sudoku_game_solver.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the basic dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading in the test image as gray scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.imread('easy.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fba0c0fcef0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(gray,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(203, 202)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gray.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying Gaussian Blurring to reduce Background Noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "dst = cv2.GaussianBlur(gray,(1,1),cv2.BORDER_DEFAULT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fba0c0eb860>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(dst,cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying Inverse Binary Threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fba0c05a8d0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret,thresh_inv = cv2.threshold(gray, 180, 255,cv2.THRESH_BINARY_INV)\n",
    "plt.imshow(thresh_inv,cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying Probabilistic Hough Transform on the Binary Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minLineLength = 100\n",
    "maxLineGap = 60\n",
    "lines = cv2.HoughLinesP(thresh_inv,1,np.pi/180,100,minLineLength=100,maxLineGap=10)\n",
    "for l in lines:\n",
    "    x1,y1,x2,y2 = l[0]\n",
    "    cv2.line(img,(x1,y1),(x2,y2),(0,255,0),2, cv2.LINE_AA)\n",
    "cv2.imwrite('hough.jpg',img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb9d07894e0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_hough = cv2.imread('hough.jpg',0)\n",
    "img_show = cv2.imread('hough.jpg')\n",
    "plt.imshow(img_show, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding Contours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "contours,hierarchy = cv2.findContours(img_hough,cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the biggest area\n",
    "cnt = contours[0]\n",
    "max_area = cv2.contourArea(cnt)\n",
    "\n",
    "for cont in contours:\n",
    "    if cv2.contourArea(cont) > max_area:\n",
    "        cnt = cont\n",
    "        max_area = cv2.contourArea(cont)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40602.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_area"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replacing the contour by it's approximate encompassing polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "epsilon = 0.01*cv2.arcLength(cnt,True)\n",
    "poly_approx = cv2.approxPolyDP(cnt, epsilon, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   0]],\n",
       "\n",
       "       [[  0, 202]],\n",
       "\n",
       "       [[201, 202]],\n",
       "\n",
       "       [[201,   0]]], dtype=int32)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_approx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying Perspective Transform on the edge detected image with the Poly_Approx points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function that takes in points\n",
    "def order_points(pts):\n",
    "    # initialzie a list of coordinates that will be ordered\n",
    "    # such that the first entry in the list is the top-left,\n",
    "    # the second entry is the top-right, the third is the\n",
    "    # bottom-right, and the fourth is the bottom-left\n",
    "    rect = np.zeros((4, 2), dtype = \"float32\")\n",
    "\n",
    "    # the top-left point will have the smallest sum, whereas\n",
    "    # the bottom-right point will have the largest sum\n",
    "    s = pts.sum(axis = 1)\n",
    "    rect[0] = pts[0]\n",
    "    rect[2] = pts[2]\n",
    "\n",
    "    # now, compute the difference between the points, the\n",
    "    # top-right point will have the smallest difference,\n",
    "    # whereas the bottom-left will have the largest difference\n",
    "    diff = np.diff(pts, axis = 1)\n",
    "    rect[3] = pts[3]\n",
    "    rect[1] = pts[1]\n",
    "\n",
    "    # return the ordered coordinates\n",
