if x == 1 and y == 1:
debug = 0
# compute the starting and ending (x, y)-coordinates of the
# current cell
startX = x * stepX
startY = y * stepY
endX = (x + 1) * stepX
endY = (y + 1) * stepY
# add the (x, y)-coordinates to our cell locations list
row.append((startX, startY, endX, endY))
# crop the cell from the warped transform image and then
# extract the digit from the cell
cell = warped[startY:endY, startX:endX]
digit = extract_digit(cell, debug=debug > 0)
# verify that the digit is not empty
if digit is not None:
foo = np.hstack([cell, digit])
# resize the cell to 28x28 pixels and then prepare the
# cell for classification
roi = cv2.resize(digit, (28, 28))
roi = roi.astype("float") / 255.0
roi = img_to_array(roi)
roi = np.expand_dims(roi, axis=0)
# classify the digit and update the sudoku board with the
# prediction
pred = model.predict(roi).argmax(axis=1)[0]
for x in range(0, 9):
# compute the starting and ending (x, y)-coordinates of the
# current cell
startX = x * stepX
startY = y * stepY
endX = (x + 1) * stepX
endY = (y + 1) * stepY
# add the (x, y)-coordinates to our cell locations list
row.append((startX, startY, endX, endY))
# crop the cell from the warped transform image and then
# extract the digit from the cell
cell = warped[startY:endY, startX:endX]
digit = extract_digit(cell, debug=args["debug"] > 0)
# verify that the digit is not empty
if digit is not None:
foo = np.hstack([cell, digit])
cv2.imshow("Cell/Digit", foo)
# resize the cell to 28x28 pixels and then prepare the
# cell for classification
roi = cv2.resize(digit, (28, 28))
roi = roi.astype("float") / 255.0
roi = img_to_array(roi)
roi = np.expand_dims(roi, axis=0)
# classify the digit and update the sudoku board with the
# prediction
# initialize the current list of cell locations
row = []
for x in range(0, 9):
# compute the starting and ending (x, y)-coordinates of the current cell
startX = x * stepX
startY = y * stepY
endX = (x + 1) * stepX
endY = (y + 1) * stepY
# add the (x, y)-coordinates to our cell locations list
row.append((startX, startY, endX, endY))
# crop the cell from the warped transform image and then extract the digit from the cell
cell = warped[startY:endY, startX:endX]
# if debug = true, it shows the image of every digits
digit = extract_digit(cell, debug=False)
# verify that the digit is not empty
if digit is not None:
foo = np.hstack([cell, digit])
# cv2.imshow("Cell/Digit", foo)
# resize the cell to 28x28 pixels and then prepare the
# cell for classification
roi = cv2.resize(digit, (28, 28))
roi = roi.astype("float") / 255.0
roi = img_to_array(roi)
roi = np.expand_dims(roi, axis=0)
# classify the digit and update the sudoku board with the
# prediction
