# Split the training data into training and test data
(TrainPathList, TestPathList, TrainLabel,
TestLabel) = train_test_split(TrainingImageList,
TrainingLabelList,
stratify=TrainingLabelList)
Datasets = [
("Train", TrainPathList, TrainLabel,
Network.DATASET_PATH + os.path.sep + Network.OUTPUT_PATH +
os.path.sep + Network.TRAIN_HDF5 + ".hdf5"),
("Test", TestPathList, TestLabel, Network.DATASET_PATH + os.path.sep +
Network.OUTPUT_PATH + os.path.sep + Network.TEST_HDF5 + ".hdf5")
]
# Create a new preprocessor to resize the images to a common size
AspectPreprocessor = Preprocessor.AspectAwarePreprocessor(
Width=Network.IMAGESIZE[0], Height=Network.IMAGESIZE[1])
for (Type, PathList, Labels, FilePath) in Datasets:
print("[INFO] Building dataset '{}'...".format(Type))
FileWriter = IO.HDF5DatasetWriter(
Dimensions=(len(PathList), Network.IMAGESIZE[0],
Network.IMAGESIZE[1], Network.CHANNELS),
OutputPath=FilePath)
# Create a new progressbar
Widgets = [
" Progress: ",
progressbar.Percentage(), " ",
progressbar.Bar(), " ",
progressbar.ETA()
]