    "    return rect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def four_point_transform(image, pts):\n",
    "    # obtain a consistent order of the points and unpack them\n",
    "    # individually\n",
    "    rect = order_points(pts)\n",
    "    (tl, tr, br, bl) = rect\n",
    "\n",
    "    # compute the width of the new image, which will be the\n",
    "    # maximum distance between bottom-right and bottom-left\n",
    "    # x-coordiates or the top-right and top-left x-coordinates\n",
    "    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))\n",
    "    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))\n",
    "    maxWidth = max(int(widthA), int(widthB))\n",
    "\n",
    "    # compute the height of the new image, which will be the\n",
    "    # maximum distance between the top-right and bottom-right\n",
    "    # y-coordinates or the top-left and bottom-left y-coordinates\n",
    "    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))\n",
    "    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))\n",
    "    maxHeight = max(int(heightA), int(heightB))\n",
    "\n",
    "    # now that we have the dimensions of the new image, construct\n",
    "    # the set of destination points to obtain a \"birds eye view\",\n",
    "    # (i.e. top-down view) of the image, again specifying points\n",
    "    # in the top-left, top-right, bottom-right, and bottom-left\n",
    "    # order\n",
    "    dst = np.array([\n",
    "        [0, 0],\n",
    "        [0, maxHeight - 1],\n",
    "        [maxWidth - 1, maxHeight - 1],\n",
    "        [maxWidth - 1, 0]], dtype = \"float32\")\n",
    "\n",
    "    # compute the perspective transform matrix and then apply it\n",
    "    M = cv2.getPerspectiveTransform(rect, dst)\n",
    "    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))\n",
    "\n",
    "    # return the warped image\n",
    "    return warped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_PT = four_point_transform(thresh_inv,poly_approx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(201, 202)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_PT.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb9d0702358>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img_PT, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite('img_PT.jpg',img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Network Model trained to recognize digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import keras\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, Lambda, MaxPooling2D # convolution layers\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten # core layers\n",
    "\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "from keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(X_train,y_train),(X_test,y_test)=mnist.load_data()\n",
    "plt.imshow(X_train[0], cmap=plt.get_cmap('gray'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(60000, 28, 28, 1)\n",
    "X_test = X_test.reshape(10000, 28, 28, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=X_train/255\n",
    "X_test = X_test / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import np_utils\n",
    "y_train=np_utils.to_categorical(y_train)\n",
    "y_test = np_utils.to_categorical(y_test)\n",
    "num_classes=y_test.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Declare the model\n",
    "model = Sequential()\n",
    "\n",
    "## Declare the layers\n",
    "layer_1 = Conv2D(32, kernel_size=3, activation='relu', input_shape=(28, 28, 1))\n",
    "layer_2 = Conv2D(64, kernel_size=3, activation='relu')\n",
    "layer_3 = Flatten()\n",
    "layer_4 = Dense(10, activation='softmax')\n",
    "\n",
    "## Add the layers to the model\n",
    "model.add(layer_1)\n",
    "model.add(layer_2)\n",
    "model.add(layer_3)\n",
