sodoku_hack.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import random\n",
    "import cv2\n",
    "from glob import glob\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Activation, Dropout, Dense, Flatten, BatchNormalization, Conv2D, MaxPooling2D\n",
    "from tensorflow.keras.optimizers import RMSprop\n",
    "from tensorflow.keras import backend as K\n",
    "from keras.preprocessing import image\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from pathlib import Path\n",
    "from PIL import Image\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "read image file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Dataponits =  10160\n",
      "[0 0 0 ... 9 9 9]\n"
     ]
    }
   ],
   "source": [
    "data = os.listdir(r\"D:\\work\\y2-1\\linearProject\\dts\\Digits\")\n",
    "data_X = []\n",
    "data_y = []\n",
    "data_classes = len(data)\n",
    "for i in range(0, data_classes):\n",
    "    data_list = os.listdir(\n",
    "        r\"D:\\work\\y2-1\\linearProject\\dts\\Digits\" + \"/\"+str(i))\n",
    "    for j in data_list:\n",
    "        pic = cv2.imread(\n",
    "            r\"D:\\work\\y2-1\\linearProject\\dts\\Digits\" + \"/\"+str(i)+\"/\"+j)\n",
    "        pic = cv2.resize(pic, (32, 32))\n",
    "        data_X.append(pic)  # img 0-9\n",
    "        data_y.append(i)  # number 0-9\n",
    "\n",
    "if len(data_X) == len(data_y):\n",
    "    print(\"Total Dataponits = \", len(data_X))\n",
    "\n",
    "# Labels and images\n",
    "data_X = np.array(data_X)\n",
    "data_y = np.array(data_y)\n",
    "\n",
    "print(data_y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "split x -> trainX ,testX & split y -> trainY ,testY\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Shape =  (7721, 32, 32, 3)\n",
      "Validation Set Shape =  (1931, 32, 32, 3)\n",
      "Test Set Shape =  (508, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "train_X, test_X, train_y, test_y = train_test_split(\n",
    "    data_X, data_y, test_size=0.05)\n",
    "train_X, valid_X, train_y, valid_y = train_test_split(\n",
    "    train_X, train_y, test_size=0.2)\n",
    "print(\"Training Set Shape = \", train_X.shape)\n",
    "print(\"Validation Set Shape = \", valid_X.shape)\n",
    "print(\"Test Set Shape = \", test_X.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing the images for neuralnet\n",
    "def Prep(img):\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # making image grayscale\n",
    "    img = cv2.equalizeHist(img)  # Histogram equalization to enhance contrast\n",
    "    img = img/255  # normalizing\n",
    "    return img\n",
    "\n",
    "\n",
    "train_X = np.array(list(map(Prep, train_X)))\n",
    "test_X = np.array(list(map(Prep, test_X)))\n",
    "valid_X = np.array(list(map(Prep, valid_X)))\n",
    "\n",
    "\n",
    "# Reshaping the images\n",
    "train_X = train_X.reshape(\n",
    "    train_X.shape[0], train_X.shape[1], train_X.shape[2], 1)\n",
    "test_X = test_X.reshape(test_X.shape[0], test_X.shape[1], test_X.shape[2], 1)\n",
    "valid_X = valid_X.reshape(\n",
    "    valid_X.shape[0], valid_X.shape[1], valid_X.shape[2], 1)\n",
    "\n",
    "# Augmentation\n",
    "datagen = ImageDataGenerator(width_shift_range=0.1, height_shift_range=0.1,\n",
    "                             zoom_range=0.2, shear_range=0.1, rotation_range=10)  # random generate img data\n",
    "datagen.fit(train_X)  # trian datagen with train_X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to_categorical is function for Converts a class vector (integers) to binary class matrix.\n",
    "train_y = to_categorical(train_y, data_classes)\n",
    "test_y = to_categorical(test_y, data_classes)\n",
