Sign_Recognition_OpenCV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import keras\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating Train and Test data frame using pandas \n",
    "train_df = pd.read_csv('sign_mnist_train.csv')\n",
    "test_df = pd.read_csv('sign_mnist_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
       "      <th>pixel5</th>\n",
       "      <th>pixel6</th>\n",
       "      <th>pixel7</th>\n",
       "      <th>pixel8</th>\n",
       "      <th>pixel9</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel775</th>\n",
       "      <th>pixel776</th>\n",
       "      <th>pixel777</th>\n",
       "      <th>pixel778</th>\n",
       "      <th>pixel779</th>\n",
       "      <th>pixel780</th>\n",
       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
       "      <th>pixel783</th>\n",
       "      <th>pixel784</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>107</td>\n",
       "      <td>118</td>\n",
       "      <td>127</td>\n",
       "      <td>134</td>\n",
       "      <td>139</td>\n",
       "      <td>143</td>\n",
       "      <td>146</td>\n",
       "      <td>150</td>\n",
       "      <td>153</td>\n",
       "      <td>...</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>206</td>\n",
       "      <td>206</td>\n",
       "      <td>206</td>\n",
       "      <td>204</td>\n",
       "      <td>203</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>155</td>\n",
       "      <td>157</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>157</td>\n",
       "      <td>156</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>...</td>\n",
       "      <td>69</td>\n",
       "      <td>149</td>\n",
       "      <td>128</td>\n",
       "      <td>87</td>\n",
       "      <td>94</td>\n",
       "      <td>163</td>\n",
       "      <td>175</td>\n",
       "      <td>103</td>\n",
       "      <td>135</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>187</td>\n",
       "      <td>188</td>\n",
       "      <td>188</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>186</td>\n",
       "      <td>187</td>\n",
       "      <td>188</td>\n",
       "      <td>187</td>\n",
       "      <td>...</td>\n",
       "      <td>202</td>\n",
       "      <td>201</td>\n",
       "      <td>200</td>\n",
       "      <td>199</td>\n",
       "      <td>198</td>\n",
       "      <td>199</td>\n",
       "      <td>198</td>\n",
       "      <td>195</td>\n",
       "      <td>194</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>211</td>\n",
       "      <td>211</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>211</td>\n",
       "      <td>210</td>\n",
       "      <td>211</td>\n",
       "      <td>210</td>\n",
       "      <td>210</td>\n",
       "      <td>...</td>\n",
       "      <td>235</td>\n",
       "      <td>234</td>\n",
       "      <td>233</td>\n",
       "      <td>231</td>\n",
       "      <td>230</td>\n",
       "      <td>226</td>\n",
       "      <td>225</td>\n",
       "      <td>222</td>\n",
       "      <td>229</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>164</td>\n",
       "      <td>167</td>\n",
       "      <td>170</td>\n",
       "      <td>172</td>\n",
       "      <td>176</td>\n",
       "      <td>179</td>\n",
       "      <td>180</td>\n",
       "      <td>184</td>\n",
       "      <td>185</td>\n",
       "      <td>...</td>\n",
       "      <td>92</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>108</td>\n",
       "      <td>133</td>\n",
       "      <td>163</td>\n",
       "      <td>157</td>\n",
       "      <td>163</td>\n",
       "      <td>164</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0      3     107     118     127     134     139     143     146     150   \n",
       "1      6     155     157     156     156     156     157     156     158   \n",
       "2      2     187     188     188     187     187     186     187     188   \n",
       "3      2     211     211     212     212     211     210     211     210   \n",
       "4     13     164     167     170     172     176     179     180     184   \n",
       "\n",
       "   pixel9  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
       "0     153  ...       207       207       207       207       206       206   \n",
       "1     158  ...        69       149       128        87        94       163   \n",
       "2     187  ...       202       201       200       199       198       199   \n",
       "3     210  ...       235       234       233       231       230       226   \n",
       "4     185  ...        92       105       105       108       133       163   \n",
       "\n",
       "   pixel781  pixel782  pixel783  pixel784  \n",
       "0       206       204       203       202  \n",
       "1       175       103       135       149  \n",
       "2       198       195       194       195  \n",
       "3       225       222       229       163  \n",
       "4       157       163       164       179  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizing the dataset\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
       "      <th>pixel5</th>\n",
       "      <th>pixel6</th>\n",
