用cms做网站的具体步骤,wordpress主题添加一个自定义页面,网站色彩策划,建设工程监理 精品课网站一、图像直方图的属性
说白了就是将图像上的各个颜色通道上的像素点的像素值进行统计#xff0c;例如#xff1a;像素值为14的像素点个数有几个#xff0c;进行显示。 图像的像素值取值范围为[0,255]#xff0c;这个范围也成为直方图的range也就是直方图的横坐标轴 每一个…一、图像直方图的属性
说白了就是将图像上的各个颜色通道上的像素点的像素值进行统计例如像素值为14的像素点个数有几个进行显示。 图像的像素值取值范围为[0,255]这个范围也成为直方图的range也就是直方图的横坐标轴 每一个像素值所对应的个数称之为bin
二、对图像进行直方图统计
image.ravel()把图像的所有像素点信息进行统计 plt.hist(image.ravel(),256,[0,256])将图像信息进行统计统计成256个bin范围为[0,255] cv2.calcHist([image],[i],None,[256],[0,256])[image]为当前出来图像[i]这里使用了一个循环也就是依次BGR三个通道None是掩膜信息这里没有用到[256]表示直方图的size[0,256]BGR三颜色的像素值的范围
import cv2
import numpy as np
from matplotlib import pyplot as pltdef plot(image):plt.hist(image.ravel(),256,[0,256])plt.show(matlab自带直方图)def hist(image):color (blue,green,red)for i,color in enumerate(color):hist cv2.calcHist([image],[i],None,[256],[0,256])plt.plot(hist,colorcolor)plt.xlim([0,256])plt.show()src cv2.imread(rG:\Juptyer_workspace\study\opencv\opencv3\a1.jpg)
cv2.imshow(image,src)
cv2.namedWindow(image,cv2.WINDOW_AUTOSIZE)plot(src)
hist(src)cv2.waitKey(0)
cv2.destroyAllWindows()效果图如下
三、直方图的均衡化
OpenCV中的直方图均衡化针对的都是灰度图
Ⅰ全局直方图均衡化
import cv2
import numpy as np
from matplotlib import pyplot as pltdef equalizeHist(image):gray cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)dst cv2.equalizeHist(gray)#yy cv2.cvtColor(dst,cv2.COLOR_GRAY2BGR)cv2.imshow(equalizeHist,dst)src cv2.imread(rG:\Juptyer_workspace\study\opencv\opencv3\mi.jpg)
cv2.imshow(image,src)
cv2.namedWindow(image,cv2.WINDOW_AUTOSIZE)equalizeHist(src)cv2.waitKey(0)
cv2.destroyAllWindows()效果图如下
Ⅱ局部直方图均衡化
import cv2
import numpy as np
from matplotlib import pyplot as pltdef clahe(image):gray cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)clahe cv2.createCLAHE(clipLimit2.0,tileGridSize(8,8))dst clahe.apply(gray)cv2.imshow(clahe,dst)src cv2.imread(rG:\Juptyer_workspace\study\opencv\opencv3\mi.jpg)
cv2.imshow(image,src)
cv2.namedWindow(image,cv2.WINDOW_AUTOSIZE)clahe(src)cv2.waitKey(0)
cv2.destroyAllWindows()效果图如下
四、直方图反向投影
Ⅰ2D直方图
cv2.calcHist([image],[0,1],None,[180,256],[0,180,0,256])其中[180,256]表示bin的个数可以修改当然范围越小越精确
import cv2
import numpy as np
from matplotlib import pyplot as pltdef hist2d(image):hsv cv2.cvtColor(image,cv2.COLOR_BGR2HSV)hist cv2.calcHist([image],[0,1],None,[180,256],[0,180,0,256])cv2.imshow(hist2d,hist)def hist2d_1(image):hsv cv2.cvtColor(image,cv2.COLOR_BGR2HSV)hist cv2.calcHist([image],[0,1],None,[180,256],[0,180,0,256])plt.imshow(hist,interpolationnearest)plt.title(2D Histogram)plt.show()src cv2.imread(rG:\Juptyer_workspace\study\opencv\opencv3\l.png)
cv2.imshow(image,src)
cv2.namedWindow(image,cv2.WINDOW_AUTOSIZE)
hist2d(src)
hist2d_1(src)
cv2.waitKey(0)
cv2.destroyAllWindows()效果图如下
Ⅱ直方图反向投影
cv2.calcHist([roi_hsv],[0,1],None,[32,48],[0,180,0,256])其中[32,48]表示bin的个数可以修改当然范围越小越精确
import cv2
import numpy as np
from matplotlib import pyplot as pltdef back_projection():sample cv2.imread(rG:\Juptyer_workspace\study\opencv\opencv3\yg1.jpg)target cv2.imread(rG:\Juptyer_workspace\study\opencv\opencv3\yg.jpg)roi_hsv cv2.cvtColor(sample,cv2.COLOR_BGR2HSV)target_hsv cv2.cvtColor(target,cv2.COLOR_BGR2HSV)cv2.imshow(sample,sample)cv2.imshow(target,target)roiHist cv2.calcHist([roi_hsv],[0,1],None,[32,48],[0,180,0,256])cv2.normalize(roiHist,roiHist,0,255,cv2.NORM_MINMAX)dst cv2.calcBackProject([target_hsv],[0,1],roiHist,[0,180,0,256],1)cv2.imshow(back_projection,dst)back_projection()
cv2.waitKey(0)
cv2.destroyAllWindows()效果图如下