    "valid_y = to_categorical(valid_y, data_classes)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Building (Nueral Net)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 32, 32, 60)        1560      \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 32, 32, 60)        90060     \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 16, 16, 60)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 16, 16, 30)        16230     \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 16, 16, 30)        8130      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 8, 8, 30)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 8, 8, 30)          0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1920)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 500)               960500    \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 500)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                5010      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,081,490\n",
      "Trainable params: 1,081,490\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Creating a Neural Network\n",
    "model = Sequential()\n",
    "\n",
    "model.add((Conv2D(60, (5, 5), input_shape=(32, 32, 1),\n",
    "          padding='Same', activation='relu')))\n",
    "model.add((Conv2D(60, (5, 5), padding=\"same\", activation='relu')))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "# model.add(Dropout(0.25))\n",
    "\n",
    "model.add((Conv2D(30, (3, 3), padding=\"same\", activation='relu')))\n",
    "model.add((Conv2D(30, (3, 3), padding=\"same\", activation='relu')))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(500, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "200/200 - 43s - loss: 0.6898 - accuracy: 0.7706 - val_loss: 0.0687 - val_accuracy: 0.9762 - 43s/epoch - 217ms/step\n",
      "Epoch 2/100\n",
      "200/200 - 42s - loss: 0.2410 - accuracy: 0.9233 - val_loss: 0.0593 - val_accuracy: 0.9819 - 42s/epoch - 209ms/step\n",
      "Epoch 3/100\n",
      "200/200 - 44s - loss: 0.1756 - accuracy: 0.9457 - val_loss: 0.0406 - val_accuracy: 0.9850 - 44s/epoch - 218ms/step\n",
      "Epoch 4/100\n",
      "200/200 - 44s - loss: 0.1356 - accuracy: 0.9600 - val_loss: 0.0308 - val_accuracy: 0.9907 - 44s/epoch - 221ms/step\n",
      "Epoch 5/100\n",
      "200/200 - 43s - loss: 0.1233 - accuracy: 0.9628 - val_loss: 0.0281 - val_accuracy: 0.9922 - 43s/epoch - 215ms/step\n",
      "Epoch 6/100\n",
      "200/200 - 41s - loss: 0.1069 - accuracy: 0.9674 - val_loss: 0.0313 - val_accuracy: 0.9896 - 41s/epoch - 207ms/step\n",
      "Epoch 7/100\n",
      "200/200 - 42s - loss: 0.1028 - accuracy: 0.9680 - val_loss: 0.0292 - val_accuracy: 0.9891 - 42s/epoch - 208ms/step\n",
      "Epoch 8/100\n",
      "200/200 - 41s - loss: 0.0997 - accuracy: 0.9716 - val_loss: 0.0207 - val_accuracy: 0.9922 - 41s/epoch - 206ms/step\n",
      "Epoch 9/100\n",
      "200/200 - 42s - loss: 0.0867 - accuracy: 0.9737 - val_loss: 0.0210 - val_accuracy: 0.9917 - 42s/epoch - 209ms/step\n",
      "Epoch 10/100\n",
      "200/200 - 44s - loss: 0.0826 - accuracy: 0.9748 - val_loss: 0.0428 - val_accuracy: 0.9876 - 44s/epoch - 222ms/step\n",
      "Epoch 11/100\n",
      "200/200 - 44s - loss: 0.0869 - accuracy: 0.9749 - val_loss: 0.0361 - val_accuracy: 0.9886 - 44s/epoch - 219ms/step\n",
      "Epoch 12/100\n",
      "200/200 - 44s - loss: 0.0847 - accuracy: 0.9759 - val_loss: 0.0335 - val_accuracy: 0.9886 - 44s/epoch - 220ms/step\n",
      "Epoch 13/100\n",
      "200/200 - 44s - loss: 0.0850 - accuracy: 0.9755 - val_loss: 0.0232 - val_accuracy: 0.9927 - 44s/epoch - 219ms/step\n",
      "Epoch 14/100\n",
      "200/200 - 44s - loss: 0.0828 - accuracy: 0.9773 - val_loss: 0.0300 - val_accuracy: 0.9902 - 44s/epoch - 219ms/step\n",
      "Epoch 15/100\n",
      "200/200 - 44s - loss: 0.0776 - accuracy: 0.9780 - val_loss: 0.0165 - val_accuracy: 0.9938 - 44s/epoch - 218ms/step\n",
      "Epoch 16/100\n",
      "200/200 - 43s - loss: 0.0776 - accuracy: 0.9769 - val_loss: 0.0159 - val_accuracy: 0.9938 - 43s/epoch - 216ms/step\n",
