def getBinaryAreas(filesSubset, lowerThreshold, DEBUG = False):
binaryImageAreas = []
for i in range(len(filesSubset)):
if i % 100 == 0 and DEBUG: print("recieved areas up to pulse: {}".format(i))
jellyimage = im.getJellyImageFromFile(str(filesSubset[i]))
jellyimagebinary = im.getBinaryJelly(jellyimage, lowerThreshold)
jellyBinaryArea = im.findBinaryArea(jellyimagebinary)
binaryImageAreas.append(jellyBinaryArea)
return binaryImageAreas
def downturnFinder(files, refactoryPeriod, lowerThresh, numberOfConsecutiveDrops, peak2InflectionDiff, peak2TroughDiff, DEBUG = False):
print('searching for peaks (downturnfinder)')
i = 0
numFiles = len(files)
peakIndicies = []
# initializes lists with 'numberOfConsecutiveDrops' of files
def reinitializeTestFramesAndAreas(j):
testFrames = [] # this list should never be more than 5 entries long, ex. [51, 52, 53, 54, 55]
testAreas = [] # this list should never be more than 5 entries long, ex. [253, 255, 256, 255, 255]
while len(testFrames) < numberOfConsecutiveDrops and j < numFiles:
image = im.getJellyImageFromFile(files[j])
binary_image = im.getBinaryJelly(image, lowerThresh)
area = im.findBinaryArea(binary_image)
testFrames.append(j)
testAreas.append(area)
j += 1
return testFrames, testAreas, j
testFrames, testAreas, i = reinitializeTestFramesAndAreas(i)
while i < numFiles:
isDownturn = dm.is_downturn(0, testAreas, numberOfConsecutiveDrops)
if DEBUG: print('i: {}, isDownturn: {}, testAreas: {}, testFrames: {}'.format(i, isDownturn, testAreas, testFrames))
if isDownturn:
peak = i - numberOfConsecutiveDrops
if peak + peak2InflectionDiff >= 0 and peak + peak2TroughDiff < numFiles:
peakIndicies.append(peak)
i += refactoryPeriod
testFrames, testAreas, i = reinitializeTestFramesAndAreas(i)
else:
testFrames.pop(0)
testAreas.pop(0)
image = im.getJellyImageFromFile(files[i])
binary_image = im.getBinaryJelly(image, lowerThresh)
area = im.findBinaryArea(binary_image)
testFrames.append(i)
testAreas.append(area)
i += 1
return peakIndicies
def reinitializeTestFramesAndAreas(j):
testFrames = [] # this list should never be more than 5 entries long, ex. [51, 52, 53, 54, 55]
testAreas = [] # this list should never be more than 5 entries long, ex. [253, 255, 256, 255, 255]
while len(testFrames) < numberOfConsecutiveDrops and j < numFiles:
image = im.getJellyImageFromFile(files[j])
binary_image = im.getBinaryJelly(image, lowerThresh)
area = im.findBinaryArea(binary_image)
testFrames.append(j)
testAreas.append(area)
j += 1
return testFrames, testAreas, j
def autoLowerThreshold(averageTroughBinaryArea, peak2TroughDiff, peaksOnBinaryImage, fileSubset, thresholdingDir, recordingName):
# completed automated based on averageTroughBinaryArea
thresholdStep = 0.005
chosenThreshold = 0.05
testTroughAverage = averageTroughBinaryArea + 1
while testTroughAverage > averageTroughBinaryArea:
chosenThreshold += thresholdStep
troughAreas = []
for i, peak in enumerate(peaksOnBinaryImage):
if peak + peak2TroughDiff < len(fileSubset):
troughInfile = fileSubset[peak + peak2TroughDiff]
troughImg = im.getJellyGrayImageFromFile(troughInfile)
binaryTroughImg = im.getBinaryJelly(troughImg, chosenThreshold)
jellyTroughBinaryArea = im.findBinaryArea(binaryTroughImg)
troughAreas.append(jellyTroughBinaryArea)
testTroughAverage = np.mean(troughAreas)
print('chosenThreshold: {} (test area, {}; target area, {})'.format(chosenThreshold, testTroughAverage,
averageTroughBinaryArea))
for i, peak in enumerate(peaksOnBinaryImage):
if peak + peak2TroughDiff < len(fileSubset):
peakInfile = fileSubset[peak]
troughInfile = fileSubset[peak + peak2TroughDiff]
peakImg = im.getJellyGrayImageFromFile(peakInfile)
troughImg = im.getJellyGrayImageFromFile(troughInfile)
binaryPeakImg = im.getBinaryJelly(peakImg, chosenThreshold)
binaryTroughImg = im.getBinaryJelly(troughImg, chosenThreshold)
im.saveJellyPlot(im.juxtaposeImages(np.array([[binaryPeakImg, binaryTroughImg]])),
(thresholdingDir / '{}_thresholdVerification_{}.png'.format(recordingName, peak)))
return chosenThreshold
prePeakInflectionDiff = None
postPeakTroughDiff = None
postInflectionTestDiff = 5
initialRefracPeriod = 60 # number of frames needed in initial assessment of peaks. More accurate comes after peak interval testing.
postPeakRefractoryPeriod = None
movementThreshold4reinitialization = 10
thresholdBinaryAreaForPeak = None
binaryImageAreas = []
for file in files:
print(file.name)
jellyimage = im.getJellyImageFromFile(str(file))
jellyimagebinary = im.getBinaryJelly(jellyimage, lowerThreshold,
upperThreshold)
jellyBinaryArea = im.findBinaryArea(jellyimagebinary)
binaryImageAreas.append(jellyBinaryArea)
# gets peak frame nums from binaryImageAreas
peaksOnBinaryImage = am.downturnFinder(files, initialRefracPeriod,
lowerThreshold, upperThreshold)
print(peaksOnBinaryImage)
troughsOnBinaryImage = dm.getTroughs(binaryImageAreas)
print(troughsOnBinaryImage)
prePeakInflectionDiff = dm.getLikelyInflectionDiff(binaryImageAreas,
peaksOnBinaryImage)
print(prePeakInflectionDiff)
prePeakInflectionDiff += 5
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)
