import numpy as np

from sklearn.datasets import  load_sample_images

import tensorflow as tf

import matplotlib.pyplot as plt

dataset = np.array(load_sample_images().images, dtype=np.float32)

batch_size, height, width, channels = dataset.shape

print(batch_size, height, width, channels)

print(type(dataset))

filters_test = tf.placeholder(tf.float32, shape=(15, 15, channels, 2))

X = tf.placeholder(tf.float32, shape=(None, height, width, channels))

dataset = dataset/255

convolution = tf.nn.conv2d(X, filter=filters_test, strides=[1, 2, 2, 1], padding='SAME')

with tf.Session() as sess:

    out = {}

    filters = np.zeros(shape=(15, 15, channels, 2))

    for i in range(2):

        if i == 0:

            filters[7, :, :, 1] = 1

        elif i == 1:

            filters[:, 7, :, 1] = 1

        output = sess.run(convolution, feed_dict={X: dataset, filters_test: filters})

        print(output)

        out['output'+str(i)] = output

print(output)

plt.imshow(dataset[0])

plt.show()

for i in out:

    # print(out[i])

    # max_value = max(out[i][0].reshape(-1, 1))

    # print(max_value)

    # out[i] = out[i]/max_value

    plt.title(i)

    plt.imshow(255*out[i][0, :, :, 1], cmap='bone')

    plt.show()