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 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()
upperThreshold = 0.9 # just used for testing initially
lowerThreshold = 0.1 # just used for testing initially
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)