def solve_image(fp):
try:
img = resize_keep_aspect(cv2.imread(fp, cv2.IMREAD_COLOR))
processed = process(img)
corners = get_corners(processed)
warped = transform(corners, processed)
mask = extract_lines(warped)
numbers = cv2.bitwise_and(warped, mask)
digits_unsorted = extract_digits(numbers)
digits_subd = subdivide(numbers)
digits_sorted = sort_digits(digits_subd, digits_unsorted, img_dims)
digits_border = add_border(digits_sorted)
puzzle = img_to_array(digits_border, img_dims)
solved = solve(puzzle.copy().tolist()
) # Solve function modifies original puzzle var
warped_img = transform(corners, img)
subd = subdivide(warped_img)
subd_soln = put_solution(subd, solved, puzzle)
warped_soln = stitch_img(subd_soln,
(warped_img.shape[0], warped_img.shape[1]))
warped_inverse = inverse_perspective(warped_soln, img,
np.array(corners))
show(warped_inverse)
except Exception as e:
print(f'Image not solvable: {e}')
def solve_image(fp, font_color, font_path):
if font_color is None:
font_color = (0, 127, 255)
if font_path is None:
font_path = './python/assets/FreeMono.ttf'
try:
img = resize_keep_aspect(cv2.imread(fp, cv2.IMREAD_COLOR))
except AttributeError:
sys.stderr.write('Error: Image path not valid')
sys.exit()
processed = process(img)
corners = get_corners(processed)
warped = transform(corners, processed)
vertical_lines, horizontal_lines = get_grid_lines(warped)
mask = create_grid_mask(vertical_lines, horizontal_lines)
numbers = cv2.bitwise_and(warped, mask)
digits_sorted = extract_digits(numbers)
digits_border = add_border(digits_sorted)
digits_subd = subdivide(numbers)
try:
digits_with_zeros = add_zeros(digits_border, digits_subd)
except IndexError:
sys.stderr.write('Error: Image too warped')
sys.exit()
try:
puzzle = img_to_array(digits_with_zeros, img_dims)
except AttributeError:
sys.stderr.write('Error: OCR predictions failed')
sys.exit()
solved = solve(
puzzle.copy().tolist()) # Solve function modifies original puzzle var
if not solved:
raise ValueError('Error: Puzzle not solvable')
warped_img = transform(corners, img)
subd = subdivide(warped_img)
subd_soln = put_solution(subd, solved, puzzle, font_color, font_path)
warped_soln = stitch_img(subd_soln,
(warped_img.shape[0], warped_img.shape[1]))
warped_inverse = inverse_perspective(warped_soln, img.copy(),
np.array(corners))
return warped_inverse
def solve_webcam(font_color, font_path, debug=False):
if font_color is None:
font_color = (0, 127, 255)
if font_path is None:
font_path = 'assets/FreeMono.ttf'
cap = cv2.VideoCapture(0)
stored_soln = []
stored_puzzle = []
# Creating placeholder grid to match against until one is taken from the sudoku puzzle
cells = [np.pad(np.ones((7, 7), np.uint8) * 255, (1, 1), 'constant', constant_values=(0, 0)) for _ in range(81)]
grid = stitch_img(cells, (81, 81))
while True:
ret, frame = cap.read()
img = resize_keep_aspect(frame)
try:
processed = process(img)
corners = get_corners(processed)
warped = transform(corners, processed)
vertical_lines, horizontal_lines = get_grid_lines(warped)
mask = create_grid_mask(vertical_lines, horizontal_lines)
# Checks to see if the mask matches a grid-like structure
template = cv2.resize(grid, (warped.shape[0],) * 2, interpolation=cv2.INTER_NEAREST)
res = cv2.matchTemplate(mask, template, cv2.TM_CCORR_NORMED)
threshold = .55
loc = np.array(np.where(res >= threshold))
if loc.size == 0:
raise ValueError('Grid template not matched')
if stored_soln and stored_puzzle:
warped_img = transform(corners, img)
subd = subdivide(warped_img)
subd_soln = put_solution(subd, stored_soln, stored_puzzle, font_color, font_path)
warped_soln = stitch_img(subd_soln, (warped_img.shape[0], warped_img.shape[1]))
warped_inverse = inverse_perspective(warped_soln, img, np.array(corners))
cv2.imshow('frame', warped_inverse)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
continue
numbers = cv2.bitwise_and(warped, mask)
digits_sorted = extract_digits(numbers)
digits_border = add_border(digits_sorted)
digits_subd = subdivide(numbers)
digits_with_zeros = add_zeros(digits_border, digits_subd)
puzzle = img_to_array(digits_with_zeros, img_dims)
if np.sum(puzzle) == 0:
raise ValueError('False positive')
if not check_if_solvable(puzzle):
raise ValueError('OCR Prediction wrong')
solved = solve(puzzle.copy().tolist())
if not solved:
raise ValueError('Puzzle not solvable')
if verify(solved):
stored_puzzle = puzzle.tolist()
stored_soln = solved
grid = mask
warped_img = transform(corners, img)
