def main():
if len(sys.argv) != 3:
print ">python sudoku.py -auto image"
print ">python sudoku.py -manual image"
sys.exit()
image = Sudoku(sys.argv[2])
# PART ONE: THRESHOLD THE IMAGE
image.threshold()
# PART TWO: FIND THE CORNERS
if sys.argv[1] == "-auto":
image.findCorners()
else:
image.findCornersManually()
# PART THREE: CORRECT THE PERSPECTIVE
# 1. get the perspective transformation matrix using the four corners
# 2. perform the perspective transformation
image.correctPerspective()
# PART FOUR: CROP THE IMAGE
image.cropImage()
# PART FOUR: FIND THE REGIONS
image.findRegions()
# PART FIVE: CALL THE SUDOKU SOLVER
solved = sudokuSolver.solve(image.toString)
print solved
def sudoku_main(image):
## find sudoku contour
sudoku_contour = find_sudoku(image)
if sudoku_contour is None:
return image
## get corners of the contour
corners = get_corners(sudoku_contour)
if corners is None:
return image
corners = sort_corners(corners)
## get top view of the board in square shape
top_view, transformation_matrix, original_shape = get_top_view(
image, corners)
if top_view is None:
return image
## OCR
grid = read_grid(top_view)
if grid is None:
return image
print(grid)
# test sudoku
test = "740030010019068502000004300056370001001800095090020600103407200500200008080001470"
if grid == test:
print("true")
# solvong the sudoku
solved = solve(test)
# write the solution over the top view
empty_boxes = [[0 for j in range(9)] for i in range(9)]
k = 0
for i in range(9):
for j in range(9):
if grid[k] == '0':
empty_boxes[i][j] = 1
k = k + 1
written = write_solution(top_view, empty_boxes, solved)
# covert the top view to original size
resized = cv2.resize(top_view, (original_shape[1], original_shape[0]),
interpolation=cv2.INTER_CUBIC)
# reverse perspective transform
warped = cv2.warpPerspective(resized,
transformation_matrix,
(image.shape[1], image.shape[0]),
flags=cv2.WARP_INVERSE_MAP)
# overlay on the original image
result = np.where(warped.sum(axis=-1, keepdims=True) != 0, warped, image)
return result
def place(self, val):
row, col = self.selected
if self.cubes[row][col].value == 0:
self.cubes[row][col].set(val)
self.update_model()
if valid(self.model, val, (row, col)) and solve(self.model):
return True
else:
self.cubes[row][col].set(0)
self.cubes[row][col].set_temp(0)
self.update_model()
return False
def main():
resizeImageIntoSquare(input("Enter the name of the image file: "))
makeIndividualSquares("img_02.png")
centerCubes()
grid=makeGrid()
print("The puzzle is: ")
sudokuSolver.printGrid(grid)
if(sudokuSolver.solve(grid)):
print("\n\nThe solved grid is: ")
sudokuSolver.printGrid(grid)
else:
print("Grid is not solvable")
deleteAllEvidence()
def solve(bo):
find = find_empty(bo)
if not find:
return True
else:
row, col = find
for i in range(1, 10):
if valid(bo, i, (row, col)):
bo[row][col] = i
if solve(bo):
return True
bo[row][col] = 0
return False
biggest = reorder(biggest)
pts1 = np.float32(np.reshape(biggest, (4, 2)))
pts2 = np.float32([[0, 0], [width - 1, 0], [0, height - 1],
[width - 1, height - 1]])
M = cv.getPerspectiveTransform(pts1, pts2)
new = cv.warpPerspective(img, M, (width, height))
new = removelines(new, 5)
#progress = []
#progress.append(np.array(cv.resize(cv.cvtColor(image,cv.COLOR_GRAY2BGR),(450,450)),np.uint8))
boxes = split(new)
#a = cropbox(boxes) #### REMEMBER TO NORMALIZE "a" ####
a = tf.keras.utils.normalize(a, axis=1)
#plt.imshow(a[25],cmap=plt.cm.binary_r)
pred = model.predict(a)
pred = pred.argmax(axis=1) * (np.amax(pred, axis=1) > 0.8)
progress.append(
np.array(dispNo(pred, np.zeros((450, 450)), color=(255, 0, 255)),
np.uint8))
pos = (pred == 0) * np.ones(81)
pos
board = pred.reshape((9, 9))
try:
sudokuSolver.solve(board)
except:
pass
board
import detector
import sudokuSolver
sudoku = detector.detect_sudoku("sudoku4.jpg")
print(sudoku)
print(sudokuSolver.solve(sudoku))
print(sudoku)