from sklearn.datasets import load_sample_image
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
 
if __name__ == '__main__':
    # Load sample images
    china = load_sample_image("china.jpg")
    flower = load_sample_image("flower.jpg")
    dataset = np.array([china, flower], dtype=np.float32)
    batch_size, height, width, channels = dataset.shape
    # Create 2 filters
    filters = np.zeros(shape=(7, 7, channels, 2), dtype=np.float32)
    filters[:, 3, :, 0] = 1 # vertical line
    filters[3, :, :, 1] = 1 # horizontal line
    # Create a graph with input X plus a convolutional layer applying the 2 filters
    X = tf.placeholder(tf.float32, shape=(None, height, width, channels))
    convolution = tf.nn.conv2d(X, filters, strides=[1,2,2,1], padding="SAME")
    with tf.Session() as sess:
        output = sess.run(convolution, feed_dict={X: dataset})
    plt.imshow(output[0, :, :, 1], cmap="gray") # plot 1st image's 2nd feature map
    plt.show()