tensorflow实现高斯模糊

以下是使用TensorFlow实现高斯模糊的示例代码：

import tensorflow as tf
import numpy as np

def gaussian_kernel(size, sigma):
    x, y = np.meshgrid(np.linspace(-1,1,size), np.linspace(-1,1,size))
    d = np.sqrt(x*x+y*y)
    kernel = np.exp(-((d**2)/(2.0*sigma**2)))
    kernel = kernel / np.sum(kernel)
    return kernel

def apply_gaussian_blur(image, kernel_size, sigma):
    # Create Gaussian kernel
    kernel = gaussian_kernel(kernel_size, sigma)
    # Reshape the kernel to match the expected shape of tf.nn.conv2d
    kernel = kernel.reshape([kernel_size, kernel_size, 1, 1])
    # Create a TensorFlow graph
    graph = tf.Graph()
    with graph.as_default():
        # Create placeholders for the input and output images
        input_image = tf.placeholder(tf.float32, shape=image.shape, name='input_image')
        output_image = tf.placeholder(tf.float32, shape=image.shape, name='output_image')
        # Create a convolutional layer with the Gaussian kernel
        conv_layer = tf.nn.conv2d(input_image, kernel, strides=[1, 1, 1, 1], padding='SAME')
        # Define the loss function as the mean squared error between the output image and the blurred image
        loss = tf.reduce_mean(tf.square(output_image - conv_layer))
        # Create an optimizer to minimize the loss
        optimizer = tf.train.AdamOptimizer(learning_rate=0.01).minimize(loss)
        # Initialize all variables
        init = tf.global_variables_initializer()
    # Run the TensorFlow graph
    with tf.Session(graph=graph) as session:
        # Initialize variables
        session.run(init)
        # Train the network for 1000 iterations
        for i in range(1000):
            _, l = session.run([optimizer, loss], feed_dict={input_image: image, output_image: image})
            if i % 100 == 0:
                print('Iteration %d, loss = %f' % (i, l))
        # Apply the Gaussian blur to the input image
        blurred_image = session.run(conv_layer, feed_dict={input_image: image})
    return blurred_image

# Load an example image
image = tf.keras.utils.get_file('elephant.jpg', 'https://storage.googleapis.com/download.tensorflow.org/example_images/elephant.jpg')
image = tf.keras.preprocessing.image.load_img(image)
image = tf.keras.preprocessing.image.img_to_array(image)

# Apply Gaussian blur to the image
blurred_image = apply_gaussian_blur(image, kernel_size=5, sigma=1.5)

# Display the original and blurred images
import matplotlib.pyplot as plt
plt.subplot(1, 2, 1)
plt.imshow(image.astype('uint8'))
plt.title('Original Image')
plt.subplot(1, 2, 2)
plt.imshow(blurred_image.astype('uint8'))
plt.title('Blurred Image')
plt.show()

该代码使用`gaussian_kernel`函数创建高斯卷积核，并使用`apply_gaussian_blur`函数将该卷积核应用于输入图像。在TensorFlow图中，使用`tf.nn.conv2d`函数创建一个卷积层，并使用均方误差作为损失函数。使用Adam优化器最小化损失函数，并通过运行图来应用高斯模糊。

在示例中，我们使用了一个名为“elephant.jpg”的示例图像，并将其应用于内核大小为5和标准差为1.5的高斯模糊。结果显示了原始图像和模糊图像。

请注意，该示例仅用于演示目的，实际应用中可能需要对内核大小和标准差进行调整以获得最佳结果。