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Auto annotation new parameters for SAM models (#17288)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Muhammad Rizwan Munawar 2024-10-31 17:35:26 +05:00 • committed by GitHub
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commit e8743f2ac9
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3 changed files with 15 additions and 4 deletions
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9
ultralytics/data/annotator.py
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@ -5,7 +5,9 @@ from pathlib import Path
from ultralytics import SAM, YOLO
def auto_annotate(data, det_model="yolo11x.pt", sam_model="sam_b.pt", device="", output_dir=None):
def auto_annotate(
data, det_model="yolo11x.pt", sam_model="sam_b.pt", device="", conf=0.25, iou=0.45, imgsz=640, output_dir=None
):
"""
Automatically annotates images using a YOLO object detection model and a SAM segmentation model.
@ -17,6 +19,9 @@ def auto_annotate(data, det_model="yolo11x.pt", sam_model="sam_b.pt", device="",
det_model (str): Path or name of the pre-trained YOLO detection model.
sam_model (str): Path or name of the pre-trained SAM segmentation model.
device (str): Device to run the models on (e.g., 'cpu', 'cuda', '0').
conf (float): Confidence threshold for detection model; default is 0.25.
iou (float): IoU threshold for filtering overlapping boxes in detection results; default is 0.45.
imgsz (int): Input image resize dimension; default is 640.
output_dir (str | None): Directory to save the annotated results. If None, a default directory is created.
Examples:
@ -36,7 +41,7 @@ def auto_annotate(data, det_model="yolo11x.pt", sam_model="sam_b.pt", device="",
output_dir = data.parent / f"{data.stem}_auto_annotate_labels"
Path(output_dir).mkdir(exist_ok=True, parents=True)
det_results = det_model(data, stream=True, device=device)
det_results = det_model(data, stream=True, device=device, conf=conf, iou=iou, imgsz=imgsz)
for result in det_results:
class_ids = result.boxes.cls.int().tolist() # noqa