image_similarity_cnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tuHlALoxomGf"
   },
   "source": [
    "# Apparel Recommendations using Convolutional Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qaklMEqkomGk"
   },
   "source": [
    "## Get the feature vectors of all apparel images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vXQS9U5VomGm"
   },
   "source": [
    "<pre>\n",
    "   Running this cell will take time, you can skip running this cell. you can download the feature vectors from given link\n",
    "   16k_data_cnn_features.npy: https://drive.google.com/open?id=0BwNkduBnePt2c1BkNzRDQ1dOVFk \n",
    "   bottleneck_features_cnn.npy : https://drive.google.com/open?id=0BwNkduBnePt2ODRxWHhUVzIyWDA\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UsZZKNo3omGo",
    "outputId": "8fd226c3-bd75-46ea-ac0b-5464f02e343d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n",
      "Using cuDNN version 5110 on context None\n",
      "Mapped name None to device cuda: GeForce GTX 1050 (0000:01:00.0)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dropout, Flatten, Dense\n",
    "from keras import applications\n",
    "from sklearn.metrics import pairwise_distances\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "from PIL import Image\n",
    "import pandas as pd\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9_ZdsSPGomG0",
    "outputId": "28d5e485-5489-4e04-c64a-7db6692341ba",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 16042 images belonging to 1 classes.\n"
     ]
    }
   ],
   "source": [
    "# https://gist.github.com/fchollet/f35fbc80e066a49d65f1688a7e99f069\n",
    "# https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html\n",
    "\n",
    "\n",
    "# dimensions of our images.\n",
    "img_width, img_height = 224, 224\n",
    "\n",
    "top_model_weights_path = 'bottleneck_fc_model.h5'\n",
    "train_data_dir = 'images2/'\n",
    "nb_train_samples = 16042\n",
    "epochs = 50\n",
    "batch_size = 1\n",
    "\n",
    "\n",
    "def save_bottlebeck_features():\n",
    "    asins = []\n",
    "    datagen = ImageDataGenerator(rescale=1. / 255)\n",
    "    \n",
    "    # build the VGG16 network\n",
    "    model = applications.VGG16(include_top=False, weights='imagenet')\n",
    "    generator = datagen.flow_from_directory(\n",
    "        train_data_dir,\n",
    "        target_size=(img_width, img_height),\n",
    "        batch_size=batch_size,\n",
    "        class_mode=None,\n",
    "        shuffle=False)\n",
    "\n",
    "    for i in generator.filenames:\n",
    "        asins.append(i[2:-5])\n",
    "\n",
    "    bottleneck_features_train = model.predict_generator(generator, nb_train_samples // batch_size)\n",
    "    bottleneck_features_train = bottleneck_features_train.reshape((16042,25088))\n",
    "    \n",
    "    np.save(open('workshop/models/16k_data_cnn_features.npy', 'wb'), bottleneck_features_train)\n",
    "    np.save(open('workshop/models/16k_data_cnn_feature_asins.npy', 'wb'), np.array(asins))\n",
    "    \n",
    "\n",
    "save_bottlebeck_features()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i7dbnIH2omG7"
   },
   "source": [
    "# load the extracted features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8IdB2CnGomG9"
   },
   "outputs": [],
   "source": [
    "bottleneck_features_train = np.load('workshop/models/16k_data_cnn_features.npy')\n",
    "asins = np.load('workshop/models/16k_data_cnn_feature_asins.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WoCoJQnRomHG"
   },
   "source": [
    "## get the most similar apparels using euclidean distance measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0zcx_9DtomHJ"
   },
   "outputs": [],
   "source": [
    "data = pd.read_pickle('workshop/pickels/16k_apperal_data_preprocessed')\n",
    "df_asins = list(data['asin'])\n",
    "asins = list(asins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k9vFv5GmomHQ",
    "outputId": "bb29c858-62d8-48b3-f173-af33df28ef85",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product Title:  burnt umber tiger tshirt zebra stripes xl  xxl \n",
