大纲
- black_tophat
- white_tophat
- convolve
- correlate
- gaussian_filter
- gaussian_laplace
- maximum_filter
- median_filter
- minimum_filter
- percentile_filter
- prewitt
- rank_filter
- sobel
- spline_filter
- uniform_filter
- 基础代码
- 代码仓库
在《使用numpy处理图片——模糊处理》一文中，我们介绍了如何使用scipy库进行滤镜处理。本文我们将通过9宫格的形式，展现不同参数时滤镜效果。
black_tophat
import sys
sys.path.append("..")
from frame import *
import scipy.ndimage as ndimage
generate('lena.png', 'black_tophat.png', ndimage.black_tophat, 1, 91, 10)
对应的size（ndimage.black_tophat第二个参数）的值
| 1 | 11 | 21 |
|---|---|---|
| 31 | 41 | 51 |
| 61 | 71 | 81 |
white_tophat
import sys
sys.path.append("..")
from frame import *
import scipy.ndimage as ndimage
def func(*args):
return ndimage.white_tophat(args[0], args[1])
generate('lena.png', 'white_tophat.png', func, 1, 91, 10)
对应的size（ndimage.white_tophat第二个参数）的值
| 1 | 11 | 21 |
|---|---|---|
| 31 | 41 | 51 |
| 61 | 71 | 81 |
convolve
import sys
sys.path.append("..")
from frame import *
import scipy.ndimage as ndimage
def func(*args):
weights = np.eye(args[1])
return ndimage.convolve(args[0], weights)
generate('lena.png', 'convolve.png', func, 1, 10, 1)
对应的weights（ndimage.convolve第二个参数）的维度是
| 1 | 2 | 3 |
|---|---|---|
| 4 | 5 | 6 |
| 7 | 8 | 9 |
correlate
import sys
sys.path.append("..")
from frame import *
import scipy.ndimage as ndimage
def func(*args):
weights = np.eye(args[1])
return ndimage.correlate(args[0], weights)
generate('lena.png', 'correlate.png', func, 1, 10, 1)
对应的weights（ndimage.correlate第二个参数）的维度是
| 1 | 2 | 3 |
|---|---|---|
| 4 | 5 | 6 |
| 7 | 8 | 9 |
gaussian_filter
import sys
sys.path.append("..")
from frame import *
import scipy.ndimage as ndimage
generate('lena.png', 'gaussian_filter.png', ndimage.gaussian_filter, 1, 10, 1)
对应的sigma（ndimage.gaussian_filter第二个参数）的值
| 1 | 2 | 3 |
|---|---|---|
| 4 | 5 | 6 |
| 7 | 8 | 9 |
gaussian_laplace
import sys
sys.path.append("..")
from frame import *
import scipy.ndimage as ndimage
generate('lena.png', 'gaussian_laplace.png', ndimage.gaussian_laplace, 0.2, 1.9, 0.2)
对应的sigma（ndimage.black_tophat第二个参数）的值
| 0.2 | 0.4 | 0.6 |
|---|---|---|
| 0.8 | 1.0 | 1.2 |
| 1.4 | 1.6 | 1.8 |
maximum_filter
import sys
sys.path.append("..")
from frame import *
import scipy.ndimage as ndimage
generate('lena.png', 'maximum_filter.png', ndimage.maximum_filter, 1, 10, 1)
对应的size（ndimage.maximum_filter第二个参数）的值
| 1 | 2 | 3 |
|---|---|---|
| 4 | 5 | 6 |
| 7 | 8 | 9 |
median_filter
import sys
sys.path.append("..")
from frame import *
import scipy.ndimage as ndimage
generate('lena.png', 'median_filter.png', ndimage.median_filter, 1, 10, 1)
对应的size（ndimage.median_filter第二个参数）的值
| 1 | 2 | 3 |
|---|---|---|
| 4 | 5 | 6 |
| 7 | 8 | 9 |
minimum_filter
import sys
sys.path.append("..")