      "Epoch 17/100\n",
      "200/200 - 43s - loss: 0.0761 - accuracy: 0.9794 - val_loss: 0.0373 - val_accuracy: 0.9881 - 43s/epoch - 215ms/step\n",
      "Epoch 18/100\n",
      "200/200 - 43s - loss: 0.0754 - accuracy: 0.9799 - val_loss: 0.0174 - val_accuracy: 0.9927 - 43s/epoch - 216ms/step\n",
      "Epoch 19/100\n",
      "200/200 - 44s - loss: 0.0708 - accuracy: 0.9802 - val_loss: 0.0299 - val_accuracy: 0.9907 - 44s/epoch - 218ms/step\n",
      "Epoch 20/100\n",
      "200/200 - 44s - loss: 0.0767 - accuracy: 0.9790 - val_loss: 0.0336 - val_accuracy: 0.9891 - 44s/epoch - 218ms/step\n",
      "Epoch 21/100\n",
      "200/200 - 43s - loss: 0.0756 - accuracy: 0.9804 - val_loss: 0.0528 - val_accuracy: 0.9907 - 43s/epoch - 215ms/step\n",
      "Epoch 22/100\n",
      "200/200 - 43s - loss: 0.0745 - accuracy: 0.9801 - val_loss: 0.0184 - val_accuracy: 0.9948 - 43s/epoch - 213ms/step\n",
      "Epoch 23/100\n",
      "200/200 - 43s - loss: 0.0700 - accuracy: 0.9796 - val_loss: 0.0327 - val_accuracy: 0.9902 - 43s/epoch - 214ms/step\n",
      "Epoch 24/100\n",
      "200/200 - 43s - loss: 0.0703 - accuracy: 0.9774 - val_loss: 0.0260 - val_accuracy: 0.9927 - 43s/epoch - 217ms/step\n",
      "Epoch 25/100\n",
      "200/200 - 44s - loss: 0.0737 - accuracy: 0.9807 - val_loss: 0.0198 - val_accuracy: 0.9927 - 44s/epoch - 218ms/step\n",
      "Epoch 26/100\n",
      "200/200 - 44s - loss: 0.0705 - accuracy: 0.9801 - val_loss: 0.0338 - val_accuracy: 0.9927 - 44s/epoch - 218ms/step\n",
      "Epoch 27/100\n",
      "200/200 - 44s - loss: 0.0685 - accuracy: 0.9817 - val_loss: 0.0273 - val_accuracy: 0.9927 - 44s/epoch - 218ms/step\n",
      "Epoch 28/100\n",
      "200/200 - 41s - loss: 0.0793 - accuracy: 0.9818 - val_loss: 0.0118 - val_accuracy: 0.9953 - 41s/epoch - 203ms/step\n",
      "Epoch 29/100\n",
      "200/200 - 40s - loss: 0.0674 - accuracy: 0.9832 - val_loss: 0.0169 - val_accuracy: 0.9943 - 40s/epoch - 202ms/step\n",
      "Epoch 30/100\n",
      "200/200 - 40s - loss: 0.0728 - accuracy: 0.9821 - val_loss: 0.0146 - val_accuracy: 0.9959 - 40s/epoch - 200ms/step\n",
      "Epoch 31/100\n",
      "200/200 - 40s - loss: 0.0675 - accuracy: 0.9831 - val_loss: 0.0147 - val_accuracy: 0.9927 - 40s/epoch - 201ms/step\n",
      "Epoch 32/100\n",
      "200/200 - 40s - loss: 0.0719 - accuracy: 0.9838 - val_loss: 0.0157 - val_accuracy: 0.9943 - 40s/epoch - 202ms/step\n",
      "Epoch 33/100\n",
      "200/200 - 40s - loss: 0.0778 - accuracy: 0.9798 - val_loss: 0.0181 - val_accuracy: 0.9953 - 40s/epoch - 201ms/step\n",
      "Epoch 34/100\n",
      "200/200 - 40s - loss: 0.0657 - accuracy: 0.9829 - val_loss: 0.0167 - val_accuracy: 0.9964 - 40s/epoch - 201ms/step\n",
      "Epoch 35/100\n",
      "200/200 - 41s - loss: 0.0672 - accuracy: 0.9838 - val_loss: 0.0203 - val_accuracy: 0.9948 - 41s/epoch - 203ms/step\n",
      "Epoch 36/100\n",
      "200/200 - 40s - loss: 0.0673 - accuracy: 0.9826 - val_loss: 0.0195 - val_accuracy: 0.9943 - 40s/epoch - 198ms/step\n",
      "Epoch 37/100\n",
      "200/200 - 42s - loss: 0.0882 - accuracy: 0.9795 - val_loss: 0.0107 - val_accuracy: 0.9964 - 42s/epoch - 208ms/step\n",
      "Epoch 38/100\n",
      "200/200 - 39s - loss: 0.0695 - accuracy: 0.9851 - val_loss: 0.0313 - val_accuracy: 0.9927 - 39s/epoch - 197ms/step\n",
      "Epoch 39/100\n",
      "200/200 - 39s - loss: 0.0716 - accuracy: 0.9828 - val_loss: 0.0165 - val_accuracy: 0.9938 - 39s/epoch - 197ms/step\n",
      "Epoch 40/100\n",
      "200/200 - 40s - loss: 0.0671 - accuracy: 0.9810 - val_loss: 0.0117 - val_accuracy: 0.9948 - 40s/epoch - 202ms/step\n",
      "Epoch 41/100\n",
      "200/200 - 42s - loss: 0.0829 - accuracy: 0.9810 - val_loss: 0.0245 - val_accuracy: 0.9912 - 42s/epoch - 212ms/step\n",
      "Epoch 42/100\n",
      "200/200 - 42s - loss: 0.0620 - accuracy: 0.9826 - val_loss: 0.0158 - val_accuracy: 0.9943 - 42s/epoch - 209ms/step\n",
