def test_custom_2(self) -> None:
puzzle = [
[0, 7, 0, 9, 3, 0, 2, 0, 0],
[5, 0, 8, 0, 0, 0, 3, 0, 9],
[0, 0, 0, 2, 7, 0, 0, 6, 0],
[8, 0, 0, 0, 4, 9, 0, 1, 0],
[0, 6, 0, 0, 0, 0, 0, 5, 0],
[0, 9, 0, 5, 8, 0, 0, 0, 3],
[0, 8, 0, 0, 9, 1, 0, 0, 0],
[7, 0, 3, 0, 0, 0, 1, 0, 5],
[0, 0, 6, 0, 5, 3, 0, 2, 0],
]
solution = [
[6, 7, 1, 9, 3, 5, 2, 8, 4],
[5, 2, 8, 6, 1, 4, 3, 7, 9],
[4, 3, 9, 2, 7, 8, 5, 6, 1],
[8, 5, 7, 3, 4, 9, 6, 1, 2],
[3, 6, 4, 1, 2, 7, 9, 5, 8],
[1, 9, 2, 5, 8, 6, 7, 4, 3],
[2, 8, 5, 7, 9, 1, 4, 3, 6],
[7, 4, 3, 8, 6, 2, 1, 9, 5],
[9, 1, 6, 4, 5, 3, 8, 2, 7],
]
assert sudoku(puzzle) == solution
def test_sample(self) -> None:
puzzle = [
[5, 3, 0, 0, 7, 0, 0, 0, 0],
[6, 0, 0, 1, 9, 5, 0, 0, 0],
[0, 9, 8, 0, 0, 0, 0, 6, 0],
[8, 0, 0, 0, 6, 0, 0, 0, 3],
[4, 0, 0, 8, 0, 3, 0, 0, 1],
[7, 0, 0, 0, 2, 0, 0, 0, 6],
[0, 6, 0, 0, 0, 0, 2, 8, 0],
[0, 0, 0, 4, 1, 9, 0, 0, 5],
[0, 0, 0, 0, 8, 0, 0, 7, 9],
]
solution = [
[5, 3, 4, 6, 7, 8, 9, 1, 2],
[6, 7, 2, 1, 9, 5, 3, 4, 8],
[1, 9, 8, 3, 4, 2, 5, 6, 7],
[8, 5, 9, 7, 6, 1, 4, 2, 3],
[4, 2, 6, 8, 5, 3, 7, 9, 1],
[7, 1, 3, 9, 2, 4, 8, 5, 6],
[9, 6, 1, 5, 3, 7, 2, 8, 4],
[2, 8, 7, 4, 1, 9, 6, 3, 5],
[3, 4, 5, 2, 8, 6, 1, 7, 9],
]
assert sudoku(puzzle) == solution
def test_custom_1(self) -> None:
puzzle = [
[0, 3, 1, 0, 0, 6, 8, 9, 0],
[0, 0, 0, 0, 9, 0, 4, 3, 2],
[4, 8, 9, 3, 0, 0, 0, 0, 0],
[0, 1, 0, 7, 0, 5, 0, 0, 4],
[0, 0, 0, 9, 6, 8, 0, 0, 0],
[9, 0, 0, 2, 0, 4, 0, 7, 0],
[0, 0, 0, 0, 0, 3, 5, 2, 7],
[3, 5, 8, 0, 2, 0, 0, 0, 0],
[0, 2, 7, 5, 0, 0, 6, 8, 0],
]
solution = [
[2, 3, 1, 4, 7, 6, 8, 9, 5],
[5, 7, 6, 8, 9, 1, 4, 3, 2],
[4, 8, 9, 3, 5, 2, 7, 1, 6],
[8, 1, 2, 7, 3, 5, 9, 6, 4],
[7, 4, 3, 9, 6, 8, 2, 5, 1],
[9, 6, 5, 2, 1, 4, 3, 7, 8],
[6, 9, 4, 1, 8, 3, 5, 2, 7],
[3, 5, 8, 6, 2, 7, 1, 4, 9],
[1, 2, 7, 5, 4, 9, 6, 8, 3],
]
assert sudoku(puzzle) == solution
def read_sudoku(reader_network, png_file, size=9):
img = mpimg.imread(png_file)
s = list()
for i in range(size):
row = list()
for j in range(size):
digit_mtr = pixel_cluster(coord_of_img(i, j, img))
spectorum = predict(reader_network, digit_mtr).tolist()
digit = spectorum.index(max(spectorum))
row.append(digit)
s.append(row)
return sudoku(s)
img_name = sys.argv[1]
if len(sys.argv) == 2: # Expect exactly one argument: the image
DEMO = True
img, corners, labels = process_img(img_name, 'knn_typed.npz', 1, DEMO)
ext_puzzle = solver.stringToArray(''.join(map(str, labels)))
