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

ultralytics 8.2.69 FastSAM prompt inference refactor (#14724)

Browse source 
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Laughing 2024-07-30 07:17:23 +08:00 • committed by GitHub
parent 82c4bdad10
commit 9532ad7cae
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
11 changed files with 187 additions and 427 deletions
Download patch file Download diff file Expand all files Collapse all files

20
tests/test_cli.py
View file

@ -68,7 +68,6 @@ def test_fastsam(task="segment", model=WEIGHTS_DIR / "FastSAM-s.pt", data="coco8
run(f"yolo segment predict model={model} source={source} imgsz=32 save save_crop save_txt")
from ultralytics import FastSAM
from ultralytics.models.fastsam import FastSAMPrompt
from ultralytics.models.sam import Predictor
# Create a FastSAM model
@ -81,21 +80,10 @@ def test_fastsam(task="segment", model=WEIGHTS_DIR / "FastSAM-s.pt", data="coco8
# Remove small regions
new_masks, _ = Predictor.remove_small_regions(everything_results[0].masks.data, min_area=20)
# Everything prompt
prompt_process = FastSAMPrompt(s, everything_results, device="cpu")
ann = prompt_process.everything_prompt()
# Bbox default shape [0,0,0,0] -> [x1,y1,x2,y2]
ann = prompt_process.box_prompt(bbox=[200, 200, 300, 300])
# Text prompt
ann = prompt_process.text_prompt(text="a photo of a dog")
# Point prompt
# Points default [[0,0]] [[x1,y1],[x2,y2]]
# Point_label default [0] [1,0] 0:background, 1:foreground
ann = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1])
prompt_process.plot(annotations=ann, output="./")
# Run inference with bboxes and points and texts prompt at the same time
results = sam_model(
source, bboxes=[439, 437, 524, 709], points=[[200, 200]], labels=[1], texts="a photo of a dog"
)
def test_mobilesam():