Visualize your own image fitler

This post was kindly contributed by SAS Programming for Data Mining - go there to comment and to read the full post.

First load necessary packages


import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Conv2D
import keras.backend as K
import scipy, imageio
import matplotlib.pyplot as plt
from PIL import Image
%matplotlib inline

Then show original picture of my Jeep


# 首先将图片读入为矩阵
# 我们可以用pyplot的imshow()方法来展示图片
#  这时我曾经拥有的牧马人
#
img_data = imageio.imread('./pics/wranglerJK.jpg')
print(img_data.data.shape)

img = Image.fromarray(img_data, 'RGB')
plt.imshow(img)

Now, build our 2-D convolutional function that takes a custom filter matrix and comput the filtered output image matrix.


def my_init(shape, dtype=None):
    new_mat = filter_mat.reshape(shape[0], shape[1], 1, 1) \
           + filter_mat.reshape(1, shape[0], shape[1], 1)  \
           + filter_mat.reshape(1, 1, shape[0], shape[1])
    return np.array(new_mat, dtype=dtype)
    #return K.random_normal(shape, dtype=dtype)


def MyFilter(filter_mat):
    print(len(filter_mat.shape))
    if len(filter_mat.shape)!=2:
        print('Invalid filter matrix. It must be 2-D')
        return []
    else:
        kernel_size=filter_mat.shape
        row, col, depth = img_data.shape
        input_shape=img_data.shape
        filter_size = row*col*depth
        print(filter_size)


        model = Sequential()
        model.add(Conv2D(depth, 
                         kernel_size=kernel_size, 
                         input_shape=input_shape, 
                         padding='same', 
                         activation='linear', 
                         data_format='channels_last',
                         kernel_initializer=my_init,
                         name='Conv')                         
                  )
        model.add(Dense(1, activation='linear'))
        model.compile(optimizer='sgd', loss='mse')
        model.summary()


        inX = model.input                                          
        outputs = [layer.output for layer in model.layers if layer.name=='Conv']     
        functions = [K.function([inX], [out]) for out in outputs]   

        layer_outs = [func([img_data.reshape(1, row, col, depth)]) for func in functions]
        activationLayer = layer_outs[0][0]
        temp = (activationLayer-np.min(activationLayer))
        normalized_activationLayer = temp/np.max( np.max(temp))
        return(normalized_activationLayer.reshape(row, col, depth))

Now, insert our own fixed filter matrix and get the output, using pyplot.imshow() to display the filtered picture.


filter_mat = np.array([-1, -3, -1, 0, 0, 0, 1, 3, 1]).reshape(3, 3)

outLayer = MyFilter(filter_mat)
plt.imshow(outLayer)

Below is the filtered picture.

This post was kindly contributed by SAS Programming for Data Mining - go there to comment and to read the full post.

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