python图像检测_OpenCV3-Python图像特征检测方法

OpenCV可以检测图像的主要特征，然后提取这些特征，使其成为图像描述符，类似于人的眼睛与大脑。这些特征可作为图像搜索的数据库。

1. 特征定义

特征就是有意义的图像区域，该区域具有独特性或易于识别性。因此，角点及高密度区域是很好的特征，而大量重复的模式或低密度区域则非好的特征。

大多数特征检测算法都会涉及图像的角点、边和斑点的识别，也涉及一些脊向的概念。

2. 特征检测和提取算法

OpenCV中常用的特征检测和提取算法有如下六种：

（1）Harris：用于检测角点；

（2）SIFT：用于检测斑点；

（3）SURF：用于检测斑点；

（4）FAST：用于检测角点；

（5）BRIEF：用于检测斑点；

（6）ORB：表示带方向的FAST算法与具有旋转不变性的BRIEF算法；

本文将依次介绍以上算法的应用。

3. Harris

cornerHarris是一个非常简单方便实用的OpenCV函数，可用来检测图像的角点。

（1）应用举例# -*- coding: utf-8 -*-

"""

Created on Sun Jun 24 10:05:04 2018

@author: lu

"""

import cv2

import numpy as np

img = cv2.imread('./chess_board.png')

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

gray = np.float32(gray)

'''

https://docs.opencv.org/3.0-beta/modules/imgproc/doc/feature_detection.html?highlight=cornerharris#cv2.cornerHarris

Python: cv2.cornerHarris(src, blockSize, ksize, k[, dst[, borderType]]) → dst

Parameters:

src – Input single-channel 8-bit or floating-point image.

blockSize – Neighborhood size (see the details on cornerEigenValsAndVecs() ).

ksize – Aperture parameter for the Sobel() operator. 3~31

k – Harris detector free parameter. See the formula below.

borderType – Pixel extrapolation method. See borderInterpolate() .

dst – Image to store the Harris detector responses. It has the type CV_32FC1 and the same size as src .

'''

#blockSize：参数值越小，标记角点的记号越小

#注意ksize参数：定义了角点检测的敏感度

dst = cv2.cornerHarris(gray,5,23,0.04)

img[dst>0.01*dst.max()]=[0,0,255]

cv2.imshow('corner',img)

cv2.waitKey()

cv2.destroyAllWindows()

（2）运行结果

4. SIFT

尽管cornerHarris函数可以很好检测角点，然而如果减小（增大）图像的大小，则可能会丢失（增加）某些部分；也就是说，其检测效果很角点本身的特性有关。因此，需要一种与图像比例无关的角点检测方法，即：尺度不变特征变换（Scale-Invariant Feature Transform , SIFT）。

注：SIFT算法并不检测关键点，关键点由DoG(Difference of Gaussians）检测（DoG是对同一图像使用不同高斯滤波器所得到的结果），SIFT算法是通过一个特征向量来描述关键点周围区域的情况。

应用举例：#创建SIFT对象

sift = cv2.xfeatures2d.SIFT_create()

'''

cv2.SIFT.detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) → keypoints, descriptors

Parameters:

img – Input 8-bit grayscale image

mask – Optional input mask that marks the regions where we should detect features.

keypoints – The input/output vector of keypoints

descriptors – The output matrix of descriptors. Pass cv::noArray() if you do not need them.

useProvidedKeypoints – Boolean flag. If it is true, the keypoint detector is not run. Instead,

the provided vector of keypoints is used and the algorithm just computes their descriptors.

'''

keypoints, descriptor = sift.detectAndCompute(gray,None)

'''

Python: cv2.drawKeypoints(image, keypoints[, outImage[, color[, flags]]]) → outImage¶

Parameters:

image – Source image.

outImage – Output image. Its content depends on the flags value defining what is drawn in the output image.

See possible flags bit values below.

keypoints – Keypoints from the source image.

flags – Flags setting drawing features. Possible flags bit values are defined by DrawMatchesFlags.

See details above in drawMatches() .

color – Color of keypoints.

Note For Python API, flags are modified as

cv2.DRAW_MATCHES_FLAGS_DEFAULT=0,

cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG=1,

cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS=2,

cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS=4

'''

img = cv2.drawKeypoints(image=img, outImage=img, keypoints = keypoints,

flags = cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, color = (51, 163, 236))

5. SURF

SURF算法吸收了SIFT算法思想，其检测速度比SIFT快好几倍；不同的是SURF采用快速Hessian算法检测关键点，而SIFT则采用DoG和SIFT检测关键点并提取周围的特征。

应用举例：cv2.xfeatures2d.SURF_create(8000), #Hessian阈值设为8000；阈值越高，能识别的特征就越少

6. FAST

FAST (Features from Accelerated Segment Test) 算法会在像素周围绘制一个圆，该圆包括16个像素；然后，FAST会将每个像素值与加上某个阈值的圆心像素值进行比较，若存在N个连续的像素值比加上一个阈值的圆心的像素值还亮（暗），则可认为圆心是角点。详细介绍查看官网。

