slash = "/" # =============================================== # Dataset Initialization, dataset = Spanish or KayPentax classes = ["Normal", "Pathol"] dataset_name = "KayPentax" dataset_path = parent_path + dataset_name work_on_augmentated = True # =============================================== # Dsp Initialization, snippet_length, snippet_hop are in milliseconds snippet_length = 1000 snippet_hop = 100 fft_length = 512 fft_hop = 128 mel_length = 128 dsp_package = [snippet_length, snippet_hop, fft_length, fft_hop, mel_length] # =============================================== all_combo = getCombination(dataset_path, classes, slash) # =============================================== # This Line is left to be modified depends on what we need # compressMelSpectrogram(dataset_path, classes, dsp_package, all_combo, slash) # compressVGGishInput(dataset_path, classes, dsp_package, all_combo, slash) # compressDictionary(dataset_path, classes, dsp_package, all_combo, slash, work_on_augmentated) # compressMFCCs(dataset_path, classes, dsp_package, all_combo, slash)
# ===============================================
# Loading data inside Pickles
aug_dict = pickle.load(temp_file_2)
unaug_dict = pickle.load(temp_file_3)
VGGish_Input_data = pickle.load(temp_file_1)
# ===============================================
if train_on_augmented:
train_dict = aug_dict
else:
train_dict = unaug_dict
# ===============================================
# Load all combos from this dataset, combo = [Name, Class] example: ["WADFJS", "Pathol"]
name_class_combo = np.asarray(getCombination(dataset_path, classes, slash))
# ===============================================
normal_name_class_combo = [x for x in name_class_combo if (x[1] == "Normal")]
pathol_name_class_combo = [x for x in name_class_combo if (x[1] == "Pathol")]
# ===============================================
normal_index_array = np.arange(len(normal_name_class_combo))
pathol_index_array = np.arange(len(normal_name_class_combo), len(name_class_combo))
# ===============================================
kf_spliter = KFold(n_splits = num_folds, shuffle = True)