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- use KMEANS_PP_CENTERS

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git-svn-id: http://moon:8086/svn/software/trunk/projects/opencv@314 b431acfa-c32f-4a4a-93f1-934dc6c82436
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jens
2016-09-17 14:07:09 +00:00
parent 3ddd89068f
commit fd3232794e
1 changed files with 2 additions and 2 deletions
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beadDetect.py
+2 -2
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@@ -214,7 +214,7 @@ Z = np.float32(plotColors)
# Define criteria = ( type, max_iter, epsilon )
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.1)
ret,label,center=cv2.kmeans(Z,10,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)
ret,label,center=cv2.kmeans(Z,10,None,criteria,10,cv2.KMEANS_PP_CENTERS)
print (center)
@@ -222,7 +222,7 @@ print (center)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(blue, green, red, zdir='z', s=10, c=(0,0,0), lw = 0, depthshade=True)
ax.scatter(center[:,2], center[:,1], center[:,0], zdir='z', s=250, facecolors=center, lw = 2, depthshade=True)
ax.scatter(center[:,2], center[:,1], center[:,0], zdir='z', s=250, facecolors=center, lw = 1, depthshade=True)
plt.show()