      "Epoch 43/100\n",
      "200/200 - 42s - loss: 0.0800 - accuracy: 0.9820 - val_loss: 0.0263 - val_accuracy: 0.9933 - 42s/epoch - 208ms/step\n",
      "Epoch 44/100\n",
      "200/200 - 42s - loss: 0.0877 - accuracy: 0.9823 - val_loss: 0.0215 - val_accuracy: 0.9959 - 42s/epoch - 208ms/step\n",
      "Epoch 45/100\n",
      "200/200 - 42s - loss: 0.0691 - accuracy: 0.9836 - val_loss: 0.0234 - val_accuracy: 0.9938 - 42s/epoch - 208ms/step\n",
      "Epoch 46/100\n",
      "200/200 - 42s - loss: 0.0725 - accuracy: 0.9854 - val_loss: 0.0182 - val_accuracy: 0.9964 - 42s/epoch - 208ms/step\n",
      "Epoch 47/100\n",
      "200/200 - 42s - loss: 0.0702 - accuracy: 0.9821 - val_loss: 0.0132 - val_accuracy: 0.9964 - 42s/epoch - 210ms/step\n",
      "Epoch 48/100\n",
      "200/200 - 42s - loss: 0.0681 - accuracy: 0.9846 - val_loss: 0.0163 - val_accuracy: 0.9964 - 42s/epoch - 210ms/step\n",
      "Epoch 49/100\n",
      "200/200 - 42s - loss: 0.0883 - accuracy: 0.9802 - val_loss: 0.0189 - val_accuracy: 0.9943 - 42s/epoch - 209ms/step\n",
      "Epoch 50/100\n",
      "200/200 - 42s - loss: 0.0595 - accuracy: 0.9856 - val_loss: 0.0185 - val_accuracy: 0.9938 - 42s/epoch - 209ms/step\n",
      "Epoch 51/100\n",
      "200/200 - 42s - loss: 0.0649 - accuracy: 0.9842 - val_loss: 0.0231 - val_accuracy: 0.9943 - 42s/epoch - 210ms/step\n",
      "Epoch 52/100\n",
      "200/200 - 42s - loss: 0.0757 - accuracy: 0.9829 - val_loss: 0.0203 - val_accuracy: 0.9948 - 42s/epoch - 208ms/step\n",
      "Epoch 53/100\n",
      "200/200 - 42s - loss: 0.0732 - accuracy: 0.9848 - val_loss: 0.0134 - val_accuracy: 0.9948 - 42s/epoch - 210ms/step\n",
      "Epoch 54/100\n",
      "200/200 - 42s - loss: 0.0843 - accuracy: 0.9809 - val_loss: 0.0096 - val_accuracy: 0.9979 - 42s/epoch - 209ms/step\n",
      "Epoch 55/100\n",
      "200/200 - 42s - loss: 0.0634 - accuracy: 0.9853 - val_loss: 0.0053 - val_accuracy: 0.9969 - 42s/epoch - 210ms/step\n",
      "Epoch 56/100\n",
      "200/200 - 42s - loss: 0.0677 - accuracy: 0.9868 - val_loss: 0.0088 - val_accuracy: 0.9959 - 42s/epoch - 209ms/step\n",
      "Epoch 57/100\n",
      "200/200 - 42s - loss: 0.0747 - accuracy: 0.9829 - val_loss: 0.0250 - val_accuracy: 0.9907 - 42s/epoch - 210ms/step\n",
      "Epoch 58/100\n",
      "200/200 - 42s - loss: 0.0638 - accuracy: 0.9840 - val_loss: 0.0919 - val_accuracy: 0.9829 - 42s/epoch - 209ms/step\n",
      "Epoch 59/100\n",
      "200/200 - 42s - loss: 0.0862 - accuracy: 0.9832 - val_loss: 0.0279 - val_accuracy: 0.9948 - 42s/epoch - 208ms/step\n",
      "Epoch 60/100\n",
      "200/200 - 41s - loss: 0.0732 - accuracy: 0.9817 - val_loss: 0.0216 - val_accuracy: 0.9959 - 41s/epoch - 206ms/step\n",
      "Epoch 61/100\n",
      "200/200 - 41s - loss: 0.0644 - accuracy: 0.9856 - val_loss: 0.0108 - val_accuracy: 0.9964 - 41s/epoch - 206ms/step\n",
      "Epoch 62/100\n",
      "200/200 - 41s - loss: 0.0681 - accuracy: 0.9853 - val_loss: 0.0314 - val_accuracy: 0.9902 - 41s/epoch - 207ms/step\n",
      "Epoch 63/100\n",
      "200/200 - 42s - loss: 0.0842 - accuracy: 0.9815 - val_loss: 0.0135 - val_accuracy: 0.9959 - 42s/epoch - 212ms/step\n",
      "Epoch 64/100\n",
      "200/200 - 39s - loss: 0.0648 - accuracy: 0.9842 - val_loss: 0.0206 - val_accuracy: 0.9938 - 39s/epoch - 195ms/step\n",
      "Epoch 65/100\n",
      "200/200 - 40s - loss: 0.0738 - accuracy: 0.9846 - val_loss: 0.0469 - val_accuracy: 0.9933 - 40s/epoch - 198ms/step\n",
      "Epoch 66/100\n",
      "200/200 - 39s - loss: 0.0943 - accuracy: 0.9838 - val_loss: 0.0340 - val_accuracy: 0.9948 - 39s/epoch - 196ms/step\n",
      "Epoch 67/100\n",
      "200/200 - 39s - loss: 0.0773 - accuracy: 0.9845 - val_loss: 0.0462 - val_accuracy: 0.9902 - 39s/epoch - 196ms/step\n",
      "Epoch 68/100\n",
      "200/200 - 39s - loss: 0.0792 - accuracy: 0.9812 - val_loss: 0.0417 - val_accuracy: 0.9922 - 39s/epoch - 196ms/step\n",