    "model.add(layer_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 61s 1ms/sample - loss: 0.1174 - accuracy: 0.9650 - val_loss: 0.0494 - val_accuracy: 0.9847\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 54s 908us/sample - loss: 0.0439 - accuracy: 0.9865 - val_loss: 0.0480 - val_accuracy: 0.9853\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 52s 860us/sample - loss: 0.0267 - accuracy: 0.9915 - val_loss: 0.0404 - val_accuracy: 0.9877\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 52s 868us/sample - loss: 0.0178 - accuracy: 0.9943 - val_loss: 0.0409 - val_accuracy: 0.9879\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 50s 828us/sample - loss: 0.0125 - accuracy: 0.9959 - val_loss: 0.0528 - val_accuracy: 0.9870\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f8618b4f4e0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('keras_digit_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting on new test image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 128)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_image = cv2.imread('DIGIT_DATA/test/8/img009-00800.png',0) #reading in grayscale\n",
    "plt.imshow(test_image,cmap='gray')\n",
    "test_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "new_model = load_model('keras_digit_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0., 0., 1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from keras.preprocessing import image\n",
    "\n",
    "ret, thresh = cv2.threshold(test_image.copy(), 75, 255, cv2.THRESH_BINARY_INV)\n",
    "resized_digit = cv2.resize(thresh, (28,28))\n",
    "resized_digit = resized_digit.reshape(1,28,28,1)\n",
    "resized_digit = tf.cast(resized_digit, tf.float32) #for linux (tf2.0)\n",
    "prediction = new_model.predict(resized_digit)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prediction(test_image):\n",
    "    \n",
    "    classes = new_model.predict_classes(test_image)\n",
    "    \n",
    "    if classes == [[0]]:\n",
    "        return 0\n",
    "    elif classes == [[1]]:\n",
    "        return 1\n",
    "    elif classes == [[2]]:\n",
    "        return 2\n",
    "    elif classes == [[3]]:\n",
    "        return 3\n",
    "    elif classes == [[4]]:\n",
    "        return 4\n",
    "    elif classes == [[5]]:\n",
    "        return 5\n",
    "    elif classes == [[6]]:\n",
    "        return 6\n",
    "    elif classes == [[7]]:\n",
    "        return 7\n",
    "    elif classes == [[8]]:\n",
    "        return 8\n",
    "    elif classes == [[9]]:\n",
    "        return 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction(resized_digit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding and extracting the sudoku digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a function to get the different position of cells\n",
    "def getCellPositions(img_PT):\n",
    "    #resizing the images to take the shape of the NN model\n",
    "    img_PT = cv2.resize(img,(252,252))\n",
    "    #computing position of each cell and storing in an array of arrays\n",
    "    cell_positions = []\n",
    "    \n",
    "    width = img_PT.shape[1]\n",
    "    height = img_PT.shape[0]\n",
    "    \n",
    "    cell_width = width//9\n",
    "    cell_height = height//9\n",
    "    \n",
    "    x1,x2,y1,y2 = 0,0,0,0\n",
    "    \n",
    "    for i in range(9):\n",
    "        y2 = y1 + cell_height\n",
    "        x1 = 0\n",
    "        for j in range(9):\n",
    "            x2 = x1 + cell_width\n",
    "            current_cell = [x1,x2,y1,y2]\n",
    "            cell_positions.append(current_cell)\n",
    "            x1 = x2\n",
    "        y1 = y2\n",
    "    return cell_positions   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using the trained model to predict the digits in each cell, if cell is empty fix with 0\n",
    "def predictDigit(cell,img):\n",
    "    pos = []\n",
    "    img = cv2.resize(img,(252,252))\n",
    "    img = img[cell[2]+2:cell[3]-3,cell[0]+2:cell[1]-3]\n",
    "    contours,hierarchy = cv2.findContours(img.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
    "    if len(contours) != 0:\n",
    "\n",
    "        for c in contours:\n",
    "            x,y,w,h = cv2.boundingRect(c)\n",
    "            # if the contour is sufficiently large, it must be a digit\n",
    "            if (w < 15 and x > 2) and (h < 25 and y > 2):#multiplied each number by 9 due to the resized image\n",
    "                #pos = (x,y,x+w,y+h)\n",
