def center_extent(image, size):
# grab the extent width and height
(eW, eH) = size
# handle when the width is greater than the height
if image.shape[1] > image.shape[0]:
image = imutils.resize(image, width = eW)
# otherwise, the height is greater than the width
else:
image = imutils.resize(image, height = eH)
# allocate memory for the extent of the image and
# grab it
extent = np.zeros((eH, eW), dtype = "uint8")
offsetX = (eW - image.shape[1]) // 2
offsetY = (eH - image.shape[0]) // 2
extent[offsetY:offsetY + image.shape[0], offsetX:offsetX + image.shape[1]] = image
# compute the center of mass of the image and then
# move the center of mass to the center of the image
(cY, cX) = np.round(mahotas.center_of_mass(extent)).astype("int32")
(dX, dY) = ((size[0] // 2) - cX, (size[1] // 2) - cY)
M = np.float32([[1, 0, dX], [0, 1, dY]])
extent = cv2.warpAffine(extent, M, size)
# return the extent of the image
return extent
def deskew(image, width):
# grab the width and height of the image and compute
# moments for the image
(h, w) = image.shape[:2]
moments = cv2.moments(image)
# deskew the image by applying an affine transformation
skew = moments["mu11"] / moments["mu02"]
M = np.float32([
[1, skew, -0.5 * w * skew],
[0, 1, 0]])
image = cv2.warpAffine(image, M, (w, h),
flags = cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
# resize the image to have a constant width
image = imutils.resize(image, width = width)
# return the deskewed image
return image
