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ultralytics 8.0.90 actions and docs improvements (#2326)

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Glenn Jocher 2023-04-29 20:16:56 +02:00 • committed by GitHub
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commit 44c7c3514d
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39 changed files with 783 additions and 143 deletions
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14
ultralytics/yolo/data/annotator.py
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@ -6,6 +6,18 @@ 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):
"""
Automatically annotates images using a YOLO object detection model and a SAM segmentation model.
Args:
data (str): Path to a folder containing images to be annotated.
det_model (str, optional): Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'.
sam_model (str, optional): Pre-trained SAM segmentation model. Defaults to 'sam_b.pt'.
device (str, optional): Device to run the models on. Defaults to an empty string (CPU or GPU, if available).
output_dir (str, None, optional): Directory to save the annotated results.
Defaults to a 'labels' folder in the same directory as 'data'.
"""
device = select_device(device)
det_model = YOLO(det_model)
sam_model = build_sam(sam_model)
@ -33,7 +45,7 @@ def auto_annotate(data, det_model='yolov8x.pt', sam_model='sam_b.pt', device='',
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:
with open(f'{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: