def evalParams(m1=MEAN_COEFF, m2=STD_COEFF, epsilon=EPSILON_FACTOR,gap=GAP_FACTOR, overlap=OVERLAP_FACTOR):
sum = 0
for stitch in xrange(stitchesNum):
data = []
tags = []
for subject in subjects:
for index in xrange(8):
try:
input = getAMCInput(joint, subject, index)
except:
continue
parts = st.createParts(input, partsAmount)
stitched = st.stitch(parts, m1, m2, epsilon,gap, overlap)
#plt.figure()
#plt.plot(stitched)
periods = pr.breakToPeriods(stitched)
periods = ut.alignByMaxMany(periods)
periods = inter.getUniformSampledVecs(periods, 100)
data = data + periods
tags = tags + [subject]*len(periods)
#st.plotParts(periods)
cl = KNeighborsClassifier()
cl.n_neighbors = 5
cl.weights = 'distance'
testSize = 1
score = crossValidate(cl, data, tags, testSize, testAmount)
#print str(m2)+' '+ str(score)
sum+=score
score = float(sum)/stitchesNum
scores[m1, m2] = score
return score
def stitchByDensity(time, angles, plot=False, startGrade=0.93, \
minimalOverlap=10, maximalOverlap=200, lengthFactor=0, density=10):
fracs = clusterByTime(time, angles, plot)
fracs = filterOutliers(fracs, plot)
cleanedParts, originalParts = cleanFracs(fracs, plot)
stitchedParts, partDescriptors = loop.stitch(cleanedParts, startGrade, minimalOverlap, \
maximalOverlap, lengthFactor, density)
return cleanedParts, stitchedParts, partDescriptors
def stitchKinect(time, angles, weights=None, plot=False, startGrade=0.93, \
minimalOverlap=10, maximalOverlap=200, lengthFactor=0, density=10):
if(plot):
plt.figure()
plt.scatter(time, angles, c='yellow', label='Low confedence level')
if(weights is not None):
time, angles = removeLowConfedence(time, angles, weights, plot)
fracs = clusterByTime(time, angles, plot)
fracs = filterOutliers(fracs, plot)
cleanedParts, originalParts = cleanFracs(fracs, plot)
parts, partDescriptors = loop.stitch(cleanedParts, startGrade, minimalOverlap, \
maximalOverlap, lengthFactor, density)
if(plot and len(parts) != 0):
plt.figure()
plt.plot(parts[-1])
loop.plotDesBypart(originalParts, cleanedParts, partDescriptors)
loop.plotReconstruction(cleanedParts, partDescriptors[-1])
plt.show()
#an.animate( parts[-1])
return parts[-1], partDescriptors[-1], fracs
#list = ut.alignByMax(list)
noiseStdFactor = 0.04
amplitude = np.max(list) - np.min(list)
var = (amplitude*noiseStdFactor)**2
print var
partsAmount = 16
noisy_parts = createParts(list, True, partsAmount, var)
parts = ma.partsmovingAverage(noisy_parts)
frameSize = math.ceil(np.sqrt(len(parts)))
fig = plt.figure()
for i,part in enumerate(parts):
curr = fig.add_subplot(frameSize, frameSize, i+1)
curr.plot(part)
merged, mergedDes = loop.stitch(parts)
print(len(merged))
bestFeatures = merged[-1]
des = mergedDes[-1]
outOff, listOff = ut.alignByBig(bestFeatures, list)
plt.figure()
plt.plot(xrange(listOff, listOff+len(list)), list, color='b')
plt.plot(xrange(outOff, outOff+len(bestFeatures)), bestFeatures, color='r')
sortingIndices = sorted(range(len(des)), key=lambda k: des[k][1])
frameSize = math.ceil(np.sqrt(len(sortingIndices)))
fig = plt.figure()
for step in xrange(1, len(sortingIndices)+1):
curr = fig.add_subplot(frameSize, frameSize, step)
for addedIndex in xrange(step):
partNum = sortingIndices[addedIndex]
def evalParams(partsAmount):
noiseStdFactor = 0.02
stances = {}
swings = {}
for subject in subjects:
for index in xrange(8):
try:
input = getAMCInput(joint, subject, index)
except:
continue
amplitude = np.max(input) - np.min(input)
var = (amplitude*noiseStdFactor)**2
for stitch in xrange(stitchesNum):
try:
noisy_parts = st.createParts(input, False, partsAmount, var)
except:
continue
parts = ma.partsmovingAverage(noisy_parts)
stitched_parts, des = loop.stitch(parts)
if(len(stitched_parts) == len(parts)):
continue
stitched = stitched_parts[-1]
input = stitched
tmpSwings, tmpStances = getSwingsAndStances(input)
key = (stitch, subject)
