train-RGB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div align=\"left\">Training the different algorithms with the dominant colors data<div/>                   <div align=\"left\">in RGB<div/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "CSV_DIR = './colors_RGB.csv' #colors.csv #colors_LAB.csv #colors_LAB_512.csv #colors_RGB.csv #colors_RGB_512.csv\n",
    "\n",
    "tf_model1= './Models/colors_model_RGB.tflite'\n",
    "tf_model2= './Models/colors_model_tf1_RGB.tflite'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### _MENU_\n",
    "1. **LOAD DATA**\n",
    "2. **SPLIT DATA**\n",
    "3. **NORMALIZE DATA**\n",
    "4. **TRAINING AND EVALUATION OF MACHINE LEARNING MODELS**  \n",
    "    4.1 Decision Trees (DT)  \n",
    "    4.2 K-Nearest-Neighbors (KNN)  \n",
    "    4.3 Logistic Regression (LR)  \n",
    "    4.4 Support Vector Machine (SVM)  \n",
    "    4.5 Convolutional Neural Network (CNN)  \n",
    "5. **EXPORT THE BEST MODEL (KNN)**  \n",
    "    5.1 Export the KNN model using concrete functions of Tensorflow 2  \n",
    "    5.2 Export the KNN model using session graph of Tensorflow 1\n",
    "6. **MAKE NEW PREDICTIONS WITH THE EXPORTED MODEL**  \n",
    "    6.1 Test the KNN exported model trained with the dominant colors of granite image 10714J_P2.jpg  \n",
    "    6.2 Test the CNN exported model of our previous work trained with all image features of granite image 10714J_P2.jpg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. LOAD DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11830I_P2_01_02.png</td>\n",
       "      <td>95.269035</td>\n",
       "      <td>91.425207</td>\n",
       "      <td>90.621158</td>\n",
       "      <td>0.126083</td>\n",
       "      <td>201.666805</td>\n",
       "      <td>198.684869</td>\n",
       "      <td>194.049263</td>\n",
       "      <td>0.242202</td>\n",
       "      <td>138.724315</td>\n",
       "      <td>134.668137</td>\n",
       "      <td>132.229649</td>\n",
       "      <td>0.275912</td>\n",
       "      <td>171.437440</td>\n",
       "      <td>167.489210</td>\n",
       "      <td>163.534256</td>\n",
       "      <td>0.355803</td>\n",
       "      <td>Granodiorite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12720V_P2.JPG</td>\n",
       "      <td>88.316395</td>\n",
       "      <td>86.958287</td>\n",
       "      <td>85.464752</td>\n",
       "      <td>0.103402</td>\n",
       "      <td>200.649128</td>\n",
       "      <td>195.801483</td>\n",
       "      <td>191.483951</td>\n",
       "      <td>0.222824</td>\n",
       "      <td>137.959806</td>\n",
       "      <td>134.586174</td>\n",
       "      <td>131.488156</td>\n",
       "      <td>0.283614</td>\n",
       "      <td>169.132867</td>\n",
       "      <td>164.719685</td>\n",
       "      <td>160.554818</td>\n",
       "      <td>0.390160</td>\n",
       "      <td>Granodiorite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10715M_P2_01_02.png</td>\n",
       "      <td>67.306529</td>\n",
       "      <td>64.026982</td>\n",
       "      <td>61.557307</td>\n",
       "      <td>0.138290</td>\n",
       "      <td>112.445387</td>\n",
       "      <td>108.708090</td>\n",
       "      <td>104.446520</td>\n",
       "      <td>0.208386</td>\n",
       "      <td>174.545574</td>\n",
       "      <td>170.638811</td>\n",
       "      <td>166.482386</td>\n",
       "      <td>0.299667</td>\n",
       "      <td>145.279065</td>\n",
       "      <td>141.066676</td>\n",
       "      <td>136.754627</td>\n",
       "      <td>0.353657</td>\n",
       "      <td>Granodiorite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12723M_P2_01_02.png</td>\n",
       "      <td>73.269358</td>\n",
       "      <td>71.906398</td>\n",
       "      <td>72.610250</td>\n",
       "      <td>0.160950</td>\n",
       "      <td>131.461649</td>\n",
       "      <td>127.162902</td>\n",
       "      <td>125.093589</td>\n",
       "      <td>0.234111</td>\n",
       "      <td>200.555570</td>\n",
       "      <td>197.370429</td>\n",
       "      <td>193.249712</td>\n",
       "      <td>0.277496</td>\n",
       "      <td>168.468192</td>\n",
       "      <td>164.323115</td>\n",
       "      <td>160.877253</td>\n",
       "      <td>0.327444</td>\n",
       "      <td>Granodiorite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11830I_P2_01_01.png</td>\n",
       "      <td>103.325967</td>\n",
       "      <td>100.205286</td>\n",
       "      <td>92.789962</td>\n",
       "      <td>0.132639</td>\n",
       "      <td>142.029063</td>\n",
       "      <td>138.541654</td>\n",
       "      <td>133.233909</td>\n",
       "      <td>0.248916</td>\n",
       "      <td>200.956211</td>\n",
       "      <td>198.408777</td>\n",
       "      <td>196.501305</td>\n",
       "      <td>0.266878</td>\n",
       "      <td>172.883147</td>\n",
       "      <td>169.818332</td>\n",
       "      <td>166.548020</td>\n",
       "      <td>0.351567</td>\n",
       "      <td>Granodiorite</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "                    0           1           2          3         4   \\\n",
       "0  11830I_P2_01_02.png   95.269035   91.425207  90.621158  0.126083   \n",
       "1        12720V_P2.JPG   88.316395   86.958287  85.464752  0.103402   \n",
