visual_aid for Blinds.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import json\n",
    "import re\n",
    "import collections\n",
    "from keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions\n",
    "from keras.preprocessing import image\n",
    "from keras.models import Model, load_model\n",
    "from keras.utils import to_categorical\n",
    "import pickle\n",
    "from time import time\n",
    "import string\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.layers import Input, Dense, Dropout, Embedding, LSTM\n",
    "from keras.layers.merge import add\n",
    "from PIL import Image\n",
    "from keras import optimizers\n",
    "import matplotlib.pyplot as plt\n",
    "import pyttsx3\n",
    "from numpy import loadtxt\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# collecting caption data from json file\n",
    "\n",
    "def collectCaption(path):\n",
    "    with open(path) as f:\n",
    "        captions = json.load(f)\n",
    "    return captions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "captions = collectCaption(\"./Dataset/annotations/captions_train2017.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['info', 'licenses', 'images', 'annotations'])\n"
     ]
    }
   ],
   "source": [
    "print(captions.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mapping ids to its images.\n",
    "id_img = {}\n",
    "for x in captions['images']:\n",
    "#     c +=1\n",
    "    id_img[str(x['id'])] = x['file_name']\n",
    "#     if c==10:\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./final Data/id_img.pkl\",\"wb\") as f:\n",
    "    pickle.dump(id_img,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "000000391895.jpg\n"
     ]
    }
   ],
   "source": [
    "print(id_img[\"391895\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "description = {}\n",
    "for anno in captions['annotations']:\n",
    "    img_id = str(anno['image_id'])\n",
    "    cap = anno['caption']\n",
    "    \n",
    "    img_name = id_img[img_id]\n",
    "    if description.get(img_name) is None:\n",
    "        description[img_name] = []\n",
    "    if len(description[img_name]) <= 5:\n",
    "        description[img_name].append(cap)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A woman wearing a net on her head cutting a cake. ',\n",
       " 'A woman cutting a large white sheet cake.',\n",
       " 'A woman wearing a hair net cutting a large sheet cake.',\n",
       " 'there is a woman that is cutting a white cake',\n",
       " \"A woman marking a cake with the back of a chef's knife. \"]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "description['000000522418.jpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data cleaning\n",
    "\n",
    "def clean_text(sent):\n",
    "    sent = sent.lower()\n",
    "    sent = re.sub(\"[^a-z]+\",\" \",sent)\n",
    "    sent = sent.split()\n",
    "    \n",
    "    sent = [s for s in sent if len(s)>1]\n",
    "    sent = \" \".join(sent)\n",
    "    return sent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cleaning description\n",
    "\n",
    "for key,caption_list in description.items():\n",
    "    for i in range(len(caption_list)):\n",
    "        caption_list[i] = clean_text(caption_list[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['woman wearing net on her head cutting cake',\n",
       " 'woman cutting large white sheet cake',\n",
       " 'woman wearing hair net cutting large sheet cake',\n",
       " 'there is woman that is cutting white cake',\n",
       " 'woman marking cake with the back of chef knife']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "description['000000522418.jpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open(\"discription.txt\",\"w\") as f:\n",
    "#     f.write(str(description))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a vocab\n",
    "description = None\n",
    "with open(\"Data\\discription.txt\",\"r\") as f:\n",
    "    description = f.read()\n",
    "json_acceptable_string = description.replace(\"'\",\"\\\"\")\n",
    "description = json.loads(json_acceptable_string)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26440\n"
     ]
    }
   ],
   "source": [
    "# vocab\n",
    "\n",
    "vocab = set()\n",
    "for key in description.keys():\n",
    "    [vocab.update(sent.split()) for sent in description[key]]\n",
    "print(len(vocab))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5210675\n"
     ]
    }
   ],
   "source": [
    "# total no. of words accross the descriptionabs\n",
    "total_words = []\n",
    "\n",
    "for key in description.keys():\n",
    "    [total_words.append(i) for des in description[key] for i in des.split()]\n",
    "print(len(total_words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26440\n"
     ]
    }
   ],
   "source": [
    "counter = collections.Counter(total_words)\n",
    "freq_cnt = dict(counter)\n",
    "print(len(freq_cnt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_freq_cnt = sorted(freq_cnt.items(),reverse=True, key=lambda x:x[1])\n",
    "\n",
    "#filter\n",
    "threshold = 4\n",
    "sorted_freq_cnt = [x for x in sorted_freq_cnt if x[1]>threshold]\n",
