{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "461dee1b-62d6-43fc-bbef-42db7566dcbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: kagglehub in c:\\users\\singh\\anaconda3\\lib\\site-packages (0.3.4)\n",
      "Requirement already satisfied: packaging in c:\\users\\singh\\anaconda3\\lib\\site-packages (from kagglehub) (23.1)\n",
      "Requirement already satisfied: requests in c:\\users\\singh\\anaconda3\\lib\\site-packages (from kagglehub) (2.31.0)\n",
      "Requirement already satisfied: tqdm in c:\\users\\singh\\anaconda3\\lib\\site-packages (from kagglehub) (4.65.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\singh\\anaconda3\\lib\\site-packages (from requests->kagglehub) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\singh\\anaconda3\\lib\\site-packages (from requests->kagglehub) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\singh\\anaconda3\\lib\\site-packages (from requests->kagglehub) (2.0.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\singh\\anaconda3\\lib\\site-packages (from requests->kagglehub) (2024.2.2)\n",
      "Requirement already satisfied: colorama in c:\\users\\singh\\anaconda3\\lib\\site-packages (from tqdm->kagglehub) (0.4.6)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install kagglehub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c3c7d28b-6d74-42d4-9d78-9f964e4b77ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import kagglehub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "81c6b0c6-86ba-47b5-bc64-36cb1d679f2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Looks like you're using an outdated `kagglehub` version, please consider updating (latest version: 0.3.6)\n"
     ]
    }
   ],
   "source": [
    "path=kagglehub.dataset_download(\"rahmasleam/flowers-dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "691fb5cf-aeb1-4fe1-ac65-a0de6b1a66a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Singh\\\\.cache\\\\kagglehub\\\\datasets\\\\rahmasleam\\\\flowers-dataset\\\\versions\\\\1'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a7bd6c4b-a1f2-49fe-9d3b-78fe019bdd27",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c9b20532-6f16-424d-9c21-c5aa51b538dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "folders=os.listdir(r\"C:\\Users\\Singh\\Downloads\\flower_photos\") # List all folders in the \"flower_photos\" directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bcd17c4d-bb28-445b-bb20-d6c6c7e082bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a8bb7124-8d25-4ab2-b9f7-efc04eda887f",
   "metadata": {},
   "outputs": [],
   "source": [
    "features=[]\n",
    "class_labels=[]\n",
    "for folder in folders:\n",
    "    for images in os.listdir(r\"C:\\Users\\Singh\\Downloads\\flower_photos\\{}\".format(folder)):\n",
    "        img=cv2.imread(r\"C:\\Users\\Singh\\Downloads\\flower_photos\\{}\\{}\".format(folder,images),0)# array repress\n",
    "        img=cv2.resize(img,(50,50))#resizing the image\n",
    "        img=img.flatten()#flattening  the image\n",
    "        features.append(img)# appending each flatten image in list\n",
    "        class_labels.append(folder)#appending each class label to list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cc21daf6-98bc-4c23-aff3-c25f66b559c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "633"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(r\"C:\\Users\\Singh\\Downloads\\flower_photos\\daisy\")) # Count the number of images in the \"daisy\" folder inside \"flower_photos.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cfe72f36-3a96-43ef-a7c8-68b9fa5a5ef5",
   "metadata": {},
   "outputs": [],
   "source": [
    "img1=cv2.imread(r\"C:\\Users\\Singh\\Downloads\\flower_photos\\daisy\\5547758_eea9edfd54_n.jpg\",0) # Load an image from the \"daisy\" folder in grayscale mode (0 indicates grayscale)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "72e3d4b8-dd96-432a-a88a-48edccb7d999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[112, 106, 102, ...,  48,  50,  52],\n",
       "       [128, 121, 117, ...,  49,  50,  52],\n",
       "       [136, 129, 124, ...,  51,  51,  53],\n",
       "       ...,\n",
       "       [250, 251, 252, ...,  21,  20,  19],\n",
       "       [248, 250, 250, ...,  23,  22,  21],\n",
       "       [251, 252, 249, ...,  25,  25,  24]], dtype=uint8)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "261e6f19-30bc-4e15-9134-3debdcb235f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(232, 320)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img1.shape # Get the shape (dimensions) of the grayscale image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9227807f-3552-472e-90c1-7ff1a68c07cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3670"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(features) # Get the number of features collected so far (assuming `features` is a list or similar)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "13569c16-3939-4617-bbe8-e69f153cb2fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3670"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(class_labels) # Get the number of class labels collected so