def getFeatureVec(fileName, chopFactor, firstRun=False, joints=None):
headers = open(fileName, "r").readline().split()
# bug in the files
headers = jm.getFileHeader(headers)
ver = jm.getVersion(headers)
featuresNames = []
vec = []
jointsHeaders = headers[2:-4]
for i, h in enumerate(jointsHeaders): # drop timestamp, frameNum and floor
if i % 4 == 3:
continue
# time, relJoints = ae.getRelative2AncestorPosition(fileName, h, ver)
if i % 4 != 0:
continue
if not joints is None and not h in joints:
continue
try: # for joints that don't have father and grandfather
time, _, angles, _ = ae.getAngleVec(fileName, h, False, ver)
if len(angles) == 0:
continue
except Exception, e: # joint without a father
continue
if firstRun:
print "getAngleVec", h, len(vec)
v, f = analyzeData(time, angles, h.split("_")[0] + " angle ")
vec += v
featuresNames += f
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 utils.kinect.angleExtraction as ae import utils.stitching.stitching.quantization as qu import matplotlib.pyplot as plt fileName = "inputs/ran_5_2_14_840.skl" joint = "AnkleRight_X" time, angles = ae.getAngleVec(fileName, joint, True) disFactor = 0.05 numOfClusters = 12 res = qu.doRambamAlgo(time, angles, numOfClusters, disFactor) plt.figure() plt.plot(res) plt.show()
import matplotlib.pyplot as plt import utils.kinect.angleExtraction as ae import utils.oldKinectExtractor as ke import algorithm.stitching as st 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
import utils.kinect.angleExtraction as ae
import matplotlib.pyplot as plt
import utils.oldKinectExtractor as ex
import itertools
import numpy as np
fileName = 'inputs/alon_multi_right.skl'#'inputs/asc_gyro_l.skl'
joint = 'AnkleRight_X'
time, angles, weights = ae.getAngleVec(fileName, joint, False)
startGrade=0.93
minimalOverlap=10
maximalOverlap=50
lengthFactor=0#-0.4
merged, des, fracs = ex.stitchKinect(time, angles, weights, True, startGrade,
minimalOverlap, maximalOverlap, lengthFactor)
time, values = zip(*fracs)
sortingIndices = sorted(des.keys(), key=lambda k: des[k][0])
timeLine = []
for i in sortingIndices:
timeLine = timeLine[:des[i][0]] + time[i]
#timeLine = list(itertools.chain(*time))
mat = {}
file = open(fileName, 'r')
headersStr = file.readline()
headers = headersStr.split()
for line in file:
splited = line.split()
mat[float(splited[0])] = splited[1:]