    "total_words = [x[0] for x in sorted_freq_cnt]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10100\n"
     ]
    }
   ],
   "source": [
    "print(len(total_words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating train images\n",
    "train_img_id = []\n",
    "for key,img in id_img.items():\n",
    "    train_img_id.append(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['000000391895.jpg', '000000522418.jpg', '000000184613.jpg', '000000318219.jpg']\n"
     ]
    }
   ],
   "source": [
    "print(train_img_id[:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118287"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_img_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare Description for the Training Data\n",
    "# Tweak - Add <s> and <e> toen to our traing data\n",
    "\n",
    "train_descriptions = {}\n",
    "\n",
    "for img_id in train_img_id:\n",
    "    train_descriptions[img_id] = []\n",
    "    for cap in description[img_id]:\n",
    "        cap_to_append = \"<s> \" + cap + \" <e>\"\n",
    "        train_descriptions[img_id].append(cap_to_append)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<s> man with red helmet on small moped on dirt road <e>',\n",
       " '<s> man riding motor bike on dirt road on the countryside <e>',\n",
       " '<s> man riding on the back of motorcycle <e>',\n",
       " '<s> dirt path with young person on motor bike rests to the foreground of verdant area with bridge and background of cloud wreathed mountains <e>',\n",
       " '<s> man in red shirt and red hat is on motorcycle on hill side <e>']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_descriptions[\"000000391895.jpg\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "= \"D:/programming/Machine learning and Deep learning/Projects/minor1.0/videoCaptioning for blinds/Datasets/coco/train2017/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W1126 12:39:28.499868 85920 deprecation_wrapper.py:119] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W1126 12:39:28.538715 85920 deprecation_wrapper.py:119] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W1126 12:39:28.552721 85920 deprecation_wrapper.py:119] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4185: The name tf.truncated_normal is deprecated. Please use tf.random.truncated_normal instead.\n",
      "\n",
      "W1126 12:39:28.647423 85920 deprecation_wrapper.py:119] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "W1126 12:39:28.649453 85920 deprecation_wrapper.py:119] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "W1126 12:39:34.011540 85920 deprecation_wrapper.py:119] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
      "\n",
      "W1126 12:39:34.306750 85920 deprecation_wrapper.py:119] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Transfer Learning\n",
    "# - images-->Features\n",
    "\n",
    "#Step-1 Download Pre-trained model--resnet-50\n",
    "\n",
    "model = ResNet50(weights='imagenet',input_shape=(224,224,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 112, 112, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 112, 112, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 56, 56, 64)   4160        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 56, 56, 256)  16640       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 56, 56, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 56, 56, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 56, 56, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 56, 56, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 56, 56, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 56, 56, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 56, 56, 256)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 28, 28, 128)  32896       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 28, 28, 512)  131584      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 28, 28, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 28, 28, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 28, 28, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 28, 28, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 28, 28, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 28, 28, 512)  0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 14, 14, 256)  131328      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 14, 14, 1024) 525312      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 14, 14, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 14, 14, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 14, 14, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 14, 14, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 14, 14, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 14, 14, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 14, 14, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 14, 14, 1024) 0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 7, 7, 512)    524800      activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 7, 7, 2048)   2099200     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 7, 7, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 7, 7, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 7, 7, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 7, 7, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 7, 7, 2048)   0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (GlobalAveragePooling2 (None, 2048)         0           activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "fc1000 (Dense)                  (None, 1000)         2049000     avg_pool[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 25,636,712\n",
