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)