n",
    "        return self.model(X, **kwargs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "952bb073",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the final model\n",
    "model = FaceDetectionModel(detection_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "de4a416b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compile the detection model\n",
    "model.compile(optimizer, classification_loss, detection_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "be76fa55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "494/494 [==============================] - 46s 85ms/step - total_loss: 0.0247 - class_loss: 7.8316e-04 - loc_loss: 0.0243 - val_loss: 0.0000e+00\n",
      "Epoch 2/10\n",
      "494/494 [==============================] - 48s 89ms/step - total_loss: 0.0239 - class_loss: 5.1848e-04 - loc_loss: 0.0237 - val_loss: 0.0000e+00\n",
      "Epoch 3/10\n",
      "494/494 [==============================] - 51s 94ms/step - total_loss: 0.0225 - class_loss: 4.8953e-04 - loc_loss: 0.0222 - val_loss: 0.0000e+00\n",
      "Epoch 4/10\n",
      "494/494 [==============================] - 51s 95ms/step - total_loss: 0.0218 - class_loss: 4.5526e-04 - loc_loss: 0.0216 - val_loss: 0.0000e+00\n",
      "Epoch 5/10\n",
      "494/494 [==============================] - 51s 94ms/step - total_loss: 0.0210 - class_loss: 6.2857e-04 - loc_loss: 0.0207 - val_loss: 0.0000e+00\n",
      "Epoch 6/10\n",
      "494/494 [==============================] - 51s 95ms/step - total_loss: 0.0206 - class_loss: 3.1283e-04 - loc_loss: 0.0205 - val_loss: 0.0000e+00\n",
      "Epoch 7/10\n",
      "494/494 [==============================] - 51s 95ms/step - total_loss: 0.0192 - class_loss: 7.1338e-04 - loc_loss: 0.0189 - val_loss: 0.0000e+00\n",
      "Epoch 8/10\n",
      "494/494 [==============================] - 51s 95ms/step - total_loss: 0.0184 - class_loss: 3.3621e-04 - loc_loss: 0.0183 - val_loss: 0.0000e+00\n",
      "Epoch 9/10\n",
      "494/494 [==============================] - 50s 92ms/step - total_loss: 0.0201 - class_loss: 4.7888e-04 - loc_loss: 0.0198 - val_loss: 0.0000e+00\n",
      "Epoch 10/10\n",
      "494/494 [==============================] - 52s 95ms/step - total_loss: 0.0192 - class_loss: 0.0013 - loc_loss: 0.0185 - val_loss: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "# train model\n",
    "history = model.fit(train_dataset, epochs=EPOCHS, validation_data=val_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5c32251f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    }
   ],
   "source": [
    "# save the model\n",
    "detection_model.save(\"model.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "412e1738",
   "metadata": {},
   "source": [
    "### load the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6a7c6c89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.load_model(\"model.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d666dcb9",
   "metadata": {},
   "source": [
    "### Use the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "926d3e15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 62ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 47ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 50ms/step\n",
      "1/1 [==============================] - 0s 52ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 42ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 56ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 39ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 55ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 64ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 33ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 36ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 37ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 37ms/step\n",
      "1/1 [==============================] - 0s 37ms/step\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 36ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 39ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 35ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 33ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 35ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 33ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n"
     ]
    }
   ],
   "source": [
    "# get the webcam\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "while cap.isOpened():\n",
    "    ret, frame = cap.read()\n",
    "    frame = frame[50:500, 50:500, :]\n",
    "\n",
    "    # ready our frames for detection\n",
    "    rgb_images = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "    resized_img = tf.image.resize(rgb_images, (120, 120))\n",
    "\n",
    "    # make predictions\n",
    "    prediction = model.predict(np.expand_dims(resized_img/255,0))\n",
    "    # get the coordinates\n",
    "    coords = prediction[1][0]\n",
    "\n",
    "    # draw square if prediction accuracy was high\n",
    "    if prediction[0] >= 0.85:\n",
    "        cv2.rectangle(frame, \n",
    "                      tuple(np.multiply(coords[:2], [450,450]).astype(int)),\n",
    "                      tuple(np.multiply(coords[2:], [450,450]).astype(int)), \n",
    "                            (255,0,0), 2)\n",
    "\n",
    "    # show the frame \n",
    "    cv2.imshow(\"Face detection\", frame)\n",
    "\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3724827b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "vscode": {
   "interpreter": {
    "hash": "26de051ba29f2982a8de78e945f0abaf191376122a1563185a90213a26c5da77"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}