      "Trainable params: 25,583,592\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.layers[-2].output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_new = Model(model.input, model.layers[-2].output)\n",
    "\n",
    "model_new.save(('./final Data/resnet' +'.h5'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_img(img):\n",
    "    img = image.load_img(img,target_size=(224,224))\n",
    "    img = image.img_to_array(img)\n",
    "    img = np.expand_dims(img,axis=0)\n",
    "    \n",
    "    #Normalization\n",
    "    \n",
    "    img = preprocess_input(img)\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_image(img):\n",
    "    img = preprocess_img(img)\n",
    "    feature_vect = model_new.predict(img)\n",
    "    feature_vect = feature_vect.reshape((-1,))\n",
    "    return feature_vect\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "encode_image(IMG_PATH+ \"000000522418.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoding_train = {}\n",
    "t0 = time()\n",
    "for ix,img_id in enumerate(train_img_id):\n",
    "    img_path = IMG_PATH+img_id\n",
    "    encoding_train[img_id] = encode_image(img_path)\n",
    "    \n",
    "    if ix%1000==0:\n",
    "        print(\"Encoding in progress time step %d \"%ix)\n",
    "end_t = time()\n",
    "print(\"total time taken :\",end_t-t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# store this on disk\n",
    "\n",
    "with open(\"encoded_train.pkl\",\"wb\") as f:\n",
    "    pickle.dump(encoding_train,f)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load img feature vectors in ram from disk\n",
    "\n",
    "with open(\"./Data/encoded_train.pkl\",\"rb\") as f:\n",
    "       encoding_train = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.44277993 0.23587297 0.35735822 ... 5.404309   0.3640494  1.1392363 ]\n"
     ]
    }
   ],
   "source": [
    "print((encoding_train['000000522418.jpg']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  preparing caption dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_2_idx = {}\n",
    "idx_2_word = {}\n",
    "\n",
    "for i,word in enumerate(total_words):\n",
    "    word_2_idx[word] = i+1\n",
    "    idx_2_word[i+1] = word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'women'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_2_idx['women']\n",
    "idx_2_word[185]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10100\n"
     ]
    }
   ],
   "source": [
    "print(len(idx_2_word))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_2_idx['<s>'] = 10101\n",
    "idx_2_word[10101] = \"<s>\"\n",
    "\n",
    "word_2_idx['<e>'] = 10102\n",
    "idx_2_word[10102] = \"<e>\"\n",
    "\n",
    "vocab_size = len(idx_2_word) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"word_2_idx.pkl\",\"wb\") as f:\n",
    "    pickle.dump(word_2_idx,f)\n",
    "with open(\"idx_2_word.pkl\",\"wb\") as f:\n",
    "    pickle.dump(idx_2_word,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10103\n"
     ]
    }
   ],
   "source": [
    "print(vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49\n"
     ]
    }
   ],
   "source": [
    "max_len = 0\n",
    "\n",
    "for key in train_descriptions.keys():\n",
    "    for cap in train_descriptions[key]:\n",
    "        max_len = max(max_len,len(cap.split()))\n",
    "print(max_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data loader(Generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_generator(train_descriptions,encoding_train,word_2_idx,max_len,batch_size):\n",
    "    X1,X2,y = [],[],[]\n",
    "    \n",
    "    n=0\n",
    "    while True:\n",
    "        for key,desc_list in train_descriptions.items():\n",
    "            n += 1\n",
    "            photo = encoding_train[key]\n",
    "            for desc in desc_list:\n",
    "                seq = [word_2_idx[word] for word in desc.split() if word in word_2_idx]\n",
    "                \n",
    "                for i in range(1,len(seq)):\n",
    "                    xi = seq[0:i]\n",
    "                    yi = seq[i]\n",
    "                    \n",
    "                    xi = pad_sequences([xi], maxlen=max_len, value=0, padding='post')[0]\n",
    "                    yi = to_categorical([yi], num_classes=vocab_size)[0]\n",
    "                    \n",
    "                    X1.append(photo)\n",
    "                    X2.append(xi)\n",
    "                    y.append(yi)\n",
    "                    \n",
    "            if n==batch_size:\n",
    "                yield [[np.array(X1),np.array(X2)],np.array(y)]\n",
    "\n",
    "                X1,X2,y = [],[],[]\n",
