image2csv.ipynb

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   "outputs": [],
   "source": [
    "#import libraries\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8f8bc5d9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-16T15:17:26.665036Z",
     "start_time": "2023-05-16T15:17:26.660864Z"
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   },
   "outputs": [],
   "source": [
    "path = '/home/tapu/Visie ML/Codes/Images'\n",
    "#for root, dirs, files in os.walk(path):\n",
    "    #print(root)    #file and folders in root dir\n",
    "    #print(dirs)    #sub dirs\n",
    "    #print(files)    #files in dirs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1d259a49",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-16T15:34:07.087274Z",
     "start_time": "2023-05-16T15:34:07.081823Z"
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   },
   "outputs": [],
   "source": [
    "img_dict = dict()\n",
    "img_name = []\n",
    "folder_name = []\n",
    "for root, dirs, files in os.walk(path):\n",
    "    for file in files:\n",
    "        img_path = os.path.join(root, file)\n",
    "        img_name.append(os.path.basename(img_path))\n",
    "        folder_name.append(os.path.basename(os.path.dirname(img_path)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "853ef4b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-16T15:34:53.871441Z",
     "start_time": "2023-05-16T15:34:53.866147Z"
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   "outputs": [
    {
     "data": {
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       "15"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "len(img_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "238da286",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-16T15:34:44.832070Z",
     "start_time": "2023-05-16T15:34:44.827357Z"
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   },
   "outputs": [
    {
     "data": {
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       "15"
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     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "len(folder_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8ee2a9bc",
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     "end_time": "2023-05-16T15:37:56.207599Z",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image name': ['m0.jpeg',\n",
       "  'm4.jpeg',\n",
       "  'm3.jpeg',\n",
       "  'm2.jpeg',\n",
       "  'm1.jpeg',\n",
       "  'b3.jpeg',\n",
       "  'b2.jpeg',\n",
       "  'b0.jpeg',\n",
       "  'b4.jpeg',\n",
       "  'b1.jpeg',\n",
       "  'c1.jpeg',\n",
       "  'c3.jpeg',\n",
       "  'c4.jpeg',\n",
       "  'c2.jpeg',\n",
       "  'c0.jpeg'],\n",
       " 'folder name': ['Mango',\n",
       "  'Mango',\n",
       "  'Mango',\n",
       "  'Mango',\n",
       "  'Mango',\n",
       "  'Bike',\n",
       "  'Bike',\n",
       "  'Bike',\n",
       "  'Bike',\n",
       "  'Bike',\n",
       "  'Car',\n",
       "  'Car',\n",
       "  'Car',\n",
       "  'Car',\n",
       "  'Car']}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_dict['image name'] = img_name\n",
    "img_dict['folder name'] = folder_name\n",
    "img_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "05da745a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-16T15:38:58.176343Z",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image name</th>\n",
       "      <th>folder name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>m0.jpeg</td>\n",
       "      <td>Mango</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Mango</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>m3.jpeg</td>\n",
       "      <td>Mango</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>m2.jpeg</td>\n",
       "      <td>Mango</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>m1.jpeg</td>\n",
       "      <td>Mango</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b3.jpeg</td>\n",
       "      <td>Bike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b2.jpeg</td>\n",
       "      <td>Bike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>b0.jpeg</td>\n",
       "      <td>Bike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>b4.jpeg</td>\n",
       "      <td>Bike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>b1.jpeg</td>\n",
       "      <td>Bike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>c1.jpeg</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>c3.jpeg</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>c4.jpeg</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>c2.jpeg</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>c0.jpeg</td>\n",
       "      <td>Car</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   image name folder name\n",
       "0     m0.jpeg       Mango\n",
       "1     m4.jpeg       Mango\n",
       "2     m3.jpeg       Mango\n",
       "3     m2.jpeg       Mango\n",
