Akshay Gupta — May 26, 2021
Advanced Computer Vision Deep Learning Image Image Analysis Libraries Project Python Unstructured Data

This article was published as a part of the Data Science Blogathon

Introduction

Hello Readers!!

Deep Learning is used in many applications such as object detection, face detection, natural language processing tasks, and many more. In this blog I am going to build a model that will be used to solve unsolved Sudoku puzzles from an image using deep learning, We are going to libraries such as OpenCV and TensorFlow. If you want to know more about OpenCV, check this link. So let’s get started.

  • If you want to know about Python Libraries For Image Processing, then check this Link.
  • For more articles, click here
Sudoku Deep Learning image

Image Source 

The blog is divided into three parts:

Part 1: Digit Classification Model

We will be first building and training a neural network on the Char74k images dataset for digits. This model will help to classify the digits from the images.

Part 2: Reading and Detecting the Sudoku From an Image

This section contains, identifying the puzzle from an image with the help of OpenCV, classify the digits in the detected Sudoku puzzle using Part-1, finally getting the values of the cells from Sudoku and stored in an array.

Part3: Solving the Puzzle

We are going to store the array that we got in Pat-2 in the form of a matrix and finally run a recursion loop to solve the puzzle.

 

IMPORTING LIBRARIES 

Let’s import all the required libraries using the below commands:

import numpy as np 
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os, random
import cv2
from glob import glob
import sklearn
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing.image import ImageDataGenerator, load_img
from keras.utils.np_utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Dropout, Dense, Flatten, BatchNormalization, Conv2D, MaxPooling2D
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras import backend as K
from tensorflow.keras.preprocessing import image
from sklearn.metrics import accuracy_score, classification_report
from pathlib import Path
from PIL import Image

Part 1: Digit Classification Model 

In this section, we are going to use a digit classification model

LOADING DATA 

We are going to use an image dataset to classify the numbers in an image. Data is specified as features like images and labels as tags.

#Loading the data 
data = os.listdir("digits/Digits" )
data_X = []     
data_y = []  
data_classes = len(data)
for i in range (0,data_classes):
data_list = os.listdir("digits/Digits" +"/"+str(i))
    for j in data_list:
pic = cv2.imread("digits/Digits" +"/"+str(i)+"/"+j)
pic = cv2.resize(pic,(32,32))
data_X.append(pic)
data_y.append(i)
if len(data_X) == len(data_y) :
print("Total Dataponits = ",len(data_X))
# Labels and images
data_X = np.array(data_X)
data_y = np.array(data_y)

 

Sudoku Deep Learning datapoints

SPLITTING DATASET 

We are splitting the dataset into the train, test, and validation sets as we do in any machine learning problem.

#Spliting the train validation and test sets
train_X, test_X, train_y, test_y = train_test_split(data_X,data_y,test_size=0.05)
train_X, valid_X, train_y, valid_y = train_test_split(train_X,train_y,test_size=0.2)
print("Training Set Shape = ",train_X.shape)
print("Validation Set Shape = ",valid_X.shape)
print("Test Set Shape = ",test_X.shape)

 

Sudoku Deep Learning training set shape

Preprocessing the images for neural net 

In a preprocessing step, we preprocess the features (images) into grayscale, normalizing and enhancing them with histogram equalization. After that, convert them to NumPp arrays then reshaping them and data augmentation.

def Prep(img):
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #making image grayscale
img = cv2.equalizeHist(img) #Histogram equalization to enhance contrast
img = img/255 #normalizing
    return img
train_X = np.array(list(map(Prep, train_X)))
test_X = np.array(list(map(Prep, test_X)))
valid_X= np.array(list(map(Prep, valid_X)))
#Reshaping the images
train_X = train_X.reshape(train_X.shape[0], train_X.shape[1], train_X.shape[2],1)
test_X = test_X.reshape(test_X.shape[0], test_X.shape[1], test_X.shape[2],1)
valid_X = valid_X.reshape(valid_X.shape[0], valid_X.shape[1], valid_X.shape[2],1)
#Augmentation
datagen = ImageDataGenerator(width_shift_range=0.1, height_shift_range=0.1, zoom_range=0.2, shear_range=0.1, rotation_range=10)
datagen.fit(train_X)

 

One Hot Encoding

In this section, we are going to use one-hot encoding to labels the classes.

train_y = to_categorical(train_y, data_classes)
test_y = to_categorical(test_y, data_classes)
valid_y = to_categorical(valid_y, data_classes)

MODEL BUILDING

We are using a convolutional neural network for model building. It consists of the following steps:

#Creating a Neural Network
model = Sequential()
model.add((Conv2D(60,(5,5),input_shape=(32, 32, 1) ,padding = 'Same' ,activation='relu')))
model.add((Conv2D(60, (5,5),padding="same",activation='relu')))
model.add(MaxPooling2D(pool_size=(2,2)))
#model.add(Dropout(0.25))
model.add((Conv2D(30, (3,3),padding="same", activation='relu')))
model.add((Conv2D(30, (3,3), padding="same", activation='relu')))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(500,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.summary()
model building Sudoku Deep Learning

