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import glob
import itertools
import os.path

#dir = 'D:\\USERS\\DAN\\OneDrive\\Projects\\Bc\\baltrip-dataset-one-class\\labels'
dir = 'D:\\USERS\\DAN\\Downloads\\Self Driving Car.v2-fixed-large.yolov5pytorch\\export\\labels'

items = glob.glob(dir + "/*.txt")
no_of_items = len(items)
items_done = 0

for item in items: #itertools.islice(items, 10):
    temp = []
    items_done += 1
    print('\n#######>-' + str(items_done) + '/' + str(no_of_items) + '-<#####################################################'
          '\n' + os.path.basename(item))
    print(str(items_done) + '/' + str(no_of_items))

    with open(item, "r") as f:
        for line in f:
            line_as_list = list(line)
            #print(line_as_list)
            if line_as_list[0] == str(1): #Class 1 is "car", Class 10 is "truck". So I can use just one condition
                #line_as_list[0] = 0 #I don't need to rename anything, because I can choose to approach the dataset as single class when training (with --single-cls)
                tmp_line = ''.join(map(str, line_as_list))
                print(line_as_list)
                print(tmp_line)
                temp.append(tmp_line)
            elif line_as_list[0] != str(1):
                line_as_list = ''
                tmp_line = ''.join(map(str, line_as_list))
                print('debug: ' + tmp_line)

    with open(item, "w") as f:
        f.writelines(temp)