我试图在python中使用一些图像分析(我必须使用python).我需要进行全局和局部直方图均衡化.全局版本运行良好,但本地版本使用7×7足迹,结果非常差.
这是全球版本:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from scipy import ndimage,misc
import scipy.io as io
from scipy.misc import toimage
import numpy as n
import pylab as py
from numpy import *
mat = io.loadmat('image.mat')
image=mat['imageD']
def histeq(im,nbr_bins=256):
#get image histogram
imhist,bins = histogram(im.flatten(),nbr_bins,normed=True)
cdf = imhist.cumsum() #cumulative distribution function
cdf = 0.6 * cdf / cdf[-1] #normalize
#use linear interpolation of cdf to find new pixel values
im2 = interp(im.flatten(),bins[:-1],cdf)
#returns image and cumulative histogram used to map
return im2.reshape(im.shape),cdf
im=image
im2,cdf = histeq(im)
要做本地版本,我试图使用像这样的通用过滤器(使用与之前加载的相同的图像):
def func(x):
cdf=[]
xhist,bins=histogram(x,256,normed=True)
cdf = xhist.cumsum()
cdf = 0.6 * cdf / cdf[-1]
im_out = interp(x,cdf)
midval=interp(x[24],cdf)
return midval
print im.shape
im3=ndimage.filters.generic_filter(im,func,size=im.shape,footprint=n.ones((7,7)))
有没有人有任何建议/想法为什么第二个版本不起作用?我真的被卡住了,任何评论都会非常感激!提前致谢!
最佳答案
您可以使用scikit-image库执行全局和局部直方图均衡.从链接中骄傲地窃取,下面是片段.均衡是使用磁盘形内核(或覆盖区)完成的,但您可以通过设置kernel = np.ones((N,M))将其更改为方形.
原文链接:https://www.f2er.com/python/439664.htmlimport numpy as np
import matplotlib
import matplotlib.pyplot as plt
from skimage import data
from skimage.util import img_as_ubyte
from skimage import exposure
import skimage.morphology as morp
from skimage.filters import rank
# Original image
img = img_as_ubyte(data.moon())
# Global equalize
img_global = exposure.equalize_hist(img)
# Local Equalization,disk shape kernel
# Better contrast with disk kernel but could be different
kernel = morp.disk(30)
img_local = rank.equalize(img,selem=kernel)
fig,(ax_img,ax_global,ax_local) = plt.subplots(1,3)
ax_img.imshow(img,cmap=plt.cm.gray)
ax_img.set_title('Low contrast image')
ax_img.set_axis_off()
ax_global.imshow(img_global,cmap=plt.cm.gray)
ax_global.set_title('Global equalization')
ax_global.set_axis_off()
ax_local.imshow(img_local,cmap=plt.cm.gray)
ax_local.set_title('Local equalization')
ax_local.set_axis_off()
plt.show()