update

demo.py

@@ -1,110 +0,0 @@
-from metaseg import (
-    SahiAutoSegmentation,
-    SegAutoMaskPredictor,
-    SegManualMaskPredictor,
-    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,
-        # yolov8, detectron2, mmdetection, torchvision
-        detection_model_type=detection_model_type,
-        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().image_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"