      "Epoch 69/100\n",
      "200/200 - 39s - loss: 0.0790 - accuracy: 0.9824 - val_loss: 0.0091 - val_accuracy: 0.9969 - 39s/epoch - 196ms/step\n",
      "Epoch 70/100\n",
      "200/200 - 39s - loss: 0.1031 - accuracy: 0.9806 - val_loss: 0.0154 - val_accuracy: 0.9943 - 39s/epoch - 196ms/step\n",
      "Epoch 71/100\n",
      "200/200 - 40s - loss: 0.0675 - accuracy: 0.9846 - val_loss: 0.0291 - val_accuracy: 0.9953 - 40s/epoch - 198ms/step\n",
      "Epoch 72/100\n",
      "200/200 - 40s - loss: 0.0799 - accuracy: 0.9821 - val_loss: 0.0269 - val_accuracy: 0.9938 - 40s/epoch - 201ms/step\n",
      "Epoch 73/100\n",
      "200/200 - 39s - loss: 0.0759 - accuracy: 0.9853 - val_loss: 0.0124 - val_accuracy: 0.9964 - 39s/epoch - 194ms/step\n",
      "Epoch 74/100\n",
      "200/200 - 39s - loss: 0.0638 - accuracy: 0.9870 - val_loss: 0.0138 - val_accuracy: 0.9964 - 39s/epoch - 196ms/step\n",
      "Epoch 75/100\n",
      "200/200 - 39s - loss: 0.0719 - accuracy: 0.9870 - val_loss: 0.0330 - val_accuracy: 0.9922 - 39s/epoch - 195ms/step\n",
      "Epoch 76/100\n",
      "200/200 - 39s - loss: 0.0895 - accuracy: 0.9823 - val_loss: 0.0252 - val_accuracy: 0.9964 - 39s/epoch - 195ms/step\n",
      "Epoch 77/100\n",
      "200/200 - 39s - loss: 0.0870 - accuracy: 0.9845 - val_loss: 0.0344 - val_accuracy: 0.9959 - 39s/epoch - 196ms/step\n",
      "Epoch 78/100\n",
      "200/200 - 39s - loss: 0.0862 - accuracy: 0.9838 - val_loss: 0.0320 - val_accuracy: 0.9943 - 39s/epoch - 197ms/step\n",
      "Epoch 79/100\n",
      "200/200 - 40s - loss: 0.0957 - accuracy: 0.9803 - val_loss: 0.0223 - val_accuracy: 0.9943 - 40s/epoch - 200ms/step\n",
      "Epoch 80/100\n",
      "200/200 - 39s - loss: 0.0672 - accuracy: 0.9843 - val_loss: 0.0122 - val_accuracy: 0.9974 - 39s/epoch - 197ms/step\n",
      "Epoch 81/100\n",
      "200/200 - 39s - loss: 0.0650 - accuracy: 0.9859 - val_loss: 0.0323 - val_accuracy: 0.9943 - 39s/epoch - 195ms/step\n",
      "Epoch 82/100\n",
      "200/200 - 39s - loss: 0.0952 - accuracy: 0.9807 - val_loss: 0.0201 - val_accuracy: 0.9959 - 39s/epoch - 195ms/step\n",
      "Epoch 83/100\n",
      "200/200 - 40s - loss: 0.0656 - accuracy: 0.9847 - val_loss: 0.0229 - val_accuracy: 0.9964 - 40s/epoch - 198ms/step\n",
      "Epoch 84/100\n",
      "200/200 - 40s - loss: 0.0800 - accuracy: 0.9848 - val_loss: 0.0369 - val_accuracy: 0.9948 - 40s/epoch - 198ms/step\n",
      "Epoch 85/100\n",
      "200/200 - 41s - loss: 0.0922 - accuracy: 0.9848 - val_loss: 0.0157 - val_accuracy: 0.9959 - 41s/epoch - 207ms/step\n",
      "Epoch 86/100\n",
      "200/200 - 41s - loss: 0.0762 - accuracy: 0.9860 - val_loss: 0.0415 - val_accuracy: 0.9938 - 41s/epoch - 207ms/step\n",
      "Epoch 87/100\n",
      "200/200 - 41s - loss: 0.0811 - accuracy: 0.9860 - val_loss: 0.0166 - val_accuracy: 0.9964 - 41s/epoch - 206ms/step\n",
      "Epoch 88/100\n",
      "200/200 - 41s - loss: 0.0672 - accuracy: 0.9864 - val_loss: 0.0679 - val_accuracy: 0.9943 - 41s/epoch - 207ms/step\n",
      "Epoch 89/100\n",
      "200/200 - 42s - loss: 0.0868 - accuracy: 0.9840 - val_loss: 0.0320 - val_accuracy: 0.9927 - 42s/epoch - 209ms/step\n",
      "Epoch 90/100\n",
      "200/200 - 42s - loss: 0.1017 - accuracy: 0.9829 - val_loss: 0.0528 - val_accuracy: 0.9912 - 42s/epoch - 208ms/step\n",
      "Epoch 91/100\n",
      "200/200 - 41s - loss: 0.0856 - accuracy: 0.9854 - val_loss: 0.0172 - val_accuracy: 0.9943 - 41s/epoch - 207ms/step\n",
      "Epoch 92/100\n",
      "200/200 - 42s - loss: 0.0844 - accuracy: 0.9827 - val_loss: 0.0225 - val_accuracy: 0.9964 - 42s/epoch - 208ms/step\n",
      "Epoch 93/100\n",
      "200/200 - 41s - loss: 0.0809 - accuracy: 0.9832 - val_loss: 0.0331 - val_accuracy: 0.9953 - 41s/epoch - 207ms/step\n",
      "Epoch 94/100\n",
      "200/200 - 41s - loss: 0.0957 - accuracy: 0.9834 - val_loss: 0.0337 - val_accuracy: 0.9943 - 41s/epoch - 207ms/step\n",
