Test-to-Binary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d54097d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import os\n",
    "from PIL import Image\n",
    "import tensorflow as tf\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# save numpy array as csv file\n",
    "from numpy import asarray\n",
    "from numpy import savetxt\n",
    "\n",
    "import csv\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4d77c629",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"StrToNum.txt\", \"rb\") as myFile:\n",
    "    StrToNum = pickle.load(myFile)\n",
    "with open(\"NumToStr.txt\", \"rb\") as myFile:\n",
    "    NumToStr = pickle.load(myFile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "603dcf4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "finalTestImgData = []\n",
    "finalClassName = []\n",
    "IMG_WIDTH=32\n",
    "IMG_HEIGHT=32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "319f183d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset_PIL(img_folder):\n",
    "    global finalTestImgData\n",
    "    global finalClassName\n",
    "    for dir1 in os.listdir(img_folder):\n",
    "        print(dir1)\n",
    "        for file in os.listdir(os.path.join(img_folder, dir1)):  \n",
    "            image_path= os.path.join(img_folder, dir1,  file)\n",
    "            image= np.array(Image.open(image_path))\n",
    "            image= np.resize(image,(IMG_HEIGHT,IMG_WIDTH))\n",
    "            image = image.astype('float32')\n",
    "            image /= 255  \n",
    "            finalTestImgData=np.append(finalTestImgData,image)\n",
    "            finalClassName=np.append(finalClassName,StrToNum[dir1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "16f24598",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "character_1_ka\n",
      "character_2_kha\n",
      "character_3_ga\n",
      "character_4_gha\n",
      "character_5_kna\n",
      "(1536000,)\n",
      "(1500,)\n",
      "[ 8.  9. 10. 11. 12.]\n"
     ]
    }
   ],
   "source": [
    "img_test_folder = 'D:\\AIML\\Devanagri dataset\\DevanagariHandwrittenCharacterDataset\\Test\\First 5'\n",
    "create_dataset_PIL(img_test_folder)\n",
    "print(finalTestImgData.shape)\n",
    "print(finalClassName.shape)\n",
    "print(np.unique(finalClassName))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9df62162",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "character_10_yna\n",
      "character_6_cha\n",
      "character_7_chha\n",
      "character_8_ja\n",
      "character_9_jha\n",
      "(3072000,)\n",
      "(3000,)\n",
      "[ 7.  8.  9. 10. 11. 12. 13. 14. 15. 16.]\n"
     ]
    }
   ],
   "source": [
    "img_test_folder = 'D:\\AIML\\Devanagri dataset\\DevanagariHandwrittenCharacterDataset\\Test\\Second 5'\n",
    "create_dataset_PIL(img_test_folder)\n",
    "print(finalTestImgData.shape)\n",
    "print(finalClassName.shape)\n",
    "print(np.unique(finalClassName))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9498c992",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "character_11_taamatar\n",
      "character_12_thaa\n",
      "character_13_daa\n",
      "character_14_dhaa\n",
      "character_15_adna\n",
      "(4608000,)\n",
      "(4500,)\n",
      "[ 7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 37. 38. 39. 40. 41.]\n"
     ]
    }
   ],
   "source": [
    "img_test_folder = 'D:\\AIML\\Devanagri dataset\\DevanagariHandwrittenCharacterDataset\\Test\\Third 5'\n",
    "create_dataset_PIL(img_test_folder)\n",
    "print(finalTestImgData.shape)\n",
    "print(finalClassName.shape)\n",
    "print(np.unique(finalClassName))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9083a5ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "character_16_tabala\n",
      "character_17_tha\n",
      "character_18_da\n",
      "character_19_dha\n",
      "character_20_na\n",
      "(6144000,)\n",
      "(6000,)\n",
      "[ 7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 37. 38. 39. 40. 41. 42. 43. 44.\n",
      " 45. 46.]\n"
     ]
    }
   ],
   "source": [
    "img_test_folder = 'D:\\AIML\\Devanagri dataset\\DevanagariHandwrittenCharacterDataset\\Test\\Fourth 5'\n",
    "create_dataset_PIL(img_test_folder)\n",
    "print(finalTestImgData.shape)\n",
    "print(finalClassName.shape)\n",
    "print(np.unique(finalClassName))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3fe63830",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "character_21_pa\n",
      "character_22_pha\n",
      "character_23_ba\n",
      "character_24_bha\n",
      "character_25_ma\n",
      "(7680000,)\n",
      "(7500,)\n",
      "[ 7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 37. 38. 39.\n",
      " 40. 41. 42. 43. 44. 45. 46.]\n"
     ]
    }
   ],
   "source": [
    "img_test_folder = 'D:\\AIML\\Devanagri dataset\\DevanagariHandwrittenCharacterDataset\\Test\\Fifth 5'\n",
    "create_dataset_PIL(img_test_folder)\n",
    "print(finalTestImgData.shape)\n",
    "print(finalClassName.shape)\n",
    "print(np.unique(finalClassName))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fb04c014",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "character_26_yaw\n",
      "character_27_ra\n",
      "character_28_la\n",
      "character_29_waw\n",
      "character_30_motosaw\n",
