adjProbs = None
precomputedMode = args.infile.endswith('.pkl') or args.infile.endswith('.mat')
if precomputedMode == True:
# If the input file is a pkl (not image) assume we've run necessary superpixel classifier, and input is class label probabilities
# Input is assumed to be a tuple [superpixels, classProbs]
print "Using pre-computed superpixels and class label probabilities"
if args.infile.endswith('.pkl'):
superPixelInput = pomio.unpickleObject(args.infile)
spix = superPixelInput[0]
classProbs = superPixelInput[1]
colourMap = pomio.msrc_classToRGB
else:
spix, classProbs = isprs.loadISPRSResultFromMatlab(args.infile)
colourMap = isprs.colourMap
else:
# Assume input image needs processing and classifying
print "Superpixel generation mode"
numberSuperPixels = args.nbSuperPixels
superPixelCompactness = args.superPixelCompactness
imgRGB = amntools.readImage(args.infile)
# Turn image into superpixels.
spix = superPixels.computeSuperPixelGraph(
imgRGB, 'slic', [numberSuperPixels, superPixelCompactness])
plt.imshow(imgRGB)
plt.title('original image')
#plt.waitforbuttonpress()
plt.figure()
if precomputedMode == True:
x = scipy.io.loadmat( args.matFn )
if x.has_key('singlepix_conf'):
# these are the per-pixel probs
classLabs = x['singlepix_label']
classProbs = x['singlepix_conf'].astype(float)
# the labels are out of order for probabilities. Paul has: impervious, bldg, car, low veg, tree, clutter
#classProbs = classProbs[:,:, np.array([1,2,5,3,4])-1]
else:
# super-pixel probs
spix, classProbsLUT = isprs.loadISPRSResultFromMatlab( args.matFn )
classLabs = spix.m_labels
# map the labels to HxWxC class probabilities matrix
classProbs = np.zeros( classLabs.shape + (classProbsLUT.shape[1],) )
for c in range( classProbsLUT.shape[1] ):
# Get a mask of matching pixels
classProbs[ :,:, c ] = classProbsLUT[ classLabs, c ]
classNames = isprs.classLabels
colourMap = isprs.colourMap
else:
print 'Computing class probabilities...'
print 'Loading classifier...'
clfr = pomio.unpickleObject(clfrFn)
ftype = 'classic'
classLabs, classProbs = classification.classifyImagePixels(imgRGB, clfr, \
ftype, True)
from matplotlib import cm
import pomio
import slic
import superPixels
import skimage
import isprs
import amntools
imgRGB = amntools.readImage(args.imagefile)
if len(args.spfile) == 0:
numberSuperPixels = args.nbSuperPixels
superPixelCompactness = args.superPixelCompactness
# Turn image into superpixels.
spix = superPixels.computeSuperPixelGraph(imgRGB, "slic", [numberSuperPixels, superPixelCompactness])
elif args.spfile.endswith(".pkl"):
superPixelInput = pomio.unpickleObject(args.spfile)
spix = superPixelInput[0]
classProbs = superPixelInput[1]
colourMap = pomio.msrc_classToRGB
elif args.spfile.endswith(".mat"):
spix, classProbs = isprs.loadISPRSResultFromMatlab(args.spfile)
colourMap = isprs.colourMap
else:
assert False
# Display superpixel boundaries on image.
plt.imshow(superPixels.generateImageWithSuperPixelBoundaries(imgRGB, spix.m_labels))
plt.show()