segmentation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "segmentation.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-n5X_1f0rEj"
      },
      "source": [
        "import cv2\n",
        "import numpy\n",
        "from keras.datasets import mnist\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import Flatten\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.layers.convolutional import MaxPooling2D\n",
        "from keras.utils import np_utils\n",
        "from keras import backend as K\n",
        "#from keras as backend as K\n",
        "#K.common.set_image_dim_ordering('th')\n",
        "from keras.models import model_from_json"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIWDPmu_0s-3"
      },
      "source": [
        "try:\n",
        "    if K.backend() == 'theano':\n",
        "        K.set_image_data_format('channels_first')\n",
        "    else:\n",
        "        K.set_image_data_format('channels_last')\n",
        "except AttributeError:\n",
        "    if K._BACKEND == 'theano':\n",
        "        K.set_image_dim_ordering('th')\n",
        "    else:\n",
        "        K.set_image_dim_ordering('tf')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lLT2ZERH1KGK"
      },
      "source": [
        "json_file = open('/content/drive/MyDrive/model/model_final.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "loaded_model = model_from_json(loaded_model_json)\n",
        "# load weights into new model\n",
        "loaded_model.load_weights(\"/content/drive/MyDrive/model/model_final.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "--ZWHU7I25BO"
      },
      "source": [
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QVUYkbqc1UXn",
        "outputId": "0a0c727b-3f79-4dd9-9b88-04ac9158d8af"
      },
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "img = np.asarray(cv2.imread('/content/drive/MyDrive/model/test4.png', 0))\n",
        "#kernel = np.ones((3,3),np.uint8)\n",
        "plt.imshow(img)\n",
        "plt.show()\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()\n",
        "#erosion = cv2.erode(img,kernel,iterations = 3)\n",
        "#dilation = cv2.dilate(img,kernel,iterations = 1)\n",
        "#img=dilation\n",
        "if img is not None:\n",
        "    #images.append(img)\n",
        "    img=~img\n",
        "    ret,thresh=cv2.threshold(img,127,255,cv2.THRESH_BINARY)\n",
        "    ctrs,ret=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\n",
        "    cnt=sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])\n",
        "    w=int(32)\n",
        "    h=int(32)\n",
        "    train_data=[]\n",
        "    #print(len(cnt))\n",
        "    rects=[]\n",
        "    for c in cnt :\n",
        "        x,y,w,h= cv2.boundingRect(c)\n",
        "        rect=[x,y,w,h]\n",
        "        rects.append(rect)\n",
        "    #print(rects)\n",
        "    bool_rect=[]\n",
        "    for r in rects:\n",
        "        l=[]\n",
        "        for rec in rects:\n",
        "            flag=0\n",
        "            if rec!=r:\n",
        "                if r[0]<(rec[0]+rec[2]+10) and rec[0]<(r[0]+r[2]+10) and r[1]<(rec[1]+rec[3]+10) and rec[1]<(r[1]+r[3]+10):\n",
        "                    flag=1\n",
        "                l.append(flag)\n",
        "            if rec==r:\n",
        "                l.append(0)\n",
        "        bool_rect.append(l)\n",
        "    #print(bool_rect)\n",
        "    dump_rect=[]\n",
        "    for i in range(0,len(cnt)):\n",
        "        for j in range(0,len(cnt)):\n",
        "            if bool_rect[i][j]==1:\n",
        "                area1=rects[i][2]*rects[i][3]\n",
        "                area2=rects[j][2]*rects[j][3]\n",
        "                if(area1==min(area1,area2)):\n",
        "                    dump_rect.append(rects[i])\n",
        "    #print(len(dump_rect)) \n",
        "    final_rect=[i for i in rects if i not in dump_rect]\n",
        "    #print(final_rect)\n",
        "    for r in final_rect:\n",
        "        x=r[0]\n",
        "        y=r[1]\n",
        "        w=r[2]\n",
