【opencv自学笔记】08下：图像直方图应用

最新推荐文章于 2022-10-06 20:12:28 发布

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最新推荐文章于 2022-10-06 20:12:28 发布

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直方图反向投影，物体追踪，找出种子图片色彩空间对应的物体
读入图像——转HSV空间——计算sample直方图——归一化——计算反射投影

import cv2 as cv
from matplotlib import pyplot as plt
import numpy as np

#直方图反向投影，物体追踪，找出种子图片色彩空间对应的物体
#读入图像——转HSV空间——计算sample直方图——归一化——计算反射投影
def back_projection_demo():
    sample=cv.imread("D:/Study/opencv/code/4.png")
    target=cv.imread("D:/Study/opencv/code/3.png")

    roi_hsv=cv.cvtColor(sample,cv.COLOR_BGR2HSV)
    target_hsv=cv.cvtColor(target,cv.COLOR_BGR2HSV)
    
    #calcHist(src,chanel,none,[bsize],[区间])
    roiHist=cv.calcHist([roi_hsv],[0,1],None,[12,24],[0,180,0,256])
    cv.normalize(roiHist,roiHist,0,255,cv.NORM_MINMAX)#归一化
    
    dst=cv.calcBackProject([target_hsv],[0,1],roiHist,[0,180,0,256],1)
    
    cv.imshow('sample',roi_hsv)
    cv.imshow('target_hsv',target_hsv)
    cv.imshow('back_projection_demo',dst)
    
def hist2d_demo(image):
    hsv=cv.cvtColor(image,cv.COLOR_BGR2HSV)
    hist=cv.calcHist([hsv],[0,1],None,[32,32],[0,180,0,256])
    plt.imshow(hist,interpolation='nearest')
    plt.title('2D Histogram')
    plt.show()


src=cv.imread("D:/Study/opencv/code/1.jpg")

cv.imshow('src',src)
hist2d_demo(src)

back_projection_demo()

cv.waitKey(0)
cv.destroyAllWindows()