import utils.kinect.angleExtraction as ae
import utils.stitching.stitching.quantization as qu
import matplotlib.pyplot as plt
import utils.oldKinectExtractor as ke
import utils.stitching.stitching as st
import utils.stitching.stitching.mineByPattern as mbp
from numpy import linalg as LA
import copy
fileName = 'inputs/ran_5_2_14_840.skl'
joint = 'AnkleRight_X'
time, values = ae.getAngleVec(fileName, joint, True)
disFactor=0.2
numOfClusters = 6
minimalCluster = 10
fracs = ke.clusterByTime(time, values, False, minimalCluster)
#originalFracs = copy.deepcopy(fracs)
prob = 0.05
fracs = ke.filterOutliers(fracs, False, prob)
i=0
parts, kuku = ke.cleanFracs(fracs, False)
pattern = [14, 15, 17, 19, 24, 28, 33, 33, 33, 33, \
33, 33, 28, 24, 19, 17, 15, 14,\
12, 12, 14, 15, 16, 17.5, 17.5, 17.5, \
16, 15, 14, 13, 12, 12]
i=1
framSize = 4
groupSize = framSize**2
minedParts = []
import numpy as np import algorithm.quantization as qu import utils.utils as pe import copy import utils.MovingAverage as ma 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 = []
import utils.angleExtraction as ae
import matplotlib.pyplot as plt
import utils.oldKinectExtractor as ke
import utils.stitching.stitching.quantization as qu
import utils.stitching.stitching as st
fileName = 'inputs/ran_5_2_14_840.skl'#asc_gyro_l.skl'
joint = 'AnkleRight_X'
time, angles, kuku = ae.getAngleVec(fileName, joint, False)
#plt.plot(angles)
minimalCluster=15
fracs = ke.clusterByTime(time, angles, False, minimalCluster)
prob = 0.3
fracs = ke.filterOutliers(fracs, False, prob)
cleanedParts, kuku = ke.cleanFracs(fracs, False)
angles = [item for sublist in cleanedParts for item in sublist]
#bins = qu.getEquallyWeighetedBins(angles, alphabetSize)
#wholeStr = qu.createStr(bins, angles)
#vec = qu.fromStr2Vec(bins, wholeStr)
#ngrams_statistics_sorted = qu.getSortedNgrams(wholeStr, n)
atoms = [
[14, 15, 17, 19, 24, 28, 33, 33, 33, 33],
[33, 33, 33, 33, 28, 24, 19, 17, 15, 14],
[15, 14, 12, 12, 14, 15, 16, 17.5, 17.5, 17.5],
[17.5, 17.5, 16, 15, 14, 13, 12, 12, 14, 15]
]
"""
str = qu.appendAtom(atoms[0], atoms[1])
subjects = []
sizeOfAtom = 10
minimalCluster = sizeOfAtom
start =0
for end in seperators:
#end = 8466828
tmpTime = []
tmpAngles = []
for t, a in zip(time, angles):
if t > start and t < end:
tmpTime.append(t)
tmpAngles.append(a)
start = end
fracs = ke.clusterByTime(tmpTime, tmpAngles, False, minimalCluster)
originalFracs = copy.deepcopy(fracs)
prob = 0.05
fracs = ke.filterOutliers(fracs, False, prob)
i=0
cleanedParts, kuku = ke.cleanFracs(fracs, False)
"""
for (t, a), part in zip(originalFracs,cleanedParts):
frameSize = math.ceil(np.sqrt(len(fracs)))
curr = fig.add_subplot(frameSize,frameSize,i+1)
plt.title(str(i))
plt.xlabel('Time in miliseconds')
plt.ylabel('Right knee angle in degrees')
curr.plot(xrange(len(a)), a, c='b')
curr.plot(part, c='g')
i+=1