        "        h=r[3]\n",
        "        im_crop =thresh[y:y+h+10,x:x+w+10]\n",
        "        \n",
        "\n",
        "        im_resize = cv2.resize(im_crop,(32,32))\n",
        "        plt.imshow(im_resize)\n",
        "        plt.show()\n",
        "        #cv2.imshow(\"work\",im_resize)\n",
        "        cv2.waitKey(0)\n",
        "        cv2.destroyAllWindows()\n",
        "\n",
        "        #im_resize=np.reshape(im_resize,(1,32,32))\n",
        "        train_data.append(im_resize)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jYY+vUffQksBUh6yLmxMWYz+nvbHg0kmbbfZrRVIl7ooGRZgIoaxdzSBq/Bb8MkWbr3m3PJ32LxzwFAfbPboLH7SYFWCr/IM1o6ZuUPQuiZP6MUjeITSC0Ef2eMoBxywv4I9q/81wpcRd0eBo3navJ8kkfksle/QzX9z/W9Ab5458ALTWLXj0zk8aRKsdICGj1PM6Nt+O1VU/JssAuK7pMkxucZfVDqql/6KWlLgrGhxXNV2LKTYGAC13D19bOgbYotNDly52r6yZzbKyTSKDo/YF0CL/0r1JEaZI40EWuruc9Y7681DThAST223k0jmkq5a7QuEz/h69EWcb96DqwYO8u+2sAFt0evxqM9Pyf+7WqxAUDBBEms7siThOhnNjt2KKaWQs7C9mdVVmQO2pTevQvZCWAoCrrJwpeX/z27mVuCsaHOnmCPb2dccgSwmL4wJr0Gly628341q/DQBzs3T+dfEXAbbIv/QK34Ur0V3XvaKCRcWBq+v+Z7qFmrC0igVAOp0cKGnkt3PXj4j/ICTPYeH6zTdSXB7Fs11nc3V0eaBNqpMyVxW/2eKp0CPq3N46dB9dQ81G4a0gIURo2PtYjJokLp3kFTZ2OS00M/uvy+wtrK5qUuaGgssYRNw1LIMRjb4CgjMjtS6SNIk1K4bwDSB1nT92pUNgS7t7MCGQWmAC8ZW4exmLy8Z1uVdyaEpzGn29lmiHkykDhtF12iuUukIZ9vk4zFXw+si3uDAisKP6B/VKzn/jITJn5CMrLH/dwSSQzVIpHJDAoOsWMzh2NV1Cq70+qfLMinienD2chPWw/3wHKwZMIlGLOv4bT4Nb2y/hl+hU9PJywrYUsdqeQjNzHdegnrOuWiNuzQF0wBQeTrvBW4O21MDRiDWFU97MTDiAlJjzA1/6tz6gxN2LrKu2cc2H99Ny4iaiSpdxOJ8ickcJBc4Y7n77Dlq+sASk5PaIMeSMnBpQe3+zJZP54U6cRbuPvlNpGU3Xwfq3Y9kQP4Siy9PIvDqXFzNnkR1y+gI8uuBsCu7IpMXapSAlCbOieGj+QKY3+/W0j30s+kTm8ktyJygvR1qr+HR/LzKbfn/M9+gI5pR344fdbdFdJkI0ncFp6xkbv97rD7wTJdfRBA4YZX1FRARXJa0OiB2BJERoVGS5aOJebpRn9Gga0rhDXShx9wK6dPFySWtm/+siMmctR3d3kU2RkVgv7Ej5beW0CiknfX4FUhqSH7Ev8DnTZ4Xv46mrm5M20wFO51930HX0cgu4dPTSMigtI3nyTmxvhjHyuvFMefI1+oafeitxTmUkBWOzkGs2eNaZ4mJpFrHzlI95ojQ1V6DHGw8nvbycsquTeTxs6HHfJ8stxJTkGgtC8EuTLGYOvZjUofmMzfiZiyIq/Foz/d9rLyOrYpOxkJJIZshBGpJL5jDhLSo8r2PzqrFKB5EocVecBtsclQyYP452L5QStXWZsVIInH/rjv3hEmZ1eIUmWhTfWhPRDpRRh4QGjCZaFPPHT2DlPQl1xt/udsTzwqLLaD3DjnlDHnpFBUiJtNtJeH8p4/S7eePpSfQIO/kfUZ7DwhNT7yJ11RIATI0asevuTvS+Yj1PJK7D1wLVVNOwNw7n8Jw5zr2nEDooJfq+/TR5fT+uN8283uIyHhqWzFe3TfBKr+Z46NJF3LdRnsSlokuS6BF2nDcFKedl5JIXHo7LZiN8xwEW25IYHNVwJkGvCyXup8G6ahujnh5Pmw9XoLt/YKbISArv6sqUO6e5J0cwfuTflnRFLzyG+yNAJGpRDIy0H2WrlTsGv8XmgVZ+sbbmxQV/p81b5bj+2AxSEvfhUm5rdB9z//EiqScxGHlQr2TwlIdJm7zc05MpuaIDi8dOJNYUgT9anhEilMoUM7W1UISE1sQkHwVhNiPbZeFsFEpIiQ25YRvS6UQ6nejbcmn2fzu5bs94mozcyeetv/K5ayC8pGbcpqqfpcH52w9zY+Pf+XfWcNi8Hb1wN9MKL2Bwm7mBNiugKHE/RUp0K8PfHk/GB8uQbjeMlt2SbU/FsPS8CX8ZENxUkkKY7nt3gy9oFxpJu9AiRl85lY8vbMJr/3cN8R8YYwcp767mrK73s23QtBMSFofUuWDFGDKm/oHL/UCsurI3Nz/2tVvY/YMmTFiaCRIOL8fEwOxo+iTkH/N9kZqdKxr9QLoWwjaH5Jolt5P2cQhR6/bgLChEOp00fmcJfBFPx1fuZMNFU/3m+9W0elQ1y890DHVQ3DORuM3bkU4n21Y2hzaBtspNgL4WJe6nQK7DwqAZD9Fi4mpcLh2E4NDNfRn10BxGxxagib92yfcuTaW5zPe/sV5EEyZujDlI1OMf83rRNYT8tAqXzUb7J3dxQ/uLmZl1/PKyQ7b/neZ3HcRZaUw7Js/uyk3/N4dRsXt9bf5fsGXUKjugadyRvvAEu/LG99s1DLZf8D5l51XxcnFPltzVC7H4D8NdU1JC23u303GyfwW+oRJtCqc8S3A4YyG8OPBjWgBbHHaiCioNfReCkFD/OWaDJ3jZTyyywbBnHiLr6RW4bMas5vZBPZn45FTuiCs6ajy4VlU/bjZvcHV0OUMm/QR9OwOGv7r03lSmlaYd830zyhNxPJLk8W+bU1MIf25vQIQdoGn6IcMVc5rEmiJ4Omkj4z6YybapvTCnJAPGQG3b+3Lo8P1d2GX9qXcStNRDNXtp7yWIzXkAaLEx3Ji93G/nroeXo/6yyAYPP3kHjd9Z6hnEclzUgytf/JHzTjYS7gy/8vfE7yTihX1oSUkAyFUbeeG3QUfd3yF1np47FJatB0CLi2X3tDi+aPWtX+yti/OTcxDh3huBvCzSxrbLp9LoCwfmFpkA6KVltHsol06Lbqs3pWgV/uOXZR1wuXupzvaZDI1Z47dzn+ES4z8OC3vsf5cZKesmjUO39OO+qZ9wX3z+SR/P0vLMb8l91mou+wfXpAJmzBXsctadCPRCcQfavlJoXDvgwJD2/Nz9vYAOAPaIyvdUh/QWIULj48yfKHvDhKljWwD0khJa/bOCryrP7DIHipNjv15Jxk81D/SCi6NoGVKPCocJITKEEAuEEJuEEBuFEOPc6xOEED8KIba7/8e71wshxGQhRI4QYp0QoruvP4Sv+dQSyyOPHSnse+7rw4ynJp5wuNWfZ4jp0naXDyz1LyFCQ15Z7KnIF/ndHwxcfsdfWqh26WDmx/1xFhQCoLVrzaD7FgW8JK0mfNOS1oSJ3zrPovpVK1qiMRmInpPHI7NGBt2E3Iqjc9+uvxO5cDNgRNF1uWSLX89/Ii13J/CglLI90BcYK4RoDzwCzJdStgbmu5cBLgVau//GAIFNwzxN7t3di7dvHUKjmUs9wr53XB++HPfiCU88bHHZiC48UkjCzWd+yx1gXtfplAwxfO/SbifroQo+rmhyxD6DNl9N84/yPcubx8fydNJGf5pZJwvL2yKrjUFV6XSyoSrDq8f/uu3n5N/uDtmQkuw3d/PyobZePYeiBmdETQvKbCXgbrDlS9rgqjCSq6rO78CLGXP8ev7jiruUco+UcrX7dQWwGUgDrgA+cO/2AXCl+/UVwAxpsBSIE0KkcgYypaQ520e1wvTbWgBEWBhFD/fhs/smnFT36oDuJH59ma/MDCiJWhRNb8/F1MiodufcWcjc4s6e7bkOC+KZRE+JA61da54558uA2Ppn5m7rgMtq9LxcFgtvLz3Pq8ePNIXyn5s+QmtnVCl05u3ks8kXYXHZvHoehYFMt3kmpI4u0rHIo+Vv+J5vreG0+KLKs7yvT4jfC9OdlM9dCJEJdAOWAclSyj3uTXuBZPfrNKCg1tsK3ev+fKwxQoiVQoiVB4rrX1d1c7WVT/5zqZGwgzEAuG1CV34ee/LZhy5A2IKjpV4Xr2d+ie0sdwvVpbNhTltPdMhF8+/D/Lu7vIAQbPlHDMOjDwTI0iNxVIR6xgCQkpSFmtdbe1dGlbL54RijAiXQ5Jf97HQ23Hh0X9I2bS+4o9Wi8y2UuQKjK1ZXNY+9fiti6ToAtMTG/H3wEr/bccLiLoSIBr4A7pNSHlG/Vhpphid1x0op35JS9pRS9kxqXL+y6qyuaq6Y+SCNPl0BGNX2Nk9szYarptDkVKsVyuD9Qaeao8kfVrPc7Jti/qg2SjO0es+JdBiuD1OHNkw467N6UT5Yly4SVh05mBpdZGe/lycw1oSJUT1+R4QY5xJWG0XOGK+eQ2HQNyEPU5ThKtX2lbLW3uQ47/ANC2wxpM/e7fnN77inDU83WeZ3O07oVyaECMEQ9o+llIdn3d132N3i/r/fvb4IqO28THevOyPQpYtey26h1fObjBrZQrD7zu4su2TSKSeibHEkIqoC10X0B6N7/ooW754wYWsu47YMZ9DvY9FWuAeUwsORkyu4Mqr0WIfxGy4k0UVHtuxCthTxq+3YsfqnQs/IPEwZTQHQ9x1gcuFFXjt27ZJAuh48uRSnQs/IPExJxgC2q6ycX8oDM74xZdeFOPMN54WW3ISxQ78NSBLbiUTLCOBdYLOU8uVam+YAN7lf3wTMrrX+RnfUTF+grJb7pt4zIu9iMu8rM6ogAvoF3Zgw9u1Tb7ED/zvQG33v/uPveAZzZ/waDl1muGak04l4P4kWrxlFxgD0bm14PmtWvWi1H0b8qTclLZWU6t4v+HVeeAXlXYxWpHRUs2VFpteOXZlco+5yl++LldVnuoQW40gxZj1yVVTw+74WfrfB4rKxZ3Zzz+QprvQkhjYKTPDAifzSzgZuAPoLIda6/wYBzwMXCyG2Axe5lwHmAjuAHOBt4C7vm+0bXi3JpPyelJqQveyWdHlpLZdEnp6/vKgyzuOaCFbitUhcI4o9vuXY2WsRSw1fuyk8nIp/Wuga1jBLFkaaQtnXu+anFrfVO8fVhInSNjUPqLgtgY8QCSSxplDKWtVEsO3Pbex3G8bsvJS0j9whj0Kwa1AsCVpg7vvjZnBIKX8Djtbfu7CO/SUw9jTt8jvL7Q5mPX4JEWuM9GAtuQnW13ReTFnJ6eZ67chNJpsj49r3Wf03l6K/uCRtCyu1CKRL95RmAHD2asd77V4H6s+s9P7G2aiWC8iLwy8J2YeMSpVOJ/Fbq9jltJLlx0SZ+kSkKZTSbDz1ZWK3GgPk/uotvl6aQfGDGVBsDKTSpxOv3vS2X+v716b+9JEDyB6nhdGTxxEx2z2AGhnJlgnp/NjhC6/cGI22/fXLLVrV9LSPW9+oKylIhIVR/JD1hHMC/EWZy4ZWdaS90unkm/2dj/KO+smIzJWeMFTztiJ+ttaTyUMDhLlduaf32HijjT1eHiA/Gg6pM2365XA4QiYulvKnKk+71386NHhxt7qqOefz8aS+ttKTpLTrvq6s+ttrXkmNP6hXEpfz10pw6T872HOUVP1gwpTRlFc7/i/QZvyFn6zphK0/sjcl7XbWb24WIItOjUujN6C3TgfAVVLC69vPD7BFgeXaVms8ETNhW3bzszXTL+e9o+B8mn2Y61nOfag98zt94pdzH40GLe6HI2Pa/HuzxydePrwXn4x52Wup8YVOM43W/3UwNXxHMaWu4Lr8kaZqROiRvZRdQ1PpE1b/YvzjNCsiqo768WeYyzo7JJx9fYyWu3Q6kT80btAVKIfGrkK2Mh7Q+oGDfFjUz+fnLHRa2Dilo6faqalzW+688ruAl3kOLnU5ST6zNKb5P52eyBhn/x7c/69P6Bzq5cmO9TqSKUrKmFPRxbvnCTCXNloPLf+Uwt+7LGA+x2PxtwgLle2Sj79jPUcTJkwXF3sKoDX9Mp//VZyRCeFeoVWImao0I2pIOp3sWO/90Nba6NLFeV8/SNynxsTkpvBw9j/rOqVigt6mwYq7xWXjmekj0DcaoQvmrOb0mbiCa6O9WyZAR4CrZgTtsH9ULz7EtBXB1YVuYQZn7JGtYfHnimn1BDMaekRw3P5TOn4CnYwwVH3ffl7Z6r04+jMNMxp7+9S4UxNXCZ8Wa5tpSaLtG6Wenn/xsG582eU9n53vZAiOu/sUuC73SppNc8efmjQ2j0/hP03Wev08P1e2Q7rrl2iNEyj9ewfPtti1anaeQKEJEwe6Bsft3zcM9p5jxHdLp5PqZQkNNiRSE0U848cAABoGSURBVCZiux/09GQS1pSwsdo3sx/tcVqY9H/X1jQQM9K59R9z/F5D5mgEx919khzUKyl+PdPjjrEP6M6sQZN9EjL1dVFn9EMlAOit0ynuVBNVGlouVQnYAFLdPDiyhjVhwnpWpWc547syfqzy33y09Y27Wy7A1NiYHVcU7WOh1fuTqZa5qjj7y/EkfGK4Y0RIKJvHp3NLbL7Xz3WqNEhxn3DwbGLnbwPAFBVFzCMFPkuwsTnNnhoTzqgQyLJ6QrWSFhbxbtmZFZ2hqJ/c1fkXzKkpAMi1m3jgg1GU+CkM8GhYXdUsskGX5SPoPPEuhuf198t5z47Ix9nCGHdwVVTweYH3p5QYsf1q2j612ZOBbR3UlYVDXqpX40sNUty/nNcPvfgQAI7ebZiU+bnPzlVhrRmclZrgxZ5fYOroLgGbv4tXZg0O6i505a76WyQrJNzpKRF7pnNn3HYKh7nT7aWk+cS1nDdlPHcV9aXMVXXsN3uZRTbImjeKCx++l+cHXk3qsB2kTlzMofEZ5Dp8H/7bzByBLdlorEmnkwNl3nWTLLKB87EkT8/fFB7OnmHV9cYdc5gGJ+4OqRNTE47KrkvCfJbRZ5cOwhfWZKIeahvKpZElbB0d6xGVzDkWtjmCt753y89sbK4ObAvyaNzW4Xe0JkmBNsMrhIkQeo/4A62NkcTkslpp+uJi8i8Oo+vce33agLBLB5urrUwpaU7WN6N57trryR61lpj/LkXflutp3doTw4j0w7PUhEAPrZG26krvjW2tq7Zx15t3IZYZpTVEWBi5/+zGknNf99o5vEWDE/dCZxUJm9wtGZNGRDvfVSn83JJC0zlGoowwm9H/VkqYCOHe/vPQ3FEz2o7dbKpO8ZkNgUZbsZkbN9wcaDPqZETMH1S3Sz9inbmifpWfPhmmpf9Kp5k5aNktPev00jLaP11E659uI89h8eoYjy5dfFzRmG5vjOOBwbfy3QXZZN++Erlyg6dwlggLQ2vdguJR/bji/34i1Q+tW02YKG9WI23h+d4R91X2akY/eT9pE5Z5Pt/uu3vw2w0vkXgahQV9RYMT90OuUEIKiwEQIWauyvrDZ+eakvs39D17jXN1aM3bXT4EoG9ELqQbgu6qsDB999k+syHQSLud+Gcj+N5a/4qGpWqRlLY+0q74TZyxSUCaMPFck9VkflzEwTH9MKcYcfzOot1kj9nI7SPG0vZ/Y486ifnJsNSm0+rb2/l4yIVkPLsY17ot6AcOeMaXzGlN2f3wWUT+GMN9333Noqcn8UDCjtM+74lSmVXzEEte4aDwND/zUpvOLa/dR9zHyz2lwO2X9eLfd8w4rYqxvsS7U7+fAeQ7EqHa+PEKs5lYs+9cBofKooh1GmFYlZmN6OHWkW5hLkq6JhCzyRC/jZsyINtnZviNHIfAXFZVk+QpBEiJWLqO8e+Mos/Yl/wyKfZhF8Txop80YaK4t5PGb9esa1RQjS7l0UvlnSLC6R/fviZMvJG2FPtTvzJ42BC027PQc/KQdjti8R+0XhHK9b88wO5zTbjCj5GDICCsiZXh2atIDqnJ/Zizrwtb1mfQ8rNqsn9fjX54tiMh0BITkU0T2TUonpEj5jOr8exaA4z+Dftt274AV1gY0m4ncnkuy2xNSY8uP/4b62B+lcaTj91B6qdLPA+v4lF9eenRN7kgov6OlzU4cf9wTz9PaKLITOesyIWA90e4HVInfGXNE92apGFyK0aYCKG4o+DwUGPsFrNfq9f5ihd3D4Qcww1lTklmz5UtSHrTmFg8Y/Jaurccx/pLpxBt8nIGsBtduhiRdzE73s+mMl3wwU2T6B127O92ZK+lrE5sgn7Q6M1pNicW6SDSi2JU5qoieUmNuOthvhf6MBHC3LZzeOaLjnz/wnnEzVqLy2ZDOqqJmL2clrOPfwxMGkvD40DEe1bJ6oO0duw+YjdzVnM2P5DC/f2/p3/UHLJDQt11mQIXOXJvxnwmx52Pvm8/0mZnXVUGV0effF31hVUmnnxsNI0+XeapPVU5pCdPPzK9Xgs7NEC3TFFFbM0AT0o0HUN8k0G5yBZK+tdGrQkREop+eckR4q21tngGVVMXlbLWR4kW/sIuHayf3c4z4bSlV3Mef+Bj7AN7AsYAX9sHttB51jgO6pXHOtQpYXVVc866a6i8sRGN31lCs6eXMGzBncd935iEJVR3au5ZDsnbx49W74anjsy5ivhvNwHGvSAGFnv1+EdDEyaeStrE/55/iYqvmnoGW08Yl47LasVVWen5qz0vgTmtKbueOouB36xl+1VTuSd+Jx1CI7xScO90aR9ajL2jUQrDZbXy8Y/nntT77dLBC8WtefTJMR5hFyGhFDzWhzcmTuKyyPofBNHgWu61kQI0H4XCTdw1ANdOY9IPU6vmvNLx0yO239V+Ed/FtUAvKUHsKGS9PZ0eYWfubE0TijvRbEYuTgAhKBru4Orocr58cjslmzJw7izAVVFBm4f/oJ9tPEtGeGcQqsxVxQsH+vDtjHNIe38zzhKjV2aKiCAh6fjd8FQtgpLsMBIXGMv6wUP8Xt6akY28I8B26SD/uyzSyhcD4Di3Ex92fg3wX5JRM3M0v3T6nGlfNmfa1nPRFsRhrjx2o0a4IDbfTti2vUfWRgoJoaJ7Uw61NXPRtcv5KuVrt5jXr3ZiM3M0eVeYaT0fkJKmv7qwj3CcUBx6iW6lx1f30+75AmKKlhorTRq7Hu7JD2NeJL2ehTwejQYn7i4/lDpxSJ2dP2SSbje6r8U9E+kXbqd2N/WsyO1816QXlJTgstv55kBnbo75ySvnL3RamHTwXDpEFnFDo71+cfdM//kCWu11/xB6deTNvjMA+KD5z1w2/XLMt7gF3maj1T/X0FeMZ8Xwiafsg7e4bAzddjUl7zej8Xc5pBxYzGEJMmeks+nxpqzu+irHmyAkRGgkD9+J/CjKaJk6Hcz7rQ/6tYu9ct3u330uzWfs8Dz0dgwz0SHU/9mjmjAxNq6AsX3+C31O7D27nBbyndG4ZM11CBFO2ofYan1vgW+lH41GzWoe7lE7LWyudtH1GOP6dulgamlr3n1/EG2mrMZZa8IZ22U9eG/UlDNG2KG+PW79wKGCOM9rPcI3N+b08gwyPzFa7QjBwQG2v7QY2oS4KO2WCBiDqmtWnv4kC3bpYMKhllz+/MNsOD+azwadxU07fZ8VWOi00Oy7GrdSzvAoLowwpFYTJr5t8zWx/7V4QvRcNhutn1xDr5kPnpKLZpujku4z7sd0lYW4GUuMKA2MAfLqgb1I/6KYnMunnfCDY0LWF4h0dyVFKWnxhY0iL2R3vl6aQe492TjdEVOus7vwwcVvH+dd9Ydm5mjOC4cLIlyev7PDTX4ZFPcGYSG1ktS27OCRvKuOuu+31nC6TRvHjwM70nTCYs9MYlr7bA7c0Y+xE/9H3/D6+yCriwYn7o3X1HxBe/toPkkXfn7+5Th3GrOfmzq0YXLfvxbtjzaFs7dWqemUJYbf+FRZZa+m04f3svDi1jR5YzGuigqcO/L5fZ1vw3B06WLAitsJ/20zAFpMDFddsOyIfTRh4sPM+cRNP1STZOMW+EueG8+UkuYn9NktLhvPHGzLdc+MJ+uJ5UdkCDr79yBnRkcmTZvCm+lLTqrV3dwsKO5Tk8xkXrOdtw6dXh3wPIeF6S///YiZeYofruI834wlK+rg7pYLMLmrZbpsNrZu+2v531yHhbuL+jDhnhvI+M9iz/zJh0Mdr521kGVPvub1arH+oMG5ZUy1NKQ6yfuDmOuqbbScafeETOVcH8/ACCt1PUd79thORXg4LpuN2DX7WWKP8LR4TxS7dHBL/iXsfbolWT8tx+mqeb88uytPnD/ntD7P8bhk85Vk3luCs9JogVee35YHEl8Gjuy+Hhb42z44n53/7EnIDytx2WwkTV3C93M6MOWeQfzjii8JEX/9Tg46Y3jjj/NJ+iaMhAV5NN67xNggBM6/dcf0+H7eajWJliHRwMmrZ7QpHNe1xZg+Nb4LV5WNQ45THw/IdVi46tWHSX1/ORJ3UakXs9nY/XX8HRLYkLkx5iC/vl3A9qd7oYebePT8r4/Y/nFFY6Y9ejvR8zYQVmlMsYkQmDq3Zcvtjfho4DTODjdRn11Px6LBibuvGb7yNpot3YAEtMTGXDPg96O2Iu9KXcCLGVfC9h248gt5Lu8yLmx/4mL8gzWEuz+/k1YTcwg5sNJYKQT06cSOIVE8O+S/Pm1x5DksuF5Kxllk/DDMWc3p9q/VR81C1ISJ6c1+ZeaUDbzx8DVEzDYmI3cW7SbrsX18/mxrRB0D3FJKWlauA5eOR/pNGsW39Oa5R99xz1N5er7QOZ3fY0T/+wmbuwItPpasiJ2ndJw9TgtDJj9M0ynLke4cB/uFXZg34FUiTfUz2SWYeTvjd0re/BGAeC2Sg3ol/953AV+v7Eabd6uIXLHMk5dhioxkx6NdmDLiHS6MsJ/xoclK3L3IwioTaZNCPD/qPcPa8GnStxytNdk7zMaBc5NJ2L4D6aimbGYaln/ZjhsHvsgGN/04mrZTK8n6Y6mRdIPxMMl5IJvpw1+nb9jxk3hOh99tLm6b8TCZC1fjwvB3b/pnInNSvuR43r7hjUoImTCTlyKvI/6HbUYRN5eOq6LiuOc1hYfj6prNtttCmXvRRK9NvJ1qjqbrf9Ywv8NZ2HtY+Dzuew63sjdXW/m/PQMZlrT8mCFwP1hDuPe/D5H52irjHhACy9DeXPTEb2SHKGEPFPFaJKvs1Qz/9WZSPg+j0YJtZJcYvSrAaBD16kjBIy6W9jo8yH9mCzsocfcqt347mta/Ga1Rc0oy/W9dekyhjjSFUj24FDHDjHQ6aTI7h8n3duGxxK117r/WbmfIgrG0mWone9VqXLWyA13ndSXs33tY32qyz8uOflUZzaR7RtDsh2U1NnRty0fnv33CD5Sro8s564WXmPLwWXz/1tmkflOArDp69UJXRgqFA2JpNiCfFzLfdpdo9u7A3qupK+F+dw/ILexlripGvDye1HfW8mqPEax8bRlPJG7wfE5dulhb7WToT2NpNcNJ89+WIt0PW+uVvXnlxdeOm0il8B0zK+J5Ys5wWsyy0nLFBqTTSW3Hp7l5BvnXZTBx1LsMjLTj7XsqkDQ8ca/V6xd27z2dv7eG0eoTW42v/Z4WzEr5huNl6b3c6VMmtrgSfVsu+oEDzJrSn2FPrHL7jw2feo7DyahNNxA1MZbsBWuQtfzq5ox0ckdnMG3k4VRo3wmJLl1MK2vO/x67lIjvl3vWax3akDC50O2fPHFSzdE8l7yOxx5fzrLxUdhk3bZrSJqay+gQEuoWVf/Vqdmnu0hdeAiX1Yrp1zUsH5JN98svwOnWALMNUpZYyF6+2lNMCiFw9u9O/3/9poQ9gLxf3oSZ119Cy5XGGM3hlropMhJnrzYUXBjOiCt+YU7i7DPeBVMXDU7cy1vA4WTqlN8FlquO7wY5HtNK05g5fhBhS41Wn5bchBsuX3BCLehzwm3cPSKZZv/JB5dO4vQVDHc+RLk7MjKySJCyuJT4LTtw2XI87zOnp5F3U3PGXDeXr+Jm+zwr8FNLLI/OHUH2O6VEbHALuxBUX9KDDs+s57W0Zcc+wDGINoW7B5KPNZgcmDCTdC2EXZclkL5BM3z+O/JJmZR/9DeYNPbe04c3x00540Lngo1Z+7rD+u2eZS0piT3DWtNuxGaeTZ9Sq9R38Ak7gDjchQwkPbuEy+XzMvxyrqtyLqZqYCUuqxVzWlOGzV/OjTEHT/l4G6uruPXJB4j70GgdCLOZ3Gd7seH6E3ePbKyu4o4H7iNy1vEFUmucQMGtbbnxpnncF7/N56LukDq37LyQ/Q9lIn6vmWPWFB7OgRu68fKjU4M+vG9ztZXLvr2ftq8eQN9ed2VDERaG4+yO7BgpmH3ha3QODfKLcgawsbqKEZMfJHWxhbJWkZz/4FKeSlpOpCl4Ipa01JxVUsqedW07rrgLIcKBRRh9YTPwuZTyKSFEFjATaAysAm6QUlYLIcKAGUAPoBgYJqXMP9Y5/Cnua+12Hhl6K3LVRjBp5H/Sga3nzjilY22sruL6Fx+kyTSjvrMwmyl4uDff3P7iSU8A8tzBNiy8s98RAupBCMwpyRT3zyT77k28kfGDz4pv/Znz1g+h0U1WnHv3edaZ05qy6cl0ll32Sr0td+ptdOlipiWJJ36+mojdR3Z4pQli++zn3fYfBiT7VHF07NKB1eUgRJj89pvxJ8cS9xNxy9iB/lJKixAiBPhNCPEd8ADwipRyphBiGjAKmOr+XyKlbCWEGA68AAzzyifxAh1Czew9O5bkVYBLp+UTFoa/35+ZWT+f1HH+LOyYNPbd3ps5pyDsAI8lbiXyLTtvzRxESK2gET0MTP1KGNdmAVdFz3KP5PvvJi2bm0rEXqMuihYTQ/69Hbn+2sPlXBuGsIMReTSyUTEjr3jrGHspYa9vhIkQwrSGOe5xUm4ZIUQk8BtwJ/AtkCKldAoh+gH/klIOEELMc79eIoQwA3uBJHmME/mz5Q4wuuBsdg+N92SjybO68NRH0094QPBTSywvTriOxu8u9wj7/jv68NHDE4Ou5XbhpsGEjYvAkRDJ7vuqWdFnelB1axWKM5nTcssACCE0DNdLK+B1YAKwVErZyr09A/hOStlRCLEBGCilLHRvywX6SCkP/umYY4AxAM3SzD3yVmae4sc7eXTp4px11xB/Q6lRx9ukUTqyN2WXW5jR671jRjh8YYlh2uirMf2yxlgRxMIOxrVaX+2gkcnhieBRKBT1g2OJ+wk1VaWUupSyK5AO9Abanq5RUsq3pJQ9pZQ9kxr7N6pAEyYWdp5Jwa1G3QlcOnEfLqH58E08cd1ttJx/C19VRnNQr6REt3r+ltsdPPfSSI+wi5BQ9t/Zhw8efjkohR2Ma9U1LEwJu0JxhnFSoZBSylIhxAKgHxAnhDBLKZ0Yol/k3q0IyAAK3W6ZWIyB1XpFmAjh2dHv82LuDUTPdmcUunTEkj9otUzj7Wb9mdokFkw1gfEmSzWJm4wwQGE2k/NcD34e9iLNzqAyoAqFomFwXHEXQiQBDrewRwAXYwySLgCGYkTM3AQcnrhrjnt5iXv7z8fytweSwVFWWk58lSHX3E70wihSvsgxyse6dJz5uyD/yP09k2qZNPaP7sWP105Qwq5QKOolJxIK2Rn4AKM0mgn4VEr5byFECwxhTwDWANdLKe3u0MkPgW7AIWC4lPKY0577e0C1Lqyuap7a34dfJvUlaWEhzoLdNRmHtdBiYii6pSMf3v+yimVWKBQB5bQHVH1NfRD3w5ToVhbbE3i76HyqnH8dWG0Xt5cnmvzilSniFAqF4nQ43Tj3BkW8FsllkTYuaz3vGHspYVcoFPWb4CyqoFAoFA0cJe4KhUIRhChxVygUiiBEibtCoVAEIUrcFQqFIghR4q5QKBRBiBJ3hUKhCEKUuCsUCkUQosRdoVAoghAl7gqFQhGEKHFXKBSKIESJu0KhUAQh9aIqpBCiAtgaaDvqIYnAwePu1fBQ16Vu1HWpm2C+Ls2llEl1bagvVSG3Hq1sZUNGCLFSXZe/oq5L3ajrUjcN9boot4xCoVAEIUrcFQqFIgipL+L+VqANqKeo61I36rrUjbouddMgr0u9GFBVKBQKhXepLy13hUKhUHgRJe4KhUIRhARc3IUQA4UQW4UQOUKIRwJtjz8RQmQIIRYIITYJITYKIca51ycIIX4UQmx3/493rxdCiMnua7VOCNE9sJ/AdwghNCHEGiHEN+7lLCHEMvdn/58QItS9Psy9nOPenhlIu32JECJOCPG5EGKLEGKzEKKfuldACHG/+/ezQQjxiRAiXN0vARZ3IYQGvA5cCrQHRggh2gfSJj/jBB6UUrYH+gJj3Z//EWC+lLI1MN+9DMZ1au3+GwNM9b/JfmMcsLnW8gvAK1LKVkAJMMq9fhRQ4l7/inu/YGUS8L2Usi3QBeP6NOh7RQiRBtwL9JRSdgQ0YDjqfgEpZcD+gH7AvFrLjwKPBtKmAF+P2cDFGNm6qe51qRhJXgBvAiNq7e/ZL5j+gHQMoeoPfAMIjAxD85/vG2Ae0M/92uzeTwT6M/jgmsQCeX/+bOpeIQ0oABLc3/83wICGfr9IKQPuljn8xRym0L2uweHuHnYDlgHJUso97k17gWT364ZyvV4FHgZc7uXGQKmU0ulerv25PdfEvb3MvX+wkQUcAKa73VXvCCGiaOD3ipSyCHgJ2AXswfj+V6Hul4CLuwIQQkQDXwD3SSnLa2+TRhOjwcSrCiH+DuyXUq4KtC31DDPQHZgqpewGVFLjggEa3r0C4B5juALj4dcUiAIGBtSoekKgxb0IyKi1nO5e12AQQoRgCPvHUspZ7tX7hBCp7u2pwH73+oZwvc4GBgsh8oGZGK6ZSUCcEOJwLaTan9tzTdzbY4FifxrsJwqBQinlMvfy5xhi35DvFYCLgDwp5QEppQOYhXEPNfT7JeDivgJo7R7ZDsUYCJkTYJv8hhBCAO8Cm6WUL9faNAe4yf36Jgxf/OH1N7ojIfoCZbW65EGBlPJRKWW6lDIT4374WUo5ElgADHXv9udrcvhaDXXvH3StVynlXqBACNHGvepCYBMN+F5xswvoK4SIdP+eDl+XBn2/AIEdUHVf00HANiAXeDzQ9vj5s5+D0Y1eB6x1/w3C8AHOB7YDPwEJ7v0FRnRRLrAeI0Ig4J/Dh9fnAuAb9+sWwHIgB/gMCHOvD3cv57i3twi03T68Hl2Ble775SsgXt0rEuBpYAuwAfgQCFP3i1TlBxQKhSIYCbRbRqFQKBQ+QIm7QqFQBCFK3BUKhSIIUeKuUCgUQYgSd4VCoQhClLgrFApFEKLEXaFQKIKQ/wcp5R2FNgkzIQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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A80C4KlcwgH0SEUFjDMMtmXnbmKaB7pNWdQx6n/RtkNdBXWE85GrjUhpXOl8D/mxINZxKoyfgeeDFQdYB3E/jdPBjGp+9ltN4Zt564BXgn4E5Q6rjb4EXgE00wjZ/AHWcT+MUfRPwXPWzdND7pE0dA90nwGdpDOK6icYflu+O+T/7NPAq8ANg5kTe12/QSYUo/QKdVAzDLhXCsEuFMOxSIQy7VAjDLhXCsEuFMOxSIf4fuUvcvLlu8NgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1q/pqDY2BO2GAA3geClfhGmo4JpJEYwzDLRFxz7imWo9JWR11H5O+DfJa1xXGI642XkHjSuebwB8NqIazaPQEvAy8WmcdwKM03g7up/HZ60Yac+aNAluBfwRmDqiOh4FXgE00wjanhjoupfEWfROwsfi5ou5j0qKOWo8J8Gs0BnHdROMfyx+P+5t9AXgD+A4wfSLb9TfozJLIfoHOLA2H3SwJh90sCYfdLAmH3SwJh90sCYfdLAmH3SyJ/wVGpvSD4hIa6AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PeBVYTyNsAz2oYwGNl+jrgXXVz5W9Piaj1NHTYwJcQGMS1/U0/rH89bC/2ReATcC/AFPH87x+gk4qROkX6KRiGHapEIZdKoRhlwph2KVCGHapEIZdKoRhlwrx/0uKsde9PrRSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TYAU17RNJfTQCtjoiHi6aa98nzero1T4ptj3mSV7L9CLsLwALizOL04BLgDV1FyHpYEmH7FsGzgHWt16rq9bQmLgTejiB575wFS6khn0iSTTmMNwQEbcN66p1n5TVUfc+6dokr3WdYTzgbONSGmc6Xwe+1aMajqExEvAy8MM66wDupfF2cDeNz15X0rhn3gDwGvB9YFaP6vg74BVgHY2wza2hjtNovEVfB6wtfpbWvU9a1FHrPgF+icYkruto/GH5w2H/Z58HNgL/AEwfy+v6G3RmSWQ/QWeWhsNuloTDbpaEw26WhMNuloTDbpaEw26WhMNulsT/Aqg8/haIcB/VAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "id": "_rBChPhXwAby",
