对于灰度uint8图像，可以使用如下所示的内容：
def tf_equalize_histogram(image):
values_range = tf.constant([0., 255.], dtype = tf.float32)
histogram = tf.histogram_fixed_width(tf.to_float(image), values_range, 256)
cdf = tf.cumsum(histogram)
cdf_min = cdf[tf.reduce_min(tf.where(tf.greater(cdf, 0)))]
img_shape = tf.shape(image)
pix_cnt = img_shape[-3] * img_shape[-2]
px_map = tf.round(tf.to_float(cdf - cdf_min) * 255. / tf.to_float(pix_cnt - 1))
px_map = tf.cast(px_map, tf.uint8)
eq_hist = tf.expand_dims(tf.gather_nd(px_map, tf.cast(image, tf.int32)), 2)
return eq_hist
测试：
import tensorflow as tf
import numpy as np
import cv2
image_ph = tf.placeholder(tf.uint8, shape = [None, None, 1])
image_eq_hist = tf_equalize_histogram(image_ph)
image = cv2.imread("./test_gs.png", 0)
image = np.reshape(image, (image.shape[0], image.shape[1], 1))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
image_eq_hist_ = sess.run(image_eq_hist, Feed_dict = {image_ph : image})
cv2.imshow("eq_cv", cv2.equalizeHist(image))
cv2.imshow("eq", image_eq_hist_)
cv2.waitKey()
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