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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"
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