        "outputId": "dda688f7-e844-4e32-e7ae-31a3018b95a6"
      },
      "source": [
        "train_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-8852729f0f48>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'shape'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TrhsUjU1xsPi"
      },
      "source": [
        "train_data = np.array(train_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQ5RQ79JwERE"
      },
      "source": [
        "train_data = train_data.reshape(-1,32,32)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uT15KHtpO-v3"
      },
      "source": [
        "for i in range(len(train_data)):\n",
        "  train_data[i] = cv2.fastNlMeansDenoising(train_data[i], train_data[i], h = 10, templateWindowSize = 7, \tsearchWindowSize = 21) \n",
        "  plt.imshow(train_data[i])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8jV3kREEQn6"
      },
      "source": [
        "train_data = np.array(train_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-INPXG-HEcR4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "784dae17-a358-4bed-a6e8-e98e39518417"
      },
      "source": [
        "train_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4, 32, 32)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "djKVUzEcFShG"
      },
      "source": [
        "train_data = train_data.reshape(-1, 32, 32, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NMhdQKcRttTb"
      },
      "source": [
        "train_data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rIN3FT0FtxbW"
      },
      "source": [
        "train_data = train_data/255"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ke8Pps7jJ9_a"
      },
      "source": [
        "train_data = 1 - train_data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41_UnG5BEYVW"
      },
      "source": [
        "pred = loaded_model.predict(train_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zJJDsqxaF6JP"
      },
      "source": [
        "import pandas as pd\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/model/labels.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynaZzbMVGbA3"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVqJmtS7GqKo"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vIgu2jibGtRx"
      },
      "source": [
        "data = np.array(data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JqPHHJOOHrIh"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DkveS_m3F44o"
      },
      "source": [
        "symbols = []\n",
        "for i in range(len(data) - 1):\n",
        "    if (data[i + 1][2]>data[i][2]):\n",
        "        symbols.append(data[i][3])\n",
        "        "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "czX-zSKMG3Vm"
      },
      "source": [
        "symbols"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1965zX0quAR"
      },
      "source": [
        "alpha = \"0abcdefghijklmnopqrstuvwxyz\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GEnAVJeoN7FZ"
      },
      "source": [
        "train_data.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iDfIZFLw1XuU",
        "outputId": "da76207e-f015-4269-9c6b-84f2034e5b59"
      },
      "source": [
        "s=''\n",
        "a=[]\n",
        "\n",
        "for i in range(len(train_data)):\n",
        "    s = s + symbols[pred[i].argmax()]\n",
        "    a.append(pred[i].argmax())\n",
        "    im_resize = cv2.resize(train_data[i],(32,32))\n",
        "    plt.imshow(im_resize)\n",
        "    plt.show()\n",
        "print(s)\n",
