maskedImg = im.applyMask2Img(binaryImg, grayImg) jellyRegion = im.findJellyRegionWithGray(binaryImg, grayImg) maxIntensity = jellyRegion.max_intensity minIntensity = jellyRegion.min_intensity print(maxIntensity) print(minIntensity) thresholdedGrayImg = im.getBinaryJelly(grayImg, lower_bound=minIntensity, upper_bound=maxIntensity) print(maskedImg) plt.hist(maskedImg.ravel()) plt.show() # im.saveJellyPlot( maskedImg, str(jellyOutDir / 'maskedImg_{}_{}.jpg'.format(stack.name, relaxedFrameNum))) im.saveJellyPlot( thresholdedGrayImg, str(jellyOutDir / 'thresholdwithmask_{}_{}.jpg'.format( stack.name, relaxedFrameNum))) im.saveHistogram( maskedImg, str(jellyOutDir / 'histogram_{}_{}.jpg'.format(stack.name, relaxedFrameNum)))
binaryCentroidDiff, str(centroidDiffBinaryOutDir / 'centroidDiffBinary {} - {}.jpg'.format( stack.name, peak - prePeakInflectionDiff))) im.saveJellyPlot( maskedImg, str(centroidDiffMaskedOutDir / 'maskedImg_{}_{}.jpg'.format( stack.name, peak - prePeakInflectionDiff))) im.saveJellyPlot( thresholdedGrayImg, str(centroidDiffThresholdedOutDir / 'thresholded_{}_{}.jpg'.format( stack.name, peak - prePeakInflectionDiff))) im.saveHistogram( maskedImg, str(centroidDiffMaskedHistogramOutDir / 'histogram4mask_{}_{}.jpg'.format( stack.name, peak - prePeakInflectionDiff))) # dealing with inflection thresholding testDiffImages = [] aggregateDiffImages = [] for i in range(1, 9): testInfile = files[peak - prePeakInflectionDiff + i] testImg = im.getJellyGrayImageFromFile(testInfile) testDiff = im.getGrayscaleImageDiff_absolute( testImg, relaxedImg) testDiffImages.append(testDiff) for i in range(len(testDiffImages)):