def doAnalysis(self):
"""
Analyse the time series motion of the features being tracked
to look for ones which are oscillating, which could indicate a
seizure
"""
nSamples = len(self.timeSeries)
nFeatures = len(self.timeSeries[0][1])
print "doAnalysis() - nSamples = %d, nFeatures = %d" % (nSamples,
nFeatures)
# Create a matrix with feature speeds in the y direction,
# and time (frame no) in the x direction.
# This means we can do an FFT on each row to get
# frequency components of each feature.
dataMat = cv.CreateMat(nFeatures, nSamples - 1, cv.CV_32FC1)
times = []
for frameNo in range(nSamples - 1):
dt = (self.timeSeries[frameNo + 1][0] -
self.timeSeries[frameNo][0]).total_seconds()
if (frameNo == 0):
times.append(dt)
else:
times.append(times[frameNo - 1] + dt)
for featNo in range(nFeatures):
ds = self.ptptdist(self.timeSeries[frameNo][1][featNo],
self.timeSeries[frameNo + 1][1][featNo])
v = ds / dt
# set the direction
#if(self.timeSeries[frameNo][1][featNo][1] >
# self.timeSeries[frameNo+1][1][featNo][1]):
# v*=-1
dataMat[featNo, frameNo] = v
#cv.ShowImage("dataMat",dataMat)
self.fftMat = cv.CreateMat(nFeatures, nSamples - 1, cv.CV_32FC1)
cv.DFT(dataMat, self.fftMat, cv.CV_DXT_ROWS)
#cv.ShowImage("fft",self.fftMat)
if (self.X11): self.doPlot(dataMat, self.fftMat, times)
# Look for the dominant frequency of each feature.
freqBinSize = 1.0 / (times[len(times) - 1] - times[0])
print "freqBinSize = %f Hz" % (freqBinSize)
self.maxAmpl = numpy.zeros((nFeatures))
self.maxFreq = numpy.zeros((nFeatures))
for featNo in range(nFeatures):
self.maxAmpl[featNo] = self.FFT_AMPL_THRESH
self.maxFreq[featNo] = 0
for freqBin in range(1, nSamples / 2):
if (self.fftMat[featNo, freqBin] > self.maxAmpl[featNo]):
self.maxAmpl[featNo] = self.fftMat[featNo, freqBin]
self.maxFreq[featNo] = freqBinSize * freqBin
self.timeSeries = []
return True
dft_A = cv.CreateMat( dft_M, dft_N, cv.CV_64FC2 )
image_Re = cv.CreateImage( (dft_N, dft_M), cv.IPL_DEPTH_64F, 1)
image_Im = cv.CreateImage( (dft_N, dft_M), cv.IPL_DEPTH_64F, 1)
# copy A to dft_A and pad dft_A with zeros
tmp = cv.GetSubRect( dft_A, (0,0, im.width, im.height))
cv.Copy( complexInput, tmp, None )
if(dft_A.width > im.width):
tmp = cv.GetSubRect( dft_A, (im.width,0, dft_N - im.width, im.height))
cv.Zero( tmp )
# no need to pad bottom part of dft_A with zeros because of
# use nonzero_rows parameter in cv.FT() call below
cv.DFT( dft_A, dft_A, cv.CV_DXT_FORWARD, complexInput.height )
cv.NamedWindow("win", 0)
cv.NamedWindow("magnitude", 0)
cv.ShowImage("win", im)
# Split Fourier in real and imaginary parts
cv.Split( dft_A, image_Re, image_Im, None, None )
# Compute the magnitude of the spectrum Mag = sqrt(Re^2 + Im^2)
cv.Pow( image_Re, image_Re, 2.0)
cv.Pow( image_Im, image_Im, 2.0)
cv.Add( image_Re, image_Im, image_Re, None)
cv.Pow( image_Re, image_Re, 0.5 )
# Compute log(1 + Mag)