def deskew(image):
    # load the image from disk
    #image = cv2.imread(imgpath)
    # convert the image to grayscale and flip the foreground and background to ensure foreground is now "white" and the background is "black"
    #gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    gray = cv2.bitwise_not(image)

    # threshold the image, setting all foreground pixels to 255 and all background pixels to 0
    thresh = cv2.threshold(gray, 0, 255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
    # grab the (x, y) coordinates of all pixel values that are greater than zero, then use these coordinates to
    # compute a rotated bounding box that contains all coordinates
    coords = np.column_stack(np.where(thresh > 0))
    angle = cv2.minAreaRect(coords)[-1]

    # the `cv2.minAreaRect` function returns values in the  range [-90, 0); as the rectangle rotates clockwise the
    # returned angle trends to 0 -- in this special case we need to add 90 degrees to the angle
    if angle < -45:
        angle = -(90 + angle)

    # otherwise, just take the inverse of the angle to make it positive
    else:
        angle = -angle

    # rotate the image to deskew it
    (h, w) = image.shape[:2]
    center = (w // 2, h // 2)
    M = cv2.getRotationMatrix2D(center, angle, 1.0)
    rotated = cv2.warpAffine(image, M, (w, h),flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
    # draw the correction angle on the image so we can validate it
    # cv2.putText(rotated, "Angle: {:.2f} degrees".format(angle),(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

    # show the output image
    print("[INFO] angle: {:.3f}".format(angle))
    return rotated
