Inference on image dataset without annotations in detectron2

Motivation

• I have a detectron2 Mask R-CNN baseline model that is good enough to predict some object boundaries accurately.
• I'd like to convert these predicted boundaries to COCO polygons to annotate the next dataset (supervised labeling).
• To do this, I need to run inference on an image dataset that does not have annotations.
• The detectron2 methods register_coco_instances and load_coco_json require images with annotations to properly label the predicted objects.

Questions

• Can I register the test dataset without an annotations file?
• If not, what's the easiest way to generate COCO or Labelme JSON files with basic image info without annotations?

Code

dataset_name = "test_data"
image_dir = "data/test"
coco_file = "data/test_annotations.json"

# Register dataset
# A COCO file is needed with image info, which I don't have
register_coco_instances(dataset_name , {}, coco_file, image_dir)
test_dict = load_coco_json(coco_file, image_dir, dataset_name=dataset_name )
metadata = MetadataCatalog.get(dataset_name)

# config details omitted for brevity
cfg = get_cfg()
predictor = DefaultPredictor(cfg)

# Make predictions for all images
for sample in test_dict:
    image_filename = sample["file_name"]
    img = cv2.imread(image_filename)
    outputs = predictor(img)
    # Display or save image with predictions to file

Here's a method to generate the image details from a directory of images and write it to an existing COCO JSON file:

from PIL import Image

def add_images_to_coco(image_dir, coco_filename):
    image_filenames = list(Path(image_dir).glob('*.jpg'))
    images = []
    for i, image_filename in enumerate(image_filenames):
        im = Image.open(image_filename)
        width, height = im.size
        image_details = {
            "id": id + 1,
            "height": height,
            "width": width,
            "file_name": str(image_filename.resolve()),
        }
        images.append(image_details)

    # This will overwrite the image tags in the COCO JSON file
    with open(coco_filename) as f:
        data = json.load(f)

    coco['images'] = images

    with open(coco_filename, 'w') as coco_file:
        json.dump(data, coco_file, indent = 4)

You'll need to create a baseline COCO JSON file with your categories if you don't already have one. It should look something like this:

{
    "images": [ ],
    "annotations": [ ],
    "categories": [
        {
            "id": 1,
            "name": "Car",
            "supercategory": "Car"
        },
        {
            "id": 2,
            "name": "Person",
            "supercategory": "Person"
        }
    ]
}