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
def differenceAngleFinder(files):
global currentSegmentEndingFrame
i = 0
# movement parameters
firstStationaryAfterMovement = 0 # first stationary frame (i) after movement
isMoving = False
isQuestionablyStationary = False
centroidBefore = None
lastStationaryCentroid = None
counter = 0
peak = 0
pulseCountInQuestionablyStationary = 0
centroid = None
data = []
movingPeaks = []
# initializes lists with 'numConsecutiveDrops' 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) < numConsecutiveDrops and j < framesInChunk:
image = im.getJellyImageFromFile(files[j])
binary_image = im.getBinaryJelly(image, lowerThreshold)
area = im.findBinaryArea(binary_image)
testFrames.append(j)
testAreas.append(area)
j += 1
return testFrames, testAreas, j
# function to save out data
def saveOutData():
df = pd.DataFrame(data,
columns=[
'global frame', 'chunk frame', 'angle',
'centroid x', 'centroid y'
])
if DEBUG: print(df.head())
dataTitle = '{}_{:03}.csv'.format(chunkName, movementSegment)
df.to_csv(str(angleOutputDir / dataTitle), index=False)
testFrames, testAreas, i = reinitializeTestFramesAndAreas(i)
try:
while i < framesInChunk:
isDownturn = dm.is_downturn(0, testAreas, numConsecutiveDrops)
if isDownturn:
peak = i - numConsecutiveDrops
print("chunk: {}, i: {}, peak: {}".format(chunkName, i, peak))
# checks that peaks are within testing bounds
if peak + peak2InflectionDiff >= 0 and peak + peak2TroughDiff < framesInChunk:
troughInfile = files[peak + peak2TroughDiff]
relaxedInfile = files[peak + peak2InflectionDiff]
troughImg = im.getJellyGrayImageFromFile(troughInfile)
relaxedImg = im.getJellyGrayImageFromFile(relaxedInfile)
centroidDiff = im.getGrayscaleImageDiff_absolute(
troughImg, relaxedImg)
binaryCentroidDiff = im.getBinaryJelly(centroidDiff,
lower_bound=0.05,
upper_bound=1)
centroidRegion = im.findJellyRegion(binaryCentroidDiff)
centroid = im.findCentroid_boundingBox(centroidRegion)
if lastStationaryCentroid is None:
lastStationaryCentroid = centroid
if CONFIRMATIONIMAGES:
im.saveJellyPlot(
im.getCentroidVerificationImg(
centroidDiff, binaryCentroidDiff, centroid),
str(confirmationImagesPath /
'{}_{}_centroid.png'.format(peak, chunkName)))
if isMoving:
data.append([
peak + lastFrameOfPreviousChunk, peak, np.nan,
centroid[0], centroid[1]
])
movedBefore = isMoving
isMoving = im.distance(centroid, lastStationaryCentroid
) > movementThreshold2KeepMoving
lastStationaryCentroid = centroid
if movedBefore and not isMoving:
firstStationaryAfterMovement = i
pulseCountInQuestionablyStationary = 0
isQuestionablyStationary = True
elif isQuestionablyStationary:
data.append([
peak + lastFrameOfPreviousChunk, peak, np.nan,
centroid[0], centroid[1]
])
isMoving = im.distance(
centroid, lastStationaryCentroid
) > movementThreshold4reinitialization
if isMoving:
movingPeaks.append(peak)
isQuestionablyStationary = False
pulseCountInQuestionablyStationary += 1
if i - firstStationaryAfterMovement > numFrames2ConfirmStationary:
# now there is confirmed time after initial stationary point
if firstStationaryAfterMovement == 0:
data = []
else:
# must mutate data to take out
data = data[:
-pulseCountInQuestionablyStationary]
currentSegmentEndingFrame = i
saveSegmentVariableParams()
saveOutData()
data = []