       "      <th>pixel7</th>\n",
       "      <th>pixel8</th>\n",
       "      <th>pixel9</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel775</th>\n",
       "      <th>pixel776</th>\n",
       "      <th>pixel777</th>\n",
       "      <th>pixel778</th>\n",
       "      <th>pixel779</th>\n",
       "      <th>pixel780</th>\n",
       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
       "      <th>pixel783</th>\n",
       "      <th>pixel784</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>150</td>\n",
       "      <td>151</td>\n",
       "      <td>...</td>\n",
       "      <td>138</td>\n",
       "      <td>148</td>\n",
       "      <td>127</td>\n",
       "      <td>89</td>\n",
       "      <td>82</td>\n",
       "      <td>96</td>\n",
       "      <td>106</td>\n",
       "      <td>112</td>\n",
       "      <td>120</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>126</td>\n",
       "      <td>128</td>\n",
       "      <td>131</td>\n",
       "      <td>132</td>\n",
       "      <td>133</td>\n",
       "      <td>134</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>136</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>104</td>\n",
       "      <td>194</td>\n",
       "      <td>183</td>\n",
       "      <td>186</td>\n",
       "      <td>184</td>\n",
       "      <td>184</td>\n",
       "      <td>184</td>\n",
       "      <td>182</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>85</td>\n",
       "      <td>88</td>\n",
       "      <td>92</td>\n",
       "      <td>96</td>\n",
       "      <td>105</td>\n",
       "      <td>123</td>\n",
       "      <td>135</td>\n",
       "      <td>143</td>\n",
       "      <td>147</td>\n",
       "      <td>...</td>\n",
       "      <td>68</td>\n",
       "      <td>166</td>\n",
       "      <td>242</td>\n",
       "      <td>227</td>\n",
       "      <td>230</td>\n",
       "      <td>227</td>\n",
       "      <td>226</td>\n",
       "      <td>225</td>\n",
       "      <td>224</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>205</td>\n",
       "      <td>207</td>\n",
       "      <td>206</td>\n",
       "      <td>207</td>\n",
       "      <td>209</td>\n",
       "      <td>210</td>\n",
       "      <td>209</td>\n",
       "      <td>210</td>\n",
       "      <td>...</td>\n",
       "      <td>154</td>\n",
       "      <td>248</td>\n",
       "      <td>247</td>\n",
       "      <td>248</td>\n",
       "      <td>253</td>\n",
       "      <td>236</td>\n",
       "      <td>230</td>\n",
       "      <td>240</td>\n",
       "      <td>253</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>188</td>\n",
       "      <td>191</td>\n",
       "      <td>193</td>\n",
       "      <td>195</td>\n",
       "      <td>199</td>\n",
       "      <td>201</td>\n",
       "      <td>202</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>40</td>\n",
       "      <td>64</td>\n",
       "      <td>48</td>\n",
       "      <td>29</td>\n",
       "      <td>46</td>\n",
       "      <td>49</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0      6     149     149     150     150     150     151     151     150   \n",
       "1      5     126     128     131     132     133     134     135     135   \n",
       "2     10      85      88      92      96     105     123     135     143   \n",
       "3      0     203     205     207     206     207     209     210     209   \n",
       "4      3     188     191     193     195     199     201     202     203   \n",
       "\n",
       "   pixel9  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
       "0     151  ...       138       148       127        89        82        96   \n",
       "1     136  ...        47       104       194       183       186       184   \n",
       "2     147  ...        68       166       242       227       230       227   \n",
       "3     210  ...       154       248       247       248       253       236   \n",
       "4     203  ...        26        40        64        48        29        46   \n",
       "\n",
       "   pixel781  pixel782  pixel783  pixel784  \n",
       "0       106       112       120       107  \n",
       "1       184       184       182       180  \n",
       "2       226       225       224       222  \n",
       "3       230       240       253       255  \n",
       "4        49        46        46        53  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: keras in c:\\users\\mohit\\anaconda3\\lib\\site-packages (2.4.3)\n",
      "Requirement already satisfied: scipy>=0.14 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from keras) (1.5.2)\n",
      "Requirement already satisfied: numpy>=1.9.1 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from keras) (1.19.2)\n",
      "Requirement already satisfied: h5py in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from keras) (2.10.0)\n",
      "Requirement already satisfied: pyyaml in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from keras) (5.3.1)\n",
      "Requirement already satisfied: six in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from h5py->keras) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in c:\\users\\mohit\\anaconda3\\lib\\site-packages (2.4.1)\n",
      "Requirement already satisfied: flatbuffers~=1.12.0 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (1.12)\n",
      "Requirement already satisfied: keras-preprocessing~=1.1.2 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (1.1.2)\n",
      "Requirement already satisfied: typing-extensions~=3.7.4 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (3.7.4.3)\n",
      "Requirement already satisfied: opt-einsum~=3.3.0 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (3.3.0)\n",
      "Requirement already satisfied: grpcio~=1.32.0 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (1.32.0)\n",
      "Requirement already satisfied: wrapt~=1.12.1 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (1.12.1)\n",
      "Requirement already satisfied: gast==0.3.3 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (0.3.3)\n",
      "Requirement already satisfied: numpy~=1.19.2 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (1.19.2)\n",
      "Requirement already satisfied: h5py~=2.10.0 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (2.10.0)\n",
      "Requirement already satisfied: astunparse~=1.6.3 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (1.6.3)\n",
      "Requirement already satisfied: six~=1.15.0 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (1.15.0)\n",
      "Requirement already satisfied: tensorboard~=2.4 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (2.5.0)\n",
      "Requirement already satisfied: absl-py~=0.10 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (0.12.0)\n",
      "Requirement already satisfied: termcolor~=1.1.0 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (1.1.0)\n",
      "Requirement already satisfied: google-pasta~=0.2 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (0.2.0)\n",
      "Requirement already satisfied: wheel~=0.35 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (0.35.1)\n",
      "Requirement already satisfied: tensorflow-estimator<2.5.0,>=2.4.0 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (2.4.0)\n",
      "Requirement already satisfied: protobuf>=3.9.2 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorflow) (3.15.8)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorboard~=2.4->tensorflow) (1.8.0)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorboard~=2.4->tensorflow) (0.6.0)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorboard~=2.4->tensorflow) (2.24.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorboard~=2.4->tensorflow) (3.3.4)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorboard~=2.4->tensorflow) (1.0.1)\n",
      "Requirement already satisfied: google-auth<2,>=1.6.3 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorboard~=2.4->tensorflow) (1.30.0)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorboard~=2.4->tensorflow) (50.3.1.post20201107)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from tensorboard~=2.4->tensorflow) (0.4.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow) (2.10)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow) (3.0.4)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow) (1.25.11)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow) (2020.6.20)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow) (4.2.2)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow) (4.7.2)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow) (0.2.8)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow) (1.3.0)\n",
      "Requirement already satisfied: pyasn1>=0.1.3 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from rsa<5,>=3.1.4; python_version >= \"3.6\"->google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow) (3.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_Train: (27455, 784)\n",
      "Shape of X_Test: (7172, 785)\n"
     ]
    }
   ],
   "source": [
    "#Preprocessing the data\n",
    "\n",
    "train = np.array(train_df)  # Creating Numpy array\n",
    "Train = train[:,1:]         # Taking everthing except the 1st column i.e, Label which is used for predictions\n",
    "Train = Train/255           # Shaping the image\n",
    "print(\"Shape of X_Train:\",Train.shape)\n",
    "X_train = np.reshape(Train,(27455,28,28,1))\n",
    "y_train = np.array(train_df['label'])  # Creating Numpy array of Labels\n",
    "y_train = to_categorical(y_train)\n",
    "X_test = np.array(test_df)             # Creating Numpy array of Test dataframe\n",
    "y_test = np.array(test_df['label'])    # Creating Numpy array of Test dataframe only Labels column\n",
    "y_test = to_categorical(y_test)\n",
    "X_Test = X_test[:,1:]      # Taking everthing except the 1st column\n",
    "print(\"Shape of X_Test:\",X_test.shape)\n",
    "X_Test = np.reshape(X_Test,(7172,28,28,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x227729bf0a0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.title('Image Produced from row 1')\n",
    "plt.imshow(np.reshape(Train[1,:],(28,28)),'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2272620c4f0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.title('Image Produced from row 2')\n",