    "                n = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Word2vec enbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"D:/programming/Machine learning and Deep learning/Projects/minor1.0/videoCaptioning for blinds/Datasets/glove.6B.50d.txt\",encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_index = {}\n",
    "for line in f:\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    \n",
    "    word_embeddings = np.array(values[1:], dtype='float')\n",
    "    embedding_index[word] = word_embeddings\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_embedding_matrix():\n",
    "    emb_dim = 50\n",
    "    matrix = np.zeros((vocab_size,emb_dim))\n",
    "    for word,idx in word_2_idx.items():\n",
    "        embedding_vector = embedding_index.get(word)\n",
    "        \n",
    "        if embedding_vector is not None:\n",
    "            matrix[idx] = embedding_vector\n",
    "    return matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_matrix = get_embedding_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10103, 50)\n"
     ]
    }
   ],
   "source": [
    "print(embedding_matrix.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Archtecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W1126 14:57:03.176997 85920 deprecation.py:506] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "# image model\n",
    "\n",
    "input_img_fearures = Input(shape=(2048,))\n",
    "inp_img1 = Dropout(0.2)(input_img_fearures)\n",
    "inp_img2 = Dense(256)(inp_img1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W1126 14:58:34.905014 85920 deprecation.py:323] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:2974: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    }
   ],
   "source": [
    "# caption model\n",
    "input_captions = Input(shape=(max_len,))\n",
    "inp_cap1 = Embedding(input_dim=vocab_size,output_dim=50, mask_zero=True)(input_captions)\n",
    "inp_cap2 = Dropout(0.2)(inp_cap1)\n",
    "inp_cap3 = LSTM(256)(inp_cap2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "decoder1 = add([inp_img2,inp_cap3])\n",
    "decoder2 = Dense(256, activation='relu')(decoder1)\n",
    "outputs = Dense(vocab_size,activation='softmax')(decoder2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine model\n",
    "model = Model(inputs=[input_img_fearures,input_captions], outputs=outputs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_3 (InputLayer)            (None, 49)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            (None, 2048)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, 49, 50)       505150      input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 2048)         0           input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 49, 50)       0           embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 256)          524544      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lstm_1 (LSTM)                   (None, 256)          314368      dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_17 (Add)                    (None, 256)          0           dense_1[0][0]                    \n",
      "                                                                 lstm_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 256)          65792       add_17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 10103)        2596471     dense_2[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 4,006,325\n",
      "Trainable params: 3,501,175\n",
      "Non-trainable params: 505,150\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# pre init embedding layer\n",
    "model.layers[2].set_weights([embedding_matrix])\n",
    "model.layers[2].trainable = False\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compile Model\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"train_discriptions.txt\",\"w\") as f:\n",
    "    f.write(str(train_descriptions))\n",
    "with open(\"word_2_idx\",\"w\") as f:\n",
    "    f.write(str(word_2_idx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training of madel\n",
    "\n",
    "epochs = 10\n",
    "batch_size = 30\n",
    "steps = len(train_descriptions)//batch_size\n",
    "\n",
    "for i in range(epochs):\n",
    "    generator = data_generator(train_descriptions,encoding_train,word_2_idx,max_len,batch_size)\n",
    "    model.fit_generator(generator,epochs=1,steps_per_epoch=steps,verbose=1)\n",
    "    model.save(('./Data/model_weights/model_'+str(i)+'.h5'))\n",
    "end_t = time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sgd = optimizers.SGD(lr=0.0001, decay=1e-6, nesterov=True)\n",
    "# model.compile(loss='categorical_crossentropy', optimizer=sgd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W1128 12:11:05.720829 13356 deprecation_wrapper.py:119] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W1128 12:11:05.793633 13356 deprecation_wrapper.py:119] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W1128 12:11:05.797620 13356 deprecation_wrapper.py:119] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "W1128 12:11:05.860450 13356 deprecation_wrapper.py:119] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "W1128 12:11:05.883392 13356 deprecation.py:506] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "W1128 12:11:07.108114 13356 deprecation.py:323] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:2974: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "W1128 12:11:07.549965 13356 deprecation_wrapper.py:119] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "W1128 12:11:15.266389 13356 deprecation_wrapper.py:119] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "W1128 12:11:20.680938 13356 deprecation_wrapper.py:119] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
