StepByStepSolverLogs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1b25438e-1200-47ec-99d7-7e90539db7cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from skimage.transform import resize\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage.io import imread\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os, pickle\n",
    "\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5215ab7-efef-409a-9e4c-be54a2306dca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 ->  3%\n",
      "3 ->  7%\n",
      "4 ->  10%\n",
      "5 ->  13%\n",
      "6 ->  17%\n",
      "7 ->  20%\n",
      "8 ->  23%\n",
      "9 ->  27%\n",
      "a ->  30%\n",
      "b ->  33%\n",
      "c ->  37%\n",
      "d ->  40%\n",
      "e ->  43%\n",
      "f ->  47%\n",
      "g ->  50%\n",
      "h ->  53%\n",
      "k ->  57%\n",
      "m ->  60%\n",
      "n ->  63%\n",
      "p ->  67%\n",
      "q ->  70%\n",
      "r ->  73%\n",
      "s ->  77%\n",
      "t ->  80%\n",
      "u ->  83%\n",
      "v ->  87%\n",
      "w ->  90%\n",
      "x ->  93%\n",
      "y ->  97%\n",
      "z ->  100%\n"
     ]
    }
   ],
   "source": [
    "target = []\n",
    "flat_data = []\n",
    "DataDirectory = './DataSet/'\n",
    "\n",
    "Categories = os.listdir(DataDirectory)\n",
    "\n",
    "for index, i in enumerate(Categories):\n",
    "    print(i, '-> ', str(round(100*(index + 1)/len(Categories))) + '%')\n",
    "    target_class = index\n",
    "    path = os.path.join(DataDirectory,i)\n",
    "    for img in os.listdir(path):\n",
    "        img_array = imread(os.path.join(path,img))\n",
    "        img_resized = resize(img_array,(40,40,3))\n",
    "        flat_data.append(img_resized.flatten())\n",
    "        target.append(target_class)\n",
    "\n",
    "flat_data = np.array(flat_data)\n",
    "target = np.array(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7bd8d760-36fb-4c55-a09b-9605f8ce953f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           0       1       2       3       4       5       6       7       8  \\\n",
      "0     0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
      "1     0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
      "2     0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
      "3     0.0625  0.0625  0.0625  0.0125  0.0125  0.0125  0.0875  0.0875  0.0875   \n",
      "4     0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
      "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
      "7130  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
      "7131  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
      "7132  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
      "7133  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
      "7134  0.3000  0.3000  0.3000  0.1000  0.1000  0.1000  0.5000  0.5000  0.5000   \n",
      "\n",
      "             9  ...    4791    4792    4793    4794    4795    4796    4797  \\\n",
      "0     0.000000  ...  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
      "1     0.000000  ...  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
      "2     0.000000  ...  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
      "3     0.162500  ...  1.0000  1.0000  1.0000  1.0000  1.0000  1.0000  1.0000   \n",
      "4     0.000000  ...  1.0000  1.0000  1.0000  1.0000  1.0000  1.0000  1.0000   \n",
      "...        ...  ...     ...     ...     ...     ...     ...     ...     ...   \n",
      "7130  0.000000  ...  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
      "7131  0.000000  ...  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
      "7132  0.000000  ...  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000   \n",
      "7133  0.015937  ...  0.2125  0.2125  0.2125  0.2125  0.2125  0.2125  0.2125   \n",
      "7134  0.900000  ...  1.0000  1.0000  1.0000  1.0000  1.0000  1.0000  1.0000   \n",
      "\n",
      "        4798    4799  Target  \n",
      "0     0.0000  0.0000       0  \n",
      "1     0.0000  0.0000       0  \n",
      "2     0.0000  0.0000       0  \n",
      "3     1.0000  1.0000       0  \n",
      "4     1.0000  1.0000       0  \n",
      "...      ...     ...     ...  \n",
      "7130  0.0000  0.0000      29  \n",
      "7131  0.0000  0.0000      29  \n",
      "7132  0.0000  0.0000      29  \n",
      "7133  0.2125  0.2125      29  \n",
      "7134  1.0000  1.0000      29  \n",
      "\n",
      "[7135 rows x 4801 columns]\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(flat_data)\n",
    "df['Target'] = target\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ea50c2d0-bac4-43d3-aad4-ac7b02e35b89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input data dimensions: (7135, 4800)\n",
