def extract(self, fileName):
f = open(fileName, 'r')
headers = f.readline().split()
headers = jm.getFileHeader(headers)
jointsIndices = self.extractor.getJointsIndices(headers)
frontDirections = []
centers = []
sholderVec = []
for line in f:
lineInFloats=[float(v) for v in line.split()]
centers.append(ae.calcJointsAverage(lineInFloats, jointsIndices))
shouldersVecOnXZPlan = ae.getVecBetweenJoints(headers, lineInFloats,\
'ShoulderRight_X', 'ShoulderLeft_X')
#sholderVec.append(shouldersVecOnXZPlan)
frontDirections.append([shouldersVecOnXZPlan[2], 0 , -shouldersVecOnXZPlan[0]])
#frontDirections.append([1,0,0])
frontDirections = zip(*ma.partsmovingAverage(zip(*frontDirections), 50, 1))
movingDirections = np.diff(centers, axis=0)
movingDirections = zip(*ma.partsmovingAverage(zip(*movingDirections), 50, 1))
advancements = []
for front, move in zip(frontDirections[:-1], movingDirections):
if np.abs(front[0])/np.abs(front[2]) > 2:
front = [ae.length(front),0,0]
product = np.dot(front, move)
advancements.append(product)
return advancements
"""
import utils.utils as ut
import utils.MovingAverage as ma
file = 'AMCs/598.amc'
joint = 'rtibia'
list = getAMCperiod(joint, file)
stride = list[112:251]
list = ut.alignByMax(stride)
#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')
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