       "4     m1.jpeg       Mango\n",
       "5     b3.jpeg        Bike\n",
       "6     b2.jpeg        Bike\n",
       "7     b0.jpeg        Bike\n",
       "8     b4.jpeg        Bike\n",
       "9     b1.jpeg        Bike\n",
       "10    c1.jpeg         Car\n",
       "11    c3.jpeg         Car\n",
       "12    c4.jpeg         Car\n",
       "13    c2.jpeg         Car\n",
       "14    c0.jpeg         Car"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_df = pd.DataFrame(img_dict)\n",
    "img_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ac928a2b",
   "metadata": {
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     "start_time": "2023-05-16T15:53:17.915905Z"
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   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cec7f901",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-16T15:57:45.458955Z",
     "start_time": "2023-05-16T15:57:45.452089Z"
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   },
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()\n",
    "img_df['Label'] = encoder.fit_transform(img_df['folder name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9f9acd07",
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     "end_time": "2023-05-16T15:57:49.730187Z",
     "start_time": "2023-05-16T15:57:49.722700Z"
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image name</th>\n",
       "      <th>folder name</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>m3.jpeg</td>\n",
       "      <td>Mango</td>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>m2.jpeg</td>\n",
       "      <td>Mango</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>b4.jpeg</td>\n",
       "      <td>Bike</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>b1.jpeg</td>\n",
       "      <td>Bike</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>c1.jpeg</td>\n",
       "      <td>Car</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>c3.jpeg</td>\n",
       "      <td>Car</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>c4.jpeg</td>\n",
       "      <td>Car</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>c2.jpeg</td>\n",
       "      <td>Car</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>c0.jpeg</td>\n",
       "      <td>Car</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   image name folder name  Label\n",
       "0     m0.jpeg       Mango      2\n",
       "1     m4.jpeg       Mango      2\n",
       "2     m3.jpeg       Mango      2\n",
       "3     m2.jpeg       Mango      2\n",
       "4     m1.jpeg       Mango      2\n",
       "5     b3.jpeg        Bike      0\n",
       "6     b2.jpeg        Bike      0\n",
       "7     b0.jpeg        Bike      0\n",
       "8     b4.jpeg        Bike      0\n",
       "9     b1.jpeg        Bike      0\n",
       "10    c1.jpeg         Car      1\n",
       "11    c3.jpeg         Car      1\n",
       "12    c4.jpeg         Car      1\n",
       "13    c2.jpeg         Car      1\n",
       "14    c0.jpeg         Car      1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "305f6fa6",
   "metadata": {
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     "end_time": "2023-05-16T15:58:17.512411Z",
     "start_time": "2023-05-16T15:58:17.507084Z"
    }
   },
   "outputs": [],
   "source": [
    "img_df.to_csv('images.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4bfa138d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-16T16:21:55.725111Z",
     "start_time": "2023-05-16T16:21:55.709748Z"
    }
   },
   "outputs": [],
   "source": [
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7b7797f1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-16T16:21:56.712138Z",
     "start_time": "2023-05-16T16:21:56.686525Z"
    }
   },
   "outputs": [],
   "source": [
    "for root, dirs, files in os.walk(path):\n",
    "    img_path = [os.path.join(root, file) for file in files]\n",
    "    imgs = []\n",
    "    for p in img_path:\n",
    "        read_img = Image.open(p).convert('L')\n",
    "        new_img = read_img.resize((32,32))\n",
    "        im = np.array(new_img)\n",
    "        imgs.append(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d358c2cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-16T16:22:29.579957Z",
     "start_time": "2023-05-16T16:22:29.573968Z"
    }
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     },
     "execution_count": 56,
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   ],
   "source": [
    "npy = np.array(imgs)\n",
    "npy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f010d56f",
   "metadata": {
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     "start_time": "2023-05-16T16:23:03.053778Z"
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   },
   "outputs": [],
   "source": [
    "np.save('imgs.npy', npy)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "01b93427",
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}