Raw_Image_Similarity

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a56a9122",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.models import load_model\n",
    "from keras_preprocessing import image\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "from utils import *\n",
    "import numpy as np\n",
    "import inspect\n",
    "import os\n",
    "import numpy as np\n",
    "from keras.applications.imagenet_utils import preprocess_input\n",
    "from keras.preprocessing import image\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "076d17d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications import resnet50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2761a270",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Intialize matplotlib parameters\n",
    "\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (15, 5),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "%matplotlib inline\n",
    "\n",
    "# pandas display data frames as tables\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5364805d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = resnet50.ResNet50()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "5d5083a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_headless_pretrained_model():\n",
    "    \"\"\"\n",
    "    Loads the pretrained version of VGG with the last layer cut off\n",
    "    :return: pre-trained headless VGG16 Keras Model\n",
    "    \"\"\"\n",
    "    print (\"Loading headless pretrained model...\")\n",
    "    pretrained_vgg16 = VGG16(weights='imagenet', include_top=True)\n",
    "    model = Model(inputs=pretrained_vgg16.input,\n",
    "                  outputs=pretrained_vgg16.get_layer('fc2').output)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "e1f2d046",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2399a347",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.vgg16 import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "32a38c55",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_headless_pretrained_model():\n",
    "    \"\"\"\n",
    "    Loads the pretrained version of VGG with the last layer cut off\n",
    "    :return: pre-trained headless VGG16 Keras Model\n",
    "    \"\"\"\n",
    "    print (\"Loading headless pretrained model...\")\n",
    "    pretrained_resnet = resnet50.ResNet50(weights='imagenet', include_top=True)\n",
    "    model = Model(inputs=pretrained_resnet.input,\n",
    "                  outputs=pretrained_resnet.get_layer('avg_pool').output)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "c4f5b758",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading headless pretrained model...\n"
     ]
    }
   ],
   "source": [
    "model = load_headless_pretrained_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "a1996311",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_6 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_6[0][0]']                \n",
      "                                                                                                  \n",
      " conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              \n",
      "                                                                                                  \n",
      " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
      "                                                                  'conv2_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
      "                                                                  'conv2_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       \n",
      "                                                                  'conv2_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
      "                                                                  'conv3_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
      "                                                                  'conv3_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
      "                                                                  'conv3_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       \n",
      "                                                                  'conv3_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
      "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
      "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
      "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
      "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
      "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
      "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n",
      "                                                                  'conv5_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
      "                                                                  'conv5_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
      "                                                                  'conv5_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " avg_pool (GlobalAveragePooling  (None, 2048)        0           ['conv5_block3_out[0][0]']       \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 23,587,712\n",
