A python utlitiy wrapper around pycocotools to generate a dataset for semantic segmentation from the original COCO dataset. This will generate a dataset consisting of a copy of images from COCO and masked images in the form of tiff files ready training on machine learning segmentation models like UNet.
opencv-pythonnumpypycocotoolsfiftyone- annotation file from coco dataset
- images from coco dataset
(see usage on how to easily download images using fiftyone)
Install dependencies:
pip install opencv-python numpy pycocotools fiftyone
Download the dataset using fiftyone:
import fiftyone
dataset = fiftyone.zoo.load_zoo_dataset("coco-2017", label_types=["segmentations"], classes=["person", "bicycle", "car"], split="train")
This will create a directory called fiftyone in your home directory where the annotation files and raw images can be found in.
Create a directory original where the raw images will be dumped (copied) and masks where the generated tiff files will be saved.
mkdir original
mkdir masks
Generating a dataset using category 1 (person), 2 (bicycle) and 3 (car).
python -m pycocosegmentor --annotation-file /path/to/annotations.json --image-dir /path/to/raw/images --original-img-dir original --mask-image-dir masks --category-ids 1 2 3