我有以下代码，用于在Python中使用OpenCV调整图像numpy数组的大小。它在我的Windows桌面上运行良好，但在Linux（ubuntu）上有内存泄漏。处理了数百张图像（9000x9000像素）后，它会占用所有内存。任何提示？
import cv2.cv as cv
def resizeMemLeak(imArray, size=(1000,1000), interp=cv.CV_INTER_LINEAR):
"""
Resize a numpy array to size given by rows and cols, the array is first
converted into cv.mat image to do the resize.
@param inArray: numpy array to be resized
@param size: int - Percentage of current size
float - Fraction of current size
tuple - Size of the output image
@type size: {int, float, tuple}
@param interp: {cv.CV_INTER_LINEAR [default],
cv.CV_INTER_NN,
cv.CV_INTER_AREA,
cv.CV_INTER_CUBIC }
@return: the resized numpy array kept the same dtype.
"""
# make a copy of the input array to avoid OpenCV step bug
import copy
imCopy = copy.deepcopy(imArray)
mat = cv.fromarray(imCopy, allowND=False)
if len(imCopy.shape) == 2:
assert mat.channels == 1
elif len(imCopy.shape) == 3:
assert mat.channels == imArray.shape[2]
else:
raise TypeError("unknown image array")
import numpy as np
if imCopy.dtype == np.uint8:
assert mat.step // mat.cols == mat.channels
elif imCopy.dtype == np.uint16:
assert mat.step // mat.cols == 2 * mat.channels
elif imCopy.dtype == np.int32:
assert mat.step // mat.cols == 4 * mat.channels
else:
raise TypeError("unknown image array")
if type(size) is int:
assert 0 < size
rows = mat.rows * size // 100
cols = mat.cols * size // 100
elif type(size) is float:
assert 0.0 < size
rows = int( mat.rows * size )
cols = int( mat.cols * size )
elif type(size) is tuple:
assert len(size) == 2
rows = int(size[0])
cols = int(size[1])
else:
raise TypeError("parameter size: wrong type.")
imResized = cv.CreateMat(rows, cols, mat.type)
cv.Resize(mat, imResized)
resized = np.asarray(imResized)
return resized
谢谢！