far (assuming `class_labels` is a list or similar)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3615aff7-79ad-472f-86f6-2dbb19db6c87",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "526870d5-168a-4dff-b8e1-dbf509ad5cf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data=pd.DataFrame(features) # Convert the `features` list or array into a pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "01fb54b7-cbe8-43e6-81e7-40247b42d522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3670, 2500)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4b914cdc-da32-4e76-bc5f-41f1899d6320",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c8a7ad09-21d0-47c4-b553-00e95bdfc8b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data=final_data.astype(np.uint8) # Convert all data in the DataFrame to `uint8` data type for storage efficiency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "41441478-1e01-4799-aa77-ca3c51c6be91",
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
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       "</div>"
      ],
      "text/plain": [
       "      0     1     2     3     4     5     6     7     8     9     ...  2490  \\\n",
       "0      143   149   160   169   167   167   145   144   144   159  ...   171   \n",
       "1      224   222   216   232   228    75    85    80   182   185  ...   120   \n",
       "2      108    81    97    84   102   120   108   106   140   128  ...    38   \n",
       "3       26    25    26    29    32    35    37    38    38    40  ...    18   \n",
       "4       20    21    36    45    46    45    40    42    37    56  ...    26   \n",
       "...    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   ...   \n",
       "3665   212   189   186   212    47    82   116   131    82    79  ...    83   \n",
       "3666   135   126   129   131   119   141   135   128   132   136  ...    48   \n",
       "3667    88    72    78    97    57    77    91    79    46    77  ...    83   \n",
       "3668   157   189   169   132   174   162   185   174   122   160  ...    57   \n",
       "3669    49    62   118   122    75   164   127    98    74    78  ...    98   \n",
       "\n",
       "      2491  2492  2493  2494  2495  2496  2497  2498  2499  \n",
       "0      174   168   149   134   128   137   137   131   127  \n",
       "1      116   107   110   108   156   169   141   125   172  \n",
       "2       44    27    26    26    27    27    31    36    40  \n",
       "3       16    15    16    21    18    24    16    20    23  \n",
       "4       42    48    42    26    25    31    39    27    35  \n",
       "...    ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "3665   103   114    40     7    30    30    86   194   215  \n",
       "3666    53    60    58    54    41    48    84   132   120  \n",
       "3667    71    81    89    91    91    87   103    80    80  \n",
       "3668   152   193    74    23    18     8    12    19    45  \n",
       "3669   146    80    45    63    47    91    56    76    57  \n",
       "\n",
       "[3670 rows x 2500 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b1f089c9-df2f-4d7c-b3ba-a3ef75d22256",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_data[\"class_labels\"]=class_labels # Add a new column \"class_labels\" to the DataFrame with the collected class labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "583cecc9-cb99-4bc9-93e5-ab67de226bd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>35</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
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       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
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       "      <td>daisy</td>\n",
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       "      <th>4</th>\n",
       "      <td>20</td>\n",
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       "      <td>42</td>\n",
       "      <td>26</td>\n",
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       "      <td>31</td>\n",
       "      <td>39</td>\n",
       "      <td>27</td>\n",
       "      <td>35</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>3665</th>\n",
       "      <td>212</td>\n",
       "      <td>189</td>\n",
       "      <td>186</td>\n",
       "      <td>212</td>\n",
       "      <td>47</td>\n",
       "      <td>82</td>\n",
       "      <td>116</td>\n",
       "      <td>131</td>\n",
       "      <td>82</td>\n",
       "      <td>79</td>\n",
       "      <td>...</td>\n",
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       "      <td>114</td>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>86</td>\n",
       "      <td>194</td>\n",
       "      <td>215</td>\n",
       "      <td>tulips</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3666</th>\n",
       "      <td>135</td>\n",
       "      <td>126</td>\n",
       "      <td>129</td>\n",
       "      <td>131</td>\n",
       "      <td>119</td>\n",
       "      <td>141</td>\n",
       "      <td>135</td>\n",
       "      <td>128</td>\n",
       "      <td>132</td>\n",
