def buildHistogram(path, level):
# Read in vocabulary & data
voc = utils.loadDataFromFile("Data/voc.pkl")
Data = utils.readImages(path)
# Transform each feature into histogram
featureHistogram = []
labels = []
index = 0
for oneImage in Data:
featureHistogram.append(
voc.buildHistogramForEachImageAtDifferentLevels(oneImage, level))
return featureHistogram
def buildHistogram(path, level):
# Read in vocabulary & data
voc = utils.loadDataFromFile("Data/voc.pkl")
trainData = utils.readImages("images/"+path)
# Transform each feature into histogram
featureHistogram = []
labels = []
index = 0
for oneImage in trainData:
featureHistogram.append(voc.buildHistogramForEachImageAtDifferentLevels(oneImage, level))
labels.append(oneImage.label)
index += 1
utils.writeDataToFile("Data/"+path+"HistogramLevel" +str(level)+ ".pkl", featureHistogram)
utils.writeDataToFile("Data/"+path+"labels.pkl", labels)
def main():
# 1) build histograms at level 1
buildHistogram("testing", 2)
buildHistogram("training", 2)
# 2) calculate EMD distance matrix
trainData = np.array(
utils.loadDataFromFile("Data/trainingHistogramLevel2.pkl"))
trainLabels = utils.loadDataFromFile("Data/traininglabels.pkl")
testData = np.array(
utils.loadDataFromFile("Data/testingHistogramLevel2.pkl"))
testLabels = utils.loadDataFromFile("Data/testinglabels.pkl")
trainData = trainData[::, 300:1500:]
testData = testData[::, 300:1500:]
# calculate EMD distance
allData = np.vstack((trainData, testData))
row, column = allData.shape
aligned_allDistance = np.zeros((row, row))
unaligned_allDistance = np.zeros((row, row))
for i in range(0, row, 1):
tempOne = allData[i].reshape(4, 300)
for j in range(i + 1, row, 1):
tempTwo = allData[j].reshape(4, 300)
unalignDis, alignedDis = EarthMoverDistance.C_EMD(
tempOne, tempTwo, "EMDone")
aligned_allDistance[i][j] = alignedDis
aligned_allDistance[j][i] = alignedDis
unaligned_allDistance[i][j] = unalignDis
unaligned_allDistance[j][i] = unalignDis
print "(" + str(i) + "," + str(j) + "): " + str(
alignedDis) + "\t" + str(unalignDis)
# 3) classify
allLabels = trainLabels + testLabels
train, test = utils.randomGenerateIndices(allLabels, 0.5)
aligned_accuracyDict = {}
unaligned_accuracyDict = {}
for type in ["rbf", "lap", "id", "isd"]:
aligned_accuracyDict[type] = []
unaligned_accuracyDict[type] = []
for type in ["rbf", "lap", "id", "isd"]:
alignedAccuracy = classification.multiClassSVM(aligned_allDistance,
train, test, allLabels,
type)
unalignedAccuracy = classification.multiClassSVM(
unaligned_allDistance, train, test, allLabels, type)
aligned_accuracyDict[type].append(alignedAccuracy)
unaligned_accuracyDict[type].append(unalignedAccuracy)
print " "
print "Aligned ----------"
for type in ["rbf", "lap", "id", "isd"]:
alignedTest = aligned_accuracyDict[type]
alignedTest = np.array(alignedTest)
mean = np.mean(alignedTest)
std = np.std(alignedTest)
print type + ": " + str(mean) + u"\u00B1" + str(std)
print " "
print "Unaligned --------------"
for type in ["rbf", "lap", "id", "isd"]:
alignedTest = unaligned_accuracyDict[type]
alignedTest = np.array(alignedTest)
mean = np.mean(alignedTest)
std = np.std(alignedTest)
print type + ": " + str(mean) + u"\u00B1" + str(std)
def main():
# 1) build histograms at level 1
buildHistogram("testing", 2)
buildHistogram("training", 2)
# 2) calculate EMD distance matrix
trainData = np.array(utils.loadDataFromFile("Data/trainingHistogramLevel2.pkl"))
trainLabels = utils.loadDataFromFile("Data/traininglabels.pkl")
testData = np.array(utils.loadDataFromFile("Data/testingHistogramLevel2.pkl"))
testLabels = utils.loadDataFromFile("Data/testinglabels.pkl")
trainData = trainData[::, 300:1500:]
testData = testData[::, 300:1500:]
# calculate EMD distance
allData = np.vstack((trainData, testData))
row, column = allData.shape
aligned_allDistance = np.zeros((row, row))
unaligned_allDistance = np.zeros((row, row))
for i in range(0, row, 1):
tempOne = allData[i].reshape(4, 300)
for j in range(i+1, row, 1):
tempTwo = allData[j].reshape(4, 300)
unalignDis, alignedDis = EarthMoverDistance.C_EMD(tempOne, tempTwo, "EMDone")
aligned_allDistance[i][j] = alignedDis
aligned_allDistance[j][i] = alignedDis
unaligned_allDistance[i][j] = unalignDis
unaligned_allDistance[j][i] = unalignDis
print "("+str(i)+","+str(j)+"): "+str(alignedDis) +"\t"+ str(unalignDis)
# 3) classify
allLabels = trainLabels + testLabels
train, test = utils.randomGenerateIndices(allLabels, 0.5)
aligned_accuracyDict = {}
unaligned_accuracyDict = {}
for type in ["rbf", "lap", "id", "isd"]:
aligned_accuracyDict[type] = []
unaligned_accuracyDict[type] = []
for type in ["rbf", "lap", "id", "isd"]:
alignedAccuracy = classification.multiClassSVM(aligned_allDistance, train, test, allLabels, type)
unalignedAccuracy = classification.multiClassSVM(unaligned_allDistance, train, test, allLabels, type)
aligned_accuracyDict[type].append(alignedAccuracy)
unaligned_accuracyDict[type].append(unalignedAccuracy)
print " "
print "Aligned ----------"
for type in ["rbf", "lap", "id", "isd"]:
alignedTest = aligned_accuracyDict[type]
alignedTest = np.array(alignedTest)
mean = np.mean(alignedTest)
std = np.std(alignedTest)
print type +": "+ str(mean) +u"\u00B1"+str(std)
print " "
print "Unaligned --------------"
for type in ["rbf", "lap", "id", "isd"]:
alignedTest = unaligned_accuracyDict[type]
alignedTest = np.array(alignedTest)
mean = np.mean(alignedTest)
std = np.std(alignedTest)
print type +": "+ str(mean) +u"\u00B1"+str(std)