（1）应用举例：# -*- coding: utf-8 -*-

"""

Created on Sun Jul 15 15:33:36 2018

@author: lu

"""

import numpy as np

import cv2

from matplotlib import pyplot as plt

img = cv2.imread("./varese.jpg",0)

# Initiate FAST object with default values

fast = cv2.FastFeatureDetector_create()

# find and draw the keypoints

kp = fast.detect(img,None)

img2 = cv2.drawKeypoints(img,kp,img, color=(255,0,0))

# Print all default params

print ("Threshold: ", fast.getThreshold()) #输出阈值

print ("nonmaxSuppression: ", fast.getNonmaxSuppression()) #是否使用非极大值抑制

print ("neighborhood: ", fast.getType())

print ("Total Keypoints with nonmaxSuppression: ", len(kp)) #特征点个数

# Disable nonmaxSuppression

fast.setNonmaxSuppression(0)

kp = fast.detect(img,None)

print ("Total Keypoints without nonmaxSuppression: ", len(kp))

img3 = cv2.drawKeypoints(img,kp,img, color=(255,0,0))

cv2.imshow('fast_true', img2)

cv2.imshow('fast_false', img3)

while (True):

if cv2.waitKey(100) & 0xff == ord("q"):

break

cv2.destroyAllWindows()

（2）运行结果

7. BRIEF

从某方面来说，BRIEF(Binary Robust Independent Elementary Features)并不是特征检测算法，它只是一个描述符。关于BRIEF算法介绍，请访问官网。

（1）应用举例# -*- coding: utf-8 -*-

"""

Created on Sun Jul 15 15:59:57 2018

@author: lu

"""

import numpy as np

import cv2

from matplotlib import pyplot as plt

img = cv2.imread('./varese.jpg',0)

# Initiate STAR detector

star = cv2.xfeatures2d.StarDetector_create()

# Initiate BRIEF extractor

brief = cv2.xfeatures2d.BriefDescriptorExtractor_create()

# find the keypoints with STAR

kp = star.detect(img,None)

# compute the descriptors with BRIEF

kp, des = brief.compute(img, kp)

print (brief.descriptorSize())

print (des.shape)

img = cv2.drawKeypoints(img,kp,img, color=(255,0,0))

cv2.imshow('brief', img)

while (True):

if cv2.waitKey(100) & 0xff == ord("q"):

break

cv2.destroyAllWindows()

（2）运行结果

8. ORB

ORB的实现基于FAST关键点检测技术和基于BRIEF描述符的技术相结合。

与SIFT和SURF相比，ORB算法有更快的速度，其包括非常重要的一步：以旋转感知（rotation-aware）的方式使用BRIEF，这样即使在训练图像与查询图像之间旋转差别较大情况下，也可能够提高匹配。

注：关于ORB实现特征匹配，将在下一篇介绍。

（1）应用举例# -*- coding: utf-8 -*-

"""

Created on Sun Jun 24 11:30:20 2018

@author: lu

"""

import cv2

import sys

import numpy as np

img = cv2.imread("./varese.jpg")

def fd(algorithm):

algorithms = {

"SIFT": cv2.xfeatures2d.SIFT_create(),

"SURF": cv2.xfeatures2d.SURF_create(8000), #Hessian阈值设为8000；阈值越高，能识别的特征就越少

"ORB": cv2.ORB_create()

}

return algorithms[algorithm]

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#1.SITF算法

img1 = np.copy(img)

fd_alg1 = fd("SIFT")

keypoints, descriptor = fd_alg1.detectAndCompute(gray,None)

img1 = cv2.drawKeypoints(image=img1, outImage=img1, keypoints = keypoints, flags = 4, color = (51, 163, 236))

#2.SURF算法

img2 = np.copy(img)

fd_alg2 = fd("SURF")

keypoints, descriptor = fd_alg2.detectAndCompute(gray,None)

img2 = cv2.drawKeypoints(image=img2, outImage=img2, keypoints = keypoints, flags = 4, color = (51, 163, 236))

#3.ORB算法

img3 = np.copy(img)

fd_alg3 = fd("ORB")

keypoints, descriptor = fd_alg3.detectAndCompute(gray,None)

img3 = cv2.drawKeypoints(image=img3, outImage=img3, keypoints = keypoints, flags = 4, color = (51, 163, 236))

cv2.imshow('Original', img)

cv2.imshow('SIFT_kps', img1)

cv2.imshow('SURF_kps', img2)

cv2.imshow('ORB_kps', img3)

while (True):

if cv2.waitKey(100) & 0xff == ord("q"):

break

cv2.destroyAllWindows()

（2）运行效果

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