i = firstStationaryAfterMovement
# peak2InflectionDiff, peak2TroughDiff, postPeakRefractoryPeriod, infflectionTestDiff,
# inflectionTestBinaryThreshold, and chosen SD are all static.
initialize_params(files, i)
isQuestionablyStationary = False
pulseCountInQuestionablyStationary = 0
# until count from current i to last stationary i reaches this point,
# the program is in a holding pattern of sorts.
else:
testInfile = files[peak + peak2InflectionDiff +
inflectionTestDiff]
testImg = im.getJellyGrayImageFromFile(testInfile)
if CONFIRMATIONIMAGES:
plt.imsave(
str(confirmationImagesPath /
'{}_{}_interestFrames.png'.format(
peak, chunkName)),
im.juxtaposeImages(
np.array([[
relaxedImg, testImg, peakImg, troughImg
]])))
if centroidBefore is not None:
reinitializeAreaPlot = im.distance(
centroid, centroidBefore
) > movementThreshold4newNormalizationImg
if reinitializeAreaPlot:
peakInfile = files[peak]
peakImg = im.getJellyGrayImageFromFile(
peakInfile)
peakDiff = im.getGrayscaleImageDiff_absolute(
troughImg, peakImg)
binaryPeakDiff = im.getBinaryJelly(
peakDiff, lower_bound=0.05, upper_bound=1)
averagedDynamicRangeMaskedImg = im.dynamicRangeImg_AreaBased(
relaxedImg, binaryPeakDiff, 5)
else:
peakInfile = files[peak]
peakImg = im.getJellyGrayImageFromFile(peakInfile)
peakDiff = im.getGrayscaleImageDiff_absolute(
troughImg, peakImg)
binaryPeakDiff = im.getBinaryJelly(
peakDiff, lower_bound=0.05, upper_bound=1)
averagedDynamicRangeMaskedImg = im.dynamicRangeImg_AreaBased(
relaxedImg, binaryPeakDiff, 5)
centroidBefore = centroid
if CONFIRMATIONIMAGES:
im.saveJellyPlot(
averagedDynamicRangeMaskedImg,
str(confirmationImagesPath /
'{}_{}_dynRng.png'.format(peak,
chunkName)))
testDiff = im.getGrayscaleImageDiff_absolute(
testImg, relaxedImg)
normalizedTestDiff = testDiff / averagedDynamicRangeMaskedImg
binaryDiffImg = im.getBinaryJelly(
normalizedTestDiff,
lower_bound=inflectionTestBinaryThreshold)
biggestRegion = im.findJellyRegion(binaryDiffImg)
if biggestRegion is not None:
local_com = im.findCentroid_regionProp(
biggestRegion)
zeroDegreePoint = (centroid[0], 0)
angle = dm.getAngle(zeroDegreePoint, centroid,
local_com)
if CONFIRMATIONIMAGES:
im.saveJellyPlot(
binaryDiffImg,
str(confirmationImagesPath /
'{}_{}_angle.png'.format(
peak, chunkName)),
[centroid, local_com, zeroDegreePoint])
else:
angle = np.nan
if CONFIRMATIONIMAGES:
im.saveJellyPlot(
binaryDiffImg,
str(confirmationImagesPath /
'{}_{}_angle.png'.format(
peak, chunkName)), [centroid])
data.append([
peak + lastFrameOfPreviousChunk, peak, angle,
centroid[0], centroid[1]
])
movedBefore = isMoving
isMoving = im.distance(
centroid, lastStationaryCentroid
) > movementThreshold4reinitialization
if isMoving and not movedBefore:
isQuestionablyStationary = False
lastStationaryCentroid = centroid
i += postPeakRefractoryPeriod
counter += 1
testFrames, testAreas, i = reinitializeTestFramesAndAreas(i)
else:
testFrames.pop(0)
testAreas.pop(0)
image = im.getJellyImageFromFile(files[i])
binary_image = im.getBinaryJelly(image, lowerThreshold)
area = im.findBinaryArea(binary_image)
testFrames.append(i)
testAreas.append(area)
i += 1
counter += 1
except Exception as error:
print('{} error occured.'.format(error))
print(
"chunkName: {}, index: {}, isMoving: {}, isQStat: {}, centroid: {}"
.format(chunkName, i, isMoving, isQuestionablyStationary,
str(centroid)))
raise
finally:
currentSegmentEndingFrame = i
saveSegmentVariableParams()
saveOutData()
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)