      "\n",
      "W1128 12:11:20.900368 13356 deprecation_wrapper.py:119] From C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "C:\\Users\\asus\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras\\engine\\saving.py:292: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "  warnings.warn('No training configuration found in save file: '\n"
     ]
    }
   ],
   "source": [
    "model = load_model('./final Data/model_12.h5')\n",
    "\n",
    "model_new = load_model('./final Data/resnet.h5')\n",
    "\n",
    "max_len = 49\n",
    "\n",
    "with open(\"./final Data/encoded_train.pkl\",\"rb\") as f:\n",
    "       encoding_train = pickle.load(f)\n",
    "\n",
    "with open(\"./final Data/idx_2_word.pkl\",\"rb\") as f:\n",
    "       idx_2_word = pickle.load(f)\n",
    "        \n",
    "with open(\"./final Data/word_2_idx.pkl\",\"rb\") as f:\n",
    "       word_2_idx = pickle.load(f)\n",
    "\n",
    "with open(\"./final Data/id_img.pkl\",\"rb\") as f:\n",
    "       id_img = pickle.load(f)\n",
    "        \n",
    "        \n",
    "with open(\"./Data/encoded_train.pkl\",\"rb\") as f:\n",
    "       encoding_train = pickle.load(f)\n",
    "        \n",
    "description = None\n",
    "with open(\"Data\\discription.txt\",\"r\") as f:\n",
    "    description = f.read()\n",
    "json_acceptable_string = description.replace(\"'\",\"\\\"\")\n",
    "description = json.loads(json_acceptable_string)\n",
    "\n",
    "net = cv2.dnn.readNet(\"./yolo/Dataset/yolov3.weights\",\"./yolo/Dataset/yolov3.cfg\")\n",
    "classes = []\n",
    "with open(\"./yolo/Dataset/coco.names\", \"r\") as f:\n",
    "    classes = [line.strip() for line in f.readlines()]\n",
    "layer_names = net.getLayerNames()\n",
    "output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pred_cap(photo):\n",
    "    in_text = \"<s>\"\n",
    "    \n",
    "    for i in range(max_len):\n",
    "        sequence = [word_2_idx[w] for w in in_text.split() if w in word_2_idx]\n",
    "        sequence = pad_sequences([sequence], maxlen=max_len, padding='post')\n",
    "        ypred = model.predict([photo,sequence])\n",
    "        ypred = ypred.argmax()\n",
    "        word = idx_2_word[ypred]\n",
    "        in_text += (' '+word)\n",
    "        \n",
    "        if word == '<e>':\n",
    "            break\n",
    "    final_cap = in_text.split()[1:-1]\n",
    "    final_cap = ' '.join(final_cap)\n",
    "    return final_cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_img(img):\n",
    "    img = image.load_img(img,target_size=(224,224))\n",
    "    \n",
    "    plt.imshow(img)\n",
    "    img = image.img_to_array(img)\n",
    "    \n",
    "    img = np.expand_dims(img,axis=0)\n",
    "    \n",
    "    #Normalization\n",
    "    \n",
    "    img = preprocess_input(img)\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_image(img):\n",
    "    img = preprocess_img(img)\n",
    "    feature_vect = model_new.predict(img)\n",
    "    \n",
    "    return feature_vect\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the bathroom is clean and the the shower\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "photo = encode_image(\"./testing_videos/WhatsApp Image 2019-11-21 at 1.28.05 AM.jpeg\")\n",
    "\n",
    "cap = pred_cap(photo)\n",
    "print(cap)\n",
    "# print(objects_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using LAPTOPS WEBCAM\n",
    "\n",
    "vocals = pyttsx3.init()\n",
    "voices = vocals.getProperty(\"voices\")\n",
    "vocals.setProperty(\"voice\",voices[1].id)\n",
    "\n",
    "\n",
    "\n",
    "# cam = cv2.VideoCapture(\"./testing_videos/index.jfif\")\n",
    "cam = cv2.VideoCapture(0)\n",
    "i=0\n",
    "\n",
    "while True:\n",
    "    ret,frame = cam.read()\n",
    "    key_pressed = cv2.waitKey(1)&0xFF\n",
    "    if ret==False:\n",
    "        print(\"Something went wrong\")\n",
    "        continue\n",
    "    if key_pressed == ord('q'):\n",
    "        break\n",
    "\n",
    "    if i%30 == 0:\n",
    "        photo = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)\n",
    "        cv2.imwrite(\"C:/Users/asus/Desktop/test images/\"+str(i)+\".jpg\", photo)\n",
    "\n",
    "        photo = encode_image(\"C:/Users/asus/Desktop/test images/\"+str(i)+\".jpg\")\n",
    "        \n",
    "        cap = pred_cap(photo)\n",
    "        \n",
    "#         -----------------------------------------------------------------------------------------\n",
    "        \n",
    "        \n",
    "        \n",
    "        img = frame\n",
    "        height, width, channels = img.shape\n",
    "        # detecting objects\n",
    "        blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)\n",
    "\n",
    "        net.setInput(blob)\n",