    "                pos.append((x,y,x+w,y+h))\n",
    "                break\n",
    "    if pos == []:\n",
    "        result = 0\n",
    "    if pos:\n",
    "        img1 = img[(pos[0][1]):(pos[0][3]),(pos[0][0]):(pos[0][2])]\n",
    "        #img1 = img[4:25,5:23]\n",
    "        #22-3-2020\n",
    "        img1 = cv2.resize(img,(28,28))\n",
    "        img1 = img1.reshape(1,28,28,1)\n",
    "        img1 = tf.cast(img1, tf.float32) #for linux (tf2.0)\n",
    "\n",
    "        result = prediction(img1)\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extractSudokuDigits(img_PT):\n",
    "    #we start looking at the middle of the cell as this is where the sudoku digit should be at\n",
    "    cell_digits,num = [],0\n",
    "    cells = getCellPositions(img_PT)\n",
    "    for cell in range(len(cells)):\n",
    "        num = predictDigit(cells[cell],img_PT)\n",
    "        cell_digits.append(num)\n",
    "    n = 9\n",
    "    cell_digits = [cell_digits[i:i+n] for i in range(0, len(cell_digits), n)] \n",
    "    return cell_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       " [0, 1, 2, 0, 3, 9, 5, 6, 7],\n",
       " [0, 3, 4, 5, 0, 6, 1, 8, 2],\n",
       " [0, 0, 1, 0, 5, 8, 2, 0, 6],\n",
       " [0, 0, 8, 6, 0, 0, 0, 0, 1],\n",
       " [0, 2, 0, 0, 0, 7, 0, 5, 0],\n",
       " [0, 0, 0, 7, 0, 5, 0, 0, 8],\n",
       " [0, 8, 0, 0, 6, 0, 7, 0, 0],\n",
       " [2, 0, 7, 0, 8, 3, 6, 1, 5]]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#testing the extraction process for one sudoku picture\n",
    "a = extractSudokuDigits(img_PT)\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solving Sudoku Puzzle with Backtracking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 0  | 0 0 0  | 0 0 0\n",
      "0 1 2  | 0 3 9  | 5 6 7\n",
      "0 3 4  | 5 0 6  | 1 8 2\n",
      "- - - - - - - - - - - - - \n",
      "0 0 1  | 0 5 8  | 2 0 6\n",
      "0 0 8  | 6 0 0  | 0 0 1\n",
      "0 2 0  | 0 0 7  | 0 5 0\n",
      "- - - - - - - - - - - - - \n",
      "0 0 0  | 7 0 5  | 0 0 8\n",
      "0 8 0  | 0 6 0  | 7 0 0\n",
      "2 0 7  | 0 8 3  | 6 1 5\n"
     ]
    }
   ],
   "source": [
    "def print_board(bo):\n",
    "    for i in range(len(bo)):\n",
    "        if i % 3 == 0 and i != 0:\n",
    "            print(\"- - - - - - - - - - - - - \")\n",
    "\n",
    "        for j in range(len(bo[0])):\n",
    "            if j % 3 == 0 and j != 0:\n",
    "                print(\" | \", end=\"\")\n",
    "\n",
    "            if j == 8:\n",
    "                print(bo[i][j])\n",
    "            else:\n",
    "                print(str(bo[i][j]) + \" \", end=\"\")\n",
    "                \n",
    "#displaying our sudoku board in its array format (digit 0 represents empty cells)             \n",
    "print_board(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#helper function to get the empty cells\n",
    "def find_empty(bo):\n",
    "    for i in range(len(bo)):\n",
    "        for j in range(len(bo[0])):\n",
    "            if bo[i][j] == 0:\n",
    "                return (i, j)\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#helper function to validate placement of new digits in empty cells\n",
    "def valid(bo, num, pos):\n",
    "    # Check row\n",
    "    for i in range(len(bo[0])):\n",
    "        if bo[pos[0]][i] == num and pos[1] != i:\n",
    "            return False\n",
    "\n",
    "    # Check column\n",
    "    for i in range(len(bo)):\n",
    "        if bo[i][pos[1]] == num and pos[0] != i:\n",
    "            return False\n",
    "\n",
    "    # Check box\n",
    "    box_x = pos[1] // 3\n",
    "    box_y = pos[0] // 3\n",
    "\n",
    "    for i in range(box_y*3, box_y*3 + 3):\n",
    "        for j in range(box_x * 3, box_x*3 + 3):\n",
    "            if bo[i][j] == num and (i,j) != pos:\n",
    "                return False\n",
    "\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def solve(bo):\n",
    "    find = find_empty(bo)\n",
    "    if not find:\n",
    "        return True\n",
    "    else:\n",
    "        row, col = find\n",
    "\n",
    "    for i in range(1,10):\n",
    "        if valid(bo, i, (row, col)):\n",
