In the following code from the pascal.ipnb notebook,
trn_anno = collections.defaultdict(lambda:[])
for o in trn_j[ANNOTATIONS]:
if not o['ignore']:
bb = o[BBOX]
bb = np.array([bb[1], bb[0], bb[3]+bb[1]-1, bb[2]+bb[0]-1])
trn_anno[o[IMG_ID]].append((bb,o[CAT_ID]))
len(trn_anno)
Instead of using collections.defaultdict(lambda:[]) expression in the first line, we could have used collections.defaultdict(list). It would have the same behavior - When each key is encountered for the first time, it is not already in the mapping; so an entry is automatically created using the default_factory function which returns an empty list.
I already have a lot of images annotated in the old Pascal/VOC XML-format, after some searching I found this way to convert into JSON (I started to build it my self until I realized that someone must have done it already):
I could’t find anywhere on the forum how to do the translation and Jermey only referencing to a already translated version:
Remember to change the ID’s because they gets prefixed based on your filenames, and will give you errors because the defaultdict approach expect the ID’s to increment from 1 (and not a prefix).
I have one question for according to the course, now I wanted to train the customized image sets for beverage brands, how I can do the annotation to get the same PASCAL VOC data formats? I used labelimg and labelme to do the annotation, I have tried searched on the google but can’t find a solution, anyone can give me the similar case or advice would be helpful, thanks!
hi Punnerud, I just saw what u posted, so use that script can transform the datasets to the PASCAL VOC json file and can use the same as the course 8 tutorial one right?
I recommend doing it on the original files first to have a working example, then on your own annotated output afterward. If I remember correctly there is some fields you have to fix. It was a small “search and replace” before running the code. Two other folks at my team also figured out the same with no help, so I think you fill figure it out.
thanks @punnerud I have already use this script to get the json file and run the original files first to get understand what’s going on and then played around with my customized image set, still get one matrix bug ValueError: Expected more than 1 value per channel when training, got input size [1, 1024], so I need change my training data size and figure out how to solve this.
