import theano

import numpy

from theano.sandbox import neighbours

from theano import tensor as T

from theano import function

from skimage import io

import time

patch = io.imread('test.jpg', True)

patch_batch = []

batch_size = 384

# 这里模拟批量处理多个图像块

for i in xrange(batch_size):

    patch_batch.append(patch)

_input = T.tensor3('_input')

norm_input = T.reshape(_input, T.concatenate([(batch_size,1),_input.shape[1:]]), ndim=4)

# neighbours.images2neibs(ten4, neib_shape, neib_step=None, mode='valid')，其中neib_shap为邻域大小，这里设置为3x3，

# neib_step指定步长，我们需要对每一个点进行归一化，因此设定为(1x1)

# 该函数返回一个shape为(batch_size x patch_width x patch_height, 9)的数组，每一行代表每个点的9个邻居

neibs = neighbours.images2neibs(norm_input, (3, 3), (1, 1), 'wrap_centered')

_means = T.mean(neibs, axis=1)

_stds = T.std(neibs, axis=1)

_flatten_input = _input.flatten()

normed_result = (_flatten_input - _means) / (_stds + 1 / 255)

# 将结果重新reshape为图像大小

reshpaed_normed_result = T.reshape(normed_result, _input.shape)

shared_patch = theano.shared(numpy.asarray(patch_batch, dtype=theano.config.floatX), borrow=True)

calc_norm = function([], reshpaed_normed_result, givens={_input:shared_patch})

start_time = time.clock()

dd = calc_norm()

end_time = time.clock()

print end_time - start_time