get_json_file_YOLO.py

-#  /!\  TO DO before : globox convert input_YOLO/labels/val/ output_../classifier/test_convert_labels/ --format yolo --save_fmt labelme --img_folder YOLO/images/val/ #path to images
-
+import argparse
 import os
 import json
 import labelme
 import base64
 import pandas as pd
 
-mode = 'train' #or val
-img_dir = str('../annot/YOLO/images/'+mode+'/')
+parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='TODO')
+parser.add_argument('-p','--path_to_json', type=str, help = 'Path of the folder that contain the .json',required=True)
+parser.add_argument('-i','--path_to_img', type=str, help = 'Path of the folder that contain the .jpg',required=True)
+parser.add_argument('-d','--direction', type=str, help = 'Directory to wich modified .json files will be stored',required=True)
+args = parser.parse_args()
+
 
-filename = 'test_convert_labels_train/' #path to .json
-out_file = 'test_convert_labels_train_2/' #direction
-img_path = '/nfs/NAS7/manip_stephane/annot/YOLO/images/train/'
+filename = args.path_to_json
+out_file = args.direction
+img_path = args.path_to_img
 
 liste_file = os.listdir(filename) 
 liste_file = pd.DataFrame(liste_file, columns =['fn'])
@@ -24,7 +27,7 @@ liste_file = liste_file.reset_index()
 
 for i, row in liste_file.iterrows():
     if len(row.fn) > 30:
-        data = labelme.LabelFile.load_image_file(img_dir+row.fn[:-4]+'jpg')
+        data = labelme.LabelFile.load_image_file(str(img_path+row.fn[:-4]+'jpg'))
         image_data = base64.b64encode(data).decode('utf-8')
     else:
         continue