      "Epoch 95/100\n",
      "200/200 - 42s - loss: 0.0929 - accuracy: 0.9845 - val_loss: 0.0362 - val_accuracy: 0.9927 - 42s/epoch - 209ms/step\n",
      "Epoch 96/100\n",
      "200/200 - 42s - loss: 0.0623 - accuracy: 0.9890 - val_loss: 0.0138 - val_accuracy: 0.9959 - 42s/epoch - 208ms/step\n",
      "Epoch 97/100\n",
      "200/200 - 42s - loss: 0.0744 - accuracy: 0.9847 - val_loss: 0.0371 - val_accuracy: 0.9948 - 42s/epoch - 209ms/step\n",
      "Epoch 98/100\n",
      "200/200 - 41s - loss: 0.0911 - accuracy: 0.9845 - val_loss: 0.0284 - val_accuracy: 0.9943 - 41s/epoch - 207ms/step\n",
      "Epoch 99/100\n",
      "200/200 - 42s - loss: 0.0849 - accuracy: 0.9834 - val_loss: 0.0605 - val_accuracy: 0.9927 - 42s/epoch - 208ms/step\n",
      "Epoch 100/100\n",
      "200/200 - 41s - loss: 0.1059 - accuracy: 0.9838 - val_loss: 0.0108 - val_accuracy: 0.9969 - 41s/epoch - 207ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: D:\\work\\y2-1\\linearProject\\model2\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: D:\\work\\y2-1\\linearProject\\model2\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.008776502683758736, 0.9960629940032959]\n",
      "Test Score =  0.008776502683758736\n",
      "Test Accuracy = 0.9960629940032959\n"
     ]
    }
   ],
   "source": [
    "# Compiling the model\n",
    "\n",
    "optimizer = tf.keras.optimizers.legacy.RMSprop(\n",
    "    learning_rate=0.001, rho=0.9, epsilon=1e-08, decay=0.0)\n",
    "model.compile(optimizer=optimizer,\n",
    "              loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Fit the model\n",
    "\n",
    "history = model.fit(datagen.flow(train_X, train_y, batch_size=32),\n",
    "                    epochs=100, validation_data=(valid_X, valid_y),\n",
    "                    verbose=2, steps_per_epoch=200)\n",
    "\n",
    "# Testing the model on the test set\n",
    "\n",
    "score = model.evaluate(test_X, test_y, verbose=0)\n",
    "model.save(r\"D:\\work\\y2-1\\linearProject\\model2\")\n",
    "print(score)\n",
    "print('Test Score = ', score[0])\n",
    "print('Test Accuracy =', score[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Randomly select an image from the dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def preprocess(image):\n",
    "    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "    blur = cv2.GaussianBlur(gray, (3, 3), 6)\n",
    "    #blur = cv2.bilateralFilter(gray,9,75,75)\n",
    "    threshold_img = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)\n",
    "    return threshold_img\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def main_outline(contour):\n",
    "    biggest = np.array([])\n",
    "    max_area = 0\n",
    "    for i in contour:\n",
    "        area = cv2.contourArea(i)\n",
    "        if area > 50:\n",
    "            peri = cv2.arcLength(i, True)\n",
    "            approx = cv2.approxPolyDP(i, 0.02 * peri, True)\n",
    "            if area > max_area and len(approx) == 4:\n",
    "                biggest = approx\n",
    "                max_area = area\n",
    "    return biggest, max_area\n",
    "\n",
    "\n",
    "def reframe(points):\n",
    "    points = points.reshape((4, 2))\n",
    "    points_new = np.zeros((4, 1, 2), dtype=np.int32)\n",
    "    add = points.sum(1)\n",
    "    points_new[0] = points[np.argmin(add)]\n",
    "    points_new[3] = points[np.argmax(add)]\n",
    "    diff = np.diff(points, axis=1)\n",
    "    points_new[1] = points[np.argmin(diff)]\n",
    "    points_new[2] = points[np.argmax(diff)]\n",
    "    return points_new\n",
    "\n",
    "\n",
    "def splitcells(img):\n",
    "    rows = np.vsplit(img, 9)\n",
    "    boxes = []\n",
    "    for r in rows:\n",
    "        cols = np.hsplit(r, 9)\n",
    "        for box in cols:\n",
    "            boxes.append(box)\n",
