TensorBlur: Efficient Image Blurring Routines in TensorFlow

Contents

1. Description
2. Quickstart
3. Sources

Description

This package provides methods for efficient image blurring using TensorFlow.

These methods can be readily used in two ways:

1. A layer in a TensorFlow graph (i.e. a neural network),
2. A standalone processing function

TensorBlur takes advantage of several convolutional tricks and GPU acceleration to make these methods extremely efficient.

Quick Start

Apply blurring to a single image:

import numpy as np
from PIL import Image
from tensorblur.gaussian import GaussianBlur

# Load an image
img = np.array(Image.open("assets/example2.jpg"))
# Create blur object
blur = GaussianBlur(size=7)
# Apply blurring
result = blur.apply(img)

Create a blur layer in a neural network

import numpy as np
from PIL import Image
import tensorflow as tf
from tensorblur import BlurLayer

# Load an image
img = np.array(Image.open("assets/example2.jpg"))

# Create Model with blur layer
inputs = tf.keras.layers.Input(shape=(128, 128, 3))
outputs = BlurLayer(min_amt=13, max_amt=13)(inputs)
model = tf.keras.Model(inputs=inputs, outputs=outputs)

# convert input to tensor
img = tf.convert_to_tensor([img], tf.float32)     

# Apply model (call `model()`)
result = model(img)

Sources

https://stackoverflow.com/questions/52012657/how-to-make-a-2d-gaussian-filter-in-tensorflow

https://computergraphics.stackexchange.com/questions/39/how-is-gaussian-blur-implemented

http://rastergrid.com/blog/2010/09/efficient-gaussian-blur-with-linear-sampling/

https://towardsdatascience.com/a-basic-introduction-to-separable-convolutions-b99ec3102728