OpenCV3 Python语言实现 笔记4

特征检测 特征提取 特征匹配

关键点检测+描述符
SIFT: DoG + SIFT
SURF: Hessian + SURF
ORB:  FAST + BRIEF

暴力匹配

FLANN匹配法

一、SIFT
Different of Gaussian(DoG) 对同一图像使用不同高斯滤波器
DoG得到感兴趣区域(关键点)，通过SIFT进行说明

import cv2
import sys
import numpy as np

imgpath = sys.argv[1]
img = cv2.imread(imgpath)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

sift = cv2.xfeatures2d.SIFT_create()#
keypoints, descriptor = sift.detectAndCompute(gray,None)#返回关键点和描述符

img = cv2.drawKeypoints(image=img, outImg=img, keypoints=keypoints,
                        flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINT,
                        color=(51,163,236))#
#cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINT每个关键点绘制圆和方向
cv2.imshow('sift_keypoints',img)
while True:
    if cv2.waitKey(1000/12) & 0xff == ord("c"):
        break
cv2.destoryAllWindow()

关键点keypoints的属性：
1.pt: 点xy坐标
2.size: 特征直径
3.angle: 特征方向
4.response: 特征强度 评估特征好坏
5.octave: 特征所在金字塔层级
6.class_id: 关键点ID

二、SURF

surf = cv2.xfeatures2d.SURF_create(8000)#阈值越小特征点越多
keypoints, descriptor = surf.detectAndCompute(gray,None)#返回关键点和描述符
img = cv2.drawKeypoints(image=img, outImg=img, keypoints=keypoints,
                        flags=4,
                        color=(51,163,236))#

三、ORB特征检测与特征匹配

import numpy as np
import cv2
from matplotlib import pyplot as plt
cv2.ocl.setUseOpenCL(False)
# query and test image