      "(9216000,)\n",
      "(9000,)\n",
      "[ 7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24.\n",
      " 25. 26. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46.]\n"
     ]
    }
   ],
   "source": [
    "img_test_folder = 'D:\\AIML\\Devanagri dataset\\DevanagariHandwrittenCharacterDataset\\Test\\Sixth 5'\n",
    "create_dataset_PIL(img_test_folder)\n",
    "print(finalTestImgData.shape)\n",
    "print(finalClassName.shape)\n",
    "print(np.unique(finalClassName))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e4a7602f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "character_31_petchiryakha\n",
      "character_32_patalosaw\n",
      "character_33_ha\n",
      "character_34_chhya\n",
      "character_35_tra\n",
      "character_36_gya\n",
      "(11059200,)\n",
      "(10800,)\n",
      "[ 1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17. 18.\n",
      " 19. 20. 21. 22. 23. 24. 25. 26. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46.]\n"
     ]
    }
   ],
   "source": [
    "img_test_folder = 'D:\\AIML\\Devanagri dataset\\DevanagariHandwrittenCharacterDataset\\Test\\Seventh 5'\n",
    "create_dataset_PIL(img_test_folder)\n",
    "print(finalTestImgData.shape)\n",
    "print(finalClassName.shape)\n",
    "print(np.unique(finalClassName))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "56f2fec0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "digit_0\n",
      "digit_1\n",
      "digit_2\n",
      "digit_3\n",
      "digit_4\n",
      "(12595200,)\n",
      "(12300,)\n",
      "[ 1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17. 18.\n",
      " 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 37. 38. 39. 40. 41.\n",
      " 42. 43. 44. 45. 46.]\n"
     ]
    }
   ],
   "source": [
    "img_test_folder = 'D:\\AIML\\Devanagri dataset\\DevanagariHandwrittenCharacterDataset\\Test\\Eighth 5'\n",
    "create_dataset_PIL(img_test_folder)\n",
    "print(finalTestImgData.shape)\n",
    "print(finalClassName.shape)\n",
    "print(np.unique(finalClassName))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fbc4d183",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "digit_5\n",
      "digit_6\n",
      "digit_7\n",
      "digit_8\n",
      "digit_9\n",
      "(14131200,)\n",
      "(13800,)\n",
      "[ 1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17. 18.\n",
      " 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36.\n",
      " 37. 38. 39. 40. 41. 42. 43. 44. 45. 46.]\n"
     ]
    }
   ],
   "source": [
    "img_test_folder = 'D:\\AIML\\Devanagri dataset\\DevanagariHandwrittenCharacterDataset\\Test\\\\Ninth 5'\n",
    "create_dataset_PIL(img_test_folder)\n",
    "print(finalTestImgData.shape)\n",
    "print(finalClassName.shape)\n",
    "print(np.unique(finalClassName))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "39d7b7e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "finalTestImgData=np.resize(finalTestImgData,(len(finalClassName),32,32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2b5fb758",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13800, 32, 32)\n",
      "[ 7.  7.  7. ... 35. 35. 35.]\n",
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
      " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45]\n"
     ]
    }
   ],
   "source": [
    "print(finalTestImgData.shape)\n",
    "#finalClassName-=1\n",
    "print(finalClassName)\n",
    "finalClassName=finalClassName.astype('int64')\n",
    "print(np.unique(finalClassName))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cc5139f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save numpy array as npy file\n",
    "from numpy import asarray\n",
    "from numpy import save\n",
    "# define data\n",
    "# save to npy file\n",
    "save('finalTestImgData.npy', finalTestImgData)\n",
    "save('finalTestClassName.npy', finalClassName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dcd3a7ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13800, 32, 32)\n",
      "(13800,)\n",
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
      " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45]\n"
     ]
    }
   ],
   "source": [
    "# load numpy array from npy file\n",
    "from numpy import load\n",
    "# load array\n",
    "testData = load('finalTestImgData.npy')\n",
    "testLabels = load('finalTestClassName.npy')\n",
    "# print the array\n",
    "print(testData.shape)\n",
    "print(testLabels.shape)\n",
    "print(np.unique(testLabels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3da2cec0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43\n",
      "character_18_da\n",
      "28\n",
      "digit_2\n",
      "43\n",
      "character_18_da\n",
      "32\n",
      "digit_6\n",
      "22\n",
      "character_27_ra\n",
      "40\n",
      "character_15_adna\n",
      "26\n",
      "digit_0\n",
      "4\n",
      "character_35_tra\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "for i in range(8):\n",
    "    r=random.randint(0, 13800)\n",
    "    print(testLabels[r])\n",
    "    print(NumToStr[testLabels[r]+1])\n",
    "    plt.subplot(241+i)\n",
    "    plt.imshow(testData[r])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8825d41a",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}