What is the most elegant way to blur parts of an image using python?

Question

I found the following answer that blurs images locally using PIL: Filter part of image using PIL, python. The proposed answer crops a part of the image, blurs it and copies it back into the original image. This creates sharp edges between the blurred part and the original image (See example below).

I want to avoid this effect.

Answer 1

To circumvent this problem one can use the following procedure:

• Given an image and a mask (values between 0 and 1)
• Blur the full input image and the mask
• Weight the original image with the blurred mask
• Weight the blurred image with the inverted blurred mask
• Addition of the weighted images

Below some example code using scipy:

import numpy as np
import matplotlib.pyplot as plt
from scipy import misc
import scipy.ndimage


def gaussian_blur(sharp_image, sigma):
    # Filter channels individually to avoid gray scale images
    blurred_image_r = scipy.ndimage.filters.gaussian_filter(sharp_image[:, :, 0], sigma=sigma)
    blurred_image_g = scipy.ndimage.filters.gaussian_filter(sharp_image[:, :, 1], sigma=sigma)
    blurred_image_b = scipy.ndimage.filters.gaussian_filter(sharp_image[:, :, 2], sigma=sigma)
    blurred_image = np.dstack((blurred_image_r, blurred_image_g, blurred_image_b))
    return blurred_image


def uniform_blur(sharp_image, uniform_filter_size):
    # The multidimensional filter is required to avoid gray scale images
    multidim_filter_size = (uniform_filter_size, uniform_filter_size, 1)
    blurred_image = scipy.ndimage.filters.uniform_filter(sharp_image, size=multidim_filter_size)
    return blurred_image


def blur_image_locally(sharp_image, mask, use_gaussian_blur, gaussian_sigma, uniform_filter_size):

    one_values_f32 = np.full(sharp_image.shape, fill_value=1.0, dtype=np.float32)
    sharp_image_f32 = sharp_image.astype(dtype=np.float32)
    sharp_mask_f32 = mask.astype(dtype=np.float32)

    if use_gaussian_blur:
        blurred_image_f32 = gaussian_blur(sharp_image_f32, sigma=gaussian_sigma)
        blurred_mask_f32 = gaussian_blur(sharp_mask_f32, sigma=gaussian_sigma)

    else:
        blurred_image_f32 = uniform_blur(sharp_image_f32, uniform_filter_size)
        blurred_mask_f32 = uniform_blur(sharp_mask_f32, uniform_filter_size)

    blurred_mask_inverted_f32 = one_values_f32 - blurred_mask_f32
    weighted_sharp_image = np.multiply(sharp_image_f32, blurred_mask_f32)
    weighted_blurred_image = np.multiply(blurred_image_f32, blurred_mask_inverted_f32)
    locally_blurred_image_f32 = weighted_sharp_image + weighted_blurred_image

    locally_blurred_image = locally_blurred_image_f32.astype(dtype=np.uint8)

    return locally_blurred_image


if __name__ == '__main__':

    sharp_image = misc.face()
    height, width, channels = sharp_image.shape
    sharp_mask = np.full((height, width, channels), fill_value=1)
    sharp_mask[int(height / 4): int(3 * height / 4), int(width / 4): int(3 * width / 4), :] = 0

    result = blur_image_locally(
        sharp_image,
        sharp_mask,
        use_gaussian_blur=True,
        gaussian_sigma=31,
        uniform_filter_size=201)
    plt.imshow(result)
    plt.show()

Result: