mirror of
https://github.com/gaomingqi/Track-Anything.git
synced 2025-12-14 15:37:50 +01:00
88 lines
2.4 KiB
Python
88 lines
2.4 KiB
Python
from metaseg import SegAutoMaskPredictor, SegManualMaskPredictor, SahiAutoSegmentation, sahi_sliced_predict
|
|
|
|
# For image
|
|
|
|
def automask_image_app(image_path, model_type, points_per_side, points_per_batch, min_area):
|
|
SegAutoMaskPredictor().image_predict(
|
|
source=image_path,
|
|
model_type=model_type, # vit_l, vit_h, vit_b
|
|
points_per_side=points_per_side,
|
|
points_per_batch=points_per_batch,
|
|
min_area=min_area,
|
|
output_path="output.png",
|
|
show=False,
|
|
save=True,
|
|
)
|
|
return "output.png"
|
|
|
|
|
|
# For video
|
|
|
|
def automask_video_app(video_path, model_type, points_per_side, points_per_batch, min_area):
|
|
SegAutoMaskPredictor().video_predict(
|
|
source=video_path,
|
|
model_type=model_type, # vit_l, vit_h, vit_b
|
|
points_per_side=points_per_side,
|
|
points_per_batch=points_per_batch,
|
|
min_area=min_area,
|
|
output_path="output.mp4",
|
|
)
|
|
return "output.mp4"
|
|
|
|
|
|
# For manuel box and point selection
|
|
|
|
def manual_app(image_path, model_type, input_point, input_label, input_box, multimask_output, random_color):
|
|
SegManualMaskPredictor().image_predict(
|
|
source=image_path,
|
|
model_type=model_type, # vit_l, vit_h, vit_b
|
|
input_point=input_point,
|
|
input_label=input_label,
|
|
input_box=input_box,
|
|
multimask_output=multimask_output,
|
|
random_color=random_color,
|
|
output_path="output.png",
|
|
show=False,
|
|
save=True,
|
|
)
|
|
return "output.png"
|
|
|
|
|
|
# For sahi sliced prediction
|
|
|
|
def sahi_autoseg_app(
|
|
image_path,
|
|
sam_model_type,
|
|
detection_model_type,
|
|
detection_model_path,
|
|
conf_th,
|
|
image_size,
|
|
slice_height,
|
|
slice_width,
|
|
overlap_height_ratio,
|
|
overlap_width_ratio,
|
|
):
|
|
boxes = sahi_sliced_predict(
|
|
image_path=image_path,
|
|
detection_model_type=detection_model_type, # yolov8, detectron2, mmdetection, torchvision
|
|
detection_model_path=detection_model_path,
|
|
conf_th=conf_th,
|
|
image_size=image_size,
|
|
slice_height=slice_height,
|
|
slice_width=slice_width,
|
|
overlap_height_ratio=overlap_height_ratio,
|
|
overlap_width_ratio=overlap_width_ratio,
|
|
)
|
|
|
|
SahiAutoSegmentation().predict(
|
|
source=image_path,
|
|
model_type=sam_model_type,
|
|
input_box=boxes,
|
|
multimask_output=False,
|
|
random_color=False,
|
|
show=False,
|
|
save=True,
|
|
)
|
|
|
|
return "output.png"
|