#
# Super Pixel inference
#
# Construct test data: a 2x2 checkerboard with 4 superpixels. Want top 2
# to come out class 0, and bottom to be class 1.
splabels = np.array(
[[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1],
[2, 2, 2, 3, 3, 3], [2, 2, 2, 3, 3, 3], [2, 2, 2, 3, 3, 3]],
dtype=np.int32)
spnodes = np.arange(4)
spedges = [[0, 1], [2, 3], [0, 2], [1, 3]]
lblWts = -np.log(
np.array([[0.01, 0.02, 0.90], [0.80, 0.02, 0.05], [0.01, 0.02, 0.87],
[0.01, 0.82, 0.03]]))
spgraph = sp.SuperPixelGraph(splabels, spnodes, spedges)
x = np.reshape([np.arange(4) * 10], (4, 1))
y = spgraph.imageFromSuperPixelData(x)
print y
res = uflow.inferenceSuperPixel(spgraph, lblWts, 'abswap', 'degreeSensitive')
print "Inference result = \n", res
expectedResult = np.array([[2, 2, 2, 0, 0, 0], [2, 2, 2, 0, 0, 0],
[2, 2, 2, 0, 0, 0], [2, 2, 2, 1, 1, 1],
[2, 2, 2, 1, 1, 1], [2, 2, 2, 1, 1, 1]])
assert (np.all(expectedResult == res))
print 'Click plot to continue...'
plt.waitforbuttonpress()
#
# Do the inference
#
print 'Performing CRF inference...'
if args.verbose:
plt.figure()
segResult = uflow.inferenceSuperPixel( \
spix,\
-np.log( np.maximum(1E-10, np.ascontiguousarray(classProbs) ) ), \
adjProbs, \
'abswap',\
args.nbrPotentialMethod,\
K )#, np.ascontiguousarray(nbrPotentialParams) )
# turn from label classes to msrc labels
segResult += 1
print ' done.'
if args.outfile and len(args.outfile)>0:
print 'Writing output label file %s' % args.outfile
outimg = pomio.msrc_convertLabelsToRGB( segResult )
skimage.io.imsave(args.outfile, outimg)
print ' done.'
# Show the result.
if dointeract:
print 'Click plot to continue...'
plt.waitforbuttonpress()
#
# Do the inference
#
print 'Performing CRF inference...'
if args.verbose:
plt.figure()
segResult = uflow.inferenceSuperPixel( \
spix,\
-np.log( np.maximum(1E-10, np.ascontiguousarray(classProbs) ) ), \
adjProbs, \
'abswap',\
args.nbrPotentialMethod,\
K )#, np.ascontiguousarray(nbrPotentialParams) )
# turn from label classes to msrc labels
segResult += 1
print ' done.'
if args.outfile and len(args.outfile) > 0:
print 'Writing output label file %s' % args.outfile
outimg = pomio.msrc_convertLabelsToRGB(segResult)
skimage.io.imsave(args.outfile, outimg)
print ' done.'
# Show the result.
[2, 2, 2, 3, 3, 3],
[2, 2, 2, 3, 3, 3],
[2, 2, 2, 3, 3, 3],
],
dtype=np.int32,
)
spnodes = np.arange(4)
spedges = [[0, 1], [2, 3], [0, 2], [1, 3]]
lblWts = -np.log(np.array([[0.01, 0.02, 0.90], [0.80, 0.02, 0.05], [0.01, 0.02, 0.87], [0.01, 0.82, 0.03]]))
spgraph = sp.SuperPixelGraph(splabels, spnodes, spedges)
x = np.reshape([np.arange(4) * 10], (4, 1))
y = spgraph.imageFromSuperPixelData(x)
print y
res = uflow.inferenceSuperPixel(spgraph, lblWts, "abswap", "degreeSensitive")
print "Inference result = \n", res
expectedResult = np.array(
[
[2, 2, 2, 0, 0, 0],
[2, 2, 2, 0, 0, 0],
[2, 2, 2, 0, 0, 0],
[2, 2, 2, 1, 1, 1],
[2, 2, 2, 1, 1, 1],
[2, 2, 2, 1, 1, 1],
]
)
assert np.all(expectedResult == res)
[0,0,0, 1,1,1],
[0,0,0, 1,1,1],
[0,0,0, 1,1,1],
[2,2,2, 3,3,3],
[2,2,2, 3,3,3],
[2,2,2, 3,3,3] ], dtype=np.int32 )
spnodes = np.arange(4)
spedges = [ [0,1], [2,3], [0,2], [1,3] ];
lblWts = -np.log( np.array( [ [ 0.01, 0.02, 0.90],
[ 0.80, 0.02, 0.05],
[ 0.01, 0.02, 0.87],
[ 0.01, 0.82, 0.03] ] ) )
spgraph = sp.SuperPixelGraph(splabels,spnodes,spedges)
x = np.reshape( [np.arange(4)*10], (4,1) )
y = spgraph.imageFromSuperPixelData( x )
print y
res = uflow.inferenceSuperPixel( spgraph, lblWts, 'abswap', 'degreeSensitive' )
print "Inference result = \n", res
expectedResult = np.array( [
[2,2,2, 0,0,0],
[2,2,2, 0,0,0],
[2,2,2, 0,0,0],
[2,2,2, 1,1,1],
[2,2,2, 1,1,1],
[2,2,2, 1,1,1] ] )
assert( np.all( expectedResult == res ) )