Opencv Python版学习笔记（一）图像直方图

之前接触c++版的Opencv一般都是用到什么就去找什么，最近安装了Python的Opencv，脚本语言就是有它的好处，直接运行就能看到好多例程：

今天看的是一个初级图像处理只是，颜色直方图，直接引用的Python版Opencv例程，需要注释的地方都加了说明

这个例子分别展示了3通道颜色直方图、灰度图像直方图、灰度直方图均衡化（也就是将直方图均匀开来，能够达到提升图像局部对比度的效果）后的直方图以及图像归一化后的直方图

import cv2
import numpy as np

bins = np.arange(256).reshape(256,1)

def hist_curve(im):
h = np.zeros((300,256,3))
if len(im.shape) == 2:#判断如果为灰度图像用白色线画，所以这里color赋值为白色
color = [(255,255,255)]
elif im.shape[2] == 3:#判断如果为彩色图像，分三个通道分别计算直方图
color = [ (255,0,0),(0,255,0),(0,0,255) ]
for ch, col in enumerate(color):#循环遍历3个通道，每次循环对划线进行颜色赋值，已达到清晰表示
hist_item = cv2.calcHist([im],[ch],None,[256],[0,255])#直方图计算[ch]为通道
cv2.normalize(hist_item,hist_item,0,255,cv2.NORM_MINMAX)#直方图归一化
hist=np.int32(np.around(hist_item))#将归一化的直方图取整
pts = np.int32(np.column_stack((bins,hist)))#将bins列与直方图列合并
cv2.polylines(h,[pts],False,col)#通过构造得到的线pts在h上画出直方图曲线
y=np.flipud(h)#由于图是倒着的，将矩阵头尾对调
return y
def hist_lines(im):
h = np.zeros((300,256,3))
if len(im.shape)!=2:
print "hist_lines applicable only for grayscale images"
#print "so converting image to grayscale for representation"
im = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)#如果图片不是灰度图转为灰度图
hist_item = cv2.calcHist([im],[0],None,[256],[0,255])
cv2.normalize(hist_item,hist_item,0,255,cv2.NORM_MINMAX)
hist=np.int32(np.around(hist_item))
for x,y in enumerate(hist):
cv2.line(h,(x,0),(x,y),(255,255,255))#以每个bin的累积高度作为纵坐标bin作为横坐标画垂直的线来表示直方图
#y = np.flipud(h)
return h

if __name__=='__main__':

import sys

if len(sys.argv)>1:
im = cv2.imread(sys.argv[1])
else :
im = cv2.imread('E:/lena.jpg')
print "usage : python hist.py <image_file>"

gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)

print ''' Histogram plotting \n
Keymap :\n
a - show histogram for color image in curve mode \n
b - show histogram in bin mode \n
c - show equalized histogram (always in bin mode) \n
d - show histogram for color image in curve mode \n
e - show histogram for a normalized image in curve mode \n
Esc - exit \n
'''

cv2.imshow('image',im)
while True:
k = cv2.waitKey(0)&0xFF
if k == ord('a'):
curve = hist_curve(im)
cv2.imshow('histogram',curve)
cv2.imshow('image',im)
print 'a'
elif k == ord('b'):
print 'b'
lines = hist_lines(im)
cv2.imshow('histogram',lines)
cv2.imshow('image',gray)
elif k == ord('c'):
print 'c'
equ = cv2.equalizeHist(gray)#直方图标准化
lines = hist_lines(equ)
cv2.imshow('histogram',lines)
cv2.imshow('image',equ)
elif k == ord('d'):
print 'd'
curve = hist_curve(gray)
cv2.imshow('histogram',curve)
cv2.imshow('image',gray)
elif k == ord('e'):
print 'e'
norm = cv2.normalize(gray,alpha = 0,beta = 255,norm_type = cv2.NORM_MINMAX)#灰度图归一化
lines = hist_lines(norm)
cv2.imshow('histogram',lines)
cv2.imshow('image',norm)
elif k == 27:
print 'ESC'
cv2.destroyAllWindows()
break
cv2.destroyAllWindows()