        "print(a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAO6ElEQVR4nO3de4xc5XnH8e+TzQINEIHBdYxxakxMK0CJoVuLBkRo0mAgiIsUIaiEXAnFEQ1SkWglRNRC1VaBqoBQ1BKZ4saJKJdyKVZLay5KhdIkwELAGNxyqyl2fOMmLi3El6d/zLG0tmZ2Z2fOXNbv9yOt9sx5z+Xh4N+emfPOeU9kJpL2f58YdAGS+sOwS4Uw7FIhDLtUCMMuFcKwS4X4ZDcrR8RZwC3ACPB3mXn9ZMsfOWskF8wf7WaXmsJLaz817XWO+/z/drS9ydbTYGx4Ywdvvr0rmrV1HPaIGAH+BvgqsBF4KiJWZ+aLrdZZMH+UJ9fM73SXasPSoxZPe501a57taHuTrafBWLL0jZZt3byNXwK8kpmvZeYvgbuA87vYnqQe6ibs84CJf0Y2VvMkDaGeX6CLiOURMR4R49vf2tXr3UlqoZuwbwImfgA/upq3l8xckZljmTk2+4iRLnYnqRvdhP0pYFFEHBMRBwAXA6vrKUtS3Tq+Gp+ZOyPiCmANja63lZn5Qm2VFe6+Dz7dsm3FcQtr3dfJf355y7bZ/LTWfWlwuupnz8yHgIdqqkVSD/kNOqkQhl0qhGGXCmHYpUIYdqkQXV2NV3c6uWllKmt+Mf2bU5YeVXsZGkKe2aVCGHapEIZdKoRhlwph2KVCeDW+x37r261vMpnV4U0mnVxxlzyzS4Uw7FIhDLtUCMMuFcKwS4Uw7FIh7Hqrwde+eF7Ltlkb7F7TcPDMLhXCsEuFMOxSIQy7VAjDLhXCsEuF6KrrLSI2AO8Du4CdmTlWR1HD6oTv/kHT+Udv+ElH27N7Tf1URz/772TmmzVsR1IP+TZeKkS3YU/g4Yh4OiKW11GQpN7o9m38aZm5KSJ+FXgkIv4zMx+fuED1R2A5wGfn+e1caVC6OrNn5qbq9zbgAWBJk2VWZOZYZo7NPmKkm91J6kLHYY+IgyPi0D3TwJnAuroKk1Svbt5XzwEeiIg92/mHzPy3WqoaoHNfOrtl29HfmX4Xm91rGhYdhz0zXwO+UGMtknrIrjepEIZdKoRhlwph2KVCGHapEH6lbR87ztg87XXsXtNM4JldKoRhlwph2KVCGHapEIZdKkSRV+OXHrW4o/W86q6ZzDO7VAjDLhXCsEuFMOxSIQy7VAjDLhViv+1667R77buv/8ckrQd3Vow0BDyzS4Uw7FIhDLtUCMMuFcKwS4Uw7FIhpux6i4iVwLnAtsw8sZo3C7gbWABsAC7KzHd6V2Zzi/7991u2LaSzO9SOG7V7Tfunds7s3wfO2mfe1cBjmbkIeKx6LWmITRn26nnrb+8z+3xgVTW9Crig5rok1azTz+xzMnPPmMtbaDzRVdIQ6/oCXWYmkK3aI2J5RIxHxPj2t3Z1uztJHeo07FsjYi5A9XtbqwUzc0VmjmXm2OwjRjrcnaRudRr21cCyanoZ8GA95UjqlXa63u4EzgCOjIiNwLXA9cA9EXEZ8DpwUS+LbGXh73XWvebAkSrRlGHPzEtaNH2l5lok9ZDfoJMKYdilQhh2qRCGXSqEYZcKMSMGnOxk8Ei71/Z29qJTJ2n9sG91aHA8s0uFMOxSIQy7VAjDLhXCsEuFMOxSIYam6+3cl86epHVz07l2rw3W2QtPadn2r6/9rI+VqB2e2aVCGHapEIZdKoRhlwph2KVCDM3V+B1nNL/irnrs/rD+m112f/RR7dtU73hmlwph2KVCGHapEIZdKoRhlwph2KVCtPP4p5XAucC2zDyxmncd8A1ge7XYNZn50FTb2r5rlO+9O2/aRXrDi9S9ds7s3wfOajL/5sxcXP1MGXRJgzVl2DPzceDtPtQiqYe6+cx+RUSsjYiVEXF4bRVJ6olOw34rcCywmMbIEje2WjAilkfEeESMf/DOjg53J6lbHYU9M7dm5q7M3A3cBiyZZNkVmTmWmWOHHD7aaZ2SutRR2CNi7oSXFwLr6ilHUq+00/V2J3AGcGREbASuBc6IiMVAAhuAb7azsze3HMbff+e8pm2H8dP2KlZfTdbtOdljuS54eWnT+f+0aE3XNakzU4Y9My9pMvv2HtQiqYf8Bp1UCMMuFcKwS4Uw7FIhDLtUiL4OODny1occ9kO72IZNL+4q/L8vbW3e8Ivad6U2eWaXCmHYpUIYdqkQhl0qhGGXCmHYpUIMzbPeVI/zXm42XCDAlr7WoeHjmV0qhGGXCmHYpUIYdqkQhl0qhFfj9zMff8mr7mrOM7tUCMMuFcKwS4Uw7FIhDLtUCMMuFaKdxz/NB34AzKHxuKcVmXlLRMwC7gYW0HgE1EWZ+U7vStWwueqVF1q23fi5E/pYyczW6jFadY8N2M6ZfSdwVWYeD5wCfCsijgeuBh7LzEXAY9VrSUNqyrBn5ubMfKaafh9YD8wDzgdWVYutAi7oVZGSujetz+wRsQA4CXgCmJOZm6umLTTe5ksaUm2HPSIOAe4DrszM9ya2ZWbS+DzfbL3lETEeEeM7+LirYiV1rq2wR8QojaDfkZn3V7O3RsTcqn0usK3Zupm5IjPHMnNslAPrqFlSB6YMe0QEjeexr8/MmyY0rQaWVdPLgAfrL09SXdq56+1U4FLg+YjY0xdwDXA9cE9EXAa8DlzUTSGfOPjgblYvyo7cNe11evGIpzM/taNl24217234tepCGxZThj0zfwxEi+av1FuOpF7xG3RSIQy7VAjDLhXCsEuFMOxSIRxwcgY6d95vDrqEjk3WPdWL7sG6ddq99snPtP42+b88s6bTcqbFM7tUCMMuFcKwS4Uw7FIhDLtUCMMuFcKut/3MG3/yxRYtw9+t1U+9uEPthv9+omXb4gMHP5aDZ3apEIZdKoRhlwph2KVCGHapEENzNX73hx8OuoSh0unV4hcv/9uaK+lMq5taJvvv+trJS1u27bxjtPXOvrKx7bq6NfnNOoO/4j4Zz+xSIQy7VAjDLhXCsEuFMOxSIQy7VIgpu94iYj7wAxqPZE5gRWbeEhHXAd8AtleLXpOZD/Wq0P1Rp91rR/3s0JorGQ47t2xt3Vjzs4dmwnh3dWunn30ncFVmPhMRhwJPR8QjVdvNmfnXvStPUl3aedbbZmBzNf1+RKwH5vW6MEn1mtZn9ohYAJwE7Llx94qIWBsRKyPi8Jprk1SjtsMeEYcA9wFXZuZ7wK3AscBiGmf+pk/pjYjlETEeEeM7+LiGkiV1oq2wR8QojaDfkZn3A2Tm1szclZm7gduAJc3WzcwVmTmWmWOjQ/7dYWl/NmXYIyKA24H1mXnThPlzJyx2IbCu/vIk1aWdq/GnApcCz0fEnv6Ka4BLImIxje64DcA3p9rQp0/Yxe/e837TtkdPbN2d1KqLaiZ0n3TavfY/17UaSw7WfHY47mzrxEz4f7a/audq/I+BaNJkn7o0g/gNOqkQhl0qhGGXCmHYpUIYdqkQfR1w8jMjH/PHs15t2vYo0++iOvPry1q2PXzvqmlvr1OTDZQIre/kGjnh11u2rV8+c7vXNJw8s0uFMOxSIQy7VAjDLhXCsEuFMOxSIYbmWW+T3Q3V6s6x+Mlz016nN1p3r227ovXdaz+/xu419Y9ndqkQhl0qhGGXCmHYpUIYdqkQhl0qxNB0vU2mVbfc4x+1XucvF/az6601u9c0LDyzS4Uw7FIhDLtUCMMuFcKwS4WY8mp8RBwEPA4cWC1/b2ZeGxHHAHcBRwBPA5dm5i97Wey+Tj9okjYfMyTtpZ0z+8fAlzPzCzQez3xWRJwC3ADcnJmfA94BLutdmZK6NWXYs+GD6uVo9ZPAl4F7q/mrgAt6UqGkWrT7fPaR6gmu24BHgFeBdzNzZ7XIRmBeb0qUVIe2wp6ZuzJzMXA0sAT4jXZ3EBHLI2I8Isa3v7WrwzIldWtaV+Mz813gR8BvA4dFxJ4LfEcDm1qssyIzxzJzbPYRI10VK6lzU4Y9ImZHxGHV9K8AXwXW0wj916vFlgEP9qpISd1r50aYucCqiBih8cfhnsz854h4EbgrIv4C+Dlwew/rlNSlKcOemWuBk5rMf43G53dJM4DfoJMKYdilQhh2qRCGXSqEYZcKEZnZv51FbAder14eCbzZt523Zh17s469zbQ6fi0zZzdr6GvY99pxxHhmjg1k59ZhHQXW4dt4qRCGXSrEIMO+YoD7nsg69mYde9tv6hjYZ3ZJ/eXbeKkQAwl7RJwVEf8VEa9ExNWDqKGqY0NEPB8Rz0bEeB/3uzIitkXEugnzZkXEIxHxcvX78AHVcV1EbKqOybMRcU4f6pgfET+KiBcj4oWI+MNqfl+PySR19PWYRMRBEfFkRDxX1fFn1fxjIuKJKjd3R8QB09pwZvb1BxihMazVQuAA4Dng+H7XUdWyAThyAPs9HTgZWDdh3l8BV1fTVwM3DKiO64A/6vPxmAucXE0fCrwEHN/vYzJJHX09JkAAh1TTo8ATwCnAPcDF1fzvAZdPZ7uDOLMvAV7JzNeyMfT0XcD5A6hjYDLzceDtfWafT2PgTujTAJ4t6ui7zNycmc9U0+/TGBxlHn0+JpPU0VfZUPsgr4MI+zzgjQmvBzlYZQIPR8TTEbF8QDXsMSczN1fTW4A5A6zliohYW73N7/nHiYkiYgGN8ROeYIDHZJ86oM/HpBeDvJZ+ge60zDwZOBv4VkScPuiCoPGXncYfokG4FTiWxjMCNgM39mvHEXEIcB9wZWa+N7Gtn8ekSR19PybZxSCvrQwi7JuA+RNetxysstcyc1P1exvwAIMdeWdrRMwFqH5vG0QRmbm1+oe2G7iNPh2TiBilEbA7MvP+anbfj0mzOgZ1TKp9T3uQ11YGEfangEXVlcUDgIuB1f0uIiIOjohD90wDZwLrJl+rp1bTGLgTBjiA555wVS6kD8ckIoLGGIbrM/OmCU19PSat6uj3MenZIK/9usK4z9XGc2hc6XwV+PaAalhIoyfgOeCFftYB3Enj7eAOGp+9LqPxzLzHgJeBR4FZA6rjh8DzwFoaYZvbhzpOo/EWfS3wbPVzTr+PySR19PWYAJ+nMYjrWhp/WP50wr/ZJ4FXgH8EDpzOdv0GnVSI0i/QScUw7FIhDLtUCMMuFcKwS4Uw7FIhDLtUCMMuFeL/AaTS1QC+SPfyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kcFR1ZXEmcAHw0LCLiIh9ImK/HdPA6cDqzmsN1EO0Bu6EEQ7guSNclfMYwjGJiKA1huGazLxhUtNQj0ldHcM+JgMb5HVYVxg/drXxLFpXOl8G/nxENRxBqyfgGeC5YdYB3EXr4+AHtL57XULrmXnLgReBfwUOHFEddwDPAqtohW3eEOo4hdZH9FW0RpZ8uvo/MtRj0qGOoR4T4Dhag7iuovXG8heT/s8+AbwE/BOw165s11/QSYUo/QKdVAzDLhXCsEuFMOxSIQy7VAjDLhXCsEuFMOxSIf4PAXPNTKSktIgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xv6uNF9K60vkL4C/6VMPxtEYCXgA2N1kH8ACtl4Mf03rvdRWte+Y9BbwM/Csws091/BPwIrCRVtiGGqjjbFov0TfSunHbhuJvpNFjMk4djR4T4BRak7hupPXE8pdj/mbXA1uBHwCHTeZx/Qk6sySyX6AzS8NhN0vCYTdLwmE3S8JhN0vCYTdLwmE3S8JhN0vi/wDRVaE0NZFkdAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ExPvD++rcJk3qqH2bRIFJXvN0I+w7gNnD7udOVlm1iNiR/d4NPEF3Z97ZJWkGQPZ7dzeKiIhd2T/aAeBuatomkibQCNgDEfF41lz7NmlWR7e2SbbuUU/ymqcbYX8RmJsdWZwIXAEsr7sISZMlHTN0G7gI2NB6qUotpzFxJ3RxAs+hcGUuo4ZtIkk05jDcFBF3DOuqdZvk1VH3Nqlskte6jjAecrTxYhpHOl8HvtelGk6mMRKwDnilzjqAB2m8Hfw1jc9e19C4Zt4zwGvAvwNTulTH/cDLwHoaYZtRQx3n0XiLvp7GTJtrs/+RWrdJizpq3SbA6TQmcV1P44Xlr4f9z74AbAb+FThyNM/rb9CZJSL1A3RmyXDYzRLhsJslwmE3S4TDbpYIh90sEQ67WSIcdrNE/C9HuOuLWPyhDAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\\omega\\flat\\subset\\nu\n",
            "[97, 287, 106, 84]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Xp_LIayG5C2"
      },
      "source": [
        "symbols.type"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5lLwYOFiG7Rk"
      },
      "source": [
        "symbols = np.array(symbols)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhXSkHteHAUS"
      },
      "source": [
        "test = cv2.imread(\"/content/y.png\", cv2.IMREAD_GRAYSCALE)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_nuDD7cLfbc"
      },
      "source": [
        "test = np.array(test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ilPz4pw-LjUV"
      },
      "source": [
        "test.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xgSlD2fELmCW"
      },
      "source": [
        "test = test.reshape(-1,32,32,1)\n",
        "test.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GV-gjpWOHHcd"
      },
      "source": [
        "prediction = loaded_model.predict()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMuZGFNZmdpy"
      },
      "source": [
        "image = cv2.imread(\"/content/d.png\", cv2.IMREAD_GRAYSCALE)\n",
        "image = np.array(image)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CATX6jonmm1O"
      },
      "source": [
        "image.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MrH8Xv-XmorS"
      },
      "source": [
        "plt.imshow(image)\n",
        "plt.show"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PQq0WyLmzUX"
      },
      "source": [
        "image = image.reshape(-1,32,32,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofVDeMUFm7Lk"
      },
      "source": [
        "y = loaded_model.predict(image)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhmQ1HsQnAJE"
      },
      "source": [
        "y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SPQoomFjnFuc"
      },
      "source": [
        "a = y.argmax()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yHxy4-gLnJXk"
      },
      "source": [
        "a"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJuSloCgnKER"
      },
      "source": [
        "symbols[a]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ULKS7gCnMwy"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}