for stanceLen, swingLen in zip(tmpStances, tmpSwings):
if(stanceLen > 55):
stances[key] = stances.get(key, []) + [stanceLen]
if(swingLen > 35):
swings[key] = swings.get(key, []) + [swingLen]
stancesCV = []
swingsCV = []
strideCV = []
strideLengths = {}
stancesLengths = {}
swingsLengths = {}
for stitch in xrange(stitchesNum):
for subject in subjects:
key = (stitch, subject)
stance = None
if not(key in stances and key in swings):
continue
stance = stances[key]
cv = getCV(stance)
if not math.isnan(cv) and 0 < cv :
stancesCV.append(cv)
stancesLengths[subject] = stancesLengths.get(subject, []) + stance
swing = swings[key]
cv = getCV(swing)
if not math.isnan(cv) and 0 < cv :
swingsCV.append(cv)
swingsLengths[subject] = swingsLengths.get(subject, []) + swing
strideLength = [x+y for x,y in zip(stance, swing)]
cv = getCV(strideLength)
if not math.isnan(cv) and 0 < cv :
strideCV.append(cv)
strideLengths[subject] = strideLengths.get(subject, []) + strideLength
stancesLengths = [stancesLengths[subject] for subject in stancesLengths]
swingsLengths = [swingsLengths[subject] for subject in swingsLengths]
strideLengths = [strideLengths[subject] for subject in strideLengths]
strideReference = [115.61538461538461, 113.66666666666667, 116.11111111111111, 126.375]
strideMeans = [np.mean(seq) for seq in strideLengths]
finalGrade = np.mean([np.abs(y-x)/x for x,y in zip(strideReference, strideMeans)])
stanceCVmean = np.mean(stancesCV)
swingCVmean = np.mean(swingsCV)
strideCVsMean = np.mean(strideCV)
titles = ['Subject: ' + str(x) for x in subjects]
#plotParts(stancesLengths, 'Stance number', 'Stance time(in frames)', titles)
#plotParts(swingsLengths, 'Swing number', 'Swing time(in frames)', titles)
#plotParts(strideLengths, 'Stride number', 'Stride time(in frames)', titles)
print 'partsAmount', partsAmount
print stanceCVmean, [np.mean(seq) for seq in stancesLengths]
print swingCVmean, [np.mean(seq) for seq in swingsLengths]
print strideCVsMean, strideMeans
return finalGrade
import numpy as np import numpy.random as rnd import matplotlib.pyplot as plt from utils.vicon.amcParser import getAMCperiod from utils.stitching.stitching import MAXIMA_ORDER, CLUSTER_COEFF, plotParts, createParts import utils.stitching.stitching as loop import utils.periodAnalysisUtils as ut from operator import add, sub import utils.LPF as LPF file = 'AMCs/598.amc' joint = 'root' list = getAMCperiod(joint, file) list = list[:538] #stride = list[112:251] #list = ut.alignByMax(stride) #list = ut.alignByMax(list) amplitude = np.max(list) - np.min(list) parts = createParts(list, True, 9, amplitude/10) merged = loop.stitch(parts) merged.sort() print(len(merged)) #out = ut.alignByMax(merged[-1]) out = LPF.clean(merged[-1]) outOff, listOff = ut.alignByBig(out, list) plt.figure() plt.plot(xrange(listOff, listOff+len(list)), list) plt.plot(xrange(outOff, outOff+len(out)), out) plt.show()
import utils.stitching.stitching as loop
fileName = 'inputs/asc_gyro_l.skl'#alon_multi_right.skl
joint = 'AnkleRight_X'
time, angles = ae.getAngleVec(fileName, joint, True)
startGrade=0.93
minimalOverlap=10
maximalOverlap=200
lengthFactor=0
density = 2
parts, stitchedParts, partDescriptors = ex.stitchByDensity(time, angles, False,
startGrade, minimalOverlap, maximalOverlap, lengthFactor, density)
for des in partDescriptors:
loop.plotDes(parts, des)
minimalOverlap=5
maximalOverlap=15
density = 5
stitchedParts, partDescriptors = loop.stitch(stitchedParts, startGrade, minimalOverlap, \
maximalOverlap, lengthFactor, density)
for des in partDescriptors:
loop.plotDes(parts, des)
"""
fracs = ex.clusterByTime(time, angles, False)
fracs = ex.filterOutliers(fracs, False)
cleanedParts, originalParts = ex.cleanFracs(fracs, False)
parts, partDescriptors = loop.stitch(cleanedParts, startGrade, minimalOverlap, \
maximalOverlap, lengthFactor, density)
"""
plt.figure()
plt.plot(stitchedParts[-1])
plt.show()