OpenCV-Python Feature2D 特征点检测(含SIFT/SURF/ORB/KAZE/FAST/BRISK/AKAZE)

对于OpenCV-Python，OpenCV2.x和OpenCV3.x的函数使用方式有很大不同。网上很多教程都还是基于OpenCV2.x，此版本已经逐渐被弃用。

本教程针对特征点检测，分析OpenCV2.x和OpenCV3.x的不同之后，并重点介绍OpenCV3.x-Python的特征点检测。

Open2.x-Python 特征点检测方法

对于OpenCV2.x-Python，特征点检测及显示方法如下：

# OpenCV2.x-Python
function = cv2.Function_Name()
keypoints = function.detect(img, None)
img2 = cv2.drawKeyPoints(img, keypoints, color=(0,255,0))

其中Function_Name就是特征检测方法的函数名，如BRISK、FastFeatureDetector等。

比如，在OpenCV2.x-Python，想使用Fast来检测特征点，示例如下：

# OpenCV2.x-Python
fast = cv2.FastFeatureDetector()
keypoints = fast.detect(img, None)
img2 = cv2.drawKeypoints(img, keypoints, color=(255,0,0))

Open3.x-Python 特征点检测方法

对于OpenCV3.x-Python，特征点检测及显示方法如下：

# OpenCV3.x-Python
# 注意有_create()后缀
function = cv2.Function_Name_create()
keypoints = function.detect(img, None)
# 注意显示之前要先将img2初始化
img2 = img.copy()
img2 = cv2.drawKeyPoints(img, keypoints, color=(0,255,0))

其中Function_Name就是特征检测方法的函数名，如BRISK、FastFeatureDetector等。

[注意1]：对于OpenCV3.x-Python，还要在Function_Name后加上_create后缀。其实这一点在opencv_doc中具体的函数python使用方法中已经注明了。

[注意2]：对于OpenCV3.x-Python，若要显示检测的特征点，需要初始化img2，才能正常显示。这里可以先使用img2 = img.copy()完成拷贝初始化。

下面就重点介绍OpenCV3.x-Python中的各种特征点检测方法的使用示例。

测试图像为标准的lena.png

AKAZE Feature Detection

#!/usr/bin/env python
# -*- coding=utf-8 -*-
# Summary: 使用OpenCV3.x-Python检测AKAZE特征点
# Author:  Amusi
# Date:    2018-03-17
# Reference: https://docs.opencv.org/master/d8/d30/classcv_1_1AKAZE.html

import cv2
import numpy

def main():

	img = cv2.imread("lena.png")
	cv2.imshow('Input Image', img)
	cv2.waitKey(0)
	
	# 检测
	akaze = cv2.AKAZE_create()
	keypoints = akaze.detect(img, None)
	
	# 显示
	# 必须要先初始化img2
	img2 = img.copy()
	img2 = cv2.drawKeypoints(img, keypoints, img2, color=(0,255,0))
	cv2.imshow('Detected AKAZE keypoints', img2)
	cv2.waitKey(0)
    
if __name__ == '__main__':
	main()

BRISK Feature Detection

#!/usr/bin/env python
# -*- coding=utf-8 -*-
# Summary: 使用OpenCV3.x-Python检测BRISK特征点
# Author:  Amusi
# Date:    2018-03-17
# Reference: https://docs.opencv.org/master/de/dbf/classcv_1_1BRISK.html

import cv2
import numpy

def main():
	img = cv2.imread("lena.png")
	cv2.imshow('Input Image', img)
	cv2.waitKey(0)


	brisk = cv2.BRISK_create()
	keypoints = brisk.detect(img, None)
	
	# 必须要先初始化img2
	img2 = img.copy()
	img2 = cv2.drawKeypoints(img, keypoints, img2, color=(0,255,0))
	cv2.imshow('Detected BRISK keypoints', img2)
	cv2.waitKey(0)
    
if __name__ == '__main__':
	main()

Fast Feature Detection

#!/usr/bin/env python
# -*- coding=utf-8 -*-
# Summary: 使用OpenCV3.x-Python检测FAST特征点
# Author:  Amusi
# Date:    2018-03-17
# Reference: https://docs.opencv.org/master/df/d74/classcv_1_1FastFeatureDetector.html

import cv2
import numpy

def main():

	img = cv2.imread("lena.png")
	cv2.imshow('Input Image', img)
	cv2.waitKey(0)

	# 2018-03-17 Amusi: OpenCV3.x FeatureDetector写法有变化
	# OpenCV2.x
	# fast = cv2.FastFeatureDetector()
	# keypoints = fast.detect(img, None)
	
	# OpenCV3.x
	# 注意有_create()后缀
	fast = cv2.FastFeatureDetector_create()
	keypoints = fast.detect(img, None)
	
	# 必须要先初始化img2
	img2 = img.copy()
	img2 = cv2.drawKeypoints(img, keypoints, img2, color=(0,255,0))
	cv2.imshow('Detected FAST keypoints', img2)
	cv2.waitKey(0)
    
if __name__ == '__main__':
	main()

KAZE Feature Detection

#!/usr/bin/env python
# -*- coding=utf-8 -*-
# Summary: 使用OpenCV3.x-Python检测KAZE特征点
# Author:  Amusi
# Date:    2018-03-17
# Reference: https://docs.opencv.org/master/d3/d61/classcv_1_1KAZE.html

import cv2
import numpy

def main():

	img = cv2.imread("lena.png")
	cv2.imshow('Input Image', img)
	cv2.waitKey(0)
	
	# 检测
	kaze = cv2.KAZE_create()
	keypoints = kaze.detect(img, None)
	
	# 显示
	# 必须要先初始化img2
	img2 = img.copy()
	img2 = cv2.drawKeypoints(img, keypoints, img2, color=(0,255,0))
	cv2.imshow('Detected KAZE keypoints', img2)
	cv2.waitKey(0)
    
if __name__ == '__main__':
	main()

ORB Feature Detection

#!/usr/bin/env python
# -*- coding=utf-8 -*-
# Summary: 使用OpenCV3.x-Python检测ORB特征点
# Author:  Amusi
# Date:    2018-03-17
# Reference: https://docs.opencv.org/master/db/d95/classcv_1_1ORB.html


import cv2
import numpy


def main():


	img = cv2.imread("lena.png")
	cv2.imshow('Input Image', img)
	cv2.waitKey(0)
	
	# 检测
	orb = cv2.ORB_create()
	keypoints = orb.detect(img, None)
	
	# 显示
	# 必须要先初始化img2
	img2 = img.copy()
	img2 = cv2.drawKeypoints(img, keypoints, img2, color=(0,255,0))
	cv2.imshow('Detected ORB keypoints', img2)
	cv2.waitKey(0)
    
if __name__ == '__main__':
	main()

----------我是可爱的分割线----------

下面介绍属于nonfree的特征检测方法，如SIFT和SURF。

这些方法在opencv-contrib中，所以想要使用前，请卸载当前非contrib版本的opencv，即pip uninstall opencv-python后；再重新安装opencv-contrib-python，即pip install opencv-contrib-python

SIFT Feature Detection

#!/usr/bin/env python
# -*- coding=utf-8 -*-
# Summary: 使用OpenCV3.x-Python检测SIFT特征点
# Author:  Amusi
# Date:    2018-03-17
# Reference: https://docs.opencv.org/master/d5/d3c/classcv_1_1xfeatures2d_1_1SIFT.html


import cv2
import numpy


def main():


	img = cv2.imread("lena.png")
	cv2.imshow('Input Image', img)
	cv2.waitKey(0)
	
	# 检测
	sift = cv2.xfeatures2d.SIFT_create()
	keypoints = sift.detect(img, None)
	
	# 显示
	# 必须要先初始化img2
	img2 = img.copy()
	img2 = cv2.drawKeypoints(img, keypoints, img2, color=(0,255,0))
	cv2.imshow('Detected SIFT keypoints', img2)
	cv2.waitKey(0)
    
if __name__ == '__main__':
	main()

SURF Feature Detection

#!/usr/bin/env python
# -*- coding=utf-8 -*-
# Summary: 使用OpenCV3.x-Python检测SURF特征点
# Author:  Amusi
# Date:    2018-03-17
# Reference: https://docs.opencv.org/master/d5/df7/classcv_1_1xfeatures2d_1_1SURF.html

import cv2
import numpy

def main():

	img = cv2.imread("lena.png")
	cv2.imshow('Input Image', img)
	cv2.waitKey(0)
	
	# 检测
	surf = cv2.xfeatures2d.SURF_create()
	keypoints = surf.detect(img, None)
	
	# 显示
	# 必须要先初始化img2
	img2 = img.copy()
	img2 = cv2.drawKeypoints(img, keypoints, img2, color=(0,255,0))
	cv2.imshow('Detected SURF keypoints', img2)
	cv2.waitKey(0)
    
if __name__ == '__main__':
	main()

注：OpenCV3.x-Python与OpenCV2.x-Python有很多函数的用法不同，虽然网上教程大多参次不齐，但可以直接去官网查看最新的用法（官网即正义）