      "Output data dimensions: (7135,)\n",
      "Dimensions of input training data: (5708, 4800)\n",
      "Dimensions of input testing data: (1427, 4800)\n",
      "Dimensions of output training data: (5708,)\n",
      "Dimensions of output testing data: (1427,)\n"
     ]
    }
   ],
   "source": [
    "x = df.iloc[:,:-1].values\n",
    "y = target\n",
    "\n",
    "print(\"Input data dimensions:\",x.shape)\n",
    "print(\"Output data dimensions:\",y.shape)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, shuffle = True, test_size = 0.2, random_state = 109, stratify = y)\n",
    "\n",
    "print(\"Dimensions of input training data:\",x_train.shape)\n",
    "print(\"Dimensions of input testing data:\",x_test.shape)\n",
    "print(\"Dimensions of output training data:\",y_train.shape)\n",
    "print(\"Dimensions of output testing data:\",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a056e551-0da6-44bb-a10e-be49cfaf6234",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3, metric = 'minkowski', p = 2)\n",
    "knn.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "50ef17cd-65d6-4961-82d1-1498ee1318bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'finalized_model.sav'\n",
    "# load the model from disk\n",
    "loaded_model = pickle.load(open(filename, 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e842b3e9-2944-4ed1-9c98-940c7d53a15a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2 15 23 ...  9 12 13]\n",
      "[ 2 15 23 ...  9 12 13]\n",
      "0.9677645409950946\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(x_test)\n",
    "print(y_test)\n",
    "print(y_pred)\n",
    "ac = accuracy_score(y_test,y_pred)\n",
    "print(ac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "23044f52-a885-453c-90cf-57568d571185",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sharp_img(img):\n",
    "    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "    plt.imshow(gray_img, cmap='gray')\n",
    "    plt.title('Grayscale Image')\n",
    "    plt.show()\n",
    "\n",
    "    blur_image = cv2.blur(gray_img, (4, 4))\n",
    "    plt.imshow(blur_image, cmap='gray')\n",
    "    plt.title('Blurred Image')\n",
    "    plt.show()\n",
    "\n",
    "    gblur_img = cv2.GaussianBlur(blur_image, (0, 0), 6)\n",
    "    plt.imshow(gblur_img, cmap='gray')\n",
    "    plt.title('Gaussian Blurred Image')\n",
    "    plt.show()\n",
    "\n",
    "    # Step 4: Sharpen the image\n",
    "    sharp_img = cv2.addWeighted(gray_img, 1.80, gblur_img, -0.60, 0)\n",
    "    plt.imshow(sharp_img, cmap='gray')\n",
    "    plt.title('Sharpened Image')\n",
    "    plt.show()\n",
    "\n",
    "    sharp_not_img = cv2.bitwise_not(sharp_img)\n",
    "    plt.imshow(sharp_not_img, cmap='gray')\n",
    "    plt.title('Inverted Image')\n",
    "    plt.show()\n",
    "\n",
    "    _, img_zeroone = cv2.threshold(sharp_not_img, 20, 255, cv2.THRESH_BINARY)\n",
    "    plt.imshow(img_zeroone, cmap='gray')\n",
    "    plt.title('Binary Image')\n",
    "    plt.show()\n",
    "\n",
    "    return img_zeroone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f86dde2c-4d4d-4268-b510-44dbfabc7f36",
   "metadata": {},
   "outputs": [],
   "source": [
    "def clear_img(img):\n",
    "    im = cv2.imread(\"mask.png\")\n",
    "    mask = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)\n",
    "    plt.imshow(mask, cmap='gray')\n",
    "    plt.title(\"Grayscale Mask\")\n",
    "    plt.show()\n",
    "\n",
    "    _, t_mask = cv2.threshold(mask, 70, 255, cv2.THRESH_BINARY)\n",
    "    plt.imshow(t_mask, cmap='gray')\n",
    "    plt.title(\"Binary Mask\")\n",
    "    plt.show()\n",
    "\n",
    "    masker = cv2.bitwise_not(t_mask)\n",
    "    plt.imshow(masker, cmap='gray')\n",
    "    plt.title(\"Inverted Mask\")\n",
    "    plt.show()\n",
    "\n",
    "    try:\n",
    "        img = cv2.bitwise_and(img, masker) \n",
    "    except:\n",
    "        pass\n",
    "    plt.imshow(img, cmap='gray')\n",
    "    plt.title(\"Masked Image\")\n",
    "    plt.show()\n",
    "\n",
    "    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2)) \n",
    "    opening_img = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) \n",
    "    plt.imshow(opening_img, cmap='gray')\n",
    "    plt.title(\"Morphologically Opened Image\")\n",
    "    plt.show()\n",
    "\n",
    "    return opening_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a40f298e-af26-45d0-a09d-680acc31693b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_image(image):\n",
    "    cv2.imshow(\"image\", image)\n",
    "    c = cv2.waitKey()\n",
    "    if c >= 0:\n",
