def __init__(self, signal_images_list, bkg_image_list, json_name):
"""
Args:
csv_file (string): Path to the csv file with annotations.
root_dir (string): Directory with all the images.
transform (callable, optional): Optional transform to be applied
on a sample.
"""
signal_dict = create_table(signal_images_list, (json_name, 'id'))
background_dict = create_table(bkg_image_list, (json_name, 'id'))
try:
print('clock: ', background_dict['clock'][0])
except:
'''
'''
# print(signal_dict[json_name][1])
# assert 0
signal_images = np.array(signal_dict[json_name], dtype=object)
background_images = np.array(background_dict[json_name], dtype=object)
dataset_size = min(len(signal_images), len(background_images))
signal_labels = np.ones(dataset_size, dtype=np.float32)
background_labels = np.zeros(dataset_size, dtype=np.float32)
self.size = dataset_size * 2
self.trainX = np.concatenate(
(signal_images[:dataset_size], background_images[:dataset_size]),
axis=0)
print(self.trainX.shape)
self.trainY = np.concatenate((signal_labels, background_labels),
axis=0)
self.image_shape = (self.trainX.shape[-1], *self.trainX[0, 0].shape)
def __init__(self, signal_images_list, bkg_image_list, json_name):
"""
Args:
csv_file (string): Path to the csv file with annotations.
root_dir (string): Directory with all the images.
transform (callable, optional): Optional transform to be applied
on a sample.
"""
signal_dict = create_table(signal_images_list, (json_name, 'vertex'))
background_dict = create_table(bkg_image_list, (json_name, 'vertex'))
signal_images = np.array(signal_dict[json_name], dtype=object)
background_images = np.array(background_dict[json_name], dtype=object)
dataset_size = min(len(signal_images), len(background_images))
signal_labels = np.ones(dataset_size, dtype=np.float32)
background_labels = np.zeros(dataset_size, dtype=np.float32)
self.size = dataset_size * 2
indices = np.arange(self.size)
np.random.shuffle(indices)
self.trainX = np.concatenate(
(signal_images[:dataset_size], background_images[:dataset_size]),
axis=0)[indices]
self.trainY = np.concatenate((signal_labels, background_labels),
axis=0)[indices]
self.image_shape = (self.trainX.shape[-1], *self.trainX[0, 0].shape)
def __init__(self, signal_images_list, bkg_image_list, json_name):
signal_dict = create_table(signal_images_list, (json_name, 'id'))
background_dict = create_table(bkg_image_list, (json_name, 'id'))
signal_images = np.array(signal_dict[json_name], dtype=object)
background_images = np.array(background_dict[json_name], dtype=object)
dataset_size = min(len(signal_images), len(background_images))
signal_labels = np.ones(dataset_size, dtype=np.float32)
background_labels = np.zeros(dataset_size, dtype=np.float32)
self.size = dataset_size * 2
self.trainX = np.concatenate(
(signal_images[:dataset_size], background_images[:dataset_size]),
axis=0)
print(self.trainX.shape)
self.trainY = np.concatenate((signal_labels, background_labels),
axis=0)
self.image_shape = (self.trainX.shape[-1], *self.trainX[0, 0].shape)
def __init__(self, signal_images_list, bkg_image_list, json_name):
signal_dict = create_table(signal_images_list, (json_name, 'vertex'))
background_dict = create_table(bkg_image_list, (json_name, 'vertex'))
print(len(signal_dict[json_name]))
signal_images = np.array(signal_dict[json_name], dtype=object)
background_images = np.array(background_dict[json_name], dtype=object)
print(signal_images.shape, 'Abigail')
dataset_size = min(len(signal_images), len(background_images))
signal_labels = np.ones(dataset_size, dtype=np.float32)
background_labels = np.zeros(dataset_size, dtype=np.float32)
self.size = dataset_size * 2
indices = np.arange(self.size)
np.random.shuffle(indices)
print(signal_images.shape, background_images.shape)
self.trainX = np.concatenate(
(signal_images[:dataset_size], background_images[:dataset_size]),
axis=0)[indices]
self.trainY = np.concatenate((signal_labels, background_labels),
axis=0)[indices]
self.image_shape = (self.trainX.shape[-1], *self.trainX[0, 0].shape)
time_index = args.time_index
qe_index = args.qe_index
json_name = str(time_index) + '_' + str(qe_index)
signal_images_list = [
str(filename.strip()) for filename in list(open(args.signallist, 'r'))
if filename != ''
]
bkg_image_list = [
str(filename.strip()) for filename in list(open(args.bglist, 'r'))
if filename != ''
]
signal_images_list = signal_images_list[:1]
bkg_image_list = bkg_image_list[:1]
signal_dict = create_table(signal_images_list, (json_name, 'vertex'))
background_dict = create_table(bkg_image_list, (json_name, 'vertex'))
signal_images = np.array(signal_dict[json_name], dtype=object)
background_images = np.array(background_dict[json_name], dtype=object)
print(len(signal_images), len(background_images))
dataset_size = min(len(signal_images), len(background_images))
signal_labels = np.ones(dataset_size, dtype=np.float32)
background_labels = np.zeros(dataset_size, dtype=np.float32)
size = dataset_size * 2
indices = np.arange(size)
np.random.shuffle(indices)
trainX = np.concatenate(
(signal_images[:dataset_size], background_images[:dataset_size]),
axis=0)[indices]
trainY = np.concatenate((signal_labels, background_labels),
axis=0)[indices]