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| import os | |
| import sys | |
| import math | |
| import tarfile | |
| class DataManager: | |
| def __init__(self, dataset_path): | |
| self.dataset_path = dataset_path | |
| def extract_dataset(self, compressed_dataset_file_name, dataset_directory): | |
| try: | |
| # extract files to dataset folder | |
| tar = tarfile.open(compressed_dataset_file_name, "r:gz") | |
| tar.extractall(dataset_directory) | |
| tar.close() | |
| print("Files extraction was successfull ...") | |
| except: | |
| print("Ecxception raised: No extraction was done ...") | |
| def make_folder(self, folder_path): | |
| try: | |
| os.mkdir(folder_path) | |
| print(folder_path, "was created ...") | |
| except: | |
| print("Ecxception raised: ", folder_path, "could not be created ...") | |
| def move_files(self, src, dst, group): | |
| for fname in group: | |
| os.rename(src + '/' + fname, dst + '/' + fname) | |
| def get_fnames_from_dict(self, dataset_dict, f_or_m): | |
| training_data, testing_data = [], [] | |
| for i in range(1,5): | |
| length_data = len(dataset_dict[f_or_m +"000" + str(i)]) | |
| length_separator = math.trunc(length_data*2/3) | |
| training_data += dataset_dict[f_or_m + "000" + str(i)][:length_separator] | |
| testing_data += dataset_dict[f_or_m + "000" + str(i)][length_separator:] | |
| return training_data, testing_data | |
| def manage(self): | |
| # read config file and get path to compressed dataset | |
| compressed_dataset_file_name = self.dataset_path | |
| dataset_directory = compressed_dataset_file_name.split(".")[0] | |
| # create a folder for the data | |
| try: | |
| os.mkdir(dataset_directory) | |
| except: | |
| pass | |
| # extract dataset | |
| self.extract_dataset(compressed_dataset_file_name, dataset_directory) | |
| # select females files and males files | |
| file_names = [fname for fname in os.listdir(dataset_directory) if ("f0" in fname or "m0" in fname)] | |
| dataset_dict = {"f0001": [], "f0002": [], "f0003": [], "f0004": [], "f0005": [], | |
| "m0001": [], "m0002": [], "m0003": [], "m0004": [], "m0005": [], } | |
| # fill in dictionary | |
| for fname in file_names: | |
| dataset_dict[fname.split('_')[0]].append(fname) | |
| # divide and group file names | |
| training_set, testing_set = {},{} | |
| training_set["females"], testing_set["females"] = self.get_fnames_from_dict(dataset_dict, "f") | |
| training_set["males" ], testing_set["males" ] = self.get_fnames_from_dict(dataset_dict, "m") | |
| # make training and testing folders | |
| self.make_folder("TrainingData") | |
| self.make_folder("TestingData") | |
| self.make_folder("TrainingData/females") | |
| self.make_folder("TrainingData/males") | |
| self.make_folder("TestingData/females") | |
| self.make_folder("TestingData/males") | |
| # move files | |
| self.move_files(dataset_directory, "TrainingData/females", training_set["females"]) | |
| self.move_files(dataset_directory, "TrainingData/males", training_set["males"]) | |
| self.move_files(dataset_directory, "TestingData/females", testing_set["females"]) | |
| self.move_files(dataset_directory, "TestingData/males", testing_set["males"]) | |
| if __name__== "__main__": | |
| data_manager = DataManager("SLR45.tgz") | |
| data_manager.manage() |