    "    return boxes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "INPUT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing puzzle to be solved\n",
    "try:\n",
    "    puzzle = cv2.imread(r\"D:\\work\\y2-1\\linearProject\\dts\\test\\su.jpg\")\n",
    "    plt.figure()\n",
    "    plt.imshow(puzzle)\n",
    "    plt.show()\n",
    "except:\n",
    "    print('Not found image')\n",
    "    # let's see what we got\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Resizing puzzle to be solved\n",
    "puzzle = cv2.resize(puzzle, (450, 450))\n",
    "# Preprocessing Puzzle\n",
    "su_puzzle = preprocess(puzzle)\n",
    "\n",
    "# Finding the outline of the sudoku puzzle in the image\n",
    "su_contour_1 = su_puzzle.copy()\n",
    "su_contour_2 = su_puzzle.copy()\n",
    "su_contour, hierarchy = cv2.findContours(\n",
    "    su_puzzle, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "cv2.drawContours(su_contour_1, su_contour, -1, (0, 255, 0), 3)\n",
    "\n",
    "black_img = np.zeros((450, 450, 3), np.uint8)\n",
    "su_biggest, su_maxArea = main_outline(su_contour)\n",
    "if su_biggest.size != 0:\n",
    "    su_biggest = reframe(su_biggest)\n",
    "    cv2.drawContours(su_contour_2, su_biggest, -1, (0, 255, 0), 10)\n",
    "    su_pts1 = np.float32(su_biggest)\n",
    "    su_pts2 = np.float32([[0, 0], [450, 0], [0, 450], [450, 450]])\n",
    "    su_matrix = cv2.getPerspectiveTransform(su_pts1, su_pts2)\n",
    "    su_imagewrap = cv2.warpPerspective(puzzle, su_matrix, (450, 450))\n",
    "    su_imagewrap = cv2.cvtColor(su_imagewrap, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(su_imagewrap)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sudoku_cell = splitcells(su_imagewrap)\n",
    "# Let's have alook at the last cell\n",
    "plt.figure()\n",
    "plt.imshow(sudoku_cell[58])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The sudoku_cell's output includes the boundaries this could lead to misclassifications by the model\n",
    "# I am cropping the cells to avoid that\n",
    "# sneeking in a bit of PIL lib as cv2 was giving some weird error that i couldn't ward off\n",
    "\n",
    "def CropCell(cells):\n",
    "    Cells_croped = []\n",
    "    for image in cells:\n",
    "\n",
    "        img = np.array(image)\n",
    "        img = img[4:46, 6:46]\n",
    "        img = Image.fromarray(img)\n",
    "        Cells_croped.append(img)\n",
    "\n",
    "    return Cells_croped\n",
    "\n",
    "\n",
    "sudoku_cell_croped = CropCell(sudoku_cell)\n",
    "# Let's have alook at the last cell\n",
    "plt.figure()\n",
    "plt.imshow(sudoku_cell_croped[58])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
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      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
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      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 24ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 23ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "1/1 [==============================] - 0s 33ms/step\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 25ms/step\n",
      "[3, 8, 0, 9, 0, 0, 2, 0, 5, 0, 0, 0, 0, 0, 8, 7, 3, 0, 0, 6, 0, 3, 0, 0, 9, 8, 0, 0, 0, 0, 0, 0, 3, 5, 0, 1, 9, 1, 0, 5, 0, 7, 0, 2, 3, 7, 0, 3, 1, 0, 0, 0, 0, 0, 0, 3, 5, 0, 0, 1, 0, 9, 0, 0, 7, 4, 6, 0, 0, 0, 0, 0, 8, 0, 1, 0, 0, 2, 0, 6, 7]\n",
      "[3 8 0 9 0 0 2 0 5 0 0 0 0 0 8 7 3 0 0 6 0 3 0 0 9 8 0 0 0 0 0 0 3 5 0 1 9\n",
      " 1 0 5 0 7 0 2 3 7 0 3 1 0 0 0 0 0 0 3 5 0 0 1 0 9 0 0 7 4 6 0 0 0 0 0 8 0\n",
      " 1 0 0 2 0 6 7]\n"
     ]
    }
   ],
   "source": [
    "def read_cells(cell, model):\n",
    "\n",
    "    result = []\n",
    "    for image in cell:\n",
    "        # preprocess the image as it was in the model\n",
    "        img = np.asarray(image)\n",
    "        img = img[4:img.shape[0] - 4, 4:img.shape[1] - 4]\n",
