def placeSudokuDigits(img_PT):
global img
#we start looking at the middle of the cell as this is where the sudoku digit should be at
img_PT = cv2.resize(
img_PT,
(252, 252)) #had to reshape the image size to fit the model shape
img_color = cv2.resize(img, (252, 252))
cells = getCellPositions(img_PT)
n = 9
cr = [cells[i:i + n]
for i in range(0, len(cells), n)] #cr meaning cells reshaped
digits = extractSudokuDigits(img_PT)
solve(digits) #have to look for a way not use this inside this function
for i in range(len(cr)):
for j in range(len(cr[i])):
pos = detectEmptyCell(cr[i][j], img_PT)
digit_text = digits[i][j]
if pos == []:
cv2.putText(img_color, str(digit_text),
((cr[i][j][0] + 8), (cr[i][j][2] + 19)),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2,
cv2.LINE_AA)
else:
continue
cv2.imshow('puzzle', img_color)
cv2.waitKey()
def placeSudokuDigitsLive(img_PT):
#we start looking at the middle of the cell as this is where the sudoku digit should be at
img_PT = cv2.resize(img_PT,(252,252)) #had to reshape the image size to fit the model shape
img_color = cv2.resize(frame,(252,252)) #img is got from the grayscale
cells = getCellPositions(img_PT)
n = 9
cr = [cells[i:i+n] for i in range(0, len(cells), n)] #cr meaning cells reshaped
digits = extractSudokuDigits(img_PT)
solve(digits)
for i in range(len(cr)):
for j in range(len(cr[i])):
pos = detectEmptyCell(cr[i][j],img_PT)
digit_text = digits[i][j]
if pos == []:
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)
else:
continue
def manually_solve(puzzle):
"""Compilation of techniques to run until puzzle is complete"""
added_on = True
count = 0
puzzle = puzzle.tolist()
while added_on:
orig_puzzle = np.copy(np_puzzle(puzzle))
puzzle = sole_candidate(puzzle)
puzzle = unique_candidate(puzzle)
puzzle = naked_subset(puzzle)
puzzle = naked_singles(puzzle)
puzzle = final_sweep(puzzle)
if np.array_equal(orig_puzzle, np_puzzle(puzzle)):
added_on = False
count += 1
#applies backtracking algorithm to unsolved puzzles
unsolved = np_puzzle(puzzle)
if (not (check_completion(unsolved) == 0)):
solve(unsolved, len(puzzle))
return unsolved
#returns solved puzzle
return np_puzzle(puzzle)
def gen_schedule(data):
try:
# Preprocess the data
professors, courses, groups, config = generate_ds(data)
# Initialize the closure function
schedule_session = solve(professors, courses, groups, config)
# Try to solve and return answer
solution = schedule_session(0, 0, groups[0]['hour_range'][0])
except Exception as err:
print('Something has failed inside the scheduling algorithm')
# print(err)
# traceback.print_exc()
return None
try:
if solution: return format_solution(courses, groups)
else: return None
except Exception as err:
print('Solution found, but reformatting failed')
return None
y = int((i * side) + (side / 2)) + correction
cv2.putText(check, str(sudoku[i][j]), (x, y),
cv2.FONT_HERSHEY_DUPLEX, 1.5, (0, 0, 0), 4)
print()
cv2.imshow('check', check)
cv2.waitKey(0)
goto(2)
if line == 2:
ans = input(
'Todos os digitos da imagem estão corretos? (Y para sim / N para não)'
)
if ans in ['y', 'Y', 'sim', 'Sim', 'SIM']:
solved = np.copy(sudoku)
if (backtracking.solve(solved)):
for m in range(9):
for n in range(9):
if sudoku[m][n] == 0:
xx = int((n * sidex) + (sidex / 2)) - correctionx
yy = int((m * sidey) + (sidey / 2)) + correctiony
cv2.putText(warp, str(solved[m][n]), (xx, yy),
cv2.FONT_HERSHEY_DUPLEX, 1.5,
(255, 255, 0), 3)
final = preprocessing.print_final(dst, corners, warp, img0)
cv2.imshow('final', final)
cv2.waitKey(0)
break
else:
print("This game has no solution!")
break
#Applying Probabilistic Hough Transform on the Binary Image
minLineLength = 100
maxLineGap = 60
lines = cv2.HoughLinesP(thresh_inv,1,np.pi/180,100,minLineLength=100,maxLineGap=10)
for l in lines:
x1,y1,x2,y2 = l[0]
cv2.line(board_segment,(x1,y1),(x2,y2),(0,255,0),2, cv2.LINE_AA)
if flag:
#using neural network model to detect the digits in the image
#new_model = load_model('keras_digit_model.h5')
a = extractSudokuDigits(thresh_inv)
#solving with backtracking
solve(a)
#putting back the solved digits on spaces that are empty
placeSudokuDigitsLive(thresh_inv) #this function won't have the plt.imshow() and also,
#the colored image would be img
flag = False
#overlaying the board segment of the image on the frame
x_offset, y_offset = (poly_approx[0][0].tolist()[0]),(poly_approx[0][0].tolist()[1])
x_end, y_end = (x_offset+board_segment.shape[1]), (y_offset+board_segment.shape[0])
frame[y_offset:y_end,x_offset:x_end] = board_segment
# Display the resulting frame
cv2.imshow('frame',frame)
def back_time(puzzle):
start = time.time()
solve(puzzle, len(puzzle))
end = time.time()
return (end - start)