main.py

import warnings
warnings.filterwarnings('ignore')
import cv2
import numpy as np
from filter import filter_one
from filter import filter_two
from angle import segment_by_angle_kmeans
from intersection import segmented_intersections
from boxes import rect
#from boxesrc import rect
from contours import max_area
#import tensorflow as tf
import sudoku
import os
import sys
#img = cv2.imread('sudoku.jpeg')


debug = True
def main():
    cap = cv2.VideoCapture(0)
    down = False
    filter = True

    while True:
        ret, img = cap.read()
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        edges = cv2.Canny(gray, 90, 150, apertureSize=3)
        kernel = np.ones((3, 3), np.uint8)
        edges = cv2.dilate(edges, kernel, iterations=1)
        kernel = np.ones((5, 5), np.uint8)
        edges = cv2.erode(edges, kernel, iterations=1)

        lines = cv2.HoughLines(edges, 1, np.pi/180, 150)

        if lines is None:
            continue

        if filter:
            line_flags = filter_one(lines)

        #print('number of Hough lines:', len(lines))

        filtered_lines = []

        if filter:
            filtered_lines = filter_two(lines,line_flags)

            #print('Number of filtered lines:', len(filtered_lines))
        else:
            filtered_lines = lines

        for line in filtered_lines:
            rho, theta = line[0]
            a = np.cos(theta)
            b = np.sin(theta)
            x0 = a*rho
            y0 = b*rho
            x1 = int(x0 + 1000*(-b))
            y1 = int(y0 + 1000*(a))
            x2 = int(x0 - 1000*(-b))
            y2 = int(y0 - 1000*(a))

            if debug:
                cv2.line(img, (x1, y1), (x2, y2), (0, 0, 255), 2)

        segmented = segment_by_angle_kmeans(filtered_lines)
        intersections = segmented_intersections(segmented) # points

        # Try except may be used here. I removed from here.
        if len(intersections) == 100 and len(filtered_lines) == 20:
            intersections = [val[0] for val in intersections]
            intersections = sorted(intersections,key=lambda val: val[0])
            img, prediction, done = rect(img, intersections)
            if done:
                val = sudoku.SolveSudoku(prediction)
                if val:
                    print(val)
                    sys.exit('Exiting')
                else:
                    print('Bad Detection')
        # Upto here

        cv2.imshow('image', img)
        if (cv2.waitKey(10) & 0xFF == ord('q')) or down == True:
            break

    cv2.destroyAllWindows()
    cap.release()

    if sudoku.SolveSudoku(prediction):
        print(sudoku.SolveSudoku(prediction))
    else:
        print('bad luck')

if __name__ == '__main__':
    main()