from frame import *
import scipy.ndimage as ndimage
generate('lena.png', 'minimum_filter.png', ndimage.minimum_filter, 1, 10, 1)
对应的size（ndimage.minimum_filter第二个参数）的值
| 1 | 2 | 3 |
|---|---|---|
| 4 | 5 | 6 |
| 7 | 8 | 9 |
percentile_filter
import sys
sys.path.append("..")
from frame import *
import scipy.ndimage as ndimage
def func(*args):
return ndimage.percentile_filter(args[0], percentile=args[1], size=args[1])
generate('lena.png', 'percentile_filter.png', func, 1, 10, 1)
对应的percentile和size（ndimage.percentile_filter第二、三个参数）的值
| 1 | 2 | 3 |
|---|---|---|
| 4 | 5 | 6 |
| 7 | 8 | 9 |
prewitt
import sys
sys.path.append("..")
from frame import *
import scipy.ndimage as ndimage
def func(*args):
return ndimage.prewitt(args[0])
generate('lena.png', 'prewitt.png', func, 1, 2, 1)
rank_filter
import sys
sys.path.append("..")
from frame import *
import scipy.ndimage as ndimage
def func(*args):
return ndimage.rank_filter(args[0], rank=args[1], size=args[1]*2)
generate('lena.png', 'rank_filter.png', func, 1, 10, 1)
对应的rank（ndimage.rank_filter第二个参数）的值
| 1 | 2 | 3 |
|---|---|---|
| 4 | 5 | 6 |
| 7 | 8 | 9 |
对应的size（ndimage.rank_filter第三个参数）的值
| 2 | 4 | 6 |
|---|---|---|
| 8 | 10 | 12 |
| 14 | 16 | 18 |
sobel
import sys
sys.path.append("..")
from frame import *
import scipy.ndimage as ndimage
def func(*args):
return ndimage.sobel(args[0])
generate('lena.png', 'sobel.png', func, 1, 2, 1)
spline_filter
import sys
sys.path.append("..")
from frame import *
import scipy.ndimage as ndimage
def func(*args):
return ndimage.spline_filter(args[0], args[1]).astype(np.uint8)
generate('lena.png', 'spline_filter.png', func, 2, 5, 1)
对应的size（ndimage.black_tophat第二个参数）的值
| 2 | 3 | 4 |
|---|
uniform_filter
import sys
sys.path.append("..")
from frame import *
import scipy.ndimage as ndimage
def func(*args):
return ndimage.uniform_filter(args[0], args[1])
generate('lena.png', 'uniform_filter.png', func, 1, 10, 1)
对应的size（ndimage.uniform_filter第二个参数）的值
| 1 | 2 | 3 |
|---|---|---|
| 4 | 5 | 6 |
| 7 | 8 | 9 |
基础代码
# frame.py
import numpy as np
from PIL import Image
import scipy.ndimage as ndimage
def generate(image_from, image_to, filter, start = 1, end = 10, step = 1):
source = np.array(Image.open(image_from))
colorDim3List = np.dsplit(source, 3)
red = colorDim3List[0].reshape(source.shape[0], source.shape[1])
green = colorDim3List[1].reshape(source.shape[0], source.shape[1])
blue = colorDim3List[2].reshape(source.shape[0], source.shape[1])
def inline_filter(red, green, blue, some_value):
redFilter = filter(red, some_value)
greenFilter = filter(green, some_value)
blueFilter = filter(blue, some_value)
return np.dstack((redFilter, greenFilter, blueFilter))
varrays = []
harrays = []
hindex = 0
for i in np.arange(start, end, step):
filter3D = inline_filter(red, green, blue, i)
harrays.append(filter3D)
hindex += 1
if hindex % 3 == 0:
varrays.append(np.hstack(harrays))
harrays = []
hindex = 0
if varrays == []:
varrays.append(np.hstack(harrays))
full3D = np.vstack(varrays)
Image.fromarray(full3D).save(image_to)
代码仓库
https://github.com/f304646673/scipy-ndimage-example/tree/main/
