That seems to have done the trick, as when doing python -m prodigy db-in new_dataset annotations_rehashed.jsonlĪnd restarting annotating new_dataset with image.manual the already-annotated images don't show up anymore. With open('annotations_rehashed.jsonl', 'w') as f: Jsonl_stream = JSONL('annotations_with_old_naming.jsonl')Įxamples = # before executing the following, I edit the jsonl file so that it contains the new filenames If you start annotating with image.manual the new_dataset, the examples are re-presented again in the Prodigy UI.įollowing your advice, I tried import prodigyįrom import JSONL To reproduce: annotate a bunch of images, export the annotations to a jsonl annotations_with_old_naming.jsonl file, rename the underlying images (also replace the filenames in the jsonl file) and load them in again into a new_dataset with db-in. To rename all image files in the source folder, don’t specify any extensions.-r means execute this command recursively for every image file in the top source folder (that is, the folder where all the files to be renamed are located), and also for the image files in all the source folder’s subfolders, sub-subfolders, and so on. Make the old image name reflect whats actually downloaded (the difference between 2 images, see highlighted area) using excelVBA or excel functions. Just to make sure I understand the question correctly: The main problem in your case is that you're now presented examples for annotation again, since they're considered different (due to the new naming)?Ĭorrect that's indeed the behavior I am seeing!

Bulk rename photos