def plotSuperPixelImage(sourceImage, labelledImage, orientation):
print "\n*Now plotting source & labelled image for visual comparison."
plt.interactive(1)
plt.figure()
pomio.showClassColours()
plt.figure()
print "*Unique labels from superpixel classification = ", np.unique(labelledImage)
plt.subplot(1,2,1)
plt.imshow(sourceImage, origin=orientation)
plt.subplot(1,2,2)
#pomio.showLabels(labelledImage)
plt.imshow(labelledImage, origin=orientation)
def plotSuperPixelImage(sourceImage, labelledImage, orientation):
print "\n*Now plotting source & labelled image for visual comparison."
plt.interactive(1)
plt.figure()
pomio.showClassColours()
plt.figure()
print "*Unique labels from superpixel classification = ", np.unique(
labelledImage)
plt.subplot(1, 2, 1)
plt.imshow(sourceImage, origin=orientation)
plt.subplot(1, 2, 2)
#pomio.showLabels(labelledImage)
plt.imshow(labelledImage, origin=orientation)
cmap = [\
( ( 0,255, 0),( 0,128, 0) ), \
( (200,100, 20),(128,128, 0) ), \
( (255, 0, 0),(128, 0, 0) ), \
( ( 0, 0,255),( 64,128, 0) ), \
( (100,100,100),(128, 64,128) ), \
]
infile = sys.argv[1]
outfile= sys.argv[2]
image = skimage.io.imread(infile)
plt.interactive(1)
plt.figure()
pomio.showClassColours()
plt.figure()
plt.imshow(image)
plt.title('input labels')
#plt.waitforbuttonpress()
# Make the output image
newimg = image.copy()
# for each colour make the transfer
nc = 3 # number colour channels
for cpair in cmap:
clrFrom = cpair[0]
nbClasses = pomio.getNumClasses()
cpnew = np.zeros( (nbRows, nbCols, nbClasses) )
for i in range( classProbs.shape[2] ):
# stuff this set of probs to new label
cpnew[:,:,clfr.classes_[i]] = classProbs[:,:,i]
classProbs = cpnew
del cpnew
maxLabel = np.argmax( classProbs, 2 )
pomio.showLabels(maxLabel, colourMap)
if args.verbose:
plt.title('raw clfr labels')
plt.figure()
pomio.showClassColours( classNames, colourMap )
plt.draw()
if 0 and args.interactive:
plt.waitforbuttonpress()
#print classProbs
if dbgMode and args.verbose:
for i in range( classProbs.shape[2] ):
plt.imshow( classProbs[:,:,i] )
plt.title( 'class %d: %s' % (i,classNames[i]) )
plt.waitforbuttonpress()
nhoodSz = args.nhoodSz
sigsq = amntools.estimateNeighbourRMSPixelDiff(imgRGB,nhoodSz) ** 2