python opencv-3.0 SIFT/SURF 特征提取与匹配

一、环境准备

conda install -c menpo opencv3
pip install lake

二、SIFT/SURF 特征提取与匹配

# coding: utf-8
from matplotlib import pyplot as plt
from lake.decorator import time_cost
import cv2
print 'cv version: ', cv2.__version__

def bgr_rgb(img):
(r, g, b) = cv2.split(img)
return cv2.merge([b, g, r])

def orb_detect(image_a, image_b):
# feature match
orb = cv2.ORB_create()

kp1, des1 = orb.detectAndCompute(image_a, None)
kp2, des2 = orb.detectAndCompute(image_b, None)

# create BFMatcher object
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

# Match descriptors.
matches = bf.match(des1, des2)

# Sort them in the order of their distance.
matches = sorted(matches, key=lambda x: x.distance)

# Draw first 10 matches.
img3 = cv2.drawMatches(image_a, kp1, image_b, kp2, matches[:100], None, flags=2)

return bgr_rgb(img3)

@time_cost
def sift_detect(img1, img2, detector='surf'):
if detector.startswith('si'):
print "sift detector......"
sift = cv2.xfeatures2d.SURF_create()
else:
print "surf detector......"
sift = cv2.xfeatures2d.SURF_create()

# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

# BFMatcher with default params
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)

# Apply ratio test
good = [[m] for m, n in matches if m.distance < 0.5 * n.distance]

# cv2.drawMatchesKnn expects list of lists as matches.
img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good, None, flags=2)

return bgr_rgb(img3)

if __name__ == "__main__":
# load image
image_a = cv2.imread('./img1.jpg')
image_b = cv2.imread('./img2.png')

# ORB
# img = orb_detect(image_a, image_b)

# SIFT or SURF
img = sift_detect(image_a, image_b)

plt.imshow(img)
plt.show()

三、输出展示

cv version:  3.1.0
surf detector......
==> time-cost: 0.187422     sift_detect