       "2  10715M_P2_01_02.png   67.306529   64.026982  61.557307  0.138290   \n",
       "3  12723M_P2_01_02.png   73.269358   71.906398  72.610250  0.160950   \n",
       "4  11830I_P2_01_01.png  103.325967  100.205286  92.789962  0.132639   \n",
       "\n",
       "           5           6           7         8           9           10  \\\n",
       "0  201.666805  198.684869  194.049263  0.242202  138.724315  134.668137   \n",
       "1  200.649128  195.801483  191.483951  0.222824  137.959806  134.586174   \n",
       "2  112.445387  108.708090  104.446520  0.208386  174.545574  170.638811   \n",
       "3  131.461649  127.162902  125.093589  0.234111  200.555570  197.370429   \n",
       "4  142.029063  138.541654  133.233909  0.248916  200.956211  198.408777   \n",
       "\n",
       "           11        12          13          14          15        16  \\\n",
       "0  132.229649  0.275912  171.437440  167.489210  163.534256  0.355803   \n",
       "1  131.488156  0.283614  169.132867  164.719685  160.554818  0.390160   \n",
       "2  166.482386  0.299667  145.279065  141.066676  136.754627  0.353657   \n",
       "3  193.249712  0.277496  168.468192  164.323115  160.877253  0.327444   \n",
       "4  196.501305  0.266878  172.883147  169.818332  166.548020  0.351567   \n",
       "\n",
       "             17  \n",
       "0  Granodiorite  \n",
       "1  Granodiorite  \n",
       "2  Granodiorite  \n",
       "3  Granodiorite  \n",
       "4  Granodiorite  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(CSV_DIR, header=None)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x shape: (283, 16) \n",
      "y shape: (283,)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.array(data[data.columns[1:-1]])\n",
    "y = data[data.columns[-1]]\n",
    "\n",
    "print('x shape:', x.shape, '\\ny shape:', y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes dict: {0: 'Diorite', 1: 'Gabbro', 2: 'Granite', 3: 'Granodiorite'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "encoder = preprocessing.LabelEncoder()\n",
    "y = encoder.fit_transform(y)\n",
    "classes_dict = dict(zip( list(range(len(np.unique(y)))), encoder.inverse_transform(list(range(len(np.unique(y))))) ))\n",
    "\n",
    "print('Classes dict:', classes_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Classes</th>\n",
       "      <th>Counts</th>\n",
       "      <th>Start index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Diorite</td>\n",
       "      <td>78</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gabbro</td>\n",
       "      <td>65</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Granite</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Granodiorite</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Classes  Counts  Start index\n",
       "0       Diorite      78          140\n",
       "1        Gabbro      65          218\n",
       "2       Granite      70           70\n",
       "3  Granodiorite      70            0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes, indices, counts = np.unique(y, return_counts=True, return_index=True)\n",
    "df = pd.DataFrame({'Classes': classes_dict.values(), 'Counts': counts, 'Start index': indices}, classes)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. SPLIT DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split as splitter\n",
    "xtrain, xtest, ytrain, ytest = splitter(x,y,train_size=0.8,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. NORMALIZE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "normalizer = MinMaxScaler()\n",
    "\n",
    "xtrainNorm = normalizer.fit_transform(xtrain)\n",
    "xtestNorm = normalizer.transform(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature value</th>\n",
       "      <th>scale</th>\n",
       "      <th>min</th>\n",
       "      <th>norm</th>\n",
       "      <th>REAL NORM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>103.214673</td>\n",
       "      <td>0.006444</td>\n",
       "      <td>-0.359617</td>\n",
       "      <td>0.305477</td>\n",
       "      <td>0.305477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99.450991</td>\n",
       "      <td>0.006404</td>\n",
       "      <td>-0.344978</td>\n",
       "      <td>0.291933</td>\n",
       "      <td>0.291933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96.959233</td>\n",
       "      <td>0.006354</td>\n",
       "      <td>-0.312888</td>\n",
       "      <td>0.303147</td>\n",
       "      <td>0.303147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.092607</td>\n",
       "      <td>5.521680</td>\n",
       "      <td>-0.257627</td>\n",
       "      <td>0.253718</td>\n",
       "      <td>0.253718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>191.606711</td>\n",
       "      <td>0.006298</td>\n",
       "      <td>-0.321887</td>\n",
       "      <td>0.884807</td>\n",
       "      <td>0.884807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>187.461758</td>\n",
       "      <td>0.006259</td>\n",
       "      <td>-0.306800</td>\n",
       "      <td>0.866467</td>\n",
       "      <td>0.866467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>179.728239</td>\n",
       "      <td>0.006266</td>\n",
       "      <td>-0.280187</td>\n",
