def __init__(self, images_dir, picture_amount_threshold, feature_method_list, row_threshold, column_threshold, picture_resolution, label_threshold, ground_truth_dir, abs_flag, log_flag, grey_picture=False, auto_canny=True, auto_canny_sigma=0.33): saved_args = locals() Arguments.logArguments('FeatureDataSet', saved_args) self.create_grey_dir = False self.images_dir = images_dir self.picture_amount_threshold = picture_amount_threshold # number of pictures self.grey_picture = grey_picture # load picture as grey/color self.feature_method_list = feature_method_list # list of feature extraction method self.row_threshold = row_threshold self.column_threshold = column_threshold self.picture_resolution = picture_resolution self.label_threshold = label_threshold # min amount of label 1 percentage self.ground_truth_dir = ground_truth_dir # dir of ground true pictures - extract only high class 1 percent self.abs_flag = abs_flag self.log_flag = log_flag self.images_pixel_dict = dict() # contain images and their pixels self.images_name_list = list() # contain pic name in data set self.X = list() # list of ndarray - feature format self.X_sobelx = list() self.X_sobely = list() self.X_laplacian = list() self.X_canny = list() self.X_scharrx = list() self.X_scharry = list() self.X_sobelx_final = list() self.X_sobely_final = list() self.X_laplacian_final = list() self.X_canny_final = list() self.X_scharrx_final = list() self.X_scharry_final = list() self.auto_canny = auto_canny self.auto_canny_sigma = auto_canny_sigma if self.auto_canny_sigma < 0 or self.auto_canny_sigma > 1: raise 'Illegal value for auto Canny Sigma parameter - need to be 0<=..<=1' self.kepsilon = 1e-8 return
def __init__(self, crf, w, image_name_list, X, ground_truth_dir, pixel_frame, evaluation_visualisation_flag, dir_visualization_name, model_name, feature_data_set_obj, baseline_features=None): saved_args = locals() del saved_args['X'] # preventing from logging Arguments.logArguments('Evaluate', saved_args) self.crf = crf self.w = w self.X = X self.image_name_list = image_name_list self.ground_truth_dir = ground_truth_dir self.pixels_frame = pixel_frame self.evaluation_visualisation_flag = evaluation_visualisation_flag self.dir_visualization_name = dir_visualization_name self.model_name = model_name self.baseline_features_dict = dict() if len(baseline_features) > 0: for f in baseline_features: attr = getattr(feature_data_set_obj, 'X_{}_final'.format(f)) self.baseline_features_dict[f] = attr self.all_f1_avg = list() # avg f1 (between all gt) self.all_f1 = list() # max f1 (max per gt) self.all_recall = list() self.all_precision = list() self.all_super_f1_avg = list() self.all_super_f1 = list() self.all_super_recall = list() self.all_super_precision = list() self.canny_f1_avg = list() self.canny_f1 = list() self.canny_recall = list() self.canny_precision = list() self.canny_super_f1_avg = list() self.canny_super_f1 = list() self.canny_super_recall = list() self.canny_super_precision = list() return
def __init__(self, ground_truth_dir, images_name_list, row_threshold, column_threshold, X, pixels_frame): saved_args = locals() del saved_args['X'] # preventing from logging Arguments.logArguments('TargetDataSet', saved_args) self.ground_truth_dir = ground_truth_dir self.images_name_list = images_name_list self.row_threshold = row_threshold self.column_threshold = column_threshold self.pixels_frame = pixels_frame self.y = list() self.X = X self.delete_indexes = list() return
def __init__(self, X, y, learners, learners_parameters, models, models_parameters, total_label_one_avg, row_threshold): saved_args = locals() del saved_args['X'] # preventing from logging del saved_args['y'] # preventing from logging Arguments.logArguments('Model', saved_args) self.X = X self.y = y self.learners = learners self.learners_parameters = learners_parameters self.models = models self.models_parameters = models_parameters self.total_label_one_avg = total_label_one_avg self.row_threshold = row_threshold # needed to define graph model self.total_accuracy_list = list() # each image and relevance accuracy return