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python histogrAm

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 = 255 * cdf / cdf[-1] #normalize #use linear interpolation...

运行Python前需要配置库: echo /usr/local/lib >> /etc/ld.so.conf.d/local.conf ldconfig!

How can I add textures to my bars and wedges? It is better to ask such question at stackoverflow. 需要不同图案填充直方图的话,如果仅仅为了看上去独特一点,可以不用histogram,可以自己用patches来根据坐标画,想画成什么小图形填充都...

在此我只想强调一下各种库的文档、示例等自带资料的重要性。 下面都是matplotlib的demo里面的,正是你需要的这些功能。 multiple_figs_demo.py #!/usr/bin/env python# Working with multiple figure windows and subplotsfrom pylab import *t

plot(x,y,marker='*') 用marker参数来指定,或者用set_marker函数,参看matplotlib的帮助 >>>from matplotlib.lines import Line2D >>> help(Line2D.set_marker) def set_marker(self, marker): """ Set the line marker

采用CLAHE (Contrast Limited Adaptive Histogram Equalization)技术来优化图像。 def limitedEqualize(img_array, limit = 4.0): img_array_list = [] for img in img_array: clahe = cv2.createCLAHE(clipLimit = limit, tileGridSize = (8,8)...

# /usr/bin/python# -*- coding: utf-8 -*-import numpy as npfrom matplotlib import pyplot as pltdata = np.random.normal(0, 1, 100)bins = np.arange(-10, 10, 1)plt.xlim([min(data)-1, max(data)+1])plt.hist(data, bins=bins, alpha=0.5...

试试直接导入numpy后运行 np.random.randn(10000) 看看有没有结果 np包有问题

累计求和 比如一个列表是这样[1,2,3,4,5] 返回是这样[1,3,6,10,15]

imtools.py里面也要有numpy 的引用才对 def histeq(im,nbr_bins=256): """对一幅灰度图像进行直方图均衡化""" #计算图像的直方图 imhist,bins = histogram(im.flatten(),nbr_bins,normed=True) cdf = imhist.cumsum() #累计分布函数 cdf = 255 *...

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