【openCV-python】ORB特征匹配

opencv 官网，有关特征检测识别的库函数及介绍：

• https://docs.opencv.org/4.3.0/db/d27/tutorial_py_table_of_contents_feature2d.html

ORB特征匹配库函数介绍、使用：

• https://docs.opencv.org/4.3.0/d1/d89/tutorial_py_orb.html

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
img = cv.imread('simple.jpg',0)
# Initiate ORB detector
orb = cv.ORB_create()
# find the keypoints with ORB
kp = orb.detect(img,None)
# compute the descriptors with ORB
kp, des = orb.compute(img, kp)
# draw only keypoints location,not size and orientation
img2 = cv.drawKeypoints(img, kp, None, color=(0,255,0), flags=0)
plt.imshow(img2), plt.show()

示例结果图片：

想要做一个输入目标图片，在测试图片上寻找目标，最后得到中心点坐标。但检测测试中，效果不好，所以只做了简单实验没往下做定位。不知道是不是因为我这个案例不适合做orb，网上做orb+Homography定位目标的也有，如下几个：

• https://verytoolz.com/blog/ee68cfe884/
• https://blog.csdn.net/qq_45832961/article/details/122769532
• https://www.jianshu.com/p/d835f1a4717c?tdsourcetag=s_pctim_aiomsg
• https://blog.csdn.net/zhoujinwang/article/details/128279154

实践测试中，使用如下为输入图片，左图为测试图片，右图为目标图片：

                   

 　　　　　　　　　测试图片　　　　　　　　　　　　　　　　　目标图片

 测试代码：

import cv2 as cv

def ORB_Feature(img1, img2):

    # 初始化ORB
    orb = cv.ORB_create()

    # 寻找关键点
    kp1 = orb.detect(img1)
    kp2 = orb.detect(img2)

    # 计算描述符
    kp1, des1 = orb.compute(img1, kp1)
    kp2, des2 = orb.compute(img2, kp2)

    # 画出关键点
    outimg1 = cv.drawKeypoints(img1, keypoints=kp1, outImage=None)
    outimg2 = cv.drawKeypoints(img2, keypoints=kp2, outImage=None)
    
    # 显示关键点
    # import numpy as np
    # outimg3 = np.hstack([outimg1, outimg2])
    # cv.imshow("Key Points", outimg3)
    # cv.waitKey(0)

    # 初始化 BFMatcher
    bf = cv.BFMatcher(cv.NORM_HAMMING)

    # 对描述子进行匹配
    matches = bf.match(des1, des2)

    # 计算最大距离和最小距离
    min_distance = matches[0].distance
    max_distance = matches[0].distance
    for x in matches:
        if x.distance < min_distance:
            min_distance = x.distance
        if x.distance > max_distance:
            max_distance = x.distance

    # 筛选匹配点
    '''
        当描述子之间的距离大于两倍的最小距离时，认为匹配有误。
        但有时候最小距离会非常小，所以设置一个经验值30作为下限。
    '''
    good_match = []
    for x in matches:
        if x.distance <= max(2 * min_distance, 30):
            good_match.append(x)

    # 绘制匹配结果
    draw_match(img1, img2, kp1, kp2, good_match)

def draw_match(img1, img2, kp1, kp2, match):
    outimage = cv.drawMatches(img1, kp1, img2, kp2, match[:25], outImg=None)
    cv.imshow("Match Result", outimage)
    cv.waitKey(0)

def rescale_image(img,opt,times):
    if opt == "up":
        scale_up_x = int(times)
        scale_up_y = int(times)
        return  cv.resize(img, None, fx= scale_up_x, fy= scale_up_y, interpolation= cv.INTER_LINEAR)
    if opt == "down":
        scale_down = int(times)
        return  cv.resize(img, None, fx= scale_down, fy= scale_down, interpolation= cv.INTER_LINEAR)
    


if __name__ == '__main__':
    # 读取图片
    image1 = cv.imread('orb1.png')
    image2 = cv.imread('orb2.png')
    image2 = rescale_image(image2, opt="up", times=5)
    ORB_Feature(image1, image2)

测试结果：（不怎么理想）