    "            bo[row][col] = i\n",
    "\n",
    "            if solve(bo):\n",
    "                return True\n",
    "\n",
    "            bo[row][col] = 0\n",
    "\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 0  | 0 0 0  | 0 0 0\n",
      "0 1 2  | 0 3 9  | 5 6 7\n",
      "0 3 4  | 5 0 6  | 1 8 2\n",
      "- - - - - - - - - - - - - \n",
      "0 0 1  | 0 5 8  | 2 0 6\n",
      "0 0 8  | 6 0 0  | 0 0 1\n",
      "0 2 0  | 0 0 7  | 0 5 0\n",
      "- - - - - - - - - - - - - \n",
      "0 0 0  | 7 0 5  | 0 0 8\n",
      "0 8 0  | 0 6 0  | 7 0 0\n",
      "2 0 7  | 0 8 3  | 6 1 5\n",
      "\n",
      "\n",
      "5 7 6  | 8 1 2  | 4 9 3\n",
      "8 1 2  | 4 3 9  | 5 6 7\n",
      "9 3 4  | 5 7 6  | 1 8 2\n",
      "- - - - - - - - - - - - - \n",
      "4 9 1  | 3 5 8  | 2 7 6\n",
      "7 5 8  | 6 2 4  | 9 3 1\n",
      "6 2 3  | 1 9 7  | 8 5 4\n",
      "- - - - - - - - - - - - - \n",
      "1 6 9  | 7 4 5  | 3 2 8\n",
      "3 8 5  | 2 6 1  | 7 4 9\n",
      "2 4 7  | 9 8 3  | 6 1 5\n"
     ]
    }
   ],
   "source": [
    "#using the function to solve digits extracted from the image\n",
    "print_board(a)\n",
    "print('\\n')\n",
    "solve(a)\n",
    "print_board(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Putting back the digits on the picture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#looking for the empty cells and returning\n",
    "def detectEmptyCell(cell,img):\n",
    "    pos = []\n",
    "    img = cv2.resize(img,(252,252))\n",
    "    img = img[cell[2]+2:cell[3]-3,cell[0]+2:cell[1]-3]\n",
    "    contours,hierarchy = cv2.findContours(img.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
    "    if len(contours) != 0:\n",
    "\n",
    "        for c in contours:\n",
    "            x,y,w,h = cv2.boundingRect(c)\n",
    "            # if the contour is sufficiently large, it must be a digit\n",
    "            if (w < 15 and x > 2) and (h < 25 and y > 2):#multiplied each number by 9 due to the resized image\n",
    "                #pos = (x,y,x+w,y+h)\n",
    "                pos.append((x,y,x+w,y+h))\n",
    "                break\n",
    "    if pos == []:    \n",
    "        return pos\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#placing the valid digits for the puzzle in the empty cells and visualizing\n",
    "def placeSudokuDigits(img_PT):\n",
    "    #we start looking at the middle of the cell as this is where the sudoku digit should be at\n",
    "    img_PT = cv2.resize(img_PT,(252,252)) #had to reshape the image size to fit the model shape\n",
    "    img_color = cv2.resize(img,(252,252))\n",
    "    cells = getCellPositions(img_PT)\n",
    "    n = 9\n",
    "    cr = [cells[i:i+n] for i in range(0, len(cells), n)] #cr meaning cells reshaped\n",
    "    digits = extractSudokuDigits(img_PT)\n",
    "    solve(digits) \n",
    "    for i in range(len(cr)):\n",
    "        for j in range(len(cr[i])):\n",
    "            pos = detectEmptyCell(cr[i][j],img_PT)\n",
    "            digit_text = digits[i][j]\n",
    "            if pos == []:\n",
    "                cv2.putText(img_color, str(digit_text), ((cr[i][j][0]+8),(cr[i][j][2]+19)),cv2.FONT_HERSHEY_SIMPLEX,0.7, (0, 0, 255), 2, cv2.LINE_AA)\n",
    "            else:\n",
    "                continue  \n",
    "    plt.imshow(img_color,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "placeSudokuDigits(img_PT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper function for solving the Puzzle via Webcam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def placeSudokuDigitsLive(img_PT):\n",
    "    #we start looking at the middle of the cell as this is where the sudoku digit should be at\n",
    "    img_PT = cv2.resize(img_PT,(252,252)) #had to reshape the image size to fit the model shape\n",
    "    img_color = cv2.resize(frame,(252,252)) #img is got from the grayscale\n",
    "    cells = getCellPositions(img_PT)\n",
    "    n = 9\n",
    "    cr = [cells[i:i+n] for i in range(0, len(cells), n)] #cr meaning cells reshaped\n",
    "    digits = extractSudokuDigits(img_PT)\n",
    "    solve(digits) \n",