       "      <td>0.846035</td>\n",
       "      <td>0.846035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.249200</td>\n",
       "      <td>9.485569</td>\n",
       "      <td>-1.617374</td>\n",
       "      <td>0.746430</td>\n",
       "      <td>0.746430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>140.941447</td>\n",
       "      <td>0.006299</td>\n",
       "      <td>-0.333342</td>\n",
       "      <td>0.554494</td>\n",
       "      <td>0.554494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>135.970186</td>\n",
       "      <td>0.006372</td>\n",
       "      <td>-0.335731</td>\n",
       "      <td>0.530671</td>\n",
       "      <td>0.530671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>130.245016</td>\n",
       "      <td>0.006276</td>\n",
       "      <td>-0.323621</td>\n",
       "      <td>0.493776</td>\n",
       "      <td>0.493776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.290752</td>\n",
       "      <td>6.707479</td>\n",
       "      <td>-1.465116</td>\n",
       "      <td>0.485096</td>\n",
       "      <td>0.485096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>165.830671</td>\n",
       "      <td>0.006915</td>\n",
       "      <td>-0.454938</td>\n",
       "      <td>0.691849</td>\n",
       "      <td>0.691849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>161.289846</td>\n",
       "      <td>0.006948</td>\n",
       "      <td>-0.447848</td>\n",
       "      <td>0.672843</td>\n",
       "      <td>0.672843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>154.388254</td>\n",
       "      <td>0.007050</td>\n",
       "      <td>-0.427251</td>\n",
       "      <td>0.661159</td>\n",
       "      <td>0.661159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.367441</td>\n",
       "      <td>6.299453</td>\n",
       "      <td>-1.733443</td>\n",
       "      <td>0.581237</td>\n",
       "      <td>0.581237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Feature value     scale       min      norm  REAL NORM\n",
       "0      103.214673  0.006444 -0.359617  0.305477   0.305477\n",
       "1       99.450991  0.006404 -0.344978  0.291933   0.291933\n",
       "2       96.959233  0.006354 -0.312888  0.303147   0.303147\n",
       "3        0.092607  5.521680 -0.257627  0.253718   0.253718\n",
       "4      191.606711  0.006298 -0.321887  0.884807   0.884807\n",
       "5      187.461758  0.006259 -0.306800  0.866467   0.866467\n",
       "6      179.728239  0.006266 -0.280187  0.846035   0.846035\n",
       "7        0.249200  9.485569 -1.617374  0.746430   0.746430\n",
       "8      140.941447  0.006299 -0.333342  0.554494   0.554494\n",
       "9      135.970186  0.006372 -0.335731  0.530671   0.530671\n",
       "10     130.245016  0.006276 -0.323621  0.493776   0.493776\n",
       "11       0.290752  6.707479 -1.465116  0.485096   0.485096\n",
       "12     165.830671  0.006915 -0.454938  0.691849   0.691849\n",
       "13     161.289846  0.006948 -0.447848  0.672843   0.672843\n",
       "14     154.388254  0.007050 -0.427251  0.661159   0.661159\n",
       "15       0.367441  6.299453 -1.733443  0.581237   0.581237"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_norms = [xtest[0][f]*normalizer.scale_[f]+normalizer.min_[f] for f in range(16)]\n",
    "norm = {'Feature value': xtest[0], 'scale': normalizer.scale_, 'min': normalizer.min_, 'norm': test_norms, 'REAL NORM': xtestNorm[0]}\n",
    "pd.DataFrame(norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Models/scales.txt', 'w') as filehandle:\n",
    "    for listitem in normalizer.scale_:\n",
    "        filehandle.write('%s\\n' % listitem)\n",
    "with open('Models/mins.txt', 'w') as filehandle:\n",
    "    for listitem in normalizer.min_:\n",
    "        filehandle.write('%s\\n' % listitem)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. TRAINING AND EVALUATION OF MACHINE LEARNING MODELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1 - Decision Trees (DT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<<MODEL REPORT DT>>\n",
      "Decision trees score: 0.7192982456140351\n",
      "Decision trees accuracy: 0.7192982456140351\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Diorite       0.69      0.65      0.67        17\n",
      "      Gabbro       0.64      0.69      0.67        13\n",
      "     Granite       0.92      0.85      0.88        13\n",
      "Granodiorite       0.67      0.71      0.69        14\n",
      "\n",
      "    accuracy                           0.72        57\n",
      "   macro avg       0.73      0.72      0.73        57\n",
      "weighted avg       0.72      0.72      0.72        57\n",
      "\n",
      "Training time: 0.01154470443725586 s\n"
     ]
    }
   ],
   "source": [
    "# Decision Trees (DT)\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "decisionTree = DecisionTreeClassifier(class_weight='balanced')\n",
    "start = time.time()\n",
    "decisionTree.fit(xtrain, ytrain)\n",
    "stop = time.time()\n",
    "\n",
    "print(\"<<MODEL REPORT DT>>\")\n",
    "print(\"Decision trees score:\",decisionTree.score(xtest, ytest))\n",
    "\n",
    "predictions = decisionTree.predict(xtest)\n",
    "print(\"Decision trees accuracy:\", metrics.accuracy_score(ytest, predictions))\n",
