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import typer
from groundingdino.util.inference import load_model, load_image, predict
from tqdm import tqdm
import torchvision
import torch
import fiftyone as fo
def main(
image_directory: str = 'test_grounding_dino',
text_prompt: str = 'bus, car',
box_threshold: float = 0.15,
text_threshold: float = 0.10,
export_dataset: bool = False,
view_dataset: bool = False,
export_annotated_images: bool = True,
weights_path : str = "groundingdino_swint_ogc.pth",
config_path: str = "../../GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py",
subsample: int = None,
):
model = load_model(config_path, weights_path)
dataset = fo.Dataset.from_images_dir(image_directory)
samples = []
if subsample is not None:
if subsample < len(dataset):
dataset = dataset.take(subsample).clone()
for sample in tqdm(dataset):
image_source, image = load_image(sample.filepath)
boxes, logits, phrases = predict(
model=model,
image=image,
caption=text_prompt,
box_threshold=box_threshold,
text_threshold=text_threshold,
)
detections = []
for box, logit, phrase in zip(boxes, logits, phrases):
rel_box = torchvision.ops.box_convert(box, 'cxcywh', 'xywh')
detections.append(
fo.Detection(
label=phrase,
bounding_box=rel_box,
confidence=logit,
))
# Store detections in a field name of your choice
sample["detections"] = fo.Detections(detections=detections)
sample.save()
# loads the voxel fiftyone UI ready for viewing the dataset.
if view_dataset:
session = fo.launch_app(dataset)
session.wait()
# exports COCO dataset ready for training
if export_dataset:
dataset.export(
'coco_dataset',
dataset_type=fo.types.COCODetectionDataset,
)
# saves bounding boxes plotted on the input images to disk
if export_annotated_images:
dataset.draw_labels(
'images_with_bounding_boxes',
label_fields=['detections']
)
if __name__ == '__main__':
typer.run(main)
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