index = 0
partitionOfData = 0.85
for label in data:
currentTargets = labels[index]
sizeOfLabel = np.shape(label)[0]
trainingPortion = int(np.ceil(sizeOfLabel*partitionOfData))
testPortion = int(np.ceil(sizeOfLabel*partitionOfData+1))
if len(Xtraining) == 0:
Xtraining = label[0:trainingPortion]
Ytraining = currentTargets[0:trainingPortion,:]
else:
Xtraining = np.concatenate((Xtraining,label[0:trainingPortion]),axis=0)
Ytraining = np.concatenate((Ytraining,currentTargets[0:trainingPortion,:]),axis=0)
index += 1
gaussiansPyramids = dataAugmentation.getGaussianPyramidOfList(Xtraining,3)
Xtraining1 = gaussiansPyramids[0];
Xtraining2 = gaussiansPyramids[1];
Xtraining3 = gaussiansPyramids[2];
Xtraining = np.expand_dims(Xtraining,1)
Xtraining = Xtraining.astype("float32")
Xtraining1 = np.expand_dims(Xtraining1,1)
Xtraining1 = Xtraining1.astype("float32")
Xtraining2 = np.expand_dims(Xtraining2,1)
Xtraining2 = Xtraining2.astype("float32")
Xtraining3 = np.expand_dims(Xtraining3,1)
Xtraining3 = Xtraining3.astype("float32")
###############START GENERATING TEST######################
laplaceImages = 0
data = list()
import numpy as np
from skimage import data, io, filters, transform
import sklearn
import scipy as sc
from PIL import Image as imLib1
from dataAugmentation import getGaussianPyramidOfList
setOfImages = list()
imageTest = io.imread('./13NaturalSceneDataset/bedroom/image_0216.jpg')
imageTest = transform.resize(imageTest,(200,200))
setOfImages.append(imageTest)
imageTest = io.imread('./13NaturalSceneDataset/bedroom/image_0200.jpg')
imageTest = transform.resize(imageTest,(200,200))
setOfImages.append(imageTest)
test = getGaussianPyramidOfList(setOfImages,3)