    "print(metrics.classification_report(ytest,predictions, target_names=classes_dict.values()))\n",
    "\n",
    "print(f\"Training time: {stop - start} s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2 - K-Nearest Neighbors (KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<<MODEL REPORT KNN>>\n",
      "KNN score: 0.7894736842105263\n",
      "KNN Model accuracy: 0.7894736842105263\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Diorite       0.72      0.76      0.74        17\n",
      "      Gabbro       0.71      0.77      0.74        13\n",
      "     Granite       0.85      0.85      0.85        13\n",
      "Granodiorite       0.92      0.79      0.85        14\n",
      "\n",
      "    accuracy                           0.79        57\n",
      "   macro avg       0.80      0.79      0.79        57\n",
      "weighted avg       0.80      0.79      0.79        57\n",
      "\n",
      "Training time: 0.0025479793548583984 s\n"
     ]
    }
   ],
   "source": [
    "# K-Nearest Neighbors (KNN)\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "start = time.time()\n",
    "knn.fit(xtrainNorm, ytrain)\n",
    "stop = time.time()\n",
    "\n",
    "print(\"<<MODEL REPORT KNN>>\")\n",
    "print(\"KNN score:\",knn.score(xtestNorm, ytest))\n",
    "\n",
    "predictions = knn.predict(xtestNorm)\n",
    "print(\"KNN Model accuracy:\", metrics.accuracy_score(ytest, predictions))\n",
    "print(metrics.classification_report(ytest,predictions, target_names=classes_dict.values()))\n",
    "\n",
    "print(f\"Training time: {stop - start} s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.3 - Logistic Regression (LR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<<MODELS REPORT LOG. REGRESSION>>\n",
      "Logistic Regression score: 0.631578947368421\n",
      "Logistic Regression Model accuracy: 0.631578947368421\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Diorite       0.67      0.47      0.55        17\n",
      "      Gabbro       0.53      0.77      0.62        13\n",
      "     Granite       0.72      1.00      0.84        13\n",
      "Granodiorite       0.62      0.36      0.45        14\n",
      "\n",
      "    accuracy                           0.63        57\n",
      "   macro avg       0.64      0.65      0.62        57\n",
      "weighted avg       0.64      0.63      0.61        57\n",
      "\n",
      "Training time: 0.04471397399902344 s\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression (LR)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "logReg = LogisticRegression(solver='lbfgs')\n",
    "start = time.time()\n",
    "logReg.fit(xtrainNorm, ytrain)\n",
    "stop = time.time()\n",
    "\n",
    "print(\"<<MODELS REPORT LOG. REGRESSION>>\")\n",
    "print(\"Logistic Regression score:\",logReg.score(xtestNorm, ytest))\n",
    "\n",
    "predictions = logReg.predict(xtestNorm)\n",
    "print(\"Logistic Regression Model accuracy:\", metrics.accuracy_score(ytest, predictions))\n",
    "print(metrics.classification_report(ytest,predictions, target_names=classes_dict.values()))\n",
    "\n",
    "print(f\"Training time: {stop - start} s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.4 - Support Vector Machines (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<<MODEL REPORT SVM>>\n",
      "Support Vector Machines: 0.5263157894736842\n",
      "Support Vector Machines Model accuracy: 0.5263157894736842\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Diorite       0.39      1.00      0.56        17\n",
      "      Gabbro       0.00      0.00      0.00        13\n",
      "     Granite       1.00      0.69      0.82        13\n",
      "Granodiorite       1.00      0.29      0.44        14\n",
      "\n",
      "    accuracy                           0.53        57\n",
      "   macro avg       0.60      0.49      0.46        57\n",
      "weighted avg       0.59      0.53      0.46        57\n",
      "\n",
      "Training time: 0.01086115837097168 s\n"
     ]
    }
   ],
   "source": [
    "# Support Vector Machines (SVM)\n",
    "from sklearn.svm import SVC\n",
    "svmc = SVC(kernel='rbf', gamma='auto')\n",
    "start = time.time()\n",
    "svmc.fit(xtrain, ytrain)\n",
    "stop = time.time()\n",
    "\n",
    "print(\"<<MODEL REPORT SVM>>\")\n",
    "print(\"Support Vector Machines:\",svmc.score(xtest, ytest))\n",
    "\n",
    "predictions = svmc.predict(xtest)\n",
    "print(\"Support Vector Machines Model accuracy:\", metrics.accuracy_score(ytest, predictions))\n",
    "print(metrics.classification_report(ytest,predictions, target_names=classes_dict.values(), zero_division=0))\n",
    "\n",
    "print(f\"Training time: {stop - start} s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.5 - Convolutional Neural Network (CNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy\n",
      "\ttraining         \t (min:    0.230, max:    0.832, cur:    0.827)\n",
      "\tvalidation       \t (min:    0.298, max:    0.772, cur:    0.702)\n",
      "Loss\n",
      "\ttraining         \t (min:    0.465, max:    1.381, cur:    0.465)\n",
      "\tvalidation       \t (min:    0.638, max:    1.359, cur:    0.675)\n",
      "Accuracy: 0.702 Loss: 0.675\n",
      "CNN Model accuracy: 0.7017543859649122\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Diorite       0.67      0.47      0.55        17\n",