      "Trainable params: 23,534,592\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7bea0c0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_list = []\n",
    "image_path = []\n",
    "img = image.load_img('/Users/haripurnapatre/Downloads/dataset/image/Qma6ncr4fEWxkzAQxfxaAs4BycA8uV9JCRBVQVKLemsYYV.jpg', target_size=(224, 224))\n",
    "x_raw = image.img_to_array(img)\n",
    "            #print(x_raw.shape)\n",
    "x_expand = np.expand_dims(x_raw, axis=0)\n",
    "            #print(\"after expand\")\n",
    "            #print(x_expand.shape)\n",
    "x = preprocess_input(x_expand)\n",
    "\n",
    "image_list.append(x)\n",
    "image_path.append('/Users/haripurnapatre/Downloads/dataset/image/Qma6ncr4fEWxkzAQxfxaAs4BycA8uV9JCRBVQVKLemsYYV.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a6aa945b",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = image.load_img('/Users/haripurnapatre/Downloads/dataset/image/QmaB5kNpEiXqjcMUMFxXe4aknU9Z2UsuJyoUKVXfHPgaAV.jpeg', target_size=(224, 224))\n",
    "x_raw = image.img_to_array(img)\n",
    "            #print(x_raw.shape)\n",
    "x_expand = np.expand_dims(x_raw, axis=0)\n",
    "            #print(\"after expand\")\n",
    "            #print(x_expand.shape)\n",
    "x1 = preprocess_input(x_expand)\n",
    "\n",
    "image_list.append(x1)\n",
    "image_path.append('/Users/haripurnapatre/Downloads/dataset/image/QmaB5kNpEiXqjcMUMFxXe4aknU9Z2UsuJyoUKVXfHPgaAV.jpeg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "89b30e5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = image.load_img('/Users/haripurnapatre/Downloads/dataset/image/QmczE2SvZEVkGvstRB4azTRHcXjt6FyUqwHYfiSB17PppE.jpg', target_size=(224, 224))\n",
    "x_raw = image.img_to_array(img)\n",
    "            #print(x_raw.shape)\n",
    "x_expand = np.expand_dims(x_raw, axis=0)\n",
    "            #print(\"after expand\")\n",
    "            #print(x_expand.shape)\n",
    "x1 = preprocess_input(x_expand)\n",
    "\n",
    "image_list.append(x1)\n",
    "image_path.append('/Users/haripurnapatre/Downloads/dataset/image/QmczE2SvZEVkGvstRB4azTRHcXjt6FyUqwHYfiSB17PppE.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "316efa7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "img = image.load_img('/Users/haripurnapatre/Downloads/dataset/image/Qma6et8dnc7YCiNrh4nBGechLoRXf6fRwgXL2xubYsV1cr.jpg', target_size=(224, 224))\n",
    "x_raw = image.img_to_array(img)\n",
    "            #print(x_raw.shape)\n",
    "x_expand = np.expand_dims(x_raw, axis=0)\n",
    "            #print(\"after expand\")\n",
    "            #print(x_expand.shape)\n",
    "x1 = preprocess_input(x_expand)\n",
    "\n",
    "image_list.append(x1)\n",
    "image_path.append('/Users/haripurnapatre/Downloads/dataset/image/Qma6et8dnc7YCiNrh4nBGechLoRXf6fRwgXL2xubYsV1cr.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "0ebfabc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'def get_feature_vector(model,img_path):\\n    images = np.zeros(shape=(1, 224, 224, 3))\\n    img = image.load_img(img_path, target_size=(224, 224))\\n    x_raw = image.img_to_array(img)\\n    x_expand = np.expand_dims(x_raw, axis=0)\\n    images[0, :, :, :] = x_expand\\n    inputs = preprocess_input(images)\\n    image_features = model.predict(inputs)\\n    \\n    return image_features,file_mapping '"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''def get_feature_vector(model,img_path):\n",
    "    images = np.zeros(shape=(1, 224, 224, 3))\n",
    "    img = image.load_img(img_path, target_size=(224, 224))\n",
    "    x_raw = image.img_to_array(img)\n",
    "    x_expand = np.expand_dims(x_raw, axis=0)\n",
    "    images[0, :, :, :] = x_expand\n",
    "    inputs = preprocess_input(images)\n",
    "    image_features = model.predict(inputs)\n",
    "    \n",
    "    return image_features,file_mapping '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "26ac966c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, f in enumerate(image_path):\n",
    "    images = np.zeros(shape=(len(image_path), 224, 224, 3))\n",
    "    img = image.load_img(f, target_size=(224, 224))\n",
    "    x_raw = image.img_to_array(img)\n",
    "    x_expand = np.expand_dims(x_raw, axis=0)\n",
    "    images[i, :, :, :] = x_expand\n",
    "inputs = preprocess_input(images)\n",
    "file_index = {i: f for i, f in enumerate(image_path)}\n",
    "images_features = model.predict(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a5c0faee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2048"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(images_features[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "3c6cb500",
   "metadata": {},
   "outputs": [],
   "source": [
    "def index_features(features, n_trees=1000, dims=2048, is_dict=False):\n",
    "    \"\"\"\n",
    "    Use Annoy to index our features to be able to query them rapidly\n",
    "    :param features: array of item features\n",
    "    :param n_trees: number of trees to use for Annoy. Higher is more precise but slower.\n",
