def buildHistogram(path, trainData, voc, training, level, sift):
if training is False:
trainData = utils.readImages(folderPath + path, sift)[0]
# 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
return [featureHistogram, labels]
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():
level = 2
sift = False
training_path = "c1_test"
testing_path = "c1_train"
# training_path = "training"
# testing_path = "testing"
# training_path = "caltech_train"
# testing_path = "caltech_test"
# read train data
[train_data, train_features] = utils.readImages(folderPath + training_path, sift)
# create vocabulary
training_voc = Vocabulary(train_features, 200)
[test_hist, test_label] = buildHistogram(testing_path, None, training_voc, False, level, sift)
[train_hist, train_label] = buildHistogram(training_path, train_data, training_voc, True, level, sift)
classification.SVM_Classify(train_hist, train_label, test_hist, test_label, "HI")