首先生成3×3×3的黑色图片
"""
生成3×3×3黑色图像
"""
def produce_image():
size = 3
x, y = ogrid[:size, :size] # 第一部分产生多行一列 第二部分产生一行多列
z = x + y
z = z[:, :, newaxis] # 增加第三维
# print(z)
img = repeat(z, 3, 2)/12 # 在第三维上复制两遍
# print(img.shape)
# print(img)
io.imshow(img, interpolation='none')
io.show()
return img
打印结果：
双线性插值反卷积代码如下：
"""
生成3×3×3黑色图像
"""
def produce_image():
size = 3
x, y = ogrid[:size, :size] # 第一部分产生多行一列 第二部分产生一行多列
z = x + y
z = z[:, :, newaxis] # 增加第三维
# print(z)
img = repeat(z, 3, 2)/12 # 在第三维上复制两遍
# print(img.shape)
# print(img)
io.imshow(img, interpolation='none')
io.show()
return img
"""
上采样 双线性插值生成卷积核
"""
def upsampling_bilinear():
#确定卷积核大小
def get_kernel_size(factor):
return 2*factor-factor%2
# 创建相关矩阵
def upsample_filt(size):
factor=(size+1)//2
if size%2==1:
center=factor-1
else:
center=factor-0.5
og=np.ogrid[:size,:size]
# print(og)
# print(og[0])
# print(og[1])
return (1-abs(og[0]-center)/factor)*(1-abs(og[1]-center)/factor)
#进行上采样卷积核
def bilinear_upsample_weights(factor,number_of_classes):
filter_size=get_kernel_size(factor)
weights=np.zeros((filter_size,filter_size,
number_of_classes,number_of_classes),dtype=np.float32)
upsample_kernel=upsample_filt(filter_size)
# print(upsample_kernel)
for i in range(number_of_classes):
weights[:,:,i,i]=upsample_kernel
# print(weights[:,:,i,i])
# print(weights)
# print(weights.shape)
return weights
weights=bilinear_upsample_weights(3,3)
return weights
if __name__ == '__main__':
import tensorflow as tf
# upsampling()
# upsampling_bilinear()
image=produce_image()
img = tf.cast(image, dtype=tf.float32)
img = tf.expand_dims(img, 0) # 增加维度
#产生卷积核
kerenel=upsampling_bilinear()
#反卷积处理
res=tf.nn.conv2d_transpose(img,kerenel,output_shape=[1,9,9,3],strides=[1,3,3,1],padding='SAME')
with tf.Session() as sess:
img = sess.run(res)
io.imshow(img[0, :, :, :] , interpolation='none')
io.show()打印结果：能较好恢复原图像
