如果在膨胀中使用简单的矩形核，一种可能性是使用tf.nn.max_pool2d作为替代。

import numpy as np
import tensorflow as tf
import cv2

image = np.random.random((28,28))
kernel_size = 3
# OpenCV dilation works with grayscale image, with H,W dimensions
dilated_cv = cv2.dilate(image, np.ones((kernel_size, kernel_size), np.uint8))
# TensorFlow maxpooling works with batch and channels: B,H,W,C dimenssions
image_w_batch_and_channels = image[None,...,None]
dilated_tf = tf.nn.max_pool2d(image_w_batch_and_channels, kernel_size, 1, "SAME")

# checking that the results are equal
np.allclose(dilated_cv, dilated_tf[0,...,0])

但是，如果您提到要对地面真实值应用膨胀，则该膨胀不需要是可微的。

from functools import partial
# be sure to put the correct output type, tf.float64 is working in that specific case because numpy defaults to float64, but it might be different in your case
dilated_tf_npfunc = tf.numpy_function(
    partial(cv2.dilate, kernel=np.ones((kernel_size, kernel_size), np.uint8)), [image]
)