melNET_test.py

# importing scripts
import melNET_test_src
import melNET_confusion_mat

# importing libs
import os
from tkinter import *
import tensorflow as tf
from tensorflow.python.platform import gfile


#######################################################################################################################
# Main >>>
def main():
    # Window  Creation: Train to melNET
    root = Tk()
    root.title("TEST with melNET")  # Title
    global applied
    applied = False

    def apply_button_applied():
        root.destroy()
        global applied
        applied = True

    def quit_button_applied():
        root.destroy()
        global applied
        applied = False

    # Variables
    _five_fold = BooleanVar()
    _save_performance = BooleanVar()
    _show_detection = BooleanVar()

    # UI
    Checkbutton(root, text="Five-Fold (Default: Single-Fold)", variable=_five_fold).grid(row=1, column=1, sticky=W)
    Label(root, text="For Single-Fold, keep class folders in: aug_Data/Single_Fold_(Aug)/Test ").grid(row=2, column=1)
    Checkbutton(root, text="Save Performance Report", variable=_save_performance).grid(row=4, column=1, sticky=W)
    Checkbutton(root, text="View Detection", variable=_show_detection).grid(row=5, column=1, sticky=W)

    Button(root, text="Quit", command=quit_button_applied, width=15).grid(row=7, column=2, sticky=W)
    Button(root, text="Apply", command=apply_button_applied, width=15).grid(row=7, column=3, sticky=W)

    root.mainloop()

    if applied:
        five_fold = _five_fold.get()
        save_performance = _save_performance.get()
        show_detection = _show_detection.get()

        # Managing directories
        accessories_path = os.getcwd() + "/Accessories"   # same name in malNET_train_src
        aug_data_path = os.getcwd() + "/aug_Data"

        result_root = os.getcwd() + "/Results"
        if tf.gfile.Exists(result_root):
            tf.gfile.DeleteRecursively(result_root)
        tf.gfile.MakeDirs(result_root)

        # Starting Test
        if tf.gfile.Exists(accessories_path) and tf.gfile.Exists(aug_data_path):
            if five_fold:
                fold_num = 5  # Five-Fold
                root = aug_data_path + "/Five_Fold_(Aug)"
                for num in range(fold_num):
                    result_path = result_root + "/Fold_0" + str(num+1)
                    tf.gfile.MakeDirs(result_path)
                    test_path = root + "/Fold_" + str(num+1) + "/Test"
                    weight_path = accessories_path + "/Five_Fold_Trained/Trained_Fold_" + str(num+1)
                    delete_all_csv(test_path)  # Deleting old results
                    if tf.gfile.Exists(test_path) and tf.gfile.Exists(weight_path):
                        folder_names = get_subfolder_names(test_path)
                        for folder in folder_names:
                            ground_truth = re.sub(r'[^a-z0-9]+', ' ', str(folder).lower())
                            class_path = test_path + "/" + folder
                            classes = melNET_test_src.main(class_path, weight_path, save_performance, show_detection,
                                                           ground_truth, result_path)
                        if save_performance:
                            merge_csv(result_path)
                    else:
                        print("Either [Test Data] or [Weight] is NOT available!")
                        exit()

            else:  # Single-Fold
                result_path = result_root + "/Fold_01"
                tf.gfile.MakeDirs(result_path)
                root = aug_data_path + "/Single_Fold_(Aug)"
                test_path = root + "/Test"
                weight_path = accessories_path + "/Single_Fold_Trained"
                delete_all_csv(test_path)  # Deleting old results
                if tf.gfile.Exists(test_path) and tf.gfile.Exists(weight_path):
                    folder_names = get_subfolder_names(test_path)
                    for folder in folder_names:
                        ground_truth = re.sub(r'[^a-z0-9]+', ' ', str(folder).lower())
                        class_path = test_path + "/" + folder
                        classes = melNET_test_src.main(class_path, weight_path, save_performance, show_detection,
                                                       ground_truth, result_path)
                    if save_performance:
                        merge_csv(test_path)
                else:
                    print("Either [Test Data] or [Weight] is NOT available!")
                    print("For Single-Fold, keep class folders in: aug_Data/Single_Fold_(Aug)/Test ")
                    exit()
        else:
            print("Necessary Files are NOT found!")
            exit()

    else:
        print("melNET has not been TESTED!")
        exit()

    if save_performance and len(classes) == 2:
        melNET_confusion_mat.main(classes)
#######################################################################################################################


# Getting Sub-Folder names in [Data] folder
def get_subfolder_names(dir):
    folder_list = []

    if len(os.listdir(dir)) == 0:
        print(dir + " is empty!\n")
        exit()
    else:
        for entry_name in os.listdir(dir):
            entry_path = os.path.join(dir, entry_name)
            if os.path.isdir(entry_path):
                folder_list.append(entry_name)
    return folder_list


def detect_all_csv(path_to_dir):
    # Reading csv file names
    filenames = os.listdir(path_to_dir)
    csv_files = []
    for filename in filenames:
        if filename.endswith('.csv'):
            csv_files.append(filename)
    return csv_files


def merge_csv(path_to_dir):
    csv_files = detect_all_csv(path_to_dir)
    # Reading from csv files and making the final one
    f_final = open(path_to_dir + '/_result.csv', 'w')
    first_file = True
    header_rem = True

    for csv_file in csv_files:
        csv_path = path_to_dir + '/' + csv_file
        f = open(csv_path)
        if first_file:
            for line in f:
                f_final.write(line)
            first_file = False

        else:
            for line in f:
                if header_rem:
                    header_rem = False
                    continue
                else:
                    f_final.write(line)
        f.close()
        os.remove(csv_path)
    f_final.close()


def delete_all_csv(path_to_dir):
    csv_files = detect_all_csv(path_to_dir)
    for csv_file in csv_files:
        csv_path = path_to_dir + '/' + csv_file
        os.remove(csv_path)


#######################################################################################################################
# Main Call Func. >>>
if __name__ == "__main__":
    main()