      "      Gabbro       0.61      0.85      0.71        13\n",
      "     Granite       0.92      0.85      0.88        13\n",
      "Granodiorite       0.67      0.71      0.69        14\n",
      "\n",
      "    accuracy                           0.70        57\n",
      "   macro avg       0.72      0.72      0.71        57\n",
      "weighted avg       0.71      0.70      0.70        57\n",
      "\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d (Conv1D)              (None, 16, 32)            96        \n",
      "_________________________________________________________________\n",
      "max_pooling1d (MaxPooling1D) (None, 8, 32)             0         \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 8, 64)             4160      \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 4, 64)             0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                16448     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4)                 260       \n",
      "=================================================================\n",
      "Total params: 20,964\n",
      "Trainable params: 20,964\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "\n",
      "Training time: 57.52258372306824 s\n"
     ]
    }
   ],
   "source": [
    "# Convolutional Neural Network (CNN)\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten, Dropout, Conv1D, MaxPooling1D\n",
    "from tensorflow.keras import optimizers\n",
    "from livelossplot.inputs.tf_keras import PlotLossesCallback\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv1D(32, kernel_size=2, padding=\"same\", input_shape = (16, 1), activation='relu'))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Conv1D(64, kernel_size=2, padding=\"same\", activation='relu'))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dense(len(classes_dict), activation='softmax'))\n",
    "model.compile(loss='sparse_categorical_crossentropy', optimizer=optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0), metrics=[\"accuracy\"])\n",
    "# model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "xtrain2 = np.expand_dims(xtrainNorm, 2); ytrain2 = np.expand_dims(ytrain, 1)\n",
    "xtest2 = np.expand_dims(xtestNorm, 2); ytest2 = np.expand_dims(ytest, 1)\n",
    "\n",
    "start = time.time()\n",
    "model.fit(xtrain2, ytrain2, epochs=100, batch_size=32, \n",
    "          validation_data=(xtest2, ytest2), callbacks=[PlotLossesCallback()], \n",
    "          verbose=1)\n",
    "stop = time.time()\n",
    "\n",
    "loss, acc = model.evaluate(xtest2, ytest2, verbose=0)\n",
    "print('Accuracy: %.3f' % acc, 'Loss: %.3f' % loss)\n",
    "\n",
    "predictions = np.argmax(model.predict(xtest2), axis=-1)\n",
    "print(\"CNN Model accuracy:\", metrics.accuracy_score(ytest2, predictions))\n",
    "print(metrics.classification_report(ytest,predictions, target_names=classes_dict.values()))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "print(f\"\\nTraining time: {stop - start} s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. EXPORT THE BEST MODEL (KNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1 - Export the KNN model using concrete functions of Tensorflow 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test 0, and the prediction is [3], the real value is 3\n",
      "Test 1, and the prediction is [1], the real value is 1\n",
      "Test 2, and the prediction is [0], the real value is 2\n",
      "Test 3, and the prediction is [1], the real value is 0\n",
      "Test 4, and the prediction is [3], the real value is 2\n",
      "Test 5, and the prediction is [1], the real value is 1\n",
      "Test 6, and the prediction is [1], the real value is 1\n",
      "Test 7, and the prediction is [2], the real value is 0\n",
      "Test 8, and the prediction is [3], the real value is 3\n",
      "Test 9, and the prediction is [0], the real value is 0\n",
      "Test 10, and the prediction is [1], the real value is 3\n",
      "Test 11, and the prediction is [0], the real value is 1\n",
      "Test 12, and the prediction is [0], the real value is 0\n",
      "Test 13, and the prediction is [1], the real value is 1\n",
      "Test 14, and the prediction is [2], the real value is 3\n",
      "Test 15, and the prediction is [0], the real value is 0\n",
      "Test 16, and the prediction is [1], the real value is 1\n",
      "Test 17, and the prediction is [0], the real value is 0\n",
      "Test 18, and the prediction is [1], the real value is 1\n",
      "Test 19, and the prediction is [3], the real value is 3\n",
      "Test 20, and the prediction is [2], the real value is 2\n",
      "Test 21, and the prediction is [3], the real value is 3\n",
      "Test 22, and the prediction is [3], the real value is 3\n",
      "Test 23, and the prediction is [0], the real value is 0\n",
      "Test 24, and the prediction is [3], the real value is 3\n",
      "Test 25, and the prediction is [2], the real value is 2\n",
      "Test 26, and the prediction is [2], the real value is 2\n",
      "Test 27, and the prediction is [1], the real value is 0\n",
      "Test 28, and the prediction is [1], the real value is 1\n",
      "Test 29, and the prediction is [2], the real value is 2\n",
      "Test 30, and the prediction is [0], the real value is 1\n",
