【PYTHON】代码存档----SURF算法的实现

【PYTHON】代码存档----SURF算法的实现

一、SURF算法

全称为Speed-up robust features（加速健壮特征），主要功能是完成两个图片之中的物体匹配。

二、代码

import cv2
import numpy as np
import logging as log
from torchvision.transforms import Resize
import glob

#img1=cv2.imread('D:/projects/video_flicker/video/result_45.jpg',cv2.IMREAD_GRAYSCALE)
#img2=cv2.imread('D:/projects/video_flicker/video/result_30.jpg',cv2.IMREAD_GRAYSCALE)
img1=cv2.imread('D:/projects/video_flicker/test/test_03.jpg',cv2.IMREAD_GRAYSCALE)
img2=cv2.imread('D:/projects/video_flicker/test/test_04.jpg',cv2.IMREAD_GRAYSCALE)
#提取特征点
#创建SURF对象
surf=cv2.xfeatures2d.SURF_create(10000)#返回关键点信息和描述符
image1=img1.copy()
image2=img2.copy()
keypoint1,descriptor1=surf.detectAndCompute(image1,None)
keypoint2,descriptor2=surf.detectAndCompute(image2,None)
#在图像上绘制关键点（关键点利用Hessian算法找到）
#DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS绘制特征点的时候绘制一个个带方向的圆
image1=cv2.drawKeypoints(image=image1,keypoints=keypoint1,outImage=image1,color=(255,0,255),flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
image2=cv2.drawKeypoints(image=image2,keypoints=keypoint2,outImage=image2,color=(255,0,255),flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

cv2.imshow('surf_keypoints1',image1)
cv2.imshow('surf_keypoints2',image2)


#特征点匹配
matcher=cv2.FlannBasedMatcher()
matchePoints=matcher.match(descriptor1,descriptor2)
print(type(matchePoints),len(matchePoints),matchePoints[0])

#提取最强匹配
minMatch=1
maxMatch=0

#for i in range(len(matchePoints)):
    #print(matchePoints[i].queryIdx,matchePoints[i].trainIdx)#只是打印索引，无法寻找点
#queryIdx为查询点索引，trainIdx为被查询点索引
for i in range(len(matchePoints)):
    if minMatch>matchePoints[i].distance:
        minMatch=matchePoints[i].distance
    if maxMatch<matchePoints[i].distance:
        maxMatch=matchePoints[i].distance
print('最佳匹配值：',minMatch)
print('最差匹配值：',maxMatch)

goodMatchePoints=[]
for i in range(len(matchePoints)):
    if matchePoints[i].distance<minMatch+(maxMatch-minMatch)/16:
        goodMatchePoints.append(matchePoints[i])

outImg = None
outImg = cv2.drawMatches(img1,keypoint1,img2,keypoint2,goodMatchePoints,outImg,matchColor=(0,0,255),flags=cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)
#打印最匹配点

for i in range(len(goodMatchePoints)):
    print('goodMatch输出',goodMatchePoints[i].queryIdx)
    print('x坐标',keypoint2[goodMatchePoints[i].queryIdx].pt[0])
    print('y坐标', keypoint2[goodMatchePoints[i].queryIdx].pt[1])
    match=cv2.circle(image2,(int(keypoint2[goodMatchePoints[i].trainIdx].pt[0]),
                             int(keypoint2[goodMatchePoints[i].trainIdx].pt[1])),4,(0,255,0),-1)


cv2.imshow('matche',outImg)
cv2.imshow('query',image2)#在image2上打印关键点位置
cv2.waitKey(0)
cv2.destroyAllWindows()

三、环境配置

需要python3.7作为开发环境，安装opencv-contrib-python3.4.2.17（之后的版本都被SURF算法申请了专利保护）

四、结果

上图为特征点匹配结果，下图在原图中确认匹配点位置（绿色），以便后续工作。
zjy----2022.6.30