images_train = file_io.FileHDF5().read(os.path.join(DIR, "train.hdf5"),
"images")
print("loading labels_train...")
labels_train = file_io.FileHDF5().read(os.path.join(DIR, "train.hdf5"),
"labels")
print("loading images_val...")
images_val = file_io.FileHDF5().read(os.path.join(DIR, "val.hdf5"),
"images")
print("loading labels_val...")
labels_val = file_io.FileHDF5().read(os.path.join(DIR, "val.hdf5"),
"labels")
print("Finish loading hdf5")
# Train detector
X_train, X_val, Y_train, Y_val, mean_value = preproc.GrayImgTrainPreprocessor(
).run(images_train, labels_train, images_val, labels_val, 2)
print("mean value of the train images : {}".format(mean_value)) # 107.524
print("Train image shape is {}, and Validation image shape is {}".format(
X_train.shape,
X_val.shape)) # (457723, 32, 32, 1), (113430, 32, 32, 1)
# mean value of the train images : 108.78421712130333
# Train image shape is (1279733, 32, 32, 1), and Validation image shape is (317081, 32, 32, 1)
train_.train_detector(X_train,
X_val,
Y_train,
Y_val,
nb_filters=NB_FILTERS,
nb_epoch=NB_EPOCH,
nb_classes=2,
save_file=DETECTOR_FILE)
if __name__ == "__main__":
images_train = file_io.FileHDF5().read(os.path.join(DIR, "train.hdf5"),
"images")
labels_train = file_io.FileHDF5().read(os.path.join(DIR, "train.hdf5"),
"labels")
images_val = file_io.FileHDF5().read(os.path.join(DIR, "val.hdf5"),
"images")
labels_val = file_io.FileHDF5().read(os.path.join(DIR, "val.hdf5"),
"labels")
# Train detector
X_train, X_val, Y_train, Y_val, mean_value = \
preproc.GrayImgTrainPreprocessor().run(images_train, labels_train, images_val, labels_val, 2)
# 107.524
print("mean value of the train images : {}".format(mean_value))
# (457723, 32, 32, 1), (113430, 32, 32, 1)
print("Train image shape is {}, and Validation image shape is {}".format(
X_train.shape, X_val.shape))
train_.train_detector(X_train,
X_val,
Y_train,
Y_val,
nb_filters=NB_FILTERS,
nb_epoch=NB_EPOCH,
nb_classes=2,
save_file=DETECTOR_FILE)
# Train recognizer