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