def train_load():
path = 'data/train'
Dir = listdir(path)
imagelist = []
ListOfName = []
count = 0
for directory in Dir:
if directory != '.DS_Store': #For some reason this directory keep bumping up
subpath = listdir(path + '/' + directory)
ListOfName.append(directory)
print(directory)
count = count + 1
for subdir in subpath:
if subdir != '.DS_Store':
Image = image(path + '/' + directory + '/' + subdir, count)
Image = Image.get_feature(reshape = True)
#Image = Image.getGaborFeatures(reshape = True)
output = count - 1
# output = numpy.zeros((121,1))
# output[count -1] = numpy.float64(1)
imagelist.append([Image, output])
#we need to randomly shuffle the data to retain the generality
shuffle(imagelist)
with open('pickle/ListOfName.pkl','w') as f:
pickle.dump(ListOfName,f)
print('got image list for train')
return imagelist, count
def test_load():
path = 'data/test'
Dir = listdir(path)
imagelist = []
ListOfName = []
for directory in Dir:
if directory != '.DS_Store': #For some reason this directory keep bumping up
ListOfName.append(directory)
Image = image(path + '/' + directory, 1000) #assign a random class to test data
Image = Image.get_feature(reshape = True)
imagelist.append([Image, 1000])
with open('pickle/TestImageName.pkl','w') as f:
pickle.dump(ListOfName,f)
print('got image list for test')
#shuffle(imagelist)
return imagelist