pytesseract.pytesseract.tesseract_cmd = "C:/Program Files/Tesseract-OCR/tesseract.exe"
# load the digit classifier from disk
print("[INFO] loading digit classifier...")
model = load_model("Model/train_digit_classifier.h5")
# train_digit_classifier
# digitClassifierModel
# load the Input image from disk and resize it
print("[INFO] processing image...")
image = cv2.imread("resources/fig2.PNG")
image = imutils.resize(image, width=600)
# find the puzzle in the image and then
# if debug = true, it shows the image
(puzzleImage, warped) = find_puzzle(image, debug=True)
# initialize our 9x9 sudoku board
board = np.zeros((9, 9), dtype="int")
# a sudoku puzzle is a 9x9 grid (81 individual cells), so we can
# infer the location of each cell by dividing the warped image
# into a 9x9 grid
stepX = warped.shape[1] // 9
stepY = warped.shape[0] // 9
# initialize a list to store the (x, y)-coordinates of each cell
# location
cellLocs = []
# loop over the grid locations
help="path to input sudoku puzzle image")
ap.add_argument("-d", "--debug", type=int, default=-1,
help="whether or not we are visualizing each step of the pipeline")
args = vars(ap.parse_args())
# load the digit classifier from disk
print("[INFO] loading digit classifier...")
model = load_model(args["model"])
# load the input image from disk and resize it
print("[INFO] processing image...")
image = cv2.imread(args["image"])
image = imutils.resize(image, width=600)
# find the puzzle in the image and then
(puzzleImage, warped) = find_puzzle(image, debug=args["debug"] > 0)
# initialize our 9x9 sudoku board
board = np.zeros((9, 9), dtype="int")
# a sudoku puzzle is a 9x9 grid (81 individual cells), so we can
# infer the location of each cell by dividing the warped image
# into a 9x9 grid
stepX = warped.shape[1] // 9
stepY = warped.shape[0] // 9
# initialize a list to store the (x, y)-coordinates of each cell
# location
cellLocs = []
# loop over the grid locations
# load the digit classifier from disk
print("[INFO] loading digit classifier...")
model = load_model(args["model"])
# load the input image from disk and resize it
print("[INFO] processing image...")
image = cv2.imread(args["image"])
cv2.imshow('sudoku', image)
image = imutils.resize(image, width=600)
# find the puzzle in the image and then
# puzzleImage là ảnh thường , warped là ảnh xám
(puzzleImage, warped) = find_puzzle(
image,
debug=args["debug"] > 0) #debug default hiện đang là -1 < 0 nên là false
# initialize our 9x9 sudoku board
board = np.zeros((9, 9), dtype="int")
# a sudoku puzzle is a 9x9 grid (81 individual cells), so we can
# infer the location of each cell by dividing the warped image
# into a 9x9 grid
# ảnh xám có tọa độ y , x
stepX = warped.shape[1] // 9
stepY = warped.shape[0] // 9
arr = np.zeros((9, 9))
for y in range(9):