    "plt.imshow(np.reshape(Train[2,:],(28,28)),'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "601/601 [==============================] - 5s 8ms/step - loss: 2.6027 - accuracy: 0.2495 - val_loss: 0.8276 - val_accuracy: 0.7627\n",
      "Epoch 2/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 0.6830 - accuracy: 0.8001 - val_loss: 0.3381 - val_accuracy: 0.9156\n",
      "Epoch 3/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 0.3009 - accuracy: 0.9185 - val_loss: 0.1418 - val_accuracy: 0.9754\n",
      "Epoch 4/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 0.1185 - accuracy: 0.9810 - val_loss: 0.0672 - val_accuracy: 0.9899\n",
      "Epoch 5/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 0.0549 - accuracy: 0.9939 - val_loss: 0.0375 - val_accuracy: 0.9953\n",
      "Epoch 6/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 0.0273 - accuracy: 0.9980 - val_loss: 0.0141 - val_accuracy: 0.9998\n",
      "Epoch 7/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 0.0132 - accuracy: 0.9997 - val_loss: 0.0088 - val_accuracy: 0.9999\n",
      "Epoch 8/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 0.0075 - accuracy: 0.9999 - val_loss: 0.0057 - val_accuracy: 1.0000\n",
      "Epoch 9/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 0.0052 - accuracy: 0.9997 - val_loss: 0.0032 - val_accuracy: 1.0000\n",
      "Epoch 10/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0034 - val_accuracy: 0.9999\n",
      "Epoch 11/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 0.0083 - accuracy: 0.9980 - val_loss: 0.0022 - val_accuracy: 1.0000\n",
      "Epoch 12/30\n",
      "601/601 [==============================] - 4s 7ms/step - loss: 0.0022 - accuracy: 0.9998 - val_loss: 0.0016 - val_accuracy: 0.9999\n",
      "Epoch 13/30\n",
      "601/601 [==============================] - 4s 7ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 9.3866e-04 - val_accuracy: 1.0000\n",
      "Epoch 14/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 7.1406e-04 - accuracy: 1.0000 - val_loss: 7.0910e-04 - val_accuracy: 1.0000\n",
      "Epoch 15/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 5.6992e-04 - accuracy: 1.0000 - val_loss: 5.7344e-04 - val_accuracy: 1.0000\n",
      "Epoch 16/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 0.0089 - accuracy: 0.9971 - val_loss: 0.0011 - val_accuracy: 1.0000\n",
      "Epoch 17/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 7.4546e-04 - accuracy: 1.0000 - val_loss: 5.6180e-04 - val_accuracy: 1.0000\n",
      "Epoch 18/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 4.5378e-04 - accuracy: 1.0000 - val_loss: 4.3517e-04 - val_accuracy: 1.0000\n",
      "Epoch 19/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 3.0414e-04 - accuracy: 1.0000 - val_loss: 3.5461e-04 - val_accuracy: 1.0000\n",
      "Epoch 20/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 2.4806e-04 - accuracy: 1.0000 - val_loss: 2.8518e-04 - val_accuracy: 1.0000\n",
      "Epoch 21/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 1.9512e-04 - accuracy: 1.0000 - val_loss: 2.0123e-04 - val_accuracy: 1.0000\n",
      "Epoch 22/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 1.6136e-04 - accuracy: 1.0000 - val_loss: 1.9202e-04 - val_accuracy: 1.0000\n",
      "Epoch 23/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 1.2975e-04 - accuracy: 1.0000 - val_loss: 1.4070e-04 - val_accuracy: 1.0000\n",
      "Epoch 24/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 1.0204e-04 - accuracy: 1.0000 - val_loss: 1.4151e-04 - val_accuracy: 1.0000\n",
      "Epoch 25/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 8.5403e-05 - accuracy: 1.0000 - val_loss: 9.4711e-05 - val_accuracy: 1.0000\n",
      "Epoch 26/30\n",
      "601/601 [==============================] - 4s 7ms/step - loss: 6.2551e-05 - accuracy: 1.0000 - val_loss: 8.5169e-05 - val_accuracy: 1.0000\n",
      "Epoch 27/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 0.0168 - accuracy: 0.9955 - val_loss: 4.4753e-04 - val_accuracy: 1.0000\n",
      "Epoch 28/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 3.8702e-04 - accuracy: 1.0000 - val_loss: 2.4184e-04 - val_accuracy: 1.0000\n",
      "Epoch 29/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 2.1528e-04 - accuracy: 1.0000 - val_loss: 1.7631e-04 - val_accuracy: 1.0000\n",
      "Epoch 30/30\n",
      "601/601 [==============================] - 4s 6ms/step - loss: 1.5021e-04 - accuracy: 1.0000 - val_loss: 1.3883e-04 - val_accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import MaxPooling2D, Conv2D, Flatten, Dropout, Dense\n",
    "model = Sequential()\n",
    "model.add(Conv2D(8, kernel_size=5, activation='relu', input_shape=(28,28,1)))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "\n",
    "model.add(Conv2D(16, kernel_size=3, activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(25, activation='softmax'))\n",
    "\n",
    "model.compile(loss=\"categorical_crossentropy\",optimizer = \"adam\", metrics=[\"accuracy\"])\n",
    "cnn = model.fit(X_train, y_train,validation_split = 0.3, epochs=30, verbose=1) \n",
    "# Increase epochs from 10 to 30 so that accuracy is enhanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          -----------Accuracy Graph-----------   \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": [
