aberystwyth
import optionimages_and_annotations
dir to the upload windowaberystwyth
"action": "data"
) takes all data from project aberystwyth
and keeps classes as they are."action": "split_masks"
) splits masks of class leaf
into connected components. As a result from one mask with a few leafs we will get separate masks for each leaf."action": "supervisely"
) saves results to the new project aberystwyth_splitted
."action": "data"
) takes all data from the project aberystwyth_splitted
and keeps classes as they are."action": "flip"
) flips the data horisontally."action": "multiply"
) creates 10 copies for each image."action": "crop"
) performs random crops (from 20% to 25% in width and from 25% to 30% in height with respect to the image size)"action": "if"
) filters the data (sends the data containing at least one object to the first branch, all other data will be sent to null
)."action": "if"
) randomly splits the data into two branches: first branch - 95% (will be tagged as train
) and second branch - 5% (will be tagged as val
)."action": "tag"
) adds the tag train
to all input images."action": "tag"
) adds the tag val
to all input images."action": "rename"
) makes copies of objects of the class leaf
and renames them as tmp-contour
"action": "line2bitmap"
) transforms the objects into their contours."action": "crop"
) crops 3px stripes from each side to drop the boundary contours."action": "supervisely"
) saves results to the new project aberystwyth_random_crop_train
.unet-leafs
. Project zenodo_random_crop_train
is used for training.unet-leafs
to the project with test images.