      "Test 31, and the prediction is [1], the real value is 0\n",
      "Test 32, and the prediction is [0], the real value is 0\n",
      "Test 33, and the prediction is [3], the real value is 3\n",
      "Test 34, and the prediction is [2], the real value is 2\n",
      "Test 35, and the prediction is [0], the real value is 0\n",
      "Test 36, and the prediction is [3], the real value is 3\n",
      "Test 37, and the prediction is [2], the real value is 2\n",
      "Test 38, and the prediction is [0], the real value is 1\n",
      "Test 39, and the prediction is [2], the real value is 2\n",
      "Test 40, and the prediction is [3], the real value is 3\n",
      "Test 41, and the prediction is [0], the real value is 3\n",
      "Test 42, and the prediction is [3], the real value is 3\n",
      "Test 43, and the prediction is [0], the real value is 0\n",
      "Test 44, and the prediction is [2], the real value is 2\n",
      "Test 45, and the prediction is [0], the real value is 0\n",
      "Test 46, and the prediction is [1], the real value is 1\n",
      "Test 47, and the prediction is [1], the real value is 1\n",
      "Test 48, and the prediction is [2], the real value is 2\n",
      "Test 49, and the prediction is [0], the real value is 0\n",
      "Test 50, and the prediction is [0], the real value is 0\n",
      "Test 51, and the prediction is [0], the real value is 0\n",
      "Test 52, and the prediction is [2], the real value is 2\n",
      "Test 53, and the prediction is [3], the real value is 3\n",
      "Test 54, and the prediction is [2], the real value is 2\n",
      "Test 55, and the prediction is [1], the real value is 1\n",
      "Test 56, and the prediction is [0], the real value is 0\n",
      "TF-KNN score: 0.7894736842105272\n",
      "tf.Tensor([3], shape=(1,), dtype=int64)\n",
      "\n",
      "Training time: 0.23437786102294922 s\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "class KNN(tf.Module):\n",
    "    def __init__(self, n_neighbors):\n",
    "        self.n_neighbors = n_neighbors; self.xtr = None; self.ytr = None\n",
    "        \n",
    "    def fit(self, x, y):\n",
    "        self.xtr = np.float32(x); self.ytr = y\n",
    "        \n",
    "    def score(self, xte, yte):\n",
    "        accuracy = 0.0\n",
    "        for i in range (len(xte)):\n",
    "            prediction = self.predict(xte[i])\n",
    "            print(\"Test %d, and the prediction is %s, the real value is %s\"%(i, prediction.numpy(), yte[i]))\n",
    "            if prediction == yte[i]:\n",
    "                accuracy += 1. / len(xte)\n",
    "        return accuracy\n",
    "    \n",
    "    @tf.function\n",
    "    def predict(self, value):\n",
    "        value = tf.cast(value, tf.float32)\n",
    "        # MANHATTAN distances\n",
    "        distances = tf.abs(tf.reduce_sum(tf.square(tf.subtract(self.xtr, value)), axis=1))\n",
    "        # The top k values and their indices. We multiply the distance by -1 to reverse the magnitude of distances,so the largest distance becomes the smallest distance.\n",
    "        knn_values, knn_indices = tf.nn.top_k(tf.scalar_mul(-1,distances),k=self.n_neighbors)\n",
    "        # We locate the exact class label set of the k closest matches in the training data\n",
    "        best_training_labels = tf.gather(self.ytr,knn_indices)\n",
    "        # We make our prediction based on the class label that appears most frequently\n",
    "        if self.n_neighbors==1:\n",
    "            prediction = tf.squeeze([best_training_labels])\n",
    "        else:\n",
    "            values, indices, counts = tf.unique_with_counts(best_training_labels)\n",
    "            max_count_index = tf.argmax(counts,0)\n",
    "            prediction = tf.gather(values, max_count_index)\n",
    "        return best_training_labels\n",
    "\n",
    "knn_model = KNN(1)\n",
    "start = time.time()\n",
    "knn_model.fit(xtrainNorm, ytrain)\n",
    "print('TF-KNN score:', knn_model.score(xtestNorm, ytest))\n",
    "stop = time.time()\n",
    "print(knn_model.predict(xtestNorm[0]))\n",
    "\n",
    "concrete_func = knn_model.predict.get_concrete_function(tf.TensorSpec(shape=[16], dtype=tf.float32))\n",
    "# Convert the model\n",
    "converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])\n",
    "tflite_model = converter.convert()\n",
    "\n",
    "# Save the model.\n",
    "with open(tf_model1, 'wb') as f:\n",
    "    f.write(tflite_model)\n",
    "    \n",
    "print(f\"\\nTraining time: {stop - start} s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2 - Export the KNN model using session graph of Tensorflow 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test 0, and the prediction is 3, the real value is 3\n",
      "Test 1, and the prediction is 1, the real value is 1\n",
      "Test 2, and the prediction is 0, the real value is 2\n",
      "Test 3, and the prediction is 1, the real value is 0\n",
      "Test 4, and the prediction is 3, the real value is 2\n",
      "Test 5, and the prediction is 1, the real value is 1\n",
      "Test 6, and the prediction is 1, the real value is 1\n",
      "Test 7, and the prediction is 2, the real value is 0\n",
      "Test 8, and the prediction is 3, the real value is 3\n",