    "#To visualize the training process \n",
    "\n",
    "plt.plot(cnn.history['accuracy'])\n",
    "plt.plot(cnn.history['val_accuracy'])\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['Train','Test'])\n",
    "print(\"          -----------Accuracy Graph-----------   \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          -----------Loss Graph-----------   \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": [
    "plt.plot(cnn.history['loss'])\n",
    "plt.plot(cnn.history['val_loss'])\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(['Train','Test'])\n",
    "print(\"          -----------Loss Graph-----------   \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 24, 24, 8)         208       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 12, 12, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 10, 10, 16)        1168      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 16)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 400)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 25)                10025     \n",
      "=================================================================\n",
      "Total params: 11,401\n",
      "Trainable params: 11,401\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "225/225 [==============================] - 1s 3ms/step - loss: 152.2921 - accuracy: 0.9034\n",
      "Accuracy:  90.33742547035217 %\n"
     ]
    }
   ],
   "source": [
    "accuracy = model.evaluate(x=X_Test,y=y_test,batch_size=32)\n",
    "print(\"Accuracy: \",accuracy[1]*100,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = model.predict(X_Test)\n",
    "prediction = (prediction > 0.5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.33742331288343"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,prediction)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now importing necessary files for openCV\n",
    "import cv2\n",
    "import numpy as np\n",
    "from keras import models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opencv-python in c:\\users\\mohit\\anaconda3\\lib\\site-packages (4.5.1.48)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from opencv-python) (1.19.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opencv-contrib-python in c:\\users\\mohit\\appdata\\roaming\\python\\python38\\site-packages (4.5.1.48)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\mohit\\anaconda3\\lib\\site-packages (from opencv-contrib-python) (1.19.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install --user opencv-contrib-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"CNN_sign_language.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    " def PRINT_LETTER(mod):\n",
    "    Dict = {\"A\":0,\"B\":1,\"C\":2,\"D\":3,\"E\":4,\"F\":5,\"G\":6,\"H\":7,\"I\":8,\"J\":9,\"K\":10,\"L\":11,\"M\":12,\"N\":13,\"O\":14,\"P\":15,\n",
    "            \"Q\":16,\"R\":17,\"S\":18,\"T\":19,\"U\":20,\"V\":21,\"W\":22,\"X\":23,\"Y\":24}\n",
    "    Letters = {y:x for x,y in Dict.items()}\n",
    "    return Letters[int(mod)];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = \"CNN_sign_language.h5\"\n",
    "CNN = models.load_model(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using cv2 to capture the frames using the webcamera\n",
    "capture = cv2.VideoCapture(0)\n",
    "org = (300,100)\n",
    "#Taking Font\n",
    "font = cv2.FONT_HERSHEY_COMPLEX\n",
    "fontScale = 1\n",
    "# Blue color in BGR\n",
    "color = (0,0,255) # We have taken Blue to represent the character\n",
    "# Line thickness of 2 px\n",
    "thickness = 2\n",
    "\n",
    "while True:\n",
    "    # Storing the frame from the video capture and showing\n",
    "    ret, frame = capture.read()\n",
    "    frame = cv2.flip(frame , 1)\n",
    "    R = frame[100:400, 320:620]\n",
    "    \n",
    "    R = cv2.cvtColor(R,cv2.COLOR_BGR2GRAY)\n",
    "    R = cv2.resize(R,(28,28),interpolation = cv2.INTER_AREA)\n",
    "    \n",
    "    #Creating a copy of that frame\n",
    "    copy = frame.copy()\n",
    "    \n",
    "    #Format - cv2.rectangle(image, start_point, end_point, color, thickness)\n",
    "    cv2.rectangle(copy, (320,100) , (620,400) ,(0,255,0) , thickness)\n",
    "    \n",
    "    #Reshaping\n",
    "    R = R.reshape(1,28,28,1)\n",
    "    \n",
    "    #Predicting the letter using our Model\n",
    "    out = CNN.predict_classes(R,1)[0]\n",
    "    letter = PRINT_LETTER(out)\n",
    "    #Printing the letter\n",
    "    #print(\"Letter = \",letter)\n",
    "    \n",
    "    #Showing the letter on the Application Screen\n",
    "    #cv2.putText(copy,letter,(200,100),cv2.FONT_HERSHEY_COMPLEX,2,(0,255,0),2)\n",
    "    cv2.putText(copy,letter, org, font, fontScale, color, thickness)\n",
    "    #Showing the Frame also\n",
    "    cv2.imshow(\"Frame\",copy)\n",
    "    \n",
    "    key=cv2.waitKey(1)   #Esc to Exit it\n",
    "    if key%256 == 27:  \n",
    "        break\n",
    "\n",
    "#When While loop terminates the camera is turned off and closing all opened applications using cv2\n",
    "capture.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}