def __init__(self, base_folder, sub_folders, label_file_name, parameters):
'''
Each sub_folder contains the image files and a csv file for the corresponding label. The read iterate through
all the sub_folders and aggregate all the images and their corresponding labels.
'''
self.base_folder = base_folder
self.sub_folders = sub_folders
self.label_file_name = label_file_name
self.emotion_count = parameters.target_size
self.width = parameters.width
self.height = parameters.height
self.shuffle = parameters.shuffle
self.training_mode = parameters.training_mode
# data augmentation parameters.determinisitc
if parameters.determinisitc:
self.max_shift = 0.0
self.max_scale = 1.0
self.max_angle = 0.0
self.max_skew = 0.0
self.do_flip = False
else:
self.max_shift = 0.08
self.max_scale = 1.05
self.max_angle = 20.0
self.max_skew = 0.05
self.do_flip = True
self.data = None
self.per_emotion_count = None
self.batch_start = 0
self.indices = 0
self.A = imgu.compute_norm_mat(self.width, self.height)
def __init__(self, base_folder, sub_folders, label_file_name, parameters):
'''
Each sub_folder contains the image files and a csv file for the corresponding label. The read iterate through
all the sub_folders and aggregate all the images and their corresponding labels.
'''
self.base_folder = base_folder
self.sub_folders = sub_folders
self.label_file_name = label_file_name
self.emotion_count = parameters.target_size
self.width = parameters.width
self.height = parameters.height
self.shuffle = parameters.shuffle
self.training_mode = parameters.training_mode
self.data = None
self.per_emotion_count = None
self.batch_start = 0
self.indices = 0
self.A, self.A_pinv = imgu.compute_norm_mat(self.width, self.height)