一、圖像直方圖的屬性

說白了就是將圖像上的各個顏色通道上的像素點的像素值進行統計，例如：像素值為14的像素點個數有幾個，進行顯示。

圖像的像素值取值范圍為[0,255]，這個范圍也成為直方圖的range也就是直方圖的橫坐標軸
每一個像素值所對應的個數稱之為bin

二、對圖像進行直方圖統計

image.ravel()把圖像的所有像素點信息進行統計
plt.hist(image.ravel(),256,[0,256])將圖像信息進行統計，統計成256個bin，范圍為[0,255]
cv2.calcHist([image],[i],None,[256],[0,256])[image]為當前出來圖像，[i]這里使用了一個循環也就是依次BGR三個通道，None是掩膜信息這里沒有用到，[256]表示直方圖的size，[0,256]BGR三顏色的像素值的范圍

import cv2 import numpy as np from matplotlib import pyplot as pltdef plot(image):plt.hist(image.ravel(),256,[0,256])plt.show("matlab自帶直方圖")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,color=color)plt.xlim([0,256])plt.show()src = cv2.imread(r"G:\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()

效果圖如下：

三、直方圖的均衡化

OpenCV中的直方圖均衡化針對的都是灰度圖

Ⅰ全局直方圖均衡化

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(r"G:\Juptyer_workspace\study\opencv\opencv3\mi.jpg") cv2.imshow("image",src) cv2.namedWindow("image",cv2.WINDOW_AUTOSIZE)equalizeHist(src)cv2.waitKey(0) cv2.destroyAllWindows()

效果圖如下：

Ⅱ局部直方圖均衡化

import cv2 import numpy as np from matplotlib import pyplot as pltdef clahe(image):gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)clahe = cv2.createCLAHE(clipLimit=2.0,tileGridSize=(8,8))dst = clahe.apply(gray)cv2.imshow("clahe",dst)src = cv2.imread(r"G:\Juptyer_workspace\study\opencv\opencv3\mi.jpg") cv2.imshow("image",src) cv2.namedWindow("image",cv2.WINDOW_AUTOSIZE)clahe(src)cv2.waitKey(0) cv2.destroyAllWindows()

效果圖如下：

四、直方圖反向投影

Ⅰ2D直方圖

cv2.calcHist([image],[0,1],None,[180,256],[0,180,0,256])其中[180,256]表示bin的個數，可以修改，當然范圍越小越精確

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,interpolation='nearest')plt.title("2D Histogram")plt.show()src = cv2.imread(r"G:\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()

效果圖如下：

Ⅱ直方圖反向投影

cv2.calcHist([roi_hsv],[0,1],None,[32,48],[0,180,0,256])其中[32,48]表示bin的個數，可以修改，當然范圍越小越精確

import cv2 import numpy as np from matplotlib import pyplot as pltdef back_projection():sample = cv2.imread(r"G:\Juptyer_workspace\study\opencv\opencv3\yg1.jpg")target = cv2.imread(r"G:\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()

效果圖如下：

總結

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