    "        return -1\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "10d1b008-b5ad-4b7e-8ee8-9a791baf23b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def isContain(c, contours):\n",
    "    for item in contours:\n",
    "        xC, yC, wC, hC = cv2.boundingRect(c)\n",
    "        xItem, yItem, wItem, hItem = cv2.boundingRect(item)\n",
    "        if (xC - xItem) in range(6) and (yC - yItem) in range(6) and wC < wItem and hC < hItem:\n",
    "            return True\n",
    "        if wC < 5 or hC < 5:\n",
    "            return True\n",
    "    return False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "832cc175-42a1-4e8d-8635-a1b9162ca16f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bestContours(contours):\n",
    "\n",
    "    contoursDict = {}\n",
    "    for index, item in enumerate(contours):\n",
    "        if not isContain(item, contours):\n",
    "            contoursDict[index] = len(item)\n",
    "    sortedItems = sorted(contoursDict.items(), key=lambda x: x[1], reverse=True)[:5]\n",
    "    keys = [item[0] for item in sortedItems]\n",
    "\n",
    "    returnedTuple = []\n",
    "    for index, item in enumerate(contours):\n",
    "        if index in keys:\n",
    "            returnedTuple.append(item)\n",
    "    return tuple(returnedTuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "75487d14-f2f7-4d41-ab10-871c9f9aeb58",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getWords(INPUT_IMAGE, OUTPUT=None):\n",
    "\n",
    "    im = cv2.imread(INPUT_IMAGE)\n",
    "    sim = sharp_img(im)\n",
    "\n",
    "\n",
    "    cl_img = clear_img(sim)\n",
    "    contours, hierarchy = cv2.findContours(cl_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
    "    output_image = cv2.cvtColor(cl_img, cv2.COLOR_GRAY2BGR)\n",
    "\n",
    "    # Draw contours\n",
    "    cv2.drawContours(output_image, contours, -1, (0, 255, 0), 2)\n",
    "    \n",
    "    cv2.imshow(\"Contours\", output_image)\n",
    "    cv2.waitKey(0)\n",
    "    cv2.destroyAllWindows()\n",
    "    contours = bestContours(contours)\n",
    "\n",
    "    indices = {}\n",
    "    clusters = []\n",
    "    for index, c in enumerate(contours):\n",
    "        x, y, w, h = cv2.boundingRect(c)\n",
    "        ROI = cl_img[y : y + h, x : x + w] #crop\n",
    "        indices[index] = x\n",
    "        clusters.append(ROI)\n",
    "    sortedItems = sorted(indices.items(), key=lambda x: x[1])\n",
    "    cluster = [clusters[item[0]] for item in sortedItems]\n",
    "    return cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ff9738c2-f86d-4d33-9543-3aabe887c565",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getCaptchaText(Captcha):\n",
    "    plt.imshow(plt.imread(Captcha))\n",
    "    plt.show()\n",
    "    clusters = getWords(Captcha)\n",
    "    text = ''\n",
    "\n",
    "    # Create a figure with subplots to display all images together\n",
    "    n_clusters = len(clusters)\n",
    "    cols = 5  # Number of columns in the grid\n",
    "    rows = (n_clusters + cols - 1) // cols  # Calculate rows needed\n",
    "\n",
    "    fig, axes = plt.subplots(rows, cols, figsize=(cols * 1, rows * 1))\n",
    "    axes = axes.flatten()  # Flatten to easily iterate over axes\n",
    "\n",
    "    for i, image in enumerate(clusters):\n",
    "        flat_data = []\n",
    "        img_resized = resize(image, (40, 40, 3))\n",
    "        flat_data.append(img_resized.flatten())\n",
    "        flat_data = np.array(flat_data)\n",
    "        y_output = loaded_model.predict(flat_data)\n",
    "        \n",
    "        axes[i].imshow(img_resized, cmap='gray')\n",
    "        axes[i].set_title(Categories[y_output[0]].replace('upper', '').replace('lower', ''))\n",
    "        axes[i].axis('off')  # Remove axes for a cleaner look\n",
    "        text += Categories[y_output[0]].replace('upper', '').replace('lower', '')\n",
    "\n",
    "    # Hide any unused subplots\n",
    "    for j in range(len(clusters), len(axes)):\n",
    "        axes[j].axis('off')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()  # Show all images in one figure\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0a373294-af1d-4d5c-b06a-5a51d22b5d44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x100 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Text Is: szxne\n"
     ]
    }
   ],
   "source": [
    "Captcha = './Golestan-Captchas/ml.gif'\n",
    "text = getCaptchaText(Captcha)\n",
    "print(\"Predicted Text Is:\", text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9faba16-71c0-44d4-8f7c-724b1c2309dc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}