1 import matplotlib.pyplot as plt2 import cv2 as cv3 def plot_demo(image):4     print(image.ravel())  ##ravel将图像3维转一维数组，便于统计频率5     # 统计为256个bin,直方图显示[0,256]所有的bin,意思是全部显示，我们可以设置只显示一部分[0,200]也可以6     plt.hist(image.ravel(), 256, [0, 256])　　7     plt.show()8 src = cv.imread('1.jpg')9 cv.imshow('inputimage',src)
10 plot_demo(src)
11 cv.waitKey(0)
12 cv.destroyAllWindows()

 1 import cv2 as cv2 import matplotlib.pyplot as plt3 def hist_image(image):4     colors = ('blue','green','red')5     for i,color in enumerate(colors):6         hist = cv.calcHist([image],[i],None,[256],[0,256])7         plt.plot(hist,color=color)8         plt.xlim([0,256])9     plt.show()
10 img = cv.imread('1.jpg')
11 cv.imshow('img',img)
12 hist_image(img)
13 cv.waitKey(0)
14 cv.destroyAllWindows()

 1 import cv2 as cv2 import matplotlib.pyplot as plt3 4 img = cv.imread('1.jpg')    #OpenCV直方图均衡化都是基于灰度图像5 gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)6 cv.imshow('gray',gray)7 dst = cv.equalizeHist(gray) #直方图均衡化，对比度增强8 cv.imshow('equalizehist',dst)9 cv.waitKey(0)
10 cv.destroyAllWindows()

1 img = cv.imread('1.jpg')    #OpenCV直方图均衡化都是基于灰度图像
2 gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
3 cv.imshow('gray',gray)
4 clahe = cv.createCLAHE(clipLimit=2.0,tileGridSize=(8,8))
5 dst = clahe.apply(gray)     #将灰度图像和局部直方图相关联,把直方图均衡化应用到灰度图 这两行必须搭配起来使用才可以
6 cv.imshow('dis',dst)
7 cv.waitKey(0)
8 cv.destroyAllWindows()

 1 def create_rag_hist(image):2     h,w,c = image.shape3     rgbHist = np.zeros([16*16*16,1],np.float32)　　#注意，np.array二维以上的，每个元素是一个数组4     bsize = 256/16  #间隔是165     for row in range(h):6         for col in range(w):7             b = image[row,col,0]8             g = image[row,col,1]9             r = image[row,col,2]
10             index = np.int(b/bsize)*16*16 + np.int(g/bsize)*16 + np.int(r/bsize)
11             rgbHist[np.int(index),0] = rgbHist[np.int(index),0] + 1 　　#二维的，所以是rgbhist[n,0]只不过每个数组只有一个元素，所以用0
12     return rgbHist
13
14 def hist_compare(image1,image2):
15     hist1 = create_rag_hist(image1)
16     hist2 = create_rag_hist(image2)##注意：compareHist里面的hist1和hist2必须是np.float32
17     match1 = cv.compareHist(hist1,hist2,cv.HISTCMP_BHATTACHARYYA)   #巴氏距离比较，越小越相似
18     match2 = cv.compareHist(hist1,hist2,cv.HISTCMP_CORREL)   #相关性比较（最大为1）：越接近1越相似
19     match3 = cv.compareHist(hist1,hist2,cv.HISTCMP_CHISQR)   #卡方比较，越小越相似
20     print("巴氏：%s    相关性：%s  卡方：%s"%(match1,match2,match3))

hist_compare(src,src)　　#当我们使用两张一样的图像比较

巴氏：0.0 相关性：1.0 卡方：0.0

hsv = cv.cvtColor(image,cv.COLOR_BGR2HSV)
hist = cv.calcHist([image],[0,1],None,[289,286],[0,289,0,286])　　#横坐标289个，范围是[0,289]，纵坐标是286个，范围是[0,286]
cv.imshow("hist2D",hist)

hsv = cv.cvtColor(image,cv.COLOR_BGR2HSV)
hist = cv.calcHist([image],[0,1],None,[289,286],[0,289,0,286])
plt.imshow(hist,interpolation="nearest")　　#插值方式，临近点差值，显示的会很漂亮
plt.title("2D Histogram")
plt.show()

def back_projection_demo():sample = cv.imread("./s2.png")target = cv.imread("./b.png")roi_hsv = cv.cvtColor(sample,cv.COLOR_BGR2HSV)tar_hsv = cv.cvtColor(target,cv.COLOR_BGR2HSV)

cv.imshow(</span><span style="color: #800000">"</span><span style="color: #800000">sample</span><span style="color: #800000">"</span><span style="color: #000000">,sample)
cv.imshow(</span><span style="color: #800000">"</span><span style="color: #800000">target</span><span style="color: #800000">"</span><span style="color: #000000">,target)<span style="background-color: #ffff00">roihist </span></span><span style="background-color: #ffff00">= cv.calcHist([roi_hsv], [0, 1], None, [324, 356], [0, 324, 0, 356])</span>　　<span style="color: #008000">#</span><span style="color: #008000">加红部分越小，匹配越放松，匹配越全面，若是bsize值越大，则要求得越精细，越不易匹配，所以导致匹配出来的比较小</span>
<span style="background-color: #ffff00">cv.normalize(roihist,roihist,0,255,cv.NORM_MINMAX)　</span>　<span style="color: #008000">#</span><span style="color: #008000">规划到0-255之间</span>
<span style="background-color: #ffff00">dst = cv.calcBackProject([tar_hsv],[0,1],roihist,[0,324,0,356],1)</span>   <span style="color: #008000">#</span><span style="color: #008000">直方图反向投影</span>
cv.imshow(<span style="color: #800000">"</span><span style="color: #800000">back_projection_demo</span><span style="color: #800000">"</span>,dst)</pre>