@@ -81,6 +81,7 @@ def __init__(self, project_id, **kwargs):
8181 self .custom_name_to_num = {v :k for k , v in self .custom_num_to_name .items ()}
8282
8383
84+
8485 def predict (self , tasks : List [Dict ], context : Optional [Dict ] = None , ** kwargs ) -> List [Dict ]:
8586 """ Inference logic for YOLO model """
8687
@@ -224,14 +225,16 @@ def fit(self, event, data, **kwargs):
224225 project_path = sample_img_path .split ("/" )[:- 1 ]
225226 image_name = sample_img_path .split ("/" )[- 1 ]
226227
228+
229+
227230 img1 = img .save (f"./datasets/temp/images/{ image_name } " )
228231 img2 = img .save (f"./datasets/temp/images/(2){ image_name } " )
229232
230233 all_new_paths .append (f"./datasets/temp/images/{ image_name } " )
231234 all_new_paths .append (f"./datasets/temp/images/(2){ image_name } " )
232235
233236 # now saving text file labels
234- txt_name = ( image_path . split ( '/' )[ - 1 ]) .rsplit ('.' , 1 )[0 ]
237+ txt_name = image_name .rsplit ('.' , 1 )[0 ]
235238
236239 with open (f'./datasets/temp/labels/{ txt_name } .txt' , 'w' ) as f :
237240 f .write ("" )
@@ -246,7 +249,7 @@ def fit(self, event, data, **kwargs):
246249
247250 value = result ['value' ]
248251 label = value ['rectanglelabels' ][0 ]
249-
252+
250253 if label in self .custom_name_to_num :
251254
252255 # these are out of 100, so you need to convert them back
@@ -263,7 +266,7 @@ def fit(self, event, data, **kwargs):
263266 trans_x = (x / 100 ) + (0.5 * w )
264267 trans_y = (y / 100 ) + (0.5 * h )
265268
266- # now getting the class label
269+ # now getting the class label
267270 label_num = self .custom_name_to_num .get (label )
268271
269272 with open (f'./datasets/temp/labels/{ txt_name } .txt' , 'a' ) as f :
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