In this step, we are going to compile the model and testing the model on the test set as shown below:

#Compiling the model
optimizer = RMSprop(lr=0.001, rho=0.9, epsilon = 1e-08, decay=0.0)
model.compile(optimizer=optimizer,loss='categorical_crossentropy',metrics=['accuracy'])
#Fit the model
history = model.fit(datagen.flow(train_X, train_y, batch_size=32),
                              epochs = 30, validation_data = (valid_X, valid_y),
                              verbose = 2, steps_per_epoch= 200)

# Testing the model on the test set
score = model.evaluate(test_X, test_y, verbose=0)
print('Test Score = ',score[0])
print('Test Accuracy =', score[1])

 

Sudoku Deep Learning test score

Part 2: Reading and Detecting the Sudoku From an Image

READING THE SUDOKU PUZZLE  

Read a Sudoku using OpenCv using the following code:

# Randomly select an image from the dataset 
folder="sudoku-box-detection/aug"
a=random.choice(os.listdir(folder))
print(a)
sudoku_a = cv2.imread(folder+'/'+a)
plt.figure()
plt.imshow(sudoku_a)
plt.show()

 

Sudoku Deep Learning puzzle reading

Preprocess the image for further analysis using the below code;

#Preprocessing image to be read
sudoku_a = cv2.resize(sudoku_a, (450,450))
# function to greyscale, blur and change the receptive threshold of image
def preprocess(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
blur = cv2.GaussianBlur(gray, (3,3),6) 
    #blur = cv2.bilateralFilter(gray,9,75,75)
threshold_img = cv2.adaptiveThreshold(blur,255,1,1,11,2)
    return threshold_img
threshold = preprocess(sudoku_a)
#let's look at what we have got
plt.figure()
plt.imshow(threshold)
plt.show()

 

DETECTING CONTOUR  1

DETECTING CONTOUR 

In this section, we are going to detect contour. We sill detect the biggest contour of the image

# Finding the outline of the sudoku puzzle in the image
contour_1 = sudoku_a.copy()
contour_2 = sudoku_a.copy()
contour, hierarchy = cv2.findContours(threshold,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(contour_1, contour,-1,(0,255,0),3)
#let's see what we got
plt.figure()
plt.imshow(contour_1)
plt.show()
DETECTING CONTOUR  1

The following code is used to get the cropped and well-aligned Sudoku by reshaping it.

def main_outline(contour):
biggest = np.array([])
max_area = 0
    for i in contour:
area = cv2.contourArea(i)
        if area >50:
peri = cv2.arcLength(i, True)
approx = cv2.approxPolyDP(i , 0.02* peri, True)
            if area > max_area and len(approx) ==4:
biggest = approx
max_area = area
    return biggest ,max_area
def reframe(points):
points = points.reshape((4, 2))
points_new = np.zeros((4,1,2),dtype = np.int32)
add = points.sum(1)
points_new[0] = points[np.argmin(add)]
points_new[3] = points[np.argmax(add)]
diff = np.diff(points, axis =1)
points_new[1] = points[np.argmin(diff)]
points_new[2] = points[np.argmax(diff)]
    return points_new
def splitcells(img):
rows = np.vsplit(img,9)
boxes = []
    for r in rows:
cols = np.hsplit(r,9)
        for box in cols:
boxes.append(box)
    return boxes
black_img = np.zeros((450,450,3), np.uint8)
biggest, maxArea = main_outline(contour)
if biggest.size != 0:
biggest = reframe(biggest)
cv2.drawContours(contour_2,biggest,-1, (0,255,0),10)
pts1 = np.float32(biggest)
pts2 = np.float32([[0,0],[450,0],[0,450],[450,450]])
matrix = cv2.getPerspectiveTransform(pts1,pts2)
  imagewrap = cv2.warpPerspective(sudoku_a,matrix,(450,450))
imagewrap =cv2.cvtColor(imagewrap, cv2.COLOR_BGR2GRAY)
plt.figure()
plt.imshow(imagewrap)
plt.show()
DETECTING CONTOUR  2
# Importing puzzle to be solved
puzzle = cv2.imread("su-puzzle/su.jpg")
#let's see what we got
plt.figure()
plt.imshow(puzzle)
plt.show()

 

