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50 lines
2.1 KiB
50 lines
2.1 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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from pathlib import Path |
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from ultralytics import SAM, YOLO |
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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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Example: |
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```python |
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from ultralytics.data.annotator import auto_annotate |
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auto_annotate(data='ultralytics/assets', det_model='yolov8n.pt', sam_model='mobile_sam.pt') |
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``` |
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""" |
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det_model = YOLO(det_model) |
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sam_model = SAM(sam_model) |
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data = Path(data) |
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if not output_dir: |
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output_dir = data.parent / f"{data.stem}_auto_annotate_labels" |
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Path(output_dir).mkdir(exist_ok=True, parents=True) |
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det_results = det_model(data, stream=True, device=device) |
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for result in det_results: |
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class_ids = result.boxes.cls.int().tolist() # noqa |
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if len(class_ids): |
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boxes = result.boxes.xyxy # Boxes object for bbox outputs |
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sam_results = sam_model(result.orig_img, bboxes=boxes, verbose=False, save=False, device=device) |
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segments = sam_results[0].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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