      "Euclidean Distance from input image: 0.0625\n",
      "Amazon Url: www.amzon.com/dp/B00JXQB5FQ\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product Title:  pink tiger tshirt zebra stripes xl  xxl \n",
      "Euclidean Distance from input image: 30.0501\n",
      "Amazon Url: www.amzon.com/dp/B00JXQASS6\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product Title:  yellow tiger tshirt tiger stripes  l \n",
      "Euclidean Distance from input image: 41.2611\n",
      "Amazon Url: www.amzon.com/dp/B00JXQCUIC\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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ivGPAd0dktux5RyK9zr5+050074ha3paMGjS6fZg54yeKqIHoOc4NYni5ftGk\nTwEZ3jAHuTiteInYCVOD3Iqrcwi4vbO3/wCelxGD/wB9CpA9KtYhBbRQjpGgUfgMVxHxGtvsmrWW\nqoPllHkyH3HI/mfyru6xvG2nnUvDV3AgzKi+bH/vLz/iPxpoDmLdwyhgeCOKv2rfKK5nQb5ZrVAx\n5Ares5M8Vo9hFy5chciurjbdGreoBrkLo5gP0rqdNfzNPgbOcxj+VZvcZYooopAFFFFAH//Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product Title:  brown  white tiger tshirt tiger stripes xl  xxl \n",
      "Euclidean Distance from input image: 44.0002\n",
      "Amazon Url: www.amzon.com/dp/B00JXQCWTO\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product Title:  kawaii pastel tops tees pink flower design \n",
      "Euclidean Distance from input image: 47.3825\n",
      "Amazon Url: www.amzon.com/dp/B071FCWD97\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product Title:  womens thin style tops tees pastel watermelon print \n",
      "Euclidean Distance from input image: 47.7184\n",
      "Amazon Url: www.amzon.com/dp/B01JUNHBRM\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product Title:  kawaii pastel tops tees baby blue flower design \n",
      "Euclidean Distance from input image: 47.9021\n",
      "Amazon Url: www.amzon.com/dp/B071SBCY9W\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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WGRmkvp7drpWm3LGMHiMsBw2T6ZGc9ju0o/FfsbQdoitc31t4gs/EGjQRTWE1oJtRuZJ0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\nZXQKKKKQgooooA//2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product Title:  edv cheetah run purple multi xl \n",
      "Euclidean Distance from input image: 48.0465\n",
      "Amazon Url: www.amzon.com/dp/B01CUPYBM0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product Title:  danskin womens vneck loose performance tee xsmall pink ombre \n",
      "Euclidean Distance from input image: 48.1019\n",
      "Amazon Url: www.amzon.com/dp/B01F7PHXY8\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Product Title:  summer alpaca 3d pastel casual loose tops tee design \n",
      "Euclidean Distance from input image: 48.1189\n",
      "Amazon Url: www.amzon.com/dp/B01I80A93G\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import display, Image, SVG, Math, YouTubeVideo\n",
    "\n",
    "def get_similar_products_cnn(doc_id, num_results):\n",
    "    doc_id = asins.index(df_asins[doc_id])\n",
    "    pairwise_dist = pairwise_distances(bottleneck_features_train, bottleneck_features_train[doc_id].reshape(1,-1))\n",
    "\n",
    "    indices = np.argsort(pairwise_dist.flatten())[0:num_results]\n",
    "    pdists  = np.sort(pairwise_dist.flatten())[0:num_results]\n",
    "\n",
    "    for i in range(len(indices)):\n",
    "        rows = data[['medium_image_url','title']].loc[data['asin']==asins[indices[i]]]\n",
    "        for indx, row in rows.iterrows():\n",
    "            display(Image(url=row['medium_image_url'], embed=True))\n",
    "            print('Product Title: ', row['title'])\n",
    "            print('Euclidean Distance from input image:', pdists[i])\n",
    "            print('Amazon Url: www.amzon.com/dp/'+ asins[indices[i]])\n",
    "\n",
    "get_similar_products_cnn(12566, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G1nvbPWuomHY"
   },
   "outputs": [],
   "source": [
    "# with GPU => per image 3.5/2 (0.175 sec), for whole data set its taking around 40min\n",
    "# with CPU => per image 12.3/20 (0.615 sec), for whole data set its taking around 160min"
   ]
  }
 ],
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