HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

ultralytics 8.2.62 add Explorer CLI model and data args (#14581)

Browse source 
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher 2024-07-22 03:29:44 +02:00 • committed by GitHub
parent f4af1bccc6
commit 3b81b95e1c
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
8 changed files with 153 additions and 127 deletions
Download patch file Download diff file Expand all files Collapse all files

24
ultralytics/data/annotator.py
View file

@ -9,20 +9,24 @@ def auto_annotate(data, det_model="yolov8x.pt", sam_model="sam_b.pt", device="",
"""
Automatically annotates images using a YOLO object detection model and a SAM segmentation model.
This function processes images in a specified directory, detects objects using a YOLO model, and then generates
segmentation masks using a SAM model. The resulting annotations are saved as text files.
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'.
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').
output_dir (str | None): Directory to save the annotated results. If None, a default directory is created.
Example:
```python
from ultralytics.data.annotator import auto_annotate
Examples:
>>> from ultralytics.data.annotator import auto_annotate
>>> auto_annotate(data='ultralytics/assets', det_model='yolov8n.pt', sam_model='mobile_sam.pt')
auto_annotate(data='ultralytics/assets', det_model='yolov8n.pt', sam_model='mobile_sam.pt')
```
Notes:
- The function creates a new directory for output if not specified.
- Annotation results are saved as text files with the same names as the input images.
- Each line in the output text file represents a detected object with its class ID and segmentation points.
"""
det_model = YOLO(det_model)
sam_model = SAM(sam_model)