How to make a 2D Gaussian Filter in Tensorflow?

ghz 7months ago ⋅ 62 views

How can I implement a 2D low pass (also known as blurring) filter in Tensorflow using a gaussian kernel?

Answers

To implement a 2D low-pass (blurring) filter in TensorFlow using a Gaussian kernel, you can use the tf.nn.conv2d function to perform convolution between the input image and the Gaussian kernel. Here's how you can implement it:

import tensorflow as tf
import numpy as np

def gaussian_kernel(kernel_size: int, sigma: float) -> np.ndarray:
    """Generates a 2D Gaussian kernel."""
    x = np.linspace(-kernel_size // 2 + 1, kernel_size // 2, kernel_size)
    y = np.linspace(-kernel_size // 2 + 1, kernel_size // 2, kernel_size)
    x, y = np.meshgrid(x, y)
    kernel = np.exp(-(x**2 + y**2) / (2 * sigma**2))
    kernel = kernel / np.sum(kernel)
    return kernel

def gaussian_blur(image: tf.Tensor, kernel_size: int, sigma: float) -> tf.Tensor:
    """Applies Gaussian blur to the input image."""
    # Generate Gaussian kernel
    kernel = gaussian_kernel(kernel_size, sigma)
    kernel = np.expand_dims(np.expand_dims(kernel, axis=-1), axis=-1)
    kernel = tf.constant(kernel, dtype=tf.float32)
    
    # Apply convolution
    blurred_image = tf.nn.conv2d(image, kernel, strides=[1, 1, 1, 1], padding='SAME')
    return blurred_image

# Example usage
input_image = tf.placeholder(tf.float32, shape=[None, None, None, 3])  # Input image placeholder
blurred_image = gaussian_blur(input_image, kernel_size=5, sigma=1.0)  # Apply Gaussian blur

# Run a session to blur an image
with tf.Session() as sess:
    # Assuming 'image' is your input image
    input_image_data = ...  # Load or generate your input image data
    blurred_image_data = sess.run(blurred_image, feed_dict={input_image: input_image_data})

In this code:

  • gaussian_kernel: This function generates a 2D Gaussian kernel given the kernel size and standard deviation (sigma).
  • gaussian_blur: This function applies Gaussian blur to the input image using the tf.nn.conv2d function with the generated Gaussian kernel.
  • input_image: This is a placeholder for the input image tensor.
  • blurred_image: This is the output tensor representing the blurred image.

You can adjust the kernel_size and sigma parameters to control the blurring effect. Larger kernel_size and smaller sigma values result in stronger blurring.

#Tensorflow