    "    for i in range(len(cr)):\n",
    "        for j in range(len(cr[i])):\n",
    "            pos = detectEmptyCell(cr[i][j],img_PT)\n",
    "            digit_text = digits[i][j]\n",
    "            if pos == []:\n",
    "                cv2.putText(img_color, str(digit_text), ((cr[i][j][0]+8),(cr[i][j][2]+19)),cv2.FONT_HERSHEY_SIMPLEX,0.7, (0, 0, 255), 2, cv2.LINE_AA)\n",
    "            else:\n",
    "                continue  \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading in live video of the Sudoku Puzzle via Webcam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PUTTING THIS ALL IN ONE CELL!\n",
    "\n",
    "import cv2\n",
    "\n",
    "# Connects to your computer's default camera\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "# Automatically grab width and height from video feed\n",
    "# (returns float which we need to convert to integer for later on!)\n",
    "width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "\n",
    "#to run some lines once\n",
    "flag = True\n",
    "\n",
    "while True:\n",
    "    \n",
    "    # Capture frame-by-frame\n",
    "    ret, frame = cap.read()\n",
    "    \n",
    "    #Convert the captured frame into grayscale\n",
    "    gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n",
    "    \n",
    "    \n",
    "    #This segment of the code works on the board segment of the frame\n",
    "    contours,hierarchy = cv2.findContours(gray,cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) \n",
    "    # find the biggest area\n",
    "    cnt = contours[0]\n",
    "    max_area = cv2.contourArea(cnt)\n",
    "\n",
    "    for cont in contours:\n",
    "        if cv2.contourArea(cont) > max_area:\n",
    "            cnt = cont\n",
    "            max_area = cv2.contourArea(cont)\n",
    "    epsilon = 0.01*cv2.arcLength(cnt,True)\n",
    "    poly_approx = cv2.approxPolyDP(cnt, epsilon, True)\n",
    "    \n",
    "    board_segment = four_point_transform(gray,poly_approx)\n",
    "\n",
    "    #Applying Gaussian Blurring to the image\n",
    "    dst = cv2.GaussianBlur(board_segment,(1,1),cv2.BORDER_DEFAULT)\n",
    "    \n",
    "    #Applying Inverse Binary Threshold to the image\n",
    "    ret,thresh_inv = cv2.threshold(dst, 180, 255,cv2.THRESH_BINARY_INV)\n",
    "    \n",
    "    #Applying Probabilistic Hough Transform on the Binary Image\n",
    "    minLineLength = 100\n",
    "    maxLineGap = 60\n",
    "    lines = cv2.HoughLinesP(thresh_inv,1,np.pi/180,100,minLineLength=100,maxLineGap=10)\n",
    "    for l in lines:\n",
    "        x1,y1,x2,y2 = l[0]\n",
    "        cv2.line(board_segment,(x1,y1),(x2,y2),(0,255,0),2, cv2.LINE_AA)\n",
    "        \n",
    "    if flag:\n",
    "        #using neural network model to detect the digits in the image\n",
    "        #new_model = load_model('keras_digit_model.h5')\n",
    "        a = extractSudokuDigits(thresh_inv)\n",
    "\n",
    "        #solving with backtracking\n",
    "        solve(a)\n",
    "\n",
    "        #putting back the solved digits on spaces that are empty\n",
    "        placeSudokuDigitsLive(thresh_inv) #this function won't have the plt.imshow() and also, \n",
    "                                    #the colored image would be img\n",
    "        \n",
    "        flag = False\n",
    "        \n",
    "    #overlaying the board segment of the image on the frame\n",
    "    x_offset, y_offset = (poly_approx[0][0].tolist()[0]),(poly_approx[0][0].tolist()[1])\n",
    "    x_end, y_end = (x_offset+board_segment.shape[1]), (y_offset+board_segment.shape[0])\n",
    "    frame[y_offset:y_end,x_offset:x_end] = board_segment\n",
    "        \n",
    "    # Display the resulting frame\n",
    "    cv2.imshow('frame',frame)\n",
    "    \n",
    "    # This command let's us quit with the \"q\" button on a keyboard.\n",
    "    # Simply pressing X on the window won't work!\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "\n",
    "# When everything done, release the capture and destroy the windows\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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