centroidDiffMaskedOutDir = dm.makeOutDir(jellyOutDir,
'centroidDiffMask')
centroidDiffThresholdedOutDir = dm.makeOutDir(
jellyOutDir, 'centroidDiffThresholded')
centroidDiffMaskedHistogramOutDir = dm.makeOutDir(
jellyOutDir, 'centroidDiffMaskHistogram')
centroidDiff = im.getGrayscaleImageDiff_absolute(
troughImg, relaxedImg)
binaryCentroidDiff = im.getBinaryJelly(centroidDiff,
lower_bound=0.05,
upper_bound=1)
maskedImg = im.applyMask2Img(binaryCentroidDiff, relaxedImg)
jellyRegion = im.findJellyRegionWithGray(binaryCentroidDiff,
maskedImg)
maxIntensity = jellyRegion.max_intensity
minIntensity = jellyRegion.min_intensity
print(maxIntensity)
print(minIntensity)
# thresholded image off of centroid image jelly mask intensities
thresholdedGrayImg = im.getBinaryJelly(relaxedImg,
lower_bound=minIntensity,
upper_bound=maxIntensity)
# saving jelly plots
im.saveJellyPlot(
centroidDiff,
str(centroidDiffGrayOutDir /
'centroidDiffGray {} - {}.jpg'.format(
grayscaleInfile = dm.getFileFromFrameNum(relaxedFrameNum, stack)
binaryImg = im.getJellyBinaryImageFromFile(binaryInfile)
grayImg = im.getJellyGrayImageFromFile(grayscaleInfile)
print(binaryImg.shape)
print(binaryImg)
plt.imshow(binaryImg)
plt.show()
print(grayImg)
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(
def selectAverageTroughBinaryArea(fileSubset, thresholdingDir, recordingName, peaksOnBinaryImage, peak2InflectionDiff, peak2TroughDiff):
maxIntensities = []
minIntensities = []
for i, peak in enumerate(peaksOnBinaryImage):
if peak + peak2InflectionDiff >= 0 and peak + peak2TroughDiff < len(fileSubset):
troughInfile = fileSubset[peak + peak2TroughDiff]
relaxedInfile = fileSubset[peak + peak2InflectionDiff]
troughImg = im.getJellyGrayImageFromFile(troughInfile)
relaxedImg = im.getJellyGrayImageFromFile(relaxedInfile)
centroidDiff = im.getGrayscaleImageDiff_absolute(troughImg, relaxedImg)
binaryCentroidDiff = im.getBinaryJelly(centroidDiff, lower_bound=0.05)
maskedImg = im.applyMask2Img(binaryCentroidDiff, relaxedImg)
jellyRegion = im.findJellyRegionWithGray(binaryCentroidDiff, maskedImg)
maxIntensity = jellyRegion.max_intensity
minIntensity = jellyRegion.min_intensity
maxIntensities.append(maxIntensity)
minIntensities.append(minIntensity)
indensityDifference = np.mean(maxIntensities) - np.mean(minIntensities)
lowerThreshold = np.mean(minIntensities) + (0.1 * indensityDifference)
print('peak2TroughDiff: {}'.format(peak2TroughDiff))
while True:
troughAreas = []
for peak in peaksOnBinaryImage:
peakInfile = fileSubset[peak]
troughInfile = fileSubset[peak+peak2TroughDiff]
peakImg = im.getJellyGrayImageFromFile(peakInfile)
troughImg = im.getJellyGrayImageFromFile(troughInfile)
binaryPeakImg = im.getBinaryJelly(peakImg, lowerThreshold)
binaryTroughImg = im.getBinaryJelly(troughImg, lowerThreshold)
im.saveJellyPlot(im.juxtaposeImages(np.array([[binaryPeakImg, binaryTroughImg]])),
(thresholdingDir / '{}_thresholdVerification_{}.png'.format(recordingName, peak)))
jellyTroughBinaryArea = im.findBinaryArea(binaryTroughImg)
troughAreas.append(jellyTroughBinaryArea)
if CHIME: dm.chime(MAC, 'input time')
print('average trough area: {}, sd of trough areas: {}'.format(np.mean(troughAreas), np.std(troughAreas)))
print('Change thresholds: ')
print('select \'1\' to change {} which is {}'.format('lowerThreshold', lowerThreshold))
print('select \'2\' to remove a peak from peaksOnBinaryImage')
print('or \'3\' to continue.')
selectionVar = dm.getSelection([1, 2, 3])
if selectionVar == '1':
lowerThreshold = dm.reassignFloatVariable(lowerThreshold, 'lowerThreshold')
dm.replaceDir(thresholdingDir)
elif selectionVar == '2':
print('peaksOnBinaryImage: {}'.format(peaksOnBinaryImage))
index2Pop = int(dm.getSelection(list(range(len(peaksOnBinaryImage)))))
peaksOnBinaryImage.pop(index2Pop)
else:
return np.mean(troughAreas)
