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55 lines
2.2 KiB
55 lines
2.2 KiB
from pathlib import Path
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from ultralytics import YOLO
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from ultralytics.vit.sam import PromptPredictor, build_sam
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from ultralytics.yolo.utils.torch_utils import select_device
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def auto_annotate(data, det_model='yolov8x.pt', sam_model='sam_b.pt', device='', output_dir=None):
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"""
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Automatically annotates images using a YOLO object detection model and a SAM segmentation model.
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Args:
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data (str): Path to a folder containing images to be annotated.
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det_model (str, optional): Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'.
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sam_model (str, optional): Pre-trained SAM segmentation model. Defaults to 'sam_b.pt'.
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device (str, optional): Device to run the models on. Defaults to an empty string (CPU or GPU, if available).
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output_dir (str, None, optional): Directory to save the annotated results.
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Defaults to a 'labels' folder in the same directory as 'data'.
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"""
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device = select_device(device)
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det_model = YOLO(det_model)
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sam_model = build_sam(sam_model)
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det_model.to(device)
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sam_model.to(device)
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if not output_dir:
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output_dir = Path(str(data)).parent / 'labels'
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Path(output_dir).mkdir(exist_ok=True, parents=True)
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prompt_predictor = PromptPredictor(sam_model)
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det_results = det_model(data, stream=True)
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for result in det_results:
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boxes = result.boxes.xyxy # Boxes object for bbox outputs
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class_ids = result.boxes.cls.int().tolist() # noqa
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prompt_predictor.set_image(result.orig_img)
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masks, _, _ = prompt_predictor.predict_torch(
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point_coords=None,
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point_labels=None,
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boxes=prompt_predictor.transform.apply_boxes_torch(boxes, result.orig_shape[:2]),
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multimask_output=False,
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)
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result.update(masks=masks.squeeze(1))
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segments = result.masks.xyn # noqa
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with open(f'{str(Path(output_dir) / Path(result.path).stem)}.txt', 'w') as f:
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for i in range(len(segments)):
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s = segments[i]
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if len(s) == 0:
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continue
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segment = map(str, segments[i].reshape(-1).tolist())
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f.write(f'{class_ids[i]} ' + ' '.join(segment) + '\n')
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