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

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, des1 = orb.detectAndCompute(img1,None)
kp2, des2 = orb.detectAndCompute(img2,None)
# 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 PythonPython (asking here since code.opencv.org is down)

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~~

ORB
    kp1 = orb.detect(sample, None)
    kp2 = orb.detect(display, None)
    # Compute the descriptors
    kp1, des1 = orb.detectAndCompute(img1,None)
orb.compute(sample, kp1)
    kp2, des2 = orb.detectAndCompute(img2,None)
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:
0.7n.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)
cv2.drawMatchesKnn(sample,kp1,display,kp2,matches,None,*draw_params)
    outImage = cv2.cvtColor(outImage, cv2.COLOR_BGR2RGB) 
 plt.imshow(outImage), plt.show()

plt.show()

It'd be a nice addition in the Feature Detection tutorial using FlannBasedMatcher in Python (asking here since code.opencv.org is down)

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) 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 < 0.7n.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()plt.show()

It'd be a nice addition in the Feature Detection tutorial using FlannBasedMatcher in Python (asking here since code.opencv.org is down)

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~~ ORB

 
 kp1 = orb.detect(sample, None)
 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 < 0.7n.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()

          5    None 
      updated 
        2017-11-22 16:35:46 -0600
     
 
 
  
 
 
 
  It'd be a nice addition in the Feature Detection tutorial using FlannBasedMatcher in Python (asking here since code.opencv.org is down)
 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 < 0.7n.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()
 
 
 
          6    None 
      updated 
        2017-11-22 16:37:34 -0600
     
 
 
  
 
 
 
  It'd be a nice addition in the Feature Detection tutorial using FlannBasedMatcher in Python (asking here since code.opencv.org is down)
 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 < 0.7n.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()
 
 
 
          7    None 
      updated 
        2017-11-22 16:38:26 -0600
     
 
 
  
 
 
 
  It'd be a nice addition in the Feature Detection tutorial using FlannBasedMatcher in Python (asking here since code.opencv.org is down)
 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 < 0.7n.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)
    cv2.drawMatchesKnn(sample,kp1,display,kp2,matches,None,**draw_params)
outImage = cv2.cvtColor(outImage, cv2.COLOR_BGR2RGB) 
 plt.imshow(outImage), plt.show()