    "        outs = net.forward(output_layers)\n",
    "\n",
    "        # showing information on the screen\n",
    "\n",
    "        boxes = []\n",
    "        confidences = []\n",
    "        class_ids = []\n",
    "\n",
    "        for out in outs:\n",
    "            for detection in out:\n",
    "                scores = detection[5:]\n",
    "                class_id = np.argmax(scores)\n",
    "                confidence = scores[class_id]\n",
    "                if confidence > 0.5:\n",
    "                    center_x = int(detection[0] * width)\n",
    "                    center_y = int(detection[1] * height)\n",
    "                    w = int(detection[2] * width)\n",
    "                    h = int(detection[3] * height)\n",
    "                    cv2.circle(img, (center_x, center_y), 10, (0, 255, 0), 2)\n",
    "\n",
    "                    x = int(center_x - w/2)\n",
    "                    y = int(center_y - h/2)\n",
    "                    boxes.append([x, y, w, h])\n",
    "                    confidences.append(float(confidence))\n",
    "                    class_ids.append(class_id)\n",
    "\n",
    "        objects = []\n",
    "        no_boxes_detected = len(boxes)\n",
    "        indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)\n",
    "        font = cv2.FONT_HERSHEY_PLAIN\n",
    "        for i in range(no_boxes_detected):\n",
    "            if i in indexes:\n",
    "                x, y, w, h = boxes[i]\n",
    "                label = str(classes[class_ids[i]])\n",
    "                objects.append(label)\n",
    "                cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 1)\n",
    "                cv2.putText(img, label, (x, y+30), font, 3, (0,0,0), 3)\n",
    "\n",
    "\n",
    "        cv2.imshow(\"Image\", img)\n",
    "\n",
    "#  ------------------------------------------------------------------------------------------------       \n",
    "    \n",
    "        objects_str = \",\".join(objects)\n",
    "        vocals.say(cap)\n",
    "        vocals.say(\"There are \"+ objects_str +\" near you\")\n",
    "        vocals.runAndWait()\n",
    "\n",
    "    #         cv2.imshow('VideoCapture',frame[:,::-1,:])\n",
    "        print(cap)\n",
    "        \n",
    "    if i==5000:\n",
    "        break\n",
    "    i += 1\n",
    "cam.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter local IP address192.168.43.49:8080\n",
      "man is laying on bed\n",
      "man is laying on bed with his feet\n",
      "man in black shirt is jumping on the wall\n",
      "man in the bathroom with his teeth\n",
      "man is holding pair of scissors\n",
      "cat is laying on top of the mirror\n",
      "black and white cat is sitting on top of cell phone\n",
      "laptop and laptop and other items on desk\n",
      "laptop and laptop and laptop and other items\n",
      "laptop with note of scissors on it\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-03ad697be4ff>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     20\u001b[0m         \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"C:/Users/asus/Desktop/test images/\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\".jpg\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mphoto\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m         \u001b[0mphoto\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mencode_image\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"C:/Users/asus/Desktop/test images/\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\".jpg\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     23\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m         \u001b[0mcap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpred_cap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mphoto\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-5-7582de5dfd85>\u001b[0m in \u001b[0;36mencode_image\u001b[1;34m(img)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mencode_image\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpreprocess_img\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0mfeature_vect\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_new\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mfeature_vect\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-4-5e950bb13c2d>\u001b[0m in \u001b[0;36mpreprocess_img\u001b[1;34m(img)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mpreprocess_img\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_img\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtarget_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m224\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m224\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimg_to_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\keras_preprocessing\\image\\utils.py\u001b[0m in \u001b[0;36mload_img\u001b[1;34m(path, grayscale, color_mode, target_size, interpolation)\u001b[0m\n\u001b[0;32m    130\u001b[0m                         \", \".join(_PIL_INTERPOLATION_METHODS.keys())))\n\u001b[0;32m    131\u001b[0m             \u001b[0mresample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_PIL_INTERPOLATION_METHODS\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m             \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwidth_height_tuple\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36mresize\u001b[1;34m(self, size, resample, box)\u001b[0m\n\u001b[0;32m   1888\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1889\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1890\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1891\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1892\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_new\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\ML_GPU\\lib\\site-packages\\PIL\\ImageFile.