#for hit in Hits_AfterNMS : print hit
# NB : Hits coordinates have not been corrected for cropping here ! Done in the next for loop
# Loop over final hits to generate ROI
for hit in Hits_AfterNMS:
#print hit
if Bool_SearchRoi: # Add offset of search ROI
hit['BBox'] = (hit['BBox'][0] + dX, hit['BBox'][1] + dY,
hit['BBox'][2], hit['BBox'][3])
# Create detected ROI
roi = Roi(*hit['BBox'])
roi.setName(hit['TemplateName'])
roi.setPosition(i) # set ROI Z-position
#roi.setProperty("class", hit["TemplateName"])
image.setSlice(i)
image.setRoi(roi)
if add_roi:
rm.add(
None, roi, i
) # Trick to be able to set Z-position when less images than the number of ROI. Here i is an digit index before the Roi Name
# Show All ROI + Associate ROI to slices
rm.runCommand("Associate", "true")
rm.runCommand("Show All with labels")
IJ.selectWindow(ImageName) # does not work always
def extractFeatures(ip, params):
sift = FloatArray2DSIFT(params)
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.)