    "        img = cv2.resize(img, (32, 32))\n",
    "        img = img / 255\n",
    "        img = img.reshape(1, 32, 32, 1)\n",
    "        # getting predictions and setting the values if probabilities are above 65%\n",
    "\n",
    "        predictions = model.predict(img)\n",
    "        # classIndex = model.predict_classes(img)\n",
    "        predict_x = model.predict(img)\n",
    "        classIndex = np.argmax(predict_x, axis=-1)\n",
    "        probabilityValue = np.amax(predictions)\n",
    "\n",
    "        if probabilityValue > 0.65:\n",
    "            result.append(classIndex[0])\n",
    "        else:\n",
    "            result.append(0)\n",
    "    return result\n",
    "\n",
    "\n",
    "grid = read_cells(sudoku_cell_croped, model)\n",
    "print(grid)\n",
    "grid = np.asarray(grid)\n",
    "print(grid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 8, 0, 9, 0, 0, 2, 0, 5],\n",
       "       [0, 0, 0, 0, 0, 8, 7, 3, 0],\n",
       "       [0, 6, 0, 3, 0, 0, 9, 8, 0],\n",
       "       [0, 0, 0, 0, 0, 3, 5, 0, 1],\n",
       "       [9, 1, 0, 5, 0, 7, 0, 2, 3],\n",
       "       [7, 0, 3, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 3, 5, 0, 0, 1, 0, 9, 0],\n",
       "       [0, 7, 4, 6, 0, 0, 0, 0, 0],\n",
       "       [8, 0, 1, 0, 0, 2, 0, 6, 7]], dtype=int64)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = np.reshape(grid, (9, 9))\n",
    "grid\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(su_imagewrap)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def Solved(quiz):\n",
    "    for row in range(9):\n",
    "        if row % 3 == 0 and row != 0:\n",
    "            print(\"---------------------\")\n",
    "\n",
    "        for col in range(9):\n",
    "            if col % 3 == 0 and col != 0:\n",
    "                print(\"|\", end=\" \")\n",
    "\n",
    "            if col == 8:\n",
    "                print(quiz[row][col])\n",
    "            else:\n",
    "                print(str(quiz[row][col]) + \" \", end=\"\")\n",
    "\n",
    "\n",
    "def solve(grid, row, col, num):\n",
    "    for x in range(9):\n",
    "        if grid[row][x] == num:\n",
    "            return False\n",
    "\n",
    "    for x in range(9):\n",
    "        if grid[x][col] == num:\n",
    "            return False\n",
    "\n",
    "    startRow = row - row % 3\n",
    "    startCol = col - col % 3\n",
    "    for i in range(3):\n",
    "        for j in range(3):\n",
    "            if grid[i + startRow][j + startCol] == num:\n",
    "                return False\n",
    "    return True\n",
    "\n",
    "\n",
    "def Suduko(grid, row, col):\n",
    "\n",
    "    M = 9\n",
    "    if (row == M - 1 and col == M):\n",
    "        return True\n",
    "    if col == M:\n",
    "        row += 1\n",
    "        col = 0\n",
    "    if grid[row][col] > 0:\n",
    "        return Suduko(grid, row, col + 1)\n",
    "    for num in range(1, M + 1, 1):\n",
    "\n",
    "        if solve(grid, row, col, num):\n",
    "\n",
    "            grid[row][col] = num\n",
    "            if Suduko(grid, row, col + 1):\n",
    "                return True\n",
    "        grid[row][col] = 0\n",
    "    return False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 8 7 | 9 6 4 | 2 1 5\n",
      "5 4 9 | 2 1 8 | 7 3 6\n",
      "1 6 2 | 3 7 5 | 9 8 4\n",
      "---------------------\n",
      "4 2 6 | 8 9 3 | 5 7 1\n",
      "9 1 8 | 5 4 7 | 6 2 3\n",
      "7 5 3 | 1 2 6 | 8 4 9\n",
      "---------------------\n",
      "6 3 5 | 7 8 1 | 4 9 2\n",
      "2 7 4 | 6 3 9 | 1 5 8\n",
      "8 9 1 | 4 5 2 | 3 6 7\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "if (Suduko(grid, 0, 0)):\n",
    "    Solved(grid)\n",
    "else:\n",
    "    print(\"Solution does not exist:(\")\n"
   ]
  }
 ],
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