print 'Extracted Sudoku:\n{}'.format(ext_puzzle)
puzzle = solver.stringToArray(''.join(map(str, PUZZLES[img_name])))
print 'Actual Sudoku:\n{}'.format(puzzle)
matches = np.array(PUZZLES[img_name]) == np.array(labels)
correct = np.count_nonzero(matches)
accuracy = correct * 100.0 / len(labels)
print 'Digit Recognition Accuracy: {}'.format(accuracy)
solution = solver.sudoku(puzzle.copy())
print 'Solution:\n{}'.format(solution)
draw_solution(img, corners, puzzle, solution)
show_img('Solution', img)
else:
for k in range(1, 21):
labels = process_img(img_name, 'knn_typed.npz', k, False)
#print labels
matches = np.array(PUZZLES[img_name]) == np.array(labels)
correct = np.count_nonzero(matches)
accuracy = correct * 100.0 / len(labels)
print '{}: {}'.format(k, accuracy)
y2max = (y + 1) * case - marge
x2min = x * case + marge
x2max = (x + 1) * case - marge
prediction = s.run(sortie,
feed_dict={
images: [
grille[y2min:y2max,
x2min:x2max].reshape(
28, 28, 1)
],
is_training:
False
})
ligne += "{:d}".format(np.argmax(prediction[0]))
grille_txt.append(ligne)
result = ss.sudoku(grille_txt)
print("Resultat:", result)
#result=None
if result is not None:
flag = 1
fond = np.zeros(shape=(taille_grille, taille_grille, 3),
dtype=np.float32)
for y in range(len(result)):
for x in range(len(result[y])):
if grille_txt[y][x] == "0":
cv2.putText(fond, "{:d}".format(result[y][x]),
((x) * case + marge + 3,
(y + 1) * case - marge - 3),
cv2.FONT_HERSHEY_SCRIPT_COMPLEX, 0.9,
(0, 0, 255), 1)
M = cv2.getPerspectiveTransform(pts2, pts1)
y2min = y * case + marge
y2max = (y + 1) * case - marge
x2min = x * case + marge
x2max = (x + 1) * case - marge
cv2.imwrite("mat" + str(y) + str(x) + ".png",
grille[y2min:y2max, x2min:x2max])
img = grille[y2min:y2max, x2min:x2max]
x = img.reshape(1, 28, 28, 1)
if x.sum() > 10000:
prediction = classifier.predict_classes(x)
ligne += "{:d}".format(prediction[0])
else:
ligne += "{:d}".format(0)
grille_txt.append(ligne)
print(grille_txt)
result = sol.sudoku(grille_txt)
print("Resultat:", result)
if result is not None:
flag = 1
fond = np.zeros(shape=(taille_grille, taille_grille, 3),
dtype=np.float32)
for y in range(len(result)):
for x in range(len(result[y])):
if grille_txt[y][x] == "0":
cv2.putText(fond, "{:d}".format(result[y][x]),
((x) * case + marge + 3,
(y + 1) * case - marge - 3),
cv2.FONT_HERSHEY_SCRIPT_COMPLEX, 0.9,
(0, 0, 255), 1)
M = cv2.getPerspectiveTransform(pts2, pts1)