直方图的反向投影

"""
直方图的反向投影
我的理解就是给一个sample 然后获取它的图像直方图 然后使用这个直方图在target里面寻找相似的特征
"""
import cv2 as cv
from matplotlib import pyplot as plt


def back_projection_demo():
    # 读取图片
    sample = cv.imread("imgsample.jpg")
    target = cv.imread("img.jpg")
    # 转换为HSV格式
    roi_hsv = cv.cvtColor(sample, cv.COLOR_BGR2HSV)
    target_hsv = cv.cvtColor(target, cv.COLOR_BGR2HSV)

    # show images
    cv.imshow("sample", sample)
    cv.imshow("target", target)
    # 计算图像直方图 参数的说明见https://blog.csdn.net/Wu123456CS/article/details/105367493
    roiHist = cv.calcHist([roi_hsv], [0, 1], None, [64, 64], [0, 180, 0, 256])
    # 图像归一化处理 参数见参考博客 https://blog.csdn.net/qq_29023939/article/details/81105806
    cv.normalize(roiHist, roiHist, 0, 255, cv.NORM_MINMAX)
    # 获取直方图的反向投影
    dst = cv.calcBackProject([target_hsv], [0, 1], roiHist, [0, 180, 0, 256], 1)
    cv.imshow("backProjectionDemo", dst)


def hist2d_demo(image):
    # 图像转换为HSV
    hsv = cv.cvtColor(image, cv.COLOR_BGR2HSV)
    # 计算图像的直方图
    hist = cv.calcHist([image], [0, 1], None, [32, 32], [0, 180, 0, 256])
    # interpolation 表示插值运算的方式
    plt.imshow(hist, interpolation='nearest')
    # 给图像添加标题
    plt.title("2D Histogram")
    plt.show()


print("--------- Hello Python ---------")
src = cv.imread("img.jpg")
hist2d_demo(src)
back_projection_demo()
cv.waitKey(0)

cv.destroyAllWindows()

效果展示

基本的注释我的写在代码里面了

参考博客

opencv中归一化函数cv2.normalize()的原理讲解
【opencv学习笔记】027之直方图反向投影 - calcBackProject函数详解