      "Test 9, and the prediction is 0, the real value is 0\n",
      "Test 10, and the prediction is 1, the real value is 3\n",
      "Test 11, and the prediction is 0, the real value is 1\n",
      "Test 12, and the prediction is 0, the real value is 0\n",
      "Test 13, and the prediction is 1, the real value is 1\n",
      "Test 14, and the prediction is 2, the real value is 3\n",
      "Test 15, and the prediction is 0, the real value is 0\n",
      "Test 16, and the prediction is 1, the real value is 1\n",
      "Test 17, and the prediction is 0, the real value is 0\n",
      "Test 18, and the prediction is 1, the real value is 1\n",
      "Test 19, and the prediction is 3, the real value is 3\n",
      "Test 20, and the prediction is 2, the real value is 2\n",
      "Test 21, and the prediction is 3, the real value is 3\n",
      "Test 22, and the prediction is 3, the real value is 3\n",
      "Test 23, and the prediction is 0, the real value is 0\n",
      "Test 24, and the prediction is 3, the real value is 3\n",
      "Test 25, and the prediction is 2, the real value is 2\n",
      "Test 26, and the prediction is 2, the real value is 2\n",
      "Test 27, and the prediction is 1, the real value is 0\n",
      "Test 28, and the prediction is 1, the real value is 1\n",
      "Test 29, and the prediction is 2, the real value is 2\n",
      "Test 30, and the prediction is 0, the real value is 1\n",
      "Test 31, and the prediction is 1, the real value is 0\n",
      "Test 32, and the prediction is 0, the real value is 0\n",
      "Test 33, and the prediction is 3, the real value is 3\n",
      "Test 34, and the prediction is 2, the real value is 2\n",
      "Test 35, and the prediction is 0, the real value is 0\n",
      "Test 36, and the prediction is 3, the real value is 3\n",
      "Test 37, and the prediction is 2, the real value is 2\n",
      "Test 38, and the prediction is 0, the real value is 1\n",
      "Test 39, and the prediction is 2, the real value is 2\n",
      "Test 40, and the prediction is 3, the real value is 3\n",
      "Test 41, and the prediction is 0, the real value is 3\n",
      "Test 42, and the prediction is 3, the real value is 3\n",
      "Test 43, and the prediction is 0, the real value is 0\n",
      "Test 44, and the prediction is 2, the real value is 2\n",
      "Test 45, and the prediction is 0, the real value is 0\n",
      "Test 46, and the prediction is 1, the real value is 1\n",
      "Test 47, and the prediction is 1, the real value is 1\n",
      "Test 48, and the prediction is 2, the real value is 2\n",
      "Test 49, and the prediction is 0, the real value is 0\n",
      "Test 50, and the prediction is 0, the real value is 0\n",
      "Test 51, and the prediction is 0, the real value is 0\n",
      "Test 52, and the prediction is 2, the real value is 2\n",
      "Test 53, and the prediction is 3, the real value is 3\n",
      "Test 54, and the prediction is 2, the real value is 2\n",
      "Test 55, and the prediction is 1, the real value is 1\n",
      "Test 56, and the prediction is 0, the real value is 0\n",
      "TF KNN Accuracy ->  78.94736842105272  %\n",
      "\n",
      "Training time: 0.0407109260559082 s\n"
     ]
    }
   ],
   "source": [
    "# TF KNN\n",
    "# Resource: https://stackoverflow.com/questions/45787046/tensorflow-knn-how-can-we-assign-the-k-parameter-for-defining-number-of-neighb\n",
    "\n",
    "import tensorflow.compat.v1 as tf1\n",
    "tf1.disable_eager_execution()\n",
    "\n",
    "#tf.float64\n",
    "knn_size=1\n",
    "colors = tf1.placeholder(dtype=tf1.float32, shape=[len(xtrainNorm[0])])\n",
    "# MANHATTAN distances\n",
    "distances = tf1.abs(tf1.reduce_sum(tf1.square(tf1.subtract(np.float32(xtrainNorm), colors)),axis=1))\n",
    "# The top k values and their indices. We multiply the distance by -1 to reverse the magnitude of distances,so the largest distance becomes the smallest distance.\n",
    "knn_values, knn_indices = tf1.nn.top_k(tf.scalar_mul(-1,distances),k=knn_size)\n",
    "# We locate the exact class label set of the k closest matches in the training data\n",
    "best_training_labels = tf1.gather(ytrain,knn_indices)\n",
    "\n",
    "# We make our prediction based on the class label that appears most frequently\n",
    "if knn_size==1:\n",
    "    prediction = tf1.squeeze(best_training_labels)\n",
    "else:\n",
    "    values, indices, counts = tf1.unique_with_counts(best_training_labels)\n",
    "    max_count_index = tf1.argmax(counts,0)\n",
    "    prediction = tf1.gather(values, max_count_index)\n",
    "\n",
    "# Convert to TF Lite format\n",
    "with tf1.Session() as sess:\n",
    "    start = time.time()\n",
    "    accuracy = 0.0\n",
    "    for i in range (len(xtestNorm)):\n",
    "        prediction_value = sess.run([prediction], feed_dict={colors:xtestNorm[i]})\n",
    "        print(\"Test %d, and the prediction is %s, the real value is %s\"%(i,prediction_value[0],ytest[i]))\n",
    "        if prediction_value[0] == ytest[i]:\n",
    "            accuracy += 1. / len(ytest)\n",
    "    print('TF KNN Accuracy -> ', accuracy * 100, ' %')\n",
    "    stop = time.time()\n",
    "    converter = tf1.lite.TFLiteConverter.from_session(sess, [colors], [best_training_labels])\n",
