Image gaussian blur based on Tensorflow

Gaussian blur filtering is performed on RGB images through conv2d
Gaussian blur filter Position matrix
Position matrix
In the figure below, the figure on the right is the blurred one
The result of a blurred image
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Tue Oct  2 13:23:27 2018

@author: myhaspl

@email:myhaspl@myhaspl.com

tf.nn.conv2d

Gaussian blur

"""

import tensorflow as tf
from PIL import Image    
import numpy as np
import math



g=tf.Graph()

with g.as_default():
    
    def getGuessValue(kerStd,posX,posY):
        return 1./(2.*math.pi*(np.power(kerStd,2)))*math.exp(-(np.power(posX,2)+np.power(posY,2))/(2.*(np.power(kerStd,2))))
    
    def getGuessKernel(kerStd):
        K11=np.column_stack((np.row_stack((np.eye(3)*getGuessValue(kerStd,-1,1),[0.,0.,0.])),np.array([0.,0.,0.,1.])))
        K12=np.column_stack((np.row_stack((np.eye(3)*getGuessValue(kerStd,0,1),[0.,0.,0.])),np.array([0.,0.,0.,1.])))        
        K13=np.column_stack((np.row_stack((np.eye(3)*getGuessValue(kerStd,1,1),[0.,0.,0.])),np.array([0.,0.,0.,1.])))      
        K21=np.column_stack((np.row_stack((np.eye(3)*getGuessValue(kerStd,-1,0),[0.,0.,0.])),np.array([0.,0.,0.,1.])))      
        K22=np.column_stack((np.row_stack((np.eye(3)*getGuessValue(kerStd,0,0),[0.,0.,0.])),np.array([0.,0.,0.,1.])))      
        K23=np.column_stack((np.row_stack((np.eye(3)*getGuessValue(kerStd,1,0),[0.,0.,0.])),np.array([0.,0.,0.,1.])))      
        K31=np.column_stack((np.row_stack((np.eye(3)*getGuessValue(kerStd,-1,-1),[0.,0.,0.])),np.array([0.,0.,0.,1.])))      
        K32=np.column_stack((np.row_stack((np.eye(3)*getGuessValue(kerStd,0,-1),[0.,0.,0.])),np.array([0.,0.,0.,1.])))      
        K33=np.column_stack((np.row_stack((np.eye(3)*getGuessValue(kerStd,1,-1),[0.,0.,0.])),np.array([0.,0.,0.,1.])))      
        print K11.shape
        kernel=tf.constant(np.array(
                [
                    [
                       K11,
                       K12,
                       K13
                    ],
                    [
                       K21,
                       K22,
                       K23                    
                    ],
                    [
                       K31,
                       K32,
                       K33                    
                    ]              
                ])
                ,dtype=tf.float32)#3*3*4*4
        return kernel

    def getImageData(fileNameList):
        imageData=[]
        for fn in fileNameList:        
            testImage = Image.open(fn)
            testImage.show() 
            imageData.append(np.array(testImage))
        return np.array(imageData,dtype=np.float32)

    imageFn=("dog.png",)
    imageData=getImageData(imageFn)
    testData=tf.constant(imageData)
    kernel=getGuessKernel(0.8)
    y=tf.cast(tf.nn.conv2d(testData,kernel,strides=[1,1,1,1],padding="SAME"), dtype=tf.int32)
    init_op = tf.global_variables_initializer()
with tf.Session(graph=g) as sess:
    print testData.get_shape()
    print kernel.eval()
    print kernel.get_shape()
    resultData=sess.run(y)[0]
    print resultData.shape
    resulImage=Image.fromarray(np.uint8(resultData))   
    resulImage.show()
    print y.get_shape()
Image gaussian blur based on Tensorflow

tf.nn.conv2d

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