def computeMapping(self, leftImage, rightImage):
leftGrey = cv2.cvtColor(leftImage, cv2.COLOR_BGR2GRAY)
rightGrey = cv2.cvtColor(rightImage, cv2.COLOR_BGR2GRAY)
orb = cv2.ORB_create()
leftKeypoints, leftDescriptors = orb.detectAndCompute(leftGrey, None)
rightKeypoints, rightDescriptors = orb.detectAndCompute(
rightGrey, None
)
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(leftDescriptors, rightDescriptors)
matches = sorted(matches, key=lambda x: x.distance)
nMatches = int(
float(self.matchPercentSlider.get()) * len(matches) / 100
)
if nMatches < 4:
return None
matches = matches[:nMatches]
motionModel = self.motionModelVar.get()
nRANSAC = int(self.nRANSACSlider.get())
RANSACThreshold = float(self.RANSACThresholdSlider.get())
return alignment.alignPair(
leftKeypoints, rightKeypoints, matches, motionModel, nRANSAC,
RANSACThreshold
)
def computeMapping(leftImage, rightImage):
leftGrey = cv2.cvtColor(leftImage, cv2.COLOR_BGR2GRAY)
rightGrey = cv2.cvtColor(rightImage, cv2.COLOR_BGR2GRAY)
#orb = cv2.ORB_create()#xfeatures2d.SIFT_create()#
if use_algorithm == "SIFT":
orb = cv2.xfeatures2d.SIFT_create()
elif use_algorithm == "SURF":
orb = cv2.xfeatures2d.SURF_create()
elif use_algorithm == "ORB":
orb = cv2.ORB_create(nfeatures=1500)
leftKeypoints, leftDescriptors = orb.detectAndCompute(leftGrey, None)
rightKeypoints, rightDescriptors = orb.detectAndCompute(rightGrey, None)
#bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
#matches = bf.match(leftDescriptors, rightDescriptors)
#matches = sorted(matches, key=lambda x: x.distance)
# Brute-Force matching with SIFT descriptors
bf = cv2.BFMatcher()
# Matching the keypoints with k-nearest neighbor (with k=2)
matches = bf.knnMatch(leftDescriptors, rightDescriptors, k=2)
#nMatches = int(
# float(20) * len(matches) / 100
#)
#if nMatches < 4:
# return None
goodMatch = []
# Performing ratio test to find good matches
for m, n in matches:
if m.distance < 0.75 * n.distance:
goodMatch.append(m)
#matches = matches[:nMatches]
motionModel = eTranslate
nRANSAC = cv2.RANSAC
RANSACThreshold = float(5.0)
return alignment.alignPair(leftKeypoints, rightKeypoints, goodMatch,
motionModel, nRANSAC, RANSACThreshold)
def test_alignPair(self):
'''Tests TODO 4'''
M = alignment.alignPair(self.f1, self.f2, self.matches,
alignment.eHomography, 1, 1)