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       "      <td>...</td>\n",
       "      <td>53</td>\n",
       "      <td>60</td>\n",
       "      <td>58</td>\n",
       "      <td>54</td>\n",
       "      <td>41</td>\n",
       "      <td>48</td>\n",
       "      <td>84</td>\n",
       "      <td>132</td>\n",
       "      <td>120</td>\n",
       "      <td>tulips</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3667</th>\n",
       "      <td>88</td>\n",
       "      <td>72</td>\n",
       "      <td>78</td>\n",
       "      <td>97</td>\n",
       "      <td>57</td>\n",
       "      <td>77</td>\n",
       "      <td>91</td>\n",
       "      <td>79</td>\n",
       "      <td>46</td>\n",
       "      <td>77</td>\n",
       "      <td>...</td>\n",
       "      <td>71</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>87</td>\n",
       "      <td>103</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>tulips</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3668</th>\n",
       "      <td>157</td>\n",
       "      <td>189</td>\n",
       "      <td>169</td>\n",
       "      <td>132</td>\n",
       "      <td>174</td>\n",
       "      <td>162</td>\n",
       "      <td>185</td>\n",
       "      <td>174</td>\n",
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       "      <td>160</td>\n",
       "      <td>...</td>\n",
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       "      <td>74</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>45</td>\n",
       "      <td>tulips</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3669</th>\n",
       "      <td>49</td>\n",
       "      <td>62</td>\n",
       "      <td>118</td>\n",
       "      <td>122</td>\n",
       "      <td>75</td>\n",
       "      <td>164</td>\n",
       "      <td>127</td>\n",
       "      <td>98</td>\n",
       "      <td>74</td>\n",
       "      <td>78</td>\n",
       "      <td>...</td>\n",
       "      <td>146</td>\n",
       "      <td>80</td>\n",
       "      <td>45</td>\n",
       "      <td>63</td>\n",
       "      <td>47</td>\n",
       "      <td>91</td>\n",
       "      <td>56</td>\n",
       "      <td>76</td>\n",
       "      <td>57</td>\n",
       "      <td>tulips</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3670 rows × 2501 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0    1    2    3    4    5    6    7    8    9  ...  2491  2492  2493  \\\n",
       "0     143  149  160  169  167  167  145  144  144  159  ...   174   168   149   \n",
       "1     224  222  216  232  228   75   85   80  182  185  ...   116   107   110   \n",
       "2     108   81   97   84  102  120  108  106  140  128  ...    44    27    26   \n",
       "3      26   25   26   29   32   35   37   38   38   40  ...    16    15    16   \n",
       "4      20   21   36   45   46   45   40   42   37   56  ...    42    48    42   \n",
       "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   ...   ...   ...   \n",
       "3665  212  189  186  212   47   82  116  131   82   79  ...   103   114    40   \n",
       "3666  135  126  129  131  119  141  135  128  132  136  ...    53    60    58   \n",
       "3667   88   72   78   97   57   77   91   79   46   77  ...    71    81    89   \n",
       "3668  157  189  169  132  174  162  185  174  122  160  ...   152   193    74   \n",
       "3669   49   62  118  122   75  164  127   98   74   78  ...   146    80    45   \n",
       "\n",
       "      2494  2495  2496  2497  2498  2499  class_labels  \n",
       "0      134   128   137   137   131   127         daisy  \n",
       "1      108   156   169   141   125   172         daisy  \n",
       "2       26    27    27    31    36    40         daisy  \n",
       "3       21    18    24    16    20    23         daisy  \n",
       "4       26    25    31    39    27    35         daisy  \n",
       "...    ...   ...   ...   ...   ...   ...           ...  \n",
       "3665     7    30    30    86   194   215        tulips  \n",
       "3666    54    41    48    84   132   120        tulips  \n",
       "3667    91    91    87   103    80    80        tulips  \n",
       "3668    23    18     8    12    19    45        tulips  \n",
       "3669    63    47    91    56    76    57        tulips  \n",
       "\n",
       "[3670 rows x 2501 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data # Display the entire DataFrame to examine its structure and data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4bd4960b-0677-40bd-8256-73ed2fb70114",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3670 entries, 0 to 3669\n",
      "Columns: 2501 entries, 0 to class_labels\n",
      "dtypes: object(1), uint8(2500)\n",
      "memory usage: 8.8+ MB\n"
     ]
    }
   ],
   "source": [
    "final_data.info() # Display detailed information about the DataFrame, including column types and memory usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b95f3573-f73a-468e-b12c-a605c93c6ded",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bab6163f-9907-4cce-9e21-c37eabb6b5a0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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