    "    tflite_model = converter.convert()\n",
    "\n",
    "# Save the model.\n",
    "with open(tf_model2, 'wb') as f:\n",
    "    f.write(tflite_model)\n",
    "    \n",
    "print(f\"\\nTraining time: {stop - start} s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. MAKE NEW PREDICTIONS WITH THE EXPORTED MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1 - Test the KNN exported model trained with the dominant colors of granite image 10714J_P2.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: [16] - Output shape: [1]\n",
      "\n",
      "For data [-0.20098896 -0.3526301  -0.32087895  0.54608387 -0.05820736 -0.3274255\n",
      " -0.3093959   0.3218976   0.06201478 -0.35897973 -0.35069126  0.17449032\n",
      " -0.09132059 -0.473368   -0.46893236  0.8213348 ] \n",
      "Result is [0] which is Diorite\n",
      "\n",
      "Execution time: 302.29783058166504 ms\n"
     ]
    }
   ],
   "source": [
    "from skimage import color\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "MAX_SIZE = (30, 30)\n",
    "CLUSTERS = 4\n",
    "\n",
    "def get_dominant_colors(img):\n",
    "    reshape = img.reshape((img.shape[0] * img.shape[1], img.shape[2]))\n",
    "    cluster = KMeans(n_clusters=CLUSTERS).fit(reshape)\n",
    "    lb = np.arange(0, len(np.unique(cluster.labels_))+1) # Get the number of different clusters\n",
    "    (hist, _) = np.histogram(cluster.labels_, bins = lb) # create histogram\n",
    "    hist = hist.astype(\"float\"); hist /= hist.sum() # normalize\n",
    "    colors = sorted([(percent, color) for (percent, color) in zip(hist, cluster.cluster_centers_)])\n",
    "    features = []\n",
    "    for (percent, color) in colors:\n",
    "        features.extend(color)\n",
    "        features.append(percent)\n",
    "    return features\n",
    "\n",
    "# Load the TFLite model and allocate tensors.\n",
    "interpreter = tf.lite.Interpreter(model_path=\"./Models/colors_model.tflite\")\n",
    "interpreter.allocate_tensors()\n",
    "\n",
    "# Get input and output tensors.\n",
    "input_details = interpreter.get_input_details()\n",
    "output_details = interpreter.get_output_details()\n",
    "\n",
    "print('Input shape:', input_details[0]['shape'], '- Output shape:', output_details[0]['shape'])\n",
    "input_size = input_details[0]['shape'][0]\n",
    "\n",
    "# Test the model on random input data.\n",
    "start = time.time()\n",
    "img = cv2.imread(\"./Rock-image-patches/Granite/10714J_P2.jpg\")\n",
    "img = cv2.resize(img, MAX_SIZE)\n",
    "img = color.rgb2lab(img)\n",
    "colors = get_dominant_colors(img)\n",
    "colorsNorm = [colors[i]*normalizer.scale_[i]+normalizer.min_[i] for i in range(input_size)]\n",
    "input_data = np.float32(colorsNorm) #input_data = np.float32(xtestNorm[0])\n",
    "interpreter.set_tensor(input_details[0]['index'], input_data)\n",
    "\n",
    "interpreter.invoke()\n",
    "stop = time.time()\n",
    "\n",
    "# The function `get_tensor()` returns a copy of the tensor data.\n",
    "# Use `tensor()` in order to get a pointer to the tensor.\n",
    "output_data = interpreter.get_tensor(output_details[0]['index'])\n",
    "print('\\nFor data', input_data,\n",
    "      '\\nResult is', output_data, 'which is', classes_dict[output_data[0]])\n",
    "\n",
    "print(f\"\\nExecution time: {(stop - start)*1000} ms\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.2 - Test the CNN exported model of our previous work trained with all image features of granite image 10714J_P2.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: [  1 100 100   3] - Output shape: [1 4]\n",
      "\n",
      "For data [126. 144. 145.] ... with shape (1, 100, 100, 3) \n",
      "output data is [[0. 0. 0. 1.]] \n",
      "Result is 1.0 which is Gabbro\n",
      "\n",
      "Execution time: 336.12704277038574 ms\n"
     ]
    }
   ],
   "source": [
    "# Load the TFLite model and allocate tensors.\n",
    "interpreter = tf.lite.Interpreter(model_path=\"./Models/rock_model.tflite\")\n",
    "interpreter.allocate_tensors()\n",
    "\n",
    "# Get input and output tensors.\n",
    "input_details = interpreter.get_input_details()\n",
    "output_details = interpreter.get_output_details()\n",
    "\n",
    "print('Input shape:', input_details[0]['shape'], '- Output shape:', output_details[0]['shape'])\n",
    "input_size = tuple(input_details[0]['shape'][1:3])\n",
    "\n",
    "start = time.time()\n",
    "img = cv2.imread(\"./Rock-image-patches/Granite/10714J_P2.jpg\");\n",
    "img = cv2.resize(img, input_size)\n",
    "input_data = np.float32([img])\n",
    "interpreter.set_tensor(input_details[0]['index'], input_data)\n",
    "\n",
    "interpreter.invoke()\n",
    "stop = time.time()\n",
    "\n",
    "# The function `get_tensor()` returns a copy of the tensor data.\n",
    "# Use `tensor()` in order to get a pointer to the tensor.\n",
    "output_data = interpreter.get_tensor(output_details[0]['index'])\n",
    "print('\\nFor data', input_data[0][0][0],\"... with shape\", input_data.shape,\n",
    "      '\\noutput data is', output_data,\n",
    "      '\\nResult is', max(output_data[0]), 'which is', classes_dict[max(output_data[0])])\n",
    "\n",
    "print(f\"\\nExecution time: {(stop - start)*1000} ms\")"
   ]
  }
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