import algorithm.partitionizing as prt from tsp_solver.greedy import solve_tsp import algorithm.mineByPattern as mp #Reading from file fileName = 'myKinect/v2RanLong.skl' joint = 'KneeLeft_X' time, frameNumbers, angles= ae.getAngleVec(fileName, joint, True, 'NEW') #Extracting clean fractions minimalCluster=20 fracs = ke.clusterByTime(time, frameNumbers, angles, False, minimalCluster) prob = 0.1 fracs = ke.filterOutliers(fracs, False, prob) i=0 cleanedParts, _ = ke.cleanFracs(fracs, False, 5, 1.5) st.plotParts(cleanedParts) #Creating pattern to mine for lenOfCycle = 35 pattern = mp.createFlippedUpattern(angles, lenOfCycle, 3) #Mining the pattern from the input fig = plt.figure() framSize= np.ceil(np.sqrt(len(cleanedParts))) minedParts = [] minedPartsAsList = [] dises = [] threshold = 0 sizeFactor=2 lengths = [] for index, part in enumerate(cleanedParts):
for t, a in zip(time, angles):
if np.abs(lastT - t) < 5:
currTime.append(t)
currAngles.append(a)
else:
if len(currAngles)>100:
marginSize = int(len(currTime)*0.1)
currAngles = currAngles[marginSize:-marginSize]
#fracs.append((currTime, currAngles))
anglesSplited.append(currAngles)
currTime = [t]
currAngles = [a]
lastT = t
#fracs = ke.clusterByTime(time, angles, False, minimalCluster)
#cleanedParts, kuku = ke.cleanFracs(fracs, False)
st.plotParts(anglesSplited)
periods =[]
strides = []
stances = []
swings = []
for cluster in anglesSplited:
maximaOrder=27
clusteringGranularity=0.5
breaked = part.breakToPeriods(cluster,maximaOrder, clusteringGranularity)
for cycle in breaked:
if len(cycle)>80 and len(cycle)<180:
#periods.append(cycle)
strides.append(cycle)
min = np.argmin(cycle)
stances.append(cycle[:min])
swings.append(cycle[min:])
