def __init__(self, dir_path='dataset/data'):
self.image_size = tuple((500, 500))
self.features = []
self.image_path_list = []
self.mse_all = []
self.labels = []
#object of feature extractor
self.fe_obj = fe.Global_feature_extraction()
self.dir_path = dir_path
self.lables = os.listdir(dir_path)
self.lables.sort()
def __init__(self, csv_path):
self.image_size = tuple((500, 500))
self.features = []
self.image_path_list = []
self.mse_all = []
self.labels = []
#object of feature extractor
self.fe_obj = fe.Global_feature_extraction()
#self.dir_path = dir_path
self.csv_path = csv_path
self.data = pd.read_csv(csv_path, index_col=0)
#|_________________________|
import numpy as np
import cv2
import global_feature_extractor as fe
import os
import glob
import pickle as pkl
#****************************** hiper parameters
image_size = tuple((500, 500))
features = []
image_path_list = []
mse_all = []
labels = []
#object of feature extractor
fe_obj = fe.Global_feature_extraction()
dir_path = 'dataset/data'
lables = os.listdir(dir_path)
lables.sort()
#******************************
print("\n\n [INFO] successfully loaded hiper parameters ...")
# loop over all the labels in the folder
count = 1
for i, label in enumerate(lables):
cur_path = dir_path + "/" + label
count = 1
features = []
labels = []
image_path_list = []