def saveMimicryEmbedding(model, outputFile, nChannels):
tm = model.getTrackModel()
trackIDs = tm.trackIDs(True)
embSize = 14
sliceNum = 0
cutoff = 10
embedding = ImageStack(embSize, embSize)
for trackID in trackIDs:
tracks = tm.trackSpots(trackID)
# Let's sort them by frame.
trackList = ArrayList(tracks)
trackLength = len(trackList)
if (trackLength <= cutoff):
trackPic = FloatProcessor(nChannels, trackLength)
trackPix = trackPic.getPixels()
#print(len(trackPix))
idxSpot = 0
for spot in trackList:
idxChan = 0
for iChan in range(nChannels):
trackPix[idxSpot * (nChannels) +
idxChan] = spot.getFeature('MEAN_INTENSITY%02d' %
(iChan + 1))
idxChan += 1
idxSpot += 1
trackPic = ImagePlus("slice", trackPic)
# padding
IJ.run(
trackPic, "Canvas Size...", "width=" + str(embSize) +
" height=" + str(embSize) + " position=Center zero")
trackPic = trackPic.getProcessor()
embedding.addSlice(str(sliceNum), trackPic)
sliceNum += 1
# linear interpolation
embedding = ImagePlus("embedding", embedding)
IJ.run(
embedding, "Size...", "width=" + str(embSize * 2) + " height=" +
str(embSize * 2) + " depth=" + str(sliceNum + 1) +
" constrain average interpolation=Bilinear")
outputFile.replace('.csv', '_mimicry_embd.tif')
IJ.saveAs(embedding, "Tiff", outputFile)
embedding.close()
return None