    "    :param dims: dimension of our features\n",
    "    :return: an Annoy tree of indexed features\n",
    "    \"\"\"\n",
    "    print (\"Indexing features...\")\n",
    "    feature_index = AnnoyIndex(dims, metric='angular')\n",
    "    for i, row in enumerate(features):\n",
    "        vec = row\n",
    "        if is_dict:\n",
    "            vec = features[row]\n",
    "        feature_index.add_item(i, vec)\n",
    "    feature_index.build(n_trees)\n",
    "    return feature_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "db99216f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indexing features...\n"
     ]
    }
   ],
   "source": [
    "from annoy import AnnoyIndex\n",
    "image_index = index_features(images_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "30528daf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def search_index_by_value(vector, feature_index, item_mapping, top_n=2):\n",
    "    \"\"\"\n",
    "    Search an Annoy index by value, return n nearest items\n",
    "    :param vector: the index of our item in our array of features\n",
    "    :param feature_index: an Annoy tree of indexed features\n",
    "    :param item_mapping: mapping from indices to paths/names\n",
    "    :param top_n: how many items to return\n",
    "    :return: an array of [index, item, distance] of size top_n\n",
    "    \"\"\"\n",
    "    distances = feature_index.get_nns_by_vector(vector, top_n, include_distances=True)\n",
    "    return [[a, item_mapping[a], distances[1][i]] for i, a in enumerate(distances[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3b44c178",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_feature_vector1(model,img_path):\n",
    "    images = np.zeros(shape=(1, 224, 224, 3))\n",
    "    img = image.load_img(img_path, target_size=(224, 224))\n",
    "    x_raw = image.img_to_array(img)\n",
    "    x_expand = np.expand_dims(x_raw, axis=0)\n",
    "    images[0, :, :, :] = x_expand\n",
    "    inputs = preprocess_input(images)\n",
    "    image_features = model.predict(inputs)\n",
    "    return image_features[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "98b80668",
   "metadata": {},
   "outputs": [],
   "source": [
    "def displayImages(results):\n",
    "    fig, ax = plt.subplots(1,len(results), figsize=(50,50))\n",
    "    img_Counter=0;\n",
    "    for result in results:\n",
    "        img_path = result[1]\n",
    "        #print(img_path)\n",
    "        ax[img_Counter].set_title(\"\")\n",
    "        ax[img_Counter].imshow(load_img(img_path, target_size=(224, 224)))\n",
    "        img_Counter = img_Counter + 1\n",
    "        \n",
    "def displayImagebyPath(img_path):\n",
    "    fig, ax = plt.subplots(1,1, figsize=(5,5))\n",
    "    img_Counter=0;\n",
    "    ax.set_title(img_path)\n",
    "    ax.imshow(load_img(img_path, target_size=(224, 224)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "284df8f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "displayImagebyPath(image_path[2]),displayImagebyPath(image_path[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "82fa4710",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from scipy import spatial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "0a3b087b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x3600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fv = get_feature_vector1(model, image_path[0])\n",
    "results = search_index_by_value(fv, image_index, file_index)\n",
    "displayImages(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "faff43ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "distances = image_index.get_nns_by_vector(fv, 4, include_distances=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "23b61d88",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'distances' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/y9/f2nfs3n574jc1n364wv_lbcw0000gn/T/ipykernel_1683/2241683950.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdistances\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'distances' is not defined"
     ]
    }
   ],
   "source": [
    "distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "44e22640",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from math import sqrt\n",
    "\n",
    "def cosineSim(a1,a2):\n",
    "    sum = 0\n",
    "    suma1 = 0\n",
    "    sumb1 = 0\n",
    "    for i,j in zip(a1, a2):\n",
    "        suma1 += i * i\n",
    "        sumb1 += j*j\n",
    "        sum += i*j\n",
    "    cosine_sim = sum / ((sqrt(suma1))*(sqrt(sumb1)))\n",
    "    return cosine_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "44f6db2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9143493476459198"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosineSim(features0,features1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "97030e4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2729632258415222"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity = -1 * (spatial.distance.cosine(images_features[2],images_features[3]) - 1)\n",
