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)):
