MaskCoco is a simple script that parse masked images to coco format for object segmentation.
Simply run the following in terminal
git clone https://github.com/georgezywang/MaskCoco.git
Additionally, this script expects the following libraries:
pathlib #pip install pathlib
PIL
numpy
skimage
shapely
as well as some built in ones. Use conda or pip to install missing dependencies
The demo parses a mask of a wave image[1] and displays an annotated image.
| Wave Image | mask |
|---|---|
 |
 |
The wave image is the original image to be segmented, and the mask captures the wave. These images can both be found in the images folder.
To play with this demo, run the following:
cd MaskCoco
python3 driver.py
Open demo.ipynb and run the cell. An annotated wave image should appear. A bounding box and a mask should be present in the image.
Annotated Wave Image
The visualizer was taken from kaggle [2]. To change the mask's color, or display more information, please visit the link in the references section. To visualize more complicated images, i.e. those with a lot of polygons, consider using the alternative detectron2 visualization tool in demo.ipynb.
This script was also inspired by this post [3]. A more optimized version is later adapted from [4].
To use a custom dataset, make changes in the driver.py file. MaskCoco.MaskParser has two required arguments, being the category dictionary and a list of images. The list of images should be initialized using the ImageLabel class from MaskCoco.py. If no image id is provided when initializing, MaskCoco will incrementally generate an id for each image.
The CLI provides a straightforward implementation, but to custom image name, path, or set is_crowded, it is recommended to adjust driver.py.
optional arguments:
-h, --help show this help message and exit
-i INPATH, --inpath INPATH
directory of input images
-o OUTPATH, --outpath OUTPATH
coco json output file path
-c CATPATH, --catpath CATPATH
path of category dictionary json file
-d, --imageid specify if use image id
One can run the program by python3 driver.py -i images -o images/dat.json -c categories.json -d
Initialize the image list and category dictionary, then initialize MaskParser from MaskCoco. The default input path is the current directory. If no output path is specified, the json file data.json will be saved in the current directory. The data can then be used for object segmentation architectures like Mask RCNN.
This script was originally used for wildfire detection
[1] http://groups.csail.mit.edu/vision/datasets/ADE20K/browse.php/?dirname=/training/w/wave/
[2] https://www.kaggle.com/stargarden/coco-image-viewer
[3] https://www.immersivelimit.com/tutorials/create-coco-annotations-from-scratch
[4] https://github.com/waspinator/pycococreator/blob/master/pycococreatortools/pycococreatortools.py