Note

This is a community plugin, an external project maintained by its respective author. Community plugins are not part of FiftyOne core and may change independently. Please review each plugin’s documentation and license before use.

FiftyOne Tile

Tile your images to squares (e.g. 960x960 pixels) in FiftyOne directly. Tested with with bounding boxes (without orientation) only.

Tiles will be saved to destination-dataset. As detections might be split over tiles boundaries, they might need refinement.

• intersecting label tag marks affected detections.

• intersection sample field stores average detection intersection.

• intersection detection field stores label intersections.

Where 1 means detection is not split and 0.1 means detection is visible only by 10% on this tile:

dataset.filter_labels("ground_truth", F("intersection") < 0.95)
dataset.sort_by(F("intersection"), reverse=False)

Walkthrough

1. If resize is set: Resize image to given width and keep aspect ratio before tileing

2. Add space around the image to make it a multiple of tiles size and place image in a random within the new boundaries.

3. Make tiles with the given tile_size and transfer available detections to the tiles.

   • Overlap tiles by padding value (in pixels)

   • Omit labels at image’s borders if the don’t reach in the image by threshold value (in pixels)

4. If save_empty is set, tiles without detections will be kept, if not omited.

5. If runs is > 1: repeat those steps n times and keep those with least detections being split by tileing.

Installation

Make sure OpenCV is installed

fiftyone plugins download https://github.com/mmoollllee/fiftyone-tile/

Python SDK

You can use the compute operators from the Python SDK!

import fiftyone as fo
import fiftyone.operators as foo

dataset = fo.load_dataset("existing-dataset")

make_tiles = foo.get_operator("@mmoollllee/tile/make_tiles")

make_tiles(
   dataset,
   output_dir="filepath/to/save/tiles", # Required
   name="task-title", # Optional identifier for this task's logs
   destination="destination_dataset_name", # defaults to current dataset name with '_tiled' suffix
   labels_field="ground_truth", # which labels to transfer to the tiles (Default: ground_truth)
   resize=1200, # resize the image before tiling (default: None)
   tile_size=960, # (default: 960)
   padding=20, # Overlap tiles by given value (default: 32),
   threshold=0.15, # Omit labels at the edged if smaller than given percentage (default: 0.15)
   save_empty=False, # Keep tiles without labels (default: False),
   test=False, # Run Tiling only for 5 samples and make destination dataset non-persistent
   runs=1, # repeat n times and keep only those with least detections being split by tileing.
   log_level=2 # 0 = no output, 1 = only total output, 2 = samples output, 3 = even more
)

Sources

Powered by code of these repos:

• WALDO

• yolo-tiling