Eli's profile - activity

Since FlannBasedMatcher.knnMatch requires CV_32F for descriptor arrays

It'd be nice to have one of the examples use ORB rather than SIFT for FlannBasedMatcher and then detect and computer with ORB

kp1 = orb.detect(sample, None)
kp2 = orb.detect(display, None)
# Compute the descriptors
kp1, des1 = orb.compute(sample, kp1)
kp2, des2 = orb.compute(display, kp2)
# Convert descriptor array to float32
des1 = des1.astype(np.float32)
des2 = des2.astype(np.float32)
# Finds the matches in order of increasing distance    
matches = fbm.knnMatch(des1,des2,k=2)
matchesMask = [[0,0] for i in range(len(matches))]

for i,(m,n) in enumerate(matches):
    if m.distance < 0.7*n.distance:
        matchesMask[i]=[1,0]
draw_params = dict(matchColor = (0,255,0),
               singlePointColor = (255,0,0),
               matchesMask = matchesMask,
               flags = 0)
outImage = cv2.drawMatchesKnn(sample,kp1,display,kp2,matches,None,**draw_params)
outImage = cv2.cvtColor(outImage, cv2.COLOR_BGR2RGB) 
plt.imshow(outImage), plt.show()

It'd be a nice addition in the Feature Detection tutorial using FlannBasedMatcher in Python (asking here since code.open

It'd be a nice addition in the Feature Detection tutorial using FlannBasedMatcher in Python (asking here since code.open

It'd be a nice addition in the Feature Detection tutorial using FlannBasedMatcher in Python (asking here since code.open

It'd be a nice addition in the Feature Detection tutorial using FlannBasedMatcher in Python (asking here since code.open

It'd be a nice addition in the Feature Detection tutorial using FlannBasedMatcher in Python (asking here since code.open

I understand hamming. I understand that values may be representative of bit strings. Numpy.astype float32 does not take

It'd be a nice addition in the Feature Detection tutorial using FlannBasedMatcher in Python Since FlannBasedMatcher.knnM