DETECTING CONTOUR  puzzle
# Finding the outline of the sudoku puzzle in the image
su_contour_1= su_puzzle.copy()
su_contour_2= sudoku_a.copy()
su_contour, hierarchy = cv2.findContours(su_puzzle,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(su_contour_1, su_contour,-1,(0,255,0),3)
black_img = np.zeros((450,450,3), np.uint8)
su_biggest, su_maxArea = main_outline(su_contour)
if su_biggest.size != 0:
su_biggest = reframe(su_biggest)
cv2.drawContours(su_contour_2,su_biggest,-1, (0,255,0),10)
su_pts1 = np.float32(su_biggest)
su_pts2 = np.float32([[0,0],[450,0],[0,450],[450,450]])
su_matrix = cv2.getPerspectiveTransform(su_pts1,su_pts2)  
su_imagewrap = cv2.warpPerspective(puzzle,su_matrix,(450,450))
su_imagewrap =cv2.cvtColor(su_imagewrap, cv2.COLOR_BGR2GRAY)
plt.figure()
plt.imshow(su_imagewrap)
plt.show()

 

DETECTING CONTOUR  4

SPLITTING THE CELLS AND CLASSIFYING DIGITS 

In this section, we are going to split the cells and classify the digits

  • First split the Sudoku into 81 cells with digits or empty spaces
  • Cropping the cells
  • Using the model to classify the digits in the cells so that the empty cells are classified as zero
  • Finally, detect the output in an array of 81 digits.
sudoku_cell = splitcells(su_imagewrap)
#Let's have alook at the last cell
plt.figure()
plt.imshow(sudoku_cell[58])
plt.show()

SPLITTING THE CELLS AND CLASSIFYING DIGITS 1
def CropCell(cells):
Cells_croped = []
    for image in cells:
img = np.array(image)
img = img[4:46, 6:46]
img = Image.fromarray(img)
Cells_croped.append(img)
    return Cells_croped
sudoku_cell_croped= CropCell(sudoku_cell)
#Let's have alook at the last cell
plt.figure()
plt.imshow(sudoku_cell_croped[58])
plt.show()

 

SPLITTING THE CELLS AND CLASSIFYING DIGITS  2

Part3: SOLVING THE SODOKU 

In this section, we are going to perform two operations:

  • Reshaping the array into a 9 x 9 matrix
  • Solving the matrix using recursion
# Reshaping the grid to a 9x9 matrix
grid = np.reshape(grid,(9,9))
grid

 

SOLVING THE SODOKU  3
#For compairing 
plt.figure()
plt.imshow(su_imagewrap)
plt.show()

 

SOLVING THE SODOKU  4

Check the below code for further solving the sudoku puzzle:

def next_box(quiz):
    for row in range(9):
        for col in range(9):
            if quiz[row][col] == 0:
                return (row, col)
    return False
#Function to fill in the possible values by evaluating rows collumns and smaller cells
def possible (quiz,row, col, n):
    #global quiz
    for i in range (0,9):
        if quiz[row][i] == n and row != i:
            return False
    for i in range (0,9):
        if quiz[i][col] == n and col != i:
            return False
row0 = (row)//3
col0 = (col)//3
    for i in range(row0*3, row0*3 + 3):
        for j in range(col0*3, col0*3 + 3):
            if quiz[i][j]==n and (i,j) != (row, col):
                return False
    return True
#Recursion function to loop over untill a valid answer is found. 
def solve(quiz):
val = next_box(quiz)
    if val is False:
        return True
    else:
row, col = val
        for n in range(1,10): #n is the possible solution
            if possible(quiz,row, col, n):
quiz[row][col]=n
                if solve(quiz):
                    return True 
                else:
quiz[row][col]=0
        return 
def Solved(quiz):
    for row in range(9):
        if row % 3 == 0 and row != 0:
print("....................")
        for col in range(9):
            if col % 3 == 0 and col != 0:
print("|", end=" ")
            if col == 8:
print(quiz[row][col])
            else:
print(str(quiz[row][col]) + " ", end="")

solve(grid)
SOLVING THE SODOKU 5

Check the below code for final output:

if solve(grid):
Solved(grid)
else:
print("Solution don't exist. Model misread digits.")
SOLVING THE SODOKU 6

Hurray!! We are done with sudoku solving using deep learning. If you want to learn more, then check the below links:

https://www.youtube.com/watch?v=G_UYXzGuqvM

https://www.kaggle.com/yashchoudhary/deep-sudoku-solver-multiple-approaches

https://www.youtube.com/watch?v=QR66rMS_ZfA

End Notes

So in this article, we had a detailed discussion on Solving Sudoku Using Deep Learning. Hope you learn something from this blog and it will help you in the future. Thanks for reading and your patience. Good luck!

You can check my articles here: Articles

Email id: [email protected]

Connect with me on LinkedIn: LinkedIn.

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

About the Author

Our Top Authors

  • Analytics Vidhya
  • Guest Blog
  • Tavish Srivastava
  • Aishwarya Singh
  • Aniruddha Bhandari
  • Abhishek Sharma
  • Aarshay Jain

Download Analytics Vidhya App for the Latest blog/Article

Leave a Reply Your email address will not be published. Required fields are marked *