python Tensorflow 实现图像的卷积处理
1.convolution.py
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()