def matchImages(original, image_to_compare, useFlann=False):
if useRootSIFT:
# extract RootSIFT descriptors
#print('Using RootSIFT')
kps = None
rs = RootSIFT()
kp_1, desc_1 = rs.compute(original, kps)
kp_2, desc_2 = rs.compute(image_to_compare, kps)
else:
sift = cv2.xfeatures2d.SIFT_create()
kp_1, desc_1 = sift.detectAndCompute(original, None)
kp_2, desc_2 = sift.detectAndCompute(image_to_compare, None)
if useFlann:
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
print('Using Flann Matcher')
matcher = cv2.FlannBasedMatcher(index_params, search_params)
else:
print('Using Brute Force Matcher')
matcher = cv2.DescriptorMatcher_create("BruteForce")
matches = matcher.knnMatch(desc_1, desc_2, k=2)
good_points = []
for m, n in matches:
if m.distance < LRatio * n.distance:
good_points.append(m)
print(len(good_points))
result = cv2.drawMatches(original, kp_1, image_to_compare, kp_2,
good_points, None)
# cv2.imshow("result", cv2.resize(result,(800,600)))
return (good_points, result)
def detectAndDescribe(self, image, useRootSIFT=False):
# convert the image to grayscale
# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# check to see if we are using OpenCV 3.X
if self.isv3:
if useRootSIFT:
# extract RootSIFT descriptors
# print('Using RootSIFT')
kps = None
rs = RootSIFT()
(kps, features) = rs.compute(image, kps)
else:
# detect and extract features from the image
descriptor = cv2.xfeatures2d.SIFT_create()
# descriptor = cv2.xfeatures2d.SURF_create()
# descriptor = cv2.ORB_create()
(kps, features) = descriptor.detectAndCompute(image, None)
# otherwise, we are using OpenCV 2.4.X
else:
# detect keypoints in the image
detector = cv2.FeatureDetector_create("SIFT")
kps = detector.detect(image)
# extract features from the image
extractor = cv2.DescriptorExtractor_create("SIFT")
(kps, features) = extractor.compute(image, kps)
# convert the keypoints from KeyPoint objects to NumPy
# arrays
kps = np.float32([kp.pt for kp in kps])
# return a tuple of keypoints and features
return (kps, features)
def getRootSIFT(gray):
sift = cv2.xfeatures2d.SIFT_create()
kp, des = sift.detectAndCompute(gray, None)
# extract RootSIFT descriptors
rs = RootSIFT()
kp, des = rs.compute(gray, kp)
return kp, des
def init_detect_extract(params):
'''
Initialize detector and extractor from parameters
'''
if params['descriptor_type'] == 'RootSIFT':
extractor = RootSIFT()
else:
extractor = cv2.DescriptorExtractor_create(params['descriptor_type'])
detector = cv2.FeatureDetector_create(params['keypoint_type'])
return detector, extractor
from __future__ import print_function
from rootsift import RootSIFT
import argparse
import cv2
import imutils
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="Path to the image")
args = vars(ap.parse_args())
# load the input image, convert it to grayscale
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# extract local invariant descriptors
extractor = RootSIFT()
(kps, descs) = extractor.compute(gray, None)
# show the shape of the keypoints and local invariant descriptors array
print("[INFO] # of keypoints detected: {}".format(len(kps)))
print("[INFO] feature vector shape: {}".format(descs.shape))
# USAGE
# python driver.py
# import the necessary packages
from rootsift import RootSIFT
import cv2
# load the image we are going to extract descriptors from and convert
# it to grayscale
image = cv2.imread("example.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# detect Difference of Gaussian keypoints in the image
detector = cv2.FeatureDetector_create("SIFT")
kps = detector.detect(gray)
# extract normal SIFT descriptors
extractor = cv2.DescriptorExtractor_create("SIFT")
(kps, descs) = extractor.compute(gray, kps)
print("SIFT: kps=%d, descriptors=%s " % (len(kps), descs.shape))
# extract RootSIFT descriptors
rs = RootSIFT()
(kps, descs) = rs.compute(gray, kps)
print("RootSIFT: kps=%d, descriptors=%s " % (len(kps), descs.shape))