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    251\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m                             \u001b[0mb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mb\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 253\u001b[1;33m                             \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merr_code\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdecoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    254\u001b[0m                             \u001b[1;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m                                 \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import requests\n",
    "url =input(\"enter local IP address\")\n",
    "url = \"http://\"+url+\"/shot.jpg\"\n",
    "\n",
    "vocals = pyttsx3.init()\n",
    "voices = vocals.getProperty(\"voices\")\n",
    "vocals.setProperty(\"voice\",voices[1].id)\n",
    "hn=0\n",
    "\n",
    "while True:\n",
    "    img_resp = requests.get(url)\n",
    "    img_array = np.array(bytearray(img_resp.content), dtype=np.uint8)\n",
    "    frame = cv2.imdecode(img_array, -1)\n",
    "    key_pressed = cv2.waitKey(1)&0xFF\n",
    "    if key_pressed == ord('q'):\n",
    "        break\n",
    "\n",
    "    if hn%10 == 0:\n",
    "        photo = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)\n",
    "        cv2.imwrite(\"C:/Users/asus/Desktop/test images/\"+str(hn)+\".jpg\", photo)\n",
    "\n",
    "        photo = encode_image(\"C:/Users/asus/Desktop/test images/\"+str(hn)+\".jpg\")\n",
    "        \n",
    "        cap = pred_cap(photo)\n",
    "        \n",
    "#         -----------------------------------------------------------------------------------------\n",
    "        \n",
    "        \n",
    "        \n",
    "        img = frame\n",
    "        height, width, channels = img.shape\n",
    "        # detecting objects\n",
    "        blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)\n",
    "\n",
    "        net.setInput(blob)\n",
    "        outs = net.forward(output_layers)\n",
    "\n",
    "        # showing information on the screen\n",
    "\n",
    "        boxes = []\n",
    "        confidences = []\n",
    "        class_ids = []\n",
    "\n",
    "        for out in outs:\n",
    "            for detection in out:\n",
    "                scores = detection[5:]\n",
    "                class_id = np.argmax(scores)\n",
    "                confidence = scores[class_id]\n",
    "                if confidence > 0.5:\n",
    "                    center_x = int(detection[0] * width)\n",
    "                    center_y = int(detection[1] * height)\n",
    "                    w = int(detection[2] * width)\n",
    "                    h = int(detection[3] * height)\n",
    "                    cv2.circle(img, (center_x, center_y), 10, (0, 255, 0), 2)\n",
    "\n",
    "                    x = int(center_x - w/2)\n",
    "                    y = int(center_y - h/2)\n",
    "                    boxes.append([x, y, w, h])\n",
    "                    confidences.append(float(confidence))\n",
    "                    class_ids.append(class_id)\n",
    "\n",
    "        objects = []\n",
    "        no_boxes_detected = len(boxes)\n",
    "        indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)\n",
    "        font = cv2.FONT_HERSHEY_PLAIN\n",
    "        for i in range(no_boxes_detected):\n",
    "            if i in indexes:\n",
    "                x, y, w, h = boxes[i]\n",
    "                label = str(classes[class_ids[i]])\n",
    "                objects.append(label)\n",
    "                cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 1)\n",
    "                cv2.putText(img, label, (x, y+30), font, 3, (0,0,0), 3)\n",
    "\n",
    "\n",
    "        cv2.imshow(\"Image\", img)\n",
    "\n",
    "#  ------------------------------------------------------------------------------------------------       \n",
    "    \n",
    "        objects_str = \",\".join(objects)\n",
    "        vocals.say(cap)\n",
    "        vocals.say(\"There are \"+ objects_str +\" near you\")\n",
    "        vocals.runAndWait()\n",
    "\n",
    "    #         cv2.imshow('VideoCapture',frame[:,::-1,:])\n",
    "        print(cap)\n",
    "        \n",
    "    if hn==5000:\n",
    "        break\n",
    "    hn += 1\n",
    "cam.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ML_GPU",
   "language": "python",
   "name": "ml_gpu"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}