- add get_area_filtered_coco method to Coco class (#75):
from sahi.utils.coco import Coco
from sahi.utils.file import save_json
# init Coco objects by specifying coco dataset paths and image folder directories
coco = Coco.from_coco_dict_or_path("coco.json")
# filter out images that contain annotations with smaller area than 50
area_filtered_coco = coco.get_area_filtered_coco(min=50)
# filter out images that contain annotations with smaller area than 50 and larger area than 10000
area_filtered_coco = coco.get_area_filtered_coco(min=50, max=10000)
# export filtered COCO dataset
save_json(area_filtered_coco.json, "area_filtered_coco.json")
- faster yolov5 conversion with mp argument (#80):
# multiprocess support
if __name__ == __main__:
coco = Coco.from_coco_dict_or_path(
"coco.json",
image_dir="coco_images/"
mp=True
)
coco.export_as_yolov5(
output_dir="output/folder/dir",
train_split_rate=0.85,
mp=True
)
- update torch and mmdet versions in workflows (#79)
- remove optional dependencies from conda (#78)