    "similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "583728b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "f52a01d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_model = resnet50.ResNet50(weights='imagenet', include_top=False, input_shape=(224,224,3))\n",
    "for layer in conv_model.layers:\n",
    "    layer.trainable = False\n",
    "x = keras.layers.Flatten()(conv_model.output)\n",
    "x = keras.layers.Dense(100, activation='relu')(x)\n",
    "x = keras.layers.Dense(100, activation='relu')(x)\n",
    "x = keras.layers.Dense(100, activation='relu')(x)\n",
    "predictions = keras.layers.Dense(2, activation='softmax')(x)\n",
    "full_model = keras.models.Model(inputs=conv_model.input, outputs=predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "f42d94bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense, Activation, Flatten\n",
    "mode = getResNet50Model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "65bf5107",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting bs4\n",
      "  Downloading bs4-0.0.1.tar.gz (1.1 kB)\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: beautifulsoup4 in ./opt/anaconda3/lib/python3.9/site-packages (from bs4) (4.10.0)\n",
      "Requirement already satisfied: soupsieve>1.2 in ./opt/anaconda3/lib/python3.9/site-packages (from beautifulsoup4->bs4) (2.2.1)\n",
      "Building wheels for collected packages: bs4\n",
      "  Building wheel for bs4 (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for bs4: filename=bs4-0.0.1-py3-none-any.whl size=1272 sha256=27dffb1b8c10aa13930b49a767375b6cc3b5c833871163214675d66aa29ed1f4\n",
      "  Stored in directory: /Users/haripurnapatre/Library/Caches/pip/wheels/73/2b/cb/099980278a0c9a3e57ff1a89875ec07bfa0b6fcbebb9a8cad3\n",
      "Successfully built bs4\n",
      "Installing collected packages: bs4\n",
      "Successfully installed bs4-0.0.1\n",
      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.4 is available.\n",
      "You should consider upgrading via the '/Users/haripurnapatre/opt/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install bs4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c43e9fbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "https://videocdn.geeksforgeeks.org/geeksforgeeks/MachineLearningHandlingMissingValues/MachineLearningHandlingMissingValues20220321132237-small.png\n",
      "https://videocdn.geeksforgeeks.org/geeksforgeeks/GunDetectionUsingPythonOpenCV/gundetectionusingpython20220310130800-small.png\n",
      "https://videocdn.geeksforgeeks.org/geeksforgeeks/HowtoPerformCRUDOperationsinFlutterApplicationusingMySQLDatabase/HowtoPerformCRUDOperationsinFlutterApplicationusingMySQLDatabase20220304112711.jpg\n",
      "https://videocdn.geeksforgeeks.org/geeksforgeeks/ImplementingSimpleandCustomAlertDialoginFlutterApplication/AlertDialoginFlutterApplication20220303121322-small.png\n",
      "https://videocdn.geeksforgeeks.org/geeksforgeeks/BuildaSimpleStopwatchApplicationinFlutter/BuildaSimpleStopwatchApplicationinFlutter20220302114511-small.png\n",
      "https://media.geeksforgeeks.org/wp-content/post-ads-banner/2022-03-27-22-30-23-GSC_icon.png\n",
      "https://media.geeksforgeeks.org/wp-content/post-ads-banner/2021-12-29-11-18-16-DSA_Ad_icon (1).png\n",
      "https://media.geeksforgeeks.org/wp-content/post-ads-banner/2021-12-29-16-30-50-CIP_Icon.png\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import requests \n",
    "from bs4 import BeautifulSoup \n",
    "    \n",
    "def getdata(url): \n",
    "    r = requests.get(url) \n",
    "    return r.text \n",
    "    \n",
    "htmldata = getdata(\"https://www.geeksforgeeks.org/\") \n",
    "soup = BeautifulSoup(htmldata, 'html.parser') \n",
    "for item in soup.find_all('img'):\n",
    "    print(item['src'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "cd754028",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = resnet50.ResNet50(weights='imagenet', include_top=False,\n",
    "                 input_shape=(224, 224, 3))\n",
    "def extract_features(img_path, model):\n",
    "    input_shape = (224, 224, 3)\n",
    "    img = image.load_img(img_path, target_size=(\n",
    "        input_shape[0], input_shape[1]))\n",
    "    img_array = image.img_to_array(img)\n",
    "    expanded_img_array = np.expand_dims(img_array, axis=0)\n",
    "    preprocessed_img = preprocess_input(expanded_img_array)\n",
    "    features = model.predict(preprocessed_img)\n",
    "    flattened_features = features.flatten()\n",
    "    normalized_features = flattened_features / norm(flattened_features)\n",
    "    return normalized_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "26c3173e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.linalg import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "876e4072",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100352\n"
     ]
    }
   ],
   "source": [
    "features0 = extract_features('/Users/haripurnapatre/Downloads/dataset/image/notape.png', model)\n",
    "print(len(features))\n",
    "features1 = extract_features('/Users/haripurnapatre/Downloads/dataset/image/noteape1.png', model)"
   ]
  }
 ],
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