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ultralytics 8.0.89 SAM predict and auto-annotate (#2298)

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Glenn Jocher 2023-04-28 00:36:50 +02:00 • committed by GitHub
parent 3e118f6170
commit 243fc4b1fe
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42
ultralytics/yolo/data/annotator.py Normal file
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from pathlib import Path
from ultralytics import YOLO
from ultralytics.vit.sam import PromptPredictor, build_sam
from ultralytics.yolo.utils.torch_utils import select_device
def auto_annotate(data, det_model='yolov8x.pt', sam_model='sam_b.pt', device='', output_dir=None):
device = select_device(device)
det_model = YOLO(det_model)
sam_model = build_sam(sam_model)
det_model.to(device)
sam_model.to(device)
if not output_dir:
output_dir = Path(str(data)).parent / 'labels'
Path(output_dir).mkdir(exist_ok=True, parents=True)
prompt_predictor = PromptPredictor(sam_model)
det_results = det_model(data, stream=True)
for result in det_results:
boxes = result.boxes.xyxy # Boxes object for bbox outputs
class_ids = result.boxes.cls.int().tolist() # noqa
prompt_predictor.set_image(result.orig_img)
masks, _, _ = prompt_predictor.predict_torch(
point_coords=None,
point_labels=None,
boxes=prompt_predictor.transform.apply_boxes_torch(boxes, result.orig_shape[:2]),
multimask_output=False,
)
result.update(masks=masks.squeeze(1))
segments = result.masks.xyn # noqa
with open(str(Path(output_dir) / Path(result.path).stem) + '.txt', 'w') as f:
for i in range(len(segments)):
s = segments[i]
if len(s) == 0:
continue
segment = map(str, segments[i].reshape(-1).tolist())
f.write(f'{class_ids[i]} ' + ' '.join(segment) + '\n')