sift.init(FloatArray2D(ip.convertToFloat().getPixels(),
ip.getWidth(), ip.getHeight()))
features = sift.run() # instances of mpicbg.imagefeatures.Feature
return features
features1 = extractFeatures(imp1.getProcessor(), p)
features2 = extractFeatures(imp2.getProcessor(), p)
# Feature locations as points in an ROI
# Store feature locations in the Roi manager for visualization later
roi_manager = RoiManager()
roi1 = PointRoi()
roi1.setName("features for cut1")
for f in features1:
roi1.addPoint(f.location[0], f.location[1])
roi_manager.addRoi(roi1)
roi2 = PointRoi()
roi2.setName("features for cut2")
for f in features2:
roi2.addPoint(f.location[0], f.location[1])
roi_manager.addRoi(roi2)
# Find matches between the two sets of features
# (only by whether the properties of the features themselves match,
# not by their spatial location.)
rod = 0.9 # ratio of distances in feature similarity space (closest/next closest match)
pointmatches = FloatArray2DSIFT.createMatches(features1, features2, rod)
