def highDimQuaternionSimilarity(euler=False):
quats_h_large=m.getQuaternionSegmentsByRawData(m.readCSVfile("captures/HorizontalArmSpin-Jibran.csv"),quats.rearrangeQuatsForLatentSpaceAlgorithm(quats.rawDataFileToQuats("captures/raw/HorizontalArmSpin-Jibran"),euler))
quats_h_small=m.getQuaternionSegmentsByRawData(m.readCSVfile("captures/HorizontalArmSpinLittleCircles-Jibran.csv"),quats.rearrangeQuatsForLatentSpaceAlgorithm(quats.rawDataFileToQuats("captures/raw/HorizontalArmSpinLittleCircles-Jibran"),euler))
names = ['Big','Big','Big','Big','Big','Big','Big','Small','Small','Small','Small','Small','Small','Small','Small','Small']
weights = [1]*np.shape((quats_h_large+quats_h_small)[0])[1]
if euler:
title="Similarity Matrix using high dimensional euler angle data"
else:
title="Similarity Matrix using high dimensional quaternion data"
m.similarityMatrix(quats_h_large+quats_h_small,names,weights,"Similarity Matrix using high dimensional euler angle data")
def lowDimQuaternionSimilarity(n_components=3,euler=False):
(quats_h_large,weights_large)=quats.doPCAonQuats("captures/raw/HorizontalArmSpin-Jibran",euler,n_components=n_components)
(quats_h_small,weights_small)=quats.doPCAonQuats("captures/raw/HorizontalArmSpinLittleCircles-Jibran",euler,n_components=n_components)
segments_quats_h_large=m.getQuaternionSegmentsByRawData(m.readCSVfile("captures/HorizontalArmSpin-Jibran.csv"),quats_h_large)
segments_quats_h_small=m.getQuaternionSegmentsByRawData(m.readCSVfile("captures/HorizontalArmSpinLittleCircles-Jibran.csv"),quats_h_small)
names = ['Big','Big','Big','Big','Big','Big','Big','Small','Small','Small','Small','Small','Small','Small','Small','Small']
weights = [1]*np.shape((segments_quats_h_large+segments_quats_h_small)[0])[1] #TODO: AVERAGE THE WEIGHTS!!
if euler:
title="Similarity Matrix using low dimensional euler angle data"
else:
title="Similarity Matrix using low dimensional quaternion data"
m.similarityMatrix(segments_quats_h_large+segments_quats_h_small,names,weights,title)
def __init__(self, data, absolutePathToVideo): if type(data) == str: data = readCSVfile(data) self.graph = graph(data) self.video = videoController(absolutePathToVideo) self.coordinator = coordinator(self.graph, self.video) self.coordinator.start()
def __init__(self, split=0.5, pca_dims=3):
print "Initialising reference data..."
bar = progressbar.ProgressBar(maxval=7, widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])
bar.start(); progress = 0
self.__data_V0=m.readCSVfile("captures/VerticalArmSpin-Dan.csv")
self.__data_V1=m.readCSVfile("captures/VerticalArmSpin-Jibran.csv")
self.__data_H0=m.readCSVfile("captures/HorizontalArmSpin-Dan.csv")
self.__data_H1=m.readCSVfile("captures/HorizontalArmSpin-Jibran.csv")
self.__data_H2=m.readCSVfile("captures/HorizontalArmSpinLittleCircles-Jibran.csv")
progress += 1; bar.update(progress)
(self.__HDsegs_V0,self.__LDsegs_V0,_) = m.getHighAndLowDimSegments(self.__data_V0, n_components=pca_dims, smoothingWindow=15); progress += 1; bar.update(progress)
(self.__HDsegs_V1,self.__LDsegs_V1,_) = m.getHighAndLowDimSegments(self.__data_V1, n_components=pca_dims, smoothingWindow=25); progress += 1; bar.update(progress)
(self.__HDsegs_H0,self.__LDsegs_H0,_) = m.getHighAndLowDimSegments(self.__data_H0, n_components=pca_dims, smoothingWindow=20); progress += 1; bar.update(progress)
(self.__HDsegs_H1,self.__LDsegs_H1,_) = m.getHighAndLowDimSegments(self.__data_H1, n_components=pca_dims, smoothingWindow=20); progress += 1; bar.update(progress)
(self.__HDsegs_H2,self.__LDsegs_H2,_) = m.getHighAndLowDimSegments(self.__data_H2, n_components=pca_dims, smoothingWindow=15); progress += 1; bar.update(progress)
self.HDtraining = {'VerticalArmSpin - Dan':self.__HDsegs_V0[:(int(len(self.__HDsegs_V0)*split))],
'VerticalArmSpin - Jibran':self.__HDsegs_V1[:(int(len(self.__HDsegs_V1)*split))],
'HorizontalArmSpin - Dan':self.__HDsegs_H0[:(int(len(self.__HDsegs_H0)*split))],
'HorizontalArmSpin - Jibran':self.__HDsegs_H1[:(int(len(self.__HDsegs_H1)*split))],
'HorizontalArmSpin - Small - Jibran':self.__HDsegs_H2[:(int(len(self.__HDsegs_H2)*split))]}
self.LDtraining = {'VerticalArmSpin - Dan':self.__LDsegs_V0[:(int(len(self.__LDsegs_V0)*split))],
'VerticalArmSpin - Jibran':self.__LDsegs_V1[:(int(len(self.__LDsegs_V1)*split))],
'HorizontalArmSpin - Dan':self.__LDsegs_H0[:(int(len(self.__LDsegs_H0)*split))],
'HorizontalArmSpin - Jibran':self.__LDsegs_H1[:(int(len(self.__LDsegs_H1)*split))],
'HorizontalArmSpin - Small - Jibran':self.__LDsegs_H2[:(int(len(self.__LDsegs_H2)*split))]}
self.HDtest = {'VerticalArmSpin - Dan':self.__HDsegs_V0[(int(len(self.__HDsegs_V0)*split)):],
'VerticalArmSpin - Jibran':self.__HDsegs_V1[(int(len(self.__HDsegs_V1)*split)):],
'HorizontalArmSpin - Dan':self.__HDsegs_H0[(int(len(self.__HDsegs_H0)*split)):],
'HorizontalArmSpin - Jibran':self.__HDsegs_H1[(int(len(self.__HDsegs_H1)*split)):],
'HorizontalArmSpin - Small - Jibran':self.__HDsegs_H2[(int(len(self.__HDsegs_H2)*split)):]}
self.LDtest = {'VerticalArmSpin - Dan':self.__LDsegs_V0[(int(len(self.__LDsegs_V0)*split)):],
'VerticalArmSpin - Jibran':self.__LDsegs_V1[(int(len(self.__LDsegs_V1)*split)):],
'HorizontalArmSpin - Dan':self.__LDsegs_H0[(int(len(self.__LDsegs_H0)*split)):],
'HorizontalArmSpin - Jibran':self.__LDsegs_H1[(int(len(self.__LDsegs_H1)*split)):],
'HorizontalArmSpin - Small - Jibran':self.__LDsegs_H2[(int(len(self.__LDsegs_H2)*split)):]}
progress += 1; bar.update(progress)
bar.finish()
def allMotionslowDimRawSimilarity(n_components=3,savePlot=False,title="Similarity matrix"):
# Load files and get segments
(raw_v1,weights_v1)=m.getLowDimensionalSegments(m.readCSVfile("captures/VerticalArmSpin-Dan.csv"),n_components)
(raw_v2,weights_v2)=m.getLowDimensionalSegments(m.readCSVfile("captures/VerticalArmSpin-Jibran.csv"),n_components)
(raw_h1,weights_h1)=m.getLowDimensionalSegments(m.readCSVfile("captures/HorizontalArmSpin-Dan.csv"),n_components)
(raw_h2,weights_h2)=m.getLowDimensionalSegments(m.readCSVfile("captures/HorizontalArmSpin-Jibran.csv"),n_components)
(raw_h3,weights_h3)=m.getLowDimensionalSegments(m.readCSVfile("captures/HorizontalArmSpinLittleCircles-Jibran.csv"),n_components)
# Discard bad segments
raw_h1 = raw_h1[4:]
raw_v2 = raw_v2[1:5]
# Make name labels
namesV1 = ["V1"]*len(raw_v1)
namesV2 = ["V2"]*len(raw_v2)
namesH1 = ["H1"]*len(raw_h1)
namesH2 = ["H2"]*len(raw_h2)
namesH3 = ["H3"]*len(raw_h3)
names = namesV1+namesV2+namesH1+namesH2+namesH3
# Calculate average weights
averageWeights = [(a+b+c+d+e)/5.0 for (a,b,c,d,e) in zip(weights_v1,weights_v2,weights_h1,weights_h2,weights_h3)]
# Crunch similarity matrix
m.similarityMatrix(raw_v1+raw_v2+raw_h1+raw_h2+raw_h3,names,averageWeights,title,savePlot)
def plotSegments():
m.getLowDimensionalSegments(m.readCSVfile("captures/HorizontalArmSpin-Jibran.csv"),n_components=1,plt=True,title="Large horizontal arm spin segments")
m.getLowDimensionalSegments(m.readCSVfile("captures/HorizontalArmSpinLittleCircles-Jibran.csv"),n_components=1,plt=True,title="Small horizontal arm spin segments")
def lowDimRawSimilarity(n_components=3):
(raw_h_large,weights_large)=m.getLowDimensionalSegments(m.readCSVfile("captures/HorizontalArmSpin-Jibran.csv"),n_components)
(raw_h_small,weights_small)=m.getLowDimensionalSegments(m.readCSVfile("captures/HorizontalArmSpinLittleCircles-Jibran.csv"),n_components)
names = ['Big','Big','Big','Big','Big','Big','Big','Small','Small','Small','Small','Small','Small','Small','Small','Small']
averageWeights = [(a+b)/2.0 for (a,b) in zip(weights_large,weights_small)]
m.similarityMatrix(raw_h_large+raw_h_small,names,averageWeights,"Similarity Matrix using raw sensor data projected to the latent space")
def highDimRawSimilarity():
raw_h_large=m.getHighDimensionalSegments(m.readCSVfile("captures/HorizontalArmSpin-Jibran.csv"))
raw_h_small=m.getHighDimensionalSegments(m.readCSVfile("captures/HorizontalArmSpinLittleCircles-Jibran.csv"))
names = ['Big','Big','Big','Big','Big','Big','Big','Small','Small','Small','Small','Small','Small','Small','Small','Small']
weights = [1]*np.shape((raw_h_large+raw_h_small)[0])[1]
m.similarityMatrix(raw_h_large+raw_h_small,names,weights,"Similarity Matrix using high dimensional raw sensor data")
