对于 PIL.ImageFilter.GaussianBlur 如何使用内核以及半径参数与标准偏差有关吗? [英] For PIL.ImageFilter.GaussianBlur how what kernel is used and does the radius parameter relate to standard deviation?
问题描述
使用 PIL 读取图像后,我通常使用 scipy.ndimage 执行高斯滤波器,如下所示
导入PIL从 scipy 导入 ndimagePIL_image = PIL.Image.open(文件名)数据 = PIL_image.getdata()数组 = np.array(list(data)).reshape(data.size[::-1]+(-1,))img = array.astype(float)fimg = ndimage.gaussian_filter(img, sigma=sigma, mode='mirror', order=0)
PIL 中有高斯模糊函数如下(来自
sigma=30, radius=30
的输出:
scipy.ndimage.gaussian_filter
和 PIL.ImageFilter.GaussianBlur
的输出非常相似,差异可以忽略不计.超过 95% 的差值小于等于 2.
PIL 版本:7.2.0,SciPy 版本:1.5.0
After reading an image with PIL I usually perform a Gaussian filter using scipy.ndimage as follow
import PIL
from scipy import ndimage
PIL_image = PIL.Image.open(filename)
data = PIL_image.getdata()
array = np.array(list(data)).reshape(data.size[::-1]+(-1,))
img = array.astype(float)
fimg = ndimage.gaussian_filter(img, sigma=sigma, mode='mirror', order=0)
There is Gaussian blur function within PIL as follows (from this answer), but I don't know how it works exactly or what kernel it uses:
from PIL import ImageFilter
fimgPIL = PIL_image.filter(ImageFilter.GaussianBlur(radius=r)
This documentation does not provide details.
Questions about PIL.ImageFilter.GaussianBlur
:
- What exactly is the radius parameter; is it equivalent to the standard deviation σ?
- For a given radius, how far out does it calculate the kernel? 2σ? 3σ? 6σ?
This comment on an answer to Gaussian Blur - standard deviation, radius and kernel size says the following, but I have not found information for PIL yet.
OpenCV uses kernel radius of
(sigma * 3)
while scipy.ndimage.gaussian_filter uses kernel radius of int(4 * sigma + 0.5)
From the source code, it looks like PIL.ImageFilter.GaussianBlur
uses PIL.ImageFilter.BoxBlur
. But I wasn't able to figure out how the radius and sigma are related.
I wrote a script to check the difference between scipy.ndimage.gaussian_filter
and PIL.ImageFilter.GaussianBlur
.
import numpy as np
from scipy import misc
from scipy.ndimage import gaussian_filter
import PIL
from PIL import ImageFilter
import matplotlib.pyplot as plt
# Load test color image
img = misc.face()
# Scipy gaussian filter
sigma = 5
img_scipy = gaussian_filter(img, sigma=(sigma,sigma,0), mode='nearest')
# PIL gaussian filter
radius = 5
PIL_image = PIL.Image.fromarray(img)
img_PIL = PIL_image.filter(ImageFilter.GaussianBlur(radius=radius))
data = img_PIL.getdata()
img_PIL = np.array(data).reshape(data.size[::-1]+(-1,))
img_PIL = img_PIL.astype(np.uint8)
# Image difference
img_diff = np.abs(np.float_(img_scipy) - np.float_(img_PIL))
img_diff = np.uint8(img_diff)
# Stats
mean_diff = np.mean(img_diff)
median_diff = np.median(img_diff)
max_diff = np.max(img_diff)
# Plot results
plt.subplot(221)
plt.imshow(img_scipy)
plt.title('SciPy (sigma = {})'.format(sigma))
plt.axis('off')
plt.subplot(222)
plt.imshow(img_PIL)
plt.title('PIL (radius = {})'.format(radius))
plt.axis('off')
plt.subplot(223)
plt.imshow(img_diff)
plt.title('Image difference \n (Mean = {:.2f}, Median = {:.2f}, Max = {:.2f})'
.format(mean_diff, median_diff, max_diff))
plt.colorbar()
plt.axis('off')
# Plot histogram
d = img_diff.flatten()
bins = list(range(int(max_diff)))
plt.subplot(224)
plt.title('Histogram of Image difference')
h = plt.hist(d, bins=bins)
for i in range(len(h[0])):
plt.text(h[1][i], h[0][i], str(int(h[0][i])))
Output for sigma=5, radius=5
:
Output for sigma=30, radius=30
:
The outputs of scipy.ndimage.gaussian_filter
and PIL.ImageFilter.GaussianBlur
are very similar and the difference is negligible. More than 95% of difference values are <= 2.
PIL version: 7.2.0, SciPy version: 1.5.0
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