subd = subdivide(warped_img)
subd_soln = put_solution(subd, solved, puzzle, font_color, font_path)
warped_soln = stitch_img(subd_soln, (warped_img.shape[0], warped_img.shape[1]))
warped_inverse = inverse_perspective(warped_soln, img, np.array(corners))
cv2.imshow('frame', warped_inverse)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
except Exception as e:
cv2.imshow('frame', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if debug:
print(e)
continue
def video_opener(debug=True):
filename = filedialog.askopenfilename(filetypes=(
("All files", "*.*")
# ,("Template files", "*.tplate")
,
("HTML files", "*.html;*.htm")))
cap = cv2.VideoCapture(filename)
stored_soln = []
stored_puzzle = []
# print(path)
# Creating placeholder grid to match against until one is taken from the sudoku puzzle
cells = [
np.pad(np.ones((7, 7), np.uint8) * 255, (1, 1),
'constant',
constant_values=(0, 0)) for _ in range(81)
]
grid = stitch_img(cells, (81, 81))
while True:
_, img = cap.read()
try:
processed = process(img)
corners, contour = get_corners(processed)
for i in corners:
i = tuple(i)
cv2.circle(img, i, 4, (0, 0, 255),
2) # this is to mark circle at the corners
# cv2.drawContours(img, [contour], 0, (0, 255, 0),
# 2) # this is to make boundry outline the max area square found in corner function
warped = transform(corners, processed)
vertical_lines, horizontal_lines = get_grid_lines(warped)
mask = create_grid_mask(vertical_lines, horizontal_lines)
# cv2.imshow("Mask",mask)
# cv2.imshow("Grid",grid)
# Checks to see if the mask matches a grid-like structure
template = cv2.resize(grid, (warped.shape[0], ) * 2,
interpolation=cv2.INTER_NEAREST)
res = cv2.matchTemplate(mask, template, cv2.TM_CCORR_NORMED)
threshold = .55
loc = np.array(np.where(res >= threshold))
if loc.size == 0:
raise ValueError('Grid template not matched')
if stored_soln and stored_puzzle:
warped_img = transform(corners, img)
subd = subdivide(warped_img)
subd_soln = put_solution(subd, stored_soln, stored_puzzle)
warped_soln = stitch_img(
subd_soln, (warped_img.shape[0], warped_img.shape[1]))
warped_inverse = inverse_perspective(warped_soln, img,
np.array(corners))
cv2.imshow('frame', warped_inverse)
# print("Continuing to next frame from image")
if cv2.waitKey(1) & 0xFF == ord('q'):
break
print("here doing continue")
continue
numbers = cv2.bitwise_and(warped, mask)
cv2.imshow("numbers", numbers)
digits_sorted = extract_digits(numbers)
digits_border = add_border(digits_sorted)
digits_subd = subdivide(numbers)
digits_with_zeros = add_zeros(digits_border, digits_subd)
puzzle = img_to_array(digits_with_zeros, 32)
print(puzzle)
if np.sum(puzzle) == 0:
raise ValueError('False positive')
if not check_if_solvable(puzzle):
raise ValueError('OCR Prediction wrong')
solved = solve(puzzle.copy().tolist())
if not solved:
raise ValueError('Puzzle not solvable')
if verify(solved):
stored_puzzle = puzzle.tolist()
stored_soln = solved
# grid = mask
warped_img = transform(corners, img)
subd = subdivide(warped_img)
subd_soln = put_solution(subd, solved, puzzle)
warped_soln = stitch_img(
subd_soln, (warped_img.shape[0], warped_img.shape[1]))
cv2.imshow("warped sol", warped_soln)
warped_inverse = inverse_perspective(warped_soln, img,
np.array(corners))
cv2.imshow('frame', warped_inverse)
print("hello sir ")
if cv2.waitKey(1) & 0xFF == ord('q'):
break
except Exception as e:
cv2.imshow('frame', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if debug:
print(e)
print("Its error continue")
continue
cv2.destroyAllWindows()
def solve_photo(img_path, debug=0):
while True:
img = cv2.imread(img_path)
processed_image = process(img)
corners, contour = get_corners(processed_image)
warped = transform(corners, processed_image)
vertical_lines, horizontal_lines = get_grid_lines(warped)
# print("v.s",vertical_lines.shape)
mask = create_grid_mask(vertical_lines, horizontal_lines)
# print("Mask shape",mask.shape)
# print("warped shape",warped.shape)
numbers = cv2.bitwise_and(warped, mask)
cv2.imshow("number", numbers)
digits_sorted_array = extract_digits(
numbers) # it store the number rowise in ascending order
digits_border_array = add_border(
digits_sorted_array
) # it is the array of the digits sorted with increased border.
digits_subd_array = subdivide(
numbers) # it store the each number box (inner box)
try:
digits_with_zeros = add_zeros(
digits_border_array, digits_subd_array
) # it will make the box black i.e 0 full to the box , that doesn't have the no.
# print(digits_with_zeros.shape)
except IndexError:
sys.stderr.write('ERROR: Image too warped')
sys.exit()
try:
puzzle = img_to_array(
digits_with_zeros, 32
) # as we have trained the model on the image of 32 * 32 pixel
print(puzzle)
except AttributeError:
sys.stderr.write('ERROR: OCR predictions failed')
sys.exit()
solved = solve(puzzle.copy().tolist()
) # Solve function modifies original puzzle var
if not solved:
raise ValueError('ERROR: Puzzle not solvable')
warped_img = transform(corners, img)
subd = subdivide(warped_img)
subd_soln = put_solution(subd, solved, puzzle)
# for i in subd_soln: # this is only for debugging purpose
# plt.imshow(i)
# plt.show()
warped_soln = stitch_img(subd_soln,
(warped_img.shape[0], warped_img.shape[1]))
plt.imshow(warped_soln)
plt.show()
warped_inverse = inverse_perspective(warped_soln, img.copy(),
np.array(corners))
cv2.imshow("Solved Soduku ", warped_inverse)
if (debug):
cv2.imshow("O", processed_image)
for i in corners:
i = tuple(i)
cv2.circle(img, i, 4, (0, 0, 255),
2) # this is to mark circle at the corners
cv2.drawContours(
img, [contour], 0, (0, 255, 0), 2
) # this is to make boundry outline the max area square found in corner function
create_grid_mask1(vertical_lines, horizontal_lines)
cv2.imshow("Warped image", warped)
cv2.imshow("Horizontal", horizontal_lines)
cv2.imshow("Vertical", vertical_lines)
cv2.imshow("Original image", img)
cv2.imshow("Numbers image", numbers)
if (cv2.waitKey(0) == 13):
break
cv2.destroyAllWindows()
def solve_webcam():
cap = cv2.VideoCapture(0)
stored_soln = []
stored_puzzle = []
while True:
ret, frame = cap.read()
img = resize_keep_aspect(frame)
try:
processed = process(img)
corners = get_corners(processed)
warped = transform(corners, processed)
mask = extract_lines(warped)
# Checks to see if the mask matches a grid-like structure
cells = [
np.pad(np.ones((7, 7), np.uint8) * 255, (1, 1),
'constant',
constant_values=(0, 0)) for _ in range(81)
]
grid = stitch_img(cells, (81, 81))
template = cv2.resize(grid, (warped.shape[0], ) * 2,
interpolation=cv2.INTER_NEAREST)
res = cv2.matchTemplate(mask, template, cv2.TM_CCORR_NORMED)
loc = np.array(np.where(res >= .6))
if loc.size == 0:
raise ValueError('Grid template not matched')
if stored_soln and stored_puzzle:
warped_img = transform(corners, img)
subd = subdivide(warped_img)
subd_soln = put_solution(subd, stored_soln, stored_puzzle)
warped_soln = stitch_img(
subd_soln, (warped_img.shape[0], warped_img.shape[1]))
warped_inverse = inverse_perspective(warped_soln, img,
np.array(corners))
cv2.imshow('frame', warped_inverse)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
continue
numbers = cv2.bitwise_and(warped, mask)
digits_unsorted = extract_digits(numbers)
digits_subd = subdivide(numbers)
digits_sorted = sort_digits(digits_subd, digits_unsorted, img_dims)
digits_border = add_border(digits_sorted)
puzzle = img_to_array(digits_border, img_dims)
if not check_if_solvable(puzzle):
raise ValueError('Puzzle not solvable')
solved = solve(puzzle.copy().tolist())
if not solved:
raise ValueError('Puzzle not solvable')
if verify(solved):
stored_puzzle = puzzle.tolist()
stored_soln = solved
warped_img = transform(corners, img)
subd = subdivide(warped_img)
subd_soln = put_solution(subd, solved, puzzle)
warped_soln = stitch_img(
subd_soln, (warped_img.shape[0], warped_img.shape[1]))
warped_inverse = inverse_perspective(warped_soln, img,
np.array(corners))
cv2.imshow('frame', warped_inverse)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
except (ValueError, cv2.error):
cv2.imshow('frame', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
continue
