create yolo annotations from downloaded images ()

create_ann_yolo.py

import glob
import os
import csv
import numpy as np
import shutil
import pandas as pd
## EDIT HERE 
# Desir classes(from class-descriptions-boxable.csv) and class number in your classes list(classes.txt)
classes = {'/m/04yx4':0,'/m/01bl7v':0,'/m/03bt1vf':1,'/m/05r655':1,'/m/01c648':2,'/m/050k8':3,'/m/0bh9flk':4}
# Box Optional Arguments 1 or 0 or 'any'
tags = {'IsOccluded':'any','IsTruncated':'any','IsGroupOf':0,'IsDepiction':0,'IsInside':'any'}
tags = list(tags.values())
# images path
images_path = glob.iglob('train/**/*.jpg', recursive=True)
images_name = [os.path.basename(x).replace('.jpg','') for x in images_path]

chunksize = 10**7

#######################################################################
if os.path.exists('train/annotations'):
    shutil.rmtree('train/annotations')
    
os.makedirs('train/annotations')


print('[ INFO ] Number of images : '+str(len(images_name)))

for reader in pd.read_csv('../csv_folder/train-annotations-bbox.csv', chunksize=chunksize):
    reader=reader.values  

    for i in range(0,reader.shape[0]):        
        
        if (reader[i][2] in classes) and  all([ True if b=='any' else a==b for a,b in zip(reader[i][8:13],tags) ]) and (reader[i][0] in images_name):  
            print('Matched : '+reader[i][0]) 

            wi = float(reader[i][5]) - float(reader[i][4])
            hi = float(reader[i][7]) - float(reader[i][6])
            x_cent = float(reader[i][4]) + wi/2
            y_cent = float(reader[i][6]) + hi/2


            with open('train/annotations/'+reader[i][0]+'.txt', 'a') as txt_file:
                txt_file.write(str(classes[reader[i][2]])+' '+str(x_cent)+' '+str(y_cent)+' '+str(wi)+' '+str(hi))
                txt_